W879 2008 This is to certify that the thesis entitled DEVELOPMENT OF A SMALL-SCALE SINGLE-TUBE PROTEOMICS APPROACH FOR THE ANALYSIS OF PROTEIN BIOMARKERS FROM BIOLOGICAL THREAT AGENTS presented by JENNIFER M. FROELICH has been accepted towards fulfillment of the requirements for the LIBRARY Michigan State University MS. degree in Criminal Justice 4&3”me MajFProfessor’s' Signature 54 A/ZIL Leos" Date MSU is an afiirmative-action, equal-opportunity employer PLACE IN RETURN BOX to remove this checkout from your record. TO AVOID FINES return on or before date due. MAY BE RECALLED with earlier due date if requested. DATE DUE DATE DUE DATE DUE DEVELOPMENT OF A SMALL-SCALE SINGLE-TUBE PROTEOMICS APPROACH FOR THE ANALYSIS OF PROTEIN BIOMARKERS FROM BIOLOGICAL THREAT AGENTS By Jennifer M. Froelich A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE School of Criminal Justice 2008 ABSTRACT DEVELOPMENT OF A SMALL-SCALE SINGLE-TUBE PROTEOMICS APPROACH FOR THE ANALYSIS OF PROTEIN BIOMARKERS FROM BIOLOGICAL THREAT AGENTS By Jennifer M. Froelich Due to the increased threat of terrorism there is currently a need to develop sensitive and specific methods for the detection and identification of biological threat agents. Here, the utility of the bottom-up tandem mass spectrometry (MS/MS) approach was investigated for the detection and identification of biological threat agents based on the analysis of unique protein biomarkers. Specifically, alternative methods for bacterial cell lysis and sample clean-up were investigated in order to address the challenges associated with the analysis of small sample quantities likely to be encountered in biological threat agent detection and identification. Guanidine hydrochloride was shown to be an effective reagent for achieving cell lysis at small scales, even for as little as 1 mg of starting cellular material. The number of proteins identified following guanidine hydrochloride lysis was found to be comparable with the number of proteins typically identified following large-scale lysis by sonication. This technique also demonstrated minimal bias in terms of the types of proteins which were identified. For sample clean-up prior to MS/MS analysis, on-line desalting was found to be an effective technique. The 2D linear quadrupole ion trap was also shown to significantly outperform the traditional 3D quadrupole ion trap, with over 2,000 proteins confidently identified following lysis of 1 mg of starting cellular material. Finally, the small-scale bottom-up MS/MS approach was successful in identifying proteins derived from a complex microbial mixture. COPyright by JENNIFER M. FROELICH 2008 ACKNOWLEGEMENTS Completion of this thesis would not have been possible without the many people who have helped me along the way. I must first thank Dr. Gary Van Berkel for allowing me to work on this research project while completing my internship at Oak Ridge National Laboratory (ORNL) as part of the US. Department of Homeland Security Fellowship Program. I would especially like to thank Dr. Nathan VerBerkmoes from ORNL for all of his guidance and support while working on this project, and for agreeing to be a member of my committee. I would also like to thank the other members of my committee, Dr. Ruth Smith, who provided me with valuable suggestions during the writing of my thesis, Dr. Gavin Reid, who has been a great mentor throughout my entire graduate career, and Dr. David Foran. There is not enough space to thank all of the individuals at ORNL who made me feel welcome during my short stay, but I would like to extend a special thank you to Dr. Robert Hettich and Dr. Melissa Thompson. I would like to thank all of the members of the Reid research group, both past and present, for their friendship, knowledge and support, as well as members of the MSU Forensic Chemistry Program for their suggestions during the preparation of my thesis presentation. Last, but certame not least, I would like to extend a huge thank you to my entire family, and my fiancé Brad Cox, who have always believed in me and have provided me with the support and encouragement I needed to make it to the end! I also wish to acknowledge the award of a US. Department of Homeland Security Fellowship, which is administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement with the Department of Energy (DOE). ‘ TABLE OF CONTENTS LIST OF TABLES vii LIST OF FIGURES viii 1. CHAPTER ONE: Introduction 1 1,1 Rinterrorism 1 1.2 Biological Threat Agents 7 1.3 Current and Developing Methods for the Detection and Identification of Biological Threat Agents 3 1.3.1 Genetic Analysis of Biological Threat Agents 3 1.3.2 Non-DNA Methods for the Analysis of Biological Threat Agents ............ 4 1.3.2.1 Immunoassays 5 1.3.2.2 Mass Spectrometry 1.4 Aims of this Thesis 11 2. CHAPTER TWO: Instr ‘ ‘iun 12 2.1 High Performance Liquid Chromatography (HPLC) 12 2.1.1 Ion-exchange High Performance Liquid Chromatography ......................... 13 2.1.2 Reversed-phase High Performance Liquid Chromatography ..................... 13 2.2 Mass Spectrometry 14 2.2.1 Ionization 15 2.2.1.1 Electrospray Ionization (ESI) 15 2.2.1.2 Matrix-assisted Laser Desorption Ionization (MALDI) ................. 17 2.2.2 Mass Analy79rs 18 2.2.2.1 The Quadrupole Ion Trap Mass Analyzer ...................................... 18 2.2.3 Detectors 76 3. CHAPTER THREE: Experimental 28 3.1 Materials 28 3.2 Cellular Lysis 28 3.2.1 Rhodopseudomonas palustris Isolate Samples 28 3.2.2 Microbial Mixture Samples ....................................................... 29 3.3 In-solution Proteolytic Digestion 79 3.4 Liquid Chromatography/Mass Spectrometry Analysis ...................................... 30 3.5 Data Analysis > 3? 4. CHAPTER FOUR: Results and Discussion 34 4.1 Introduction 34 4. 2 Development of a Bacterial Cell Lysis T L ' , 35 4. 3 Comparison of Sample Clean-up Techniques Prior to MS/MS Analysis .......... 39 4.4 Application of Small-scale Bacterial Cell Lysis and Sample Clean-up Techniques to Reduced Amounts of Starting Cellular Material .............................. 41 4.5 Assessment of Reproducibility 43 4.6 Assessment of Bias 44 4.7 Comparison of Mass Spectrometry Instr ‘ ‘inn 47 4.8 Protein Quantitation 49 4.9 Application to a Complex Microbial Mixture 50 4.10 Summary 53 5. CHAPTER FIVE: Conclusions and Future Directions 55 5.1 Conclusions 55 5.2 Future Directions 58 REFERENCES 59 LIST OF TABLES TABLE PAGE 4.1 Percent sequence coverage and spectral count number corresponding to trypsin for the 25, 5 and 1 mg aliquots of R. palustris subjected to lysis using guanidine HCl, desalted on-line and analyzed using the LCQ ................................ 50 vii LIST OF FIGURES FIGURE PAGE 1.1 Schematic of the two-dimensional liquid chromatography (LC/LC) “bottom- up” tandem mass spectrometry (MS/MS) approach for protein identification ......... 8 2.1 Components of a mass spectrometer 14 2.2 Schematic of electrospray ionization (ESI) 16 2.3 Diagram of a three~dimensional quadrupole ion trap 19 2.4 Typical Mathieu stability diagram for the quadrupole ion trap. The larger balls represent high mass ions whereas the smaller balls represent low mass ions 77 2.5 Principle of tandem mass spectrometry (MS/MS) 75 2.6 Diagram of a two-dimensional linear quadrupole ion trap 76 2.7 Schematic of a continuous-dynode electron multiplier coupled with a conversion dynnde 77 3.1 Schematic of the column system used for two-dimensional liquid chromatography separation 31 4.1 Schematic of the experimental approach used in this study .................................... 35 4.2 Venn diagram for the 25 mg aliquot of R. palustris subjected to lysis using guanidine HCl or urea. Samples were desalted using solid-phase extraction and analyzed using the LCQ. The numbers represent the total number of identified proteins shared between the samples and unique to each sample ........... 37 4.3 Liquid chromatography-mass spectrometry analysis of the peptide mixture resulting from guanidine HCl lysis and tryptic digestion of 25 mg of R. palustris. (A) Base peak chromatogram following reversed-phase separation (30% ammonium acetate salt step). (B) Mass spectrum obtained from time point 1 of the base peak chromatogram. (C) MS/MS product ion spectrum obtained from dissociation of the doubly protonated precursor ion (m/z 735.7) of the predicted peptide HYDVIVAPVVTEK from the R. palustris rpIW SOS ribosomal protein L23 38 viii 4.4 4.5 4.6 4.7 4.8 4.9 4.10 Venn diagrams for the 5 mg aliquot of R. palustris subjected to lysis using (A) guanidine HCl or urea and desalted using solid-phase microextraction, (B) guanidine HCl or urea and desalted on-line, (C) guanidine HCl and desalted either on-line or using solid-phase microextraction, and (D) urea and desalted either on-line or using solid-phase microextraction. Samples were analyzed using the LCQ. The numbers represent the total number of identified proteins Shared between the samples and unique to each sample ......... Venn diagram for the 1 mg aliquot of R. palustris subjected to lysis using guanidine HCl or urea. Samples were desalted on-line and analyzed using the LCQ. The numbers represent the total number of identified proteins shared between the samples and unique to each sample Venn diagrams for replicate analysis of the 5 mg (panel A) and 1 mg (panel B) aliquots of R. palustris subjected to lysis using guanidine HCl. Samples were desalted on-line and analyzed using the LCQ. The numbers represent the total number of identified proteins shared between the samples and unique to each sample Total number of non-redundant proteins identified following LCQ-MS/MS analysis of the 5 mg aliquot of R. palustris subjected to lysis using guanidine HCl and desalted on-line. The identified proteins are organized according to the functional categories of R. palustris. The non-bolded and bolded percentages represent the percentage of the identified proteome and the percentage of the predicted proteome, respectively Total number of non-redundant proteins identified following LCQ-MS/MS analysis of the 1 mg aliquot of R. palustris subjected to lysis using guanidine HCl and desalted on-line. The identified proteins are organized according to the functional categories of R. palustrz's. The non-bolded and bolded percentages represent the percentage of the identified proteome and the percentage of the predicted proteome, respectively Total number of proteins identified following LTQ-MS/MS analysis of the 1 mg aliquot of R. palustris subjected to lysis using guanidine HCl and desalted on-line. The identified proteins are organized according to the functional categories of R. palustris. The non-bolded and bolded percentages represent the percentage of the identified proteome and the percentage of the predicted proteome, respectively Number of proteins identified for each species present in a four microbe mixture as a function of the amount of starting cellular material subjected to lysis using guanidine HCl. All samples were desalted on-line and analyzed using the LCQ ..40 42 44 46 47 48 51 CHAPTER ONE Introduction 1.1 Bioterrorism Bioterrorism is the intentional release of pathogens or biological toxins into the air, water or food supply with the intent to cause harm to humans, animals or plants [1]. Acts of bioterrorism date as far back as the ancient Roman civilization, and have continued into the let century [2]. In the United States, the first known bioterrorist attack occurred in 1984 when members of the Rajneesh religious cult intentionally contaminated restaurant salad bars and grocery produce with Salmonella typhimurium in the state of Oregon [3]. Although this attack did not result in any deaths, 751 people developed food poisoning. In September and October 2001, Bacillus anthracis spores were deliberately released via mailed letters, resulting in the death of five individuals from inhalation anthrax and the infection of 17 others [4]. The use of biological agents by terrorist groups or individuals has become a growing concern in recent years [5]. Biological agents are generally difficult to detect after their initial release, usually do not cause an immediate illness, and some, such as the smallpox and Ebola viruses [6], can be easily passed from one individual to another. Compared to nuclear or conventional weapons, biological weapons are relatively inexpensive to produce. Technological advances have also made it feasible for biological agents to be manipulated such that their ability to cause disease can be increased, they can become more resistant to current medicines, and they can be more effectively released into the environment. In addition, biological agents can be genetically modified so as to preclude their detection and identification by currently available methods. Thus, there is a need to develop sensitive and specific methods for the real-time, on-site detection and identification of biological agents. However, it is just as critical to develop methods for linking the strain of a biological agent used in an attack to the individual or group responsible, which has led to the emerging field of microbial forensics [7]. Under these circumstances, detection and identification would not be as time-critical. 1.2 Biological Threat Agents There are numerous bacterial pathogens, viruses and biological toxins which pose serious health risks to humans, animals and plants, and therefore have the potential to be employed as biological threat agents [6]. These biological agents have been identified and classified by the Centers for Disease Control and Prevention (CDC) according to the severity Of the illness they cause, the likelihood that they will result in death, and how easily they can be transmitted from person-to-person [8]. Some bacterial pathogens of concern include Bacillus anthracis, which is the causative agent of anthrax, and Yersinia pestis, which causes pneumonic plague. The deoxyribonucleic acid (DNA) virus variola major, which causes smallpox, and the ribonucleic acid (RNA) filoviruses, which cause Ebola hemorrhagic fever and Marburg hemorrhagic fever, have been identified as potential biological threat agents. Biological toxins of concern include, but are not limited to, the Clostridium botulinum neurotoxins, ricin, staphylococcal enterotoxin B, and Clostridium perfinigens epsilon toxin, all of which are proteins. Of the bacteria, viruses and biological toxins which have been recognized as potential biological threat agents, several have been employed in bioterrorist attacks or crimes. For example, the Japanese cult Aum Shinrikyo, best known for the sarin nerve gas attacks in Tokyo, attempted, without success, to release a liquid suspension of Bacillus anthracis from a building roof top in Japan during the early 1990’s [9]. In 1978, Georgi Markov, a Bulgarian exile, was assassinated by a member of the Bulgarian secret police after ricin was injected into his leg with the tip of an improvised umbrella [2]. 1.3 Current and Developing Methods for the Detection and Identification of Biological Threat Agents There are numerous methods which currently exist, or are under development, for the detection and identification of biological threat agents [7, 10]. Many of these techniques differentiate biological threat agents based on the presence of unique biomarkers. Biomarkers may belong to any one of the three major classes of macromolecules including DNA/RNA, lipids and proteins. For bacteria and viruses, detection methods are typically based on the presence of DNA/RNA, lipid or protein biomarkers, while the detection of biological toxins is limited to proteins. The most widely employed techniques used to detect and identify biological threat agents based on the presence of DNA/RNA, lipid and protein biomarkers are described in the following sections. 1.3.1 Genetic Analysis of Biological Threat Agents Determination of the entire genomic sequence of a bacterium or virus would be the most reliable genetic-based method available for their detection and identification. However, the time and cost associated with whole genome sequencing renders this technique impractical for routine analysis. Thus, the detection and identification of biological threat agents is typically based on the recognition of unique regions of DNA [6, 7, 11, 12]. For bacteria and DNA viruses, this is most commonly achieved by amplification of the DNA region of interest using the polymerase chain reaction (PCR), followed by DNA sequencing. For RNA viruses, the genetic material is first converted to DNA, followed by amplification, in a process called reverse transcription (RT)-PCR. This genetic-based approach enables the unique identification of species, as well as strains, of biological threat agents, and recent advances in PCR technology have shortened the analysis time to a few minutes. However, PCR requires prior knowledge of the target sequences to be amplified, and therefore, will only detect and identify those biological threat agents which are specifically sought after. The sensitivity of PCR also makes this approach susceptible to contamination, which can lead to false-positive results. Finally, protein toxins do not contain genetic material, and thus will not be detected and identified using any genetic-based approach. 1.3.2 N on-DNA Methods for the Analysis of Biological Threat Agents The detection and identification of biological threat agents can also be based on the presence of unique lipid or protein biomarkers. Pyrolysis coupled with mass spectrometry has been used to detect and identify bacterial pathogens based on the analysis of fatty acid signatures derived from membrane phospholipids [13, 14]. This technique is rapid, robust and has been integrated with a chemical weapons detector for real-time, on-site detection [14]. However, unlike DNA and lipids, proteins are present in high abundance in all potential biological threat agents, therefore detection is not limited to bacteria and viruses. In addition, a high degree of protein variability exists between species type. Thus, the analysis of unique proteins would likely provide the most reliable method for biological threat agent detection and identification. 1.3.2.1 Immunoassays Immunoassays are one group of methods commonly used to detect protein toxins, or proteins liberated from a bacterium or virus [6, 7, 12, 15]. Immunoassays utilize antibodies which are specific for a protein of interest. Typically the antibodies are labeled with an enzyme, radioisotope, or fluorescent molecule which enables the detection of the biological toxin or protein upon antibody binding. The most widely used immunoassays for the detection and identification of biological threat agents include the mmtngraphic lateral flow assay, enzyme-linked immunosorbant assay (ELISA), time-resolved fluorescence assay (TRF), and electrochemiluminescent assay (ECL) [15]. These techniques. are relatively inexpensive and can quickly screen a large number of samples. However, prior knowledge of the likely biological toxins or proteins present within a sample is required in order to choose the correct antibodies for screening. Immunoassays also suffer from insufficient sensitivity, and lack the necessary specificity required to detect and identify biological threat agents with high confidence. 1.3.2.2 Mass Spectrometry Mass spectrometry has emerged as an attractive option for the detection and identification of biological threat agents based on the analysis of protein biomarkers. This is attributed to the speed, sensitivity and specificity associated with mass spectrometry 5 ‘ instrumentation, as well as advances in mass spectrometry-based protein identification strategies. Although mass spectrometry is amenable to the analysis of unknowns, protein identification is dependent upon the target protein sequence being contained within a protein sequence database. One mass spectrometry-based approach which has been developed to detect and identify biological threat agents involves the analysis of intact proteins liberated from a bacterium, virus or biological toxin using matrix-assisted laser desorption ionization (MALDI) coupled with time-of—flight (TOF) mass spectrometry [16-21]. In this approach, biological threat agents are identified by comparing the protein profiles from experimentally generated mass spectra with the mass spectra contained within spectral libraries. Alternatively, the experimentally determined molecular masses of the observed proteins are compared with theoretical molecular masses determined from the available protein sequence databases. While this approach is rapid and requires minimal sample preparation, it is not amenable to the direct analysis of complex protein mixtures, and would require prior separation or isolation techniques to be employed. In addition, tandem mass spectrometry (MS/MS) cannot be performed using a single TOF mass analyzer, therefore biological threat agent identification is based solely on the masses of the observed proteins. MS/MS would provide additional structural information regarding the amino acid sequence of a protein, which would likely improve the specificity of biological threat agent identification. In addition to MALDI-TOF, the detection and identification of biological threat agents based on the analysis of intact proteins has also been achieved using electrospray ionization (ESI) coupled with Fourier transform ion cyclotron resonance (FT -ICR) [22] 6 ‘ and quadrupole ion trap [23] mass analyzers. In addition to the mass analysis of intact proteins, MS/MS can also be performed on individual mass selected proteins using each of these mass analyzers. This would enable biological threat agents to be identified with higher confidence. However, the analysis of intact proteins via this approach is still not without limitations. These limitations have been described in detail elsewhere and will not be discussed here [24]. An alternative methodology to the analysis of intact proteins via each of the “top down” approaches described above is the “bottom-up” or “shotgun” tandem mass spectrometry approach illustrated in Figure 1.1 [25-30]. For the analysis of bacterial pathogens, bacterial cells are first subjected to lysis in order to release the protein complement of the cell. The protein mixture is then subjected to proteolytic digestion, using an enzyme such as trypsin. The resultant peptide mixture is then separated using one- or two-dimensional capillary liquid chromatography and introduced to the mass Spectrometer by ESI, or by MALDI. Following their mass analysis, individual protonated precursor ions are then automatically isolated and subjected to dissociation by tandem mass spectrometry [31, 32]. The identity of each peptide, and its protein of origin, is then typically achieved using sophisticated bioinformatic approaches which correlate the uninterpreted product ion spectrum with theoretically generated product ion spectra determined from peptides of the same mass contained within a known protein sequence database [33-35]. The utility of the bottom-up tandem mass spectrometry approach for the analysis of protein biomarkers from bacterial pathogens has been recently investigated for biological threat agent detection and identification [36, 37]. Mixtures of viral proteins or biological toxins could also be analyzed by this approach. 7 ‘ analysis _..=t_. “in“! ESI or MALDI Data spectrum spectrometry Mass ‘onization Protein Peptide digestion separation Sample f'.\ I f \ AA}. _ J‘ “ 0'? HPLC ’ I fractionation 3 I an a I Sample preparation .0 0 ° 0 Cell culture Figure 1.1 Schematic of the two-dimensional liquid chromatography (LC/LC) “bottom- up” tandem mass spectrometry (MS/MS) approach for protein identification. (Reproduced and modified from reference 32) While the general bottom-up MS/MS approach is amenable to the analysis of complex mixtures, and is well-established for the large-scale analysis of proteins [26, 27, 30], problems may arise when analyzing limited quantities of starting cellular material, as would often be encountered in biological threat agent detection and identification. Perhaps one of the greatest challenges in analyzing limited quantities of starting cellular material is bacterial cell lysis. Generally, efficient cell lysis is achieved through manual disruption of the cell membrane using either sonication or French press [38, 39]. When sonication is used, a metal probe oscillating at high frequency (typically 20-50 kHz) is inserted into a sample of interest. The high frequency generates a region of high pressure, which disrupts the cell membrane and causes the cells to break open. French press, on the other hand, passes cells through a narrow valve under high pressure (typically 10,000-20,000 psi). After the sample passes through the valve, the transition from high to low pressure creates a shear force which causes the cell membrane to be disrupted. Using either of these techniques, 1-4 grams of starting cellular material is typically subjected to lysis. However, when analyzing limited quantities of starting cellular material (Le, a few milligrams), extensive sample losses are likely to occur when the sample comes into contact with the surface of the metal sonication probe, or surfaces of the valve used during French press. With limited sample size, care must also be taken to avoid cross—contamination. Thus, it would be beneficial to perform all stages of sample preparation, including cell lysis and proteolytic digestion, in a single tube prior to mass spectrometry analysis. To achieve this goal, there are several alternative cellular lysis techniques which could be utilized including enzymatic- [40-43] and detergent-based methods, as well as the combined use of solvents and detergents [44, 45]. However, for enzymatic-based methods, the excess enzyme required to disrupt the cell membrane could potentially interfere with the ability to detect and identify endogenous proteins in the sample. It has also been demonstrated that some cell membranes are refractory to disruption by certain enzymes. For example, gram-positive bacteria are generally difficult to lyse using the commonly employed enzyme lysozyrne [45]. For those methods which employ detergents, it has been shown that even trace amounts of detergent in the sample can affect the recovery and separation efficiency of peptides via reversed-phase chromatography [46], as well as interfere with mass spectrometry analysis [47]. In addition, it has been demonstrated that detergents can denature the enzymes employed for proteolytic digestion, which can ultimately result in the reduction or loss of enzyme activity [48]. Wang et al. have recently described a “single-tube” method for achieving cell lysis in the absence of both enzyme and detergent, which utilizes the organic co- solvent trifluoroethanol (TFE)[49]. The advantage of this technique is that sample clean- up is not required prior to mass spectrometry analysis. However, the addition of TFE to the sample only assists in cell lysis, as sonication is still required. The problems of sample loss and cross-contamination associated with sonication would therefore still be an issue. Thus, it is desirable to explore alternative cellular lysis techniques which could not only minimize sample loss and cross-contamination, but also eliminate the problems associated with enzymatic- and detergent-based approaches. Another challenge with analyzing limited quantities of starting cellular material is the sample clean-up steps required following cellular lysis and proteolytic digestion. Typical bottom-up MS/MS approaches utilize solid-phase extraction to remove the salts introduced by buffers and denaturants, and to concentrate the peptide mixture prior to analysis. Using this technique, a peptide mixture is passed through a cartridge containing a chromatographic packing, typically reversed—phase. Peptides within the mixture bind to the chromatographic packing, while salts pass through the cartridge unretained. The peptides are then recovered by choosing appropriate solvents for elution. Typical binding capacities for traditional solid-phase extraction techniques range from 100 rig—3 mg. However, extensive sample losses are likely to occur when applying this sample clean-up technique to peptide mixtures derived from smaller quantities of starting cellular material. As a result, there is also a need to explore alternative approaches for sample clean-up following cellular lysis and proteolytic digestion. 1.4 Aims of this Thesis The aims of this thesis are: l. to develop a method for the efficient lysis of milligram amounts of starting cellular material while minimizing sample loss and cross-contamination, 2. to explore alternative techniques for sample clean-up prior to liquid chromatography/ mass spectrometry analysis, and; 3. to develop a two-dimensional liquid chromatography bottom-up tandem mass spectrometry approach for the analysis of protein biomarkers fiom low milligram amounts of starting cellular material which incorporates the optimal conditions for bacterial cell lysis and sample clean-up at small scales. ‘ CHAPTER TWO Instrumentation The ability to identify large numbers of proteins by mass spectrometry (MS) requires the extensive fractionation of complex peptide mixtures generated by proteolytic digestion. Chromatographic techniques play an important role in achieving separation of these complex peptide mixtures prior to MS analysis. 2.1 High Performance Liquid Chromatography (HPLC) Chromatographic separations are achieved based on differences in the physical or chemical interactions of the individual components with two different phases, the mobile phase and the stationary phase. High performance liquid chromatography (HPLC) utilizes a liquid mobile phase and a solid stationary phase, both influencing the mechanism by which separation is achieved. Initially, a sample of interest is dissolved in an appropriate liquid solvent, and is then loaded at high pressure onto a column containing the stationary phase. The liquid mobile phase is then continuously pumped through the column, carrying the sample through the column toward the detector. Components which have a stronger interaction with the mobile phase than with the stationary phase will elute fi'om the column at shorter times, while components which have a stronger interaction with the stationary phase than with the mobile phase will elute from the column at later times. The time at which a component elutes from the column is referred to as the retention time. Depending upon the complexity of the mixture to be analyzed, multidimensional HPLC separation techniques may be required to achieve more extensive fractionation. QR For example, two-dimensional HPLC (LC/LC) separates the components of a mixture using two different separation mechanisms. In the studies reported herein, ion—exchange chromatography and reversed-phase chromatography were performed in series. 2.1.1 Ion-exchange High Performance Liquid Chromatography Ion—exchange HPLC separates the components of a mixture based on charge. The stationary phase contains covalently bound charge-bearing functional groups, to which a counterion is attached. When a sample is introduced to the column, the charged components of the sample displace the counterions of the stationary phase and are retained. Each component is displaced from the stationary phase and eluted from the column by gradually changing the composition of the mobile phase (i.e., the pH or ionic strength). Anion exchange and cation exchange are two types of ion-exchange HPLC which are often used to separate charged molecules including proteins, peptides, nucleotides and amino acids. 2.1.2 Reversed-phase High Performance Liquid Chromatography Reversed—phase HPLC separates the components of a mixture based on differences in hydrophobicity. Each component interacts with the stationary phase, typically composed of hydrophobic alkyl chains covalently bonded to small-diameter (3— 5 pm), porous silica particles, through an adsorption mechanism. The mobile phase is a relatively polar solvent, typically water, containing low concentrations of an ion-pairing reagent and an organic modifier. As the concentration of the organic modifier in the mobile phase is gradually increased, the components are desorbed from the hydrophobic IL stationary phase in the order of least hydrophobic to most hydrophobic. Reversed-phase HPLC is commonly used to separate complex mixtures of peptides and proteins. 2.2 Mass Spectrometry Mass spectrometry is an analytical technique which involves the ionization of a sample to generate gas-phase ions, separation of the resultant ions based on their individual mass-to—charge (m/z) ratios, and measurement of the m/z and abundance of each ion reaching the detector. Optionally, individual gas-phase ions can be isolated and subjected to fragmentation, followed by mass analysis of the resultant product ions. Mass spectrometry is an attractive technique in the field of forensic science due to its speed, sensitivity and specificity. The main components of a mass spectrometer are illustrated in Figure 2.1. ! _ IongFormation Ion Separation Ion Detection Sample Ionization Mass Introduction :> [ Analyzer Jq Detector ] Vacuum Pump f—W Data Handling Data System Data Output Li__i__i_l Mass spectrum 'cj'C:= Figure 2.1 Components of a mass spectrometer (adapted from “What is mass spectrometry”. www.asms.org). 2.2.1 Ionization 2.2.1.1 Electrospray Ionization (ESI) Electrospray ionization (ESI), an atmospheric pressure ionization technique, was first introduced by Fenn and coworkers in the late 1980’s [50]. ESI results in minimal analyte fragmentation, is suitable for analyzing polar and ionic compounds, and produces multiply charged pseudomolecular ions ([M+nH+]"+), which enable high molecular weight species, such as peptides and proteins, to be readily analyzed by many types of mass analyzers. ESI can be performed in direct infusion mode, whereby a sample of interest is dissolved in an appropriate liquid solvent and then introduced to the mass spectrometer via a syringe pump. However, ESI can also be readily coupled with HPLC to directly ionize an analyte as it elutes from a chromatographic column. The ability to couple ESI directly with HPLC is particularly advantageous for the analysis of complex mixtures, whereby separation prior to mass spectrometry analysis is required. When sample quantity is limited, as'is often encountered in many forensic applications, the use of nanoelectrospray ionization (nESI), which is typically operated at flow rates of less than 1 uL/min, may be beneficial [51]. The principle of ESI is illustrated in Figure 2.2. Initially, a sample is pumped through a small diameter stainless steel or fused silica capillary tubing, the tip of which is maintained at atmospheric pressure in the source region of the mass spectrometer. A high potential is applied between the capillary tip and a counter electrode, which results in charge accumulation at the surface of the liquid. A combination of charge repulsion at the surface, and the presence of an electric field, enables the surface tension of the liquid to be overcome and the liquid subsequently expands into a Taylor cone. The tip of the ‘ Taylor cone then elongates into a liquid filament, which breaks to yield an electrostatic spray of charged droplets. Finally, solvent evaporation, in the presence of high temperature and/or a sheath gas, and charge repulsion, results in a series of coulomb fission events to yield gas-phase ions. Capillary tip Q Q + @ G @0 + Counter + electrode 63 e meleeaee * e... g + ' Q + + Charged droplets Gas-phase ions High Voltage Power Supply Figure 2.2 Schematic of electrospray ionization (ESI). (Reproduced and modified from reference 55) The mechanism by which gas—phase ions are produced fi'om the charged droplets has been explained by two competing theories, the charge residue model (CRM) [52] and the ion evaporation model (IBM) [53, 54]. In the CRM, successively smaller droplets are produced when the surface charge density of the droplet exceeds the Rayleigh stability limit (i.e., the force due to the repulsion of the surface charges becomes equal to the surface tension force of the liquid). The result of this process is the formation of droplets ‘ which contain only a single analyte molecule. A free gas-phase ion is then produced as the remaining solvent evaporates. In the IBM, it is also proposed that a series of Rayleigh instabilities produce successively smaller droplets. However, in contrast to the CRM, the surface electric field of the droplet becomes strong enough to overcome the solvation forces, causing direct emission of the analyte ions from the droplet surface into the gas- phase. Experimental evidence suggests that large molecules, such as proteins, ionize according to the CRM, while small molecules ionize according to the IBM [55]. 2.2.1.2 Matrix-assisted Laser Desorption Ionization (MALDI) Matrix-assisted laser desorption ionization (MALDI) was developed by Karas and Hillencarnp in the late 1980’s for the purpose of analyzing large molecular weight proteins [56]. MALDI is performed by dissolving an analyte of interest in an excess of matrix containing a UV absorbing chromophore. After the analyte-containing matrix has been spotted onto a metal target and allowed to crystallize, the target is placed under vacuum in the source region of the mass spectrometer. The target is then bombarded with short duration laser pulses, which induces rapid heating of the crystals and sublimation of both the analyte and matrix into the gas-phase. Although formation of analyte and matrix ions can occur at any time during this process, the exact mechanism of ionization is still the subject of much debate [57]. Similar to ESI, MALDI results in limited analyte fragmentation and is suitable for analyzing polar and ionic compounds. Although MALDI is used for the analysis of high molecular weight species, the formation of singly charged pseudomolecular ions via this technique limits the types of mass analyzers which may be employed. Due to the nature of the ionization process, MALDI cannot be coupled with HPLC. For this reason, MALDI was not explored as a potential ionization technique in the studies reported herein. 2.2.2 Mass Analyzers The mass analyzer component of the mass spectrometer separates gas-phase ions according to their individual m/z ratios. While a number of different mass analyzers are available, the quadrupole ion trap mass analyzer was used in this study, and is described in more detail below. 2.2.2.1 The Quadrupole Ion Trap Mass Analyzer The three-dimensional quadrupole ion trap mass analyzer, illustrated in Figure 2.3, consists of a hyperbolic ring electrode with an internal radius re, and two hyperbolic end-cap electrodes each positioned at a distance zo fi'om the center of the trap. In most commercial instrument platforms, the end-cap electrodes are maintained at ground, and a potential, (Do, is applied to the ring electrode. The applied potential is expressed in equation (1), where U is the direct current (DC) potential, V is the zero-to-peak amplitude of the radio fiequency (RF) voltage, 0) is the angular frequency (to = 21w, where t) is the fiequency of the RF field), and t is time. (Do = + (U - V cosmt) (l) The potential applied to the ring electrode creates a three-dimensional (3D) quadrupolar electric field within the trap. As ions are injected into the trap, via a small hole in the entrance end-cap electrode, ions of all m/z values with stable oscillating trajectories (i.e., trajectories which do not exceed the dimensions of the trap) are stored. To help focus ions toward the center of the trap, and therefore improve the overall trapping efficiency, a continuous flow of helium bath gas is introduced to the trap at a pressure of approximately 1 mTorr. Ion focusing is achieved via collisions between the stored ions and the helium gas. Ions in End-cap I electrode A Ring electrode Ions out End-cap electrode Figure 2.3 Diagram of a three-dimensional quadrupole ion trap. (Reproduced and modified from reference 58) The regions within the trap where ions of particular m/z values have a stable oscillating trajectory can be determined by considering ion motion within the trap under the influence of an applied potential. Although an ion will experience motion in the x, y and 2 dimensions, the cylindrical symmetry of the trap enables ion motion to be described using the coordinates z and r. The equations of motion inside a quadrupole ion trap are given by equations (2) and (3) dzz 4 e ———(U—Vcoscot)z=0 (2) dt2 m(ro2 +2202) 2 (U—Vcoscot)r=0 (3) dt m(ro2 + 220 j where e is the charge of an electron (1.60 x 10'19 C), 2 is the number of charges on a given ion, and m is the mass of an ion. The similarities between these two equations and the Mathieu equation, given by equation (4), enable the working equations of ion motion in an ion trap to be derived. In equation (4), u represents either 2 or r in equations (2) and (3), respectively, qu and au are dimensionless trapping parameters, and 5 = rot/2. dzu F + (au — 2qu cos 21,")u = 0 (4) 20 The working equations of ion motion, expressed in the form of the Mathieu equation are given by equations (5) and (6) below. According to these equations, an ion will only have a stable trajectory if the motion of the ion does not exceed 20 an r0 for a defined set of operating conditions (i.e., U, V and (o). —16zeU a = a = —2a = —— (5) u 2 r m(r02 + 27.02 )a)2 82eV m(r02 +2202 )602 (6) qu =qz=_2qr = A visual representation of the regions of ion stability, referred to as a Mathieu stability diagram, is obtained by plotting az as a function of qz. The stability diagram for a 3D quadrupole ion trap is depicted in Figure 2.4. Ions located within the bounded region of the stability diagam have stable trajectories, while those lying outside of the bounded region have unstable trajectories. Typically, a DC potential, U, is not applied to the ring electrode, therefore a2 is equal to zero, and the ion trap is operated along the qz axis. According to equation (6), the m/z value of an ion is inversely proportional to qz, thus low m/z ions (represented by the smaller balls in Figure 2.4) have larger qz values, and high m/z ions (represented by the larger balls) have smaller qz values. In equation (6), the term e is a constant, and re, 20 and to are fixed values, therefore the qz value of an ion with a particular m/z increases as the amplitude of the applied RF potential, V, increases. When qz reaches a value of 0.908 (stability limit), the ion no longer has a stable trajectory and is ejected from the trap in the 20 dimension for detection. A mass spectrum can 21 ‘ therefore be acquired by linearly scanning V to progressively destabilize ions of increasing m/z value. This method of mass analysis is referred to as ion ejection at the stability limit. RF ejection, q = 0.86 a ‘ Stable ions q =0.908, :3 =1 ‘12 Figure 2.4 Typical Mathieu stability diagram for the quadrupole ion trap. The larger balls represent high mass ions whereas the smaller balls represent low mass ions. (Reproduced and modified from reference 59) Mass analysis however, is typically achieved by resonance ejection. In this technique, a supplementary high amplitude RF potential is applied to the end-cap electrodes, typically at a qz value of 0.86. The amplitude of the RF voltage applied to the ring electrode is then linearly scanned, which causes the secular fi'equency at which an iOn oscillates in the trap, f2, to slowly increase. The secular frequency of an ion, which 22 . due to its inertia, is not equal to the fundamental frequency, v, of the applied RF potential, is expressed by equation (7) f.= ; m where 3,, is a fundamental stability parameter and is approximated by equation (8) for q values less than 0.4. flz= al+ 7 (8) When the secular frequency. of an ion matches the applied fi'equency of the end-cap electrodes, the kinetic energy of the ion will rapidly increase to the point where the ion’s trajectory becomes unstable and the ion is ejected fiom the trap, resulting in higher mass resolution when compared to mass analysis via ion ejection at the stability limit. One of the main advantages to using a quadrupole ion trap mass analyzer is the ability to perform multiple stages of mass analysis to obtain detailed structural information for an ion of interest. Tandem mass spectrometry (MS/MS) involves the isolation of a precursor ion, fragmentation of the precursor ion via energetic collisions with an inert gas, and mass analysis of the resultant product ions (Figure 2.5). A precursor ion of a selected m/z ratio is typically isolated by applying a supplementary high amplitude, broadband resonance ejection RF signal to the end-cap electrodes to eject ” ‘L all ions except for the precursor ion of interest. The isolated precursor ion is then activated by applying a low amplitude RF resonance excitation signal to the end-cap electrodes with a frequency corresponding to the secular frequency of motion of the isolated ion. The applied potential increases the axial motion and kinetic energy of the precursor ion and energetic collisions with the background helium buffer gas converts a fraction of the kinetic energy into internal energy. This enables the ion to reach a vibrationally excited state, and fragmentation is induced. The secular frequencies of motion Of the resultant product ions are not in resonance with the supplementary RF signal, therefore the product ions are focused to the center of the trap following collisions with the helium gas. A mass spectrum is acquired by sequentially ejecting the product ions from the trap in the same manner as described above. Multistage tandem mass Spectrometry (MSn) experiments are performed by incorporating additional ion isolation and fragmentation events following initial MS/MS analysis. 24 M S spectrum % abundance __._-...._-__._a m/z Precu rsorion l selection 0 0 C as ‘O C D .D (U °\° m/z MS/MSl MS/MSspectrum 8 . C (U "D C :3 .D «I I I _ m/z Figure 2.5 Principle of tandem mass spectrometry (MS/MS). (Reproduced and modified from reference 59) Although the 3D quadrupole ion trap is considered a relatively high sensitivity mass analyzer, it suffers from low injection and trapping efficiencies and limited ion storage capacity. To address these disadvantages, the two-dimensional linear quadrupole ion trap was recently introduced [60, 61]. The linear quadrupole ion trap, illustrated in Figure 2.6, stores ions in a two-dimensional electric field, which is created by applying 25 an RF potential to four hyperbolic rods, and static DC potentials applied to the electrodes located at each end of the rods. The linear quadrupole ion trap has a greater ion storage capacity, because ions can be stored along the entire length of the trap. The extent to which ions experience the applied RF potential in the axial dimension is also minimal, therefore resulting in greater ion trapping efficiencies. In the studies reported herein, a three-dimensional quadrupole ion trap and a two-dimensional linear quadrupole ion trap were both employed. Slot for ion ejection Hyperbolic rods Figure 2.6 Diagram of a two-dimensional linear quadrupole ion trap. (Reproduced and modified from reference 61) 2.2.3 Detectors The ion detection system of the mass spectrometer is responsible for converting the ion beam exiting the mass analyzer into a measurable electrical current. The most commonly used ion detection system consists of a continuous-dynode electron multiplier coupled with a conversion dynode (Figure 2.7). When an ion strikes the surface of the 26 high voltage (HV) conversion dynode, secondary particles are emitted, which are then accelerated into the electron multiplier. Secondary electrons are ejected fiom the electrically resistive surface of the electron multiplier following collisions between the surface and the secondary particles. AS the electrons travel further into the electron multiplier, repeated collisions with the surface cause additional electrons to be expelled due to the presence of a positive electrical field along the surface. The data acquisition system then plots the amplified electric current, which corresponds to the abundance of the ion, as a function of ion rn/z to obtain a mass spectrum. Electron multiplier Positive ions Resistive conductive ® (9 surface 69 Secondary \ electrons / I 4’ l l -HV Conversion dynode Cascade of electrons To ground via amplifier Figure 2.7 Schematic of a continuous-dynode electron multiplier coupled with a conversion dynode. (Reproduced and modified from reference 62) 27 CHAPTER THREE Experimental 3.1 Materials Unless stated otherwise, all reagents were analytical reagent (AR) grade and used as supplied without further purification. Guanidine hydrochloride, dithiothreitol (DTT), Trizma® base, Trizma® hydrochloride, urea, calcium chloride, ammonium acetate and ethylenediaminetetraacetic acid (EDTA) were purchased from Sigma Chemical Co. (St. Louis, MO, USA). Formic acid was obtained from EM Science (Darmstadt, Germany). Sequencing grade modified trypsin was from Promega (Madison, WI, USA). HPLC grade water and acetonitrile used in all solutions and HPLC applications were purchased from Burdick & Jackson (Muskegon, MI, USA). Wild-type Rhodopseudomonas palustris CGA009 was grown under photoheterotrophic conditions with benzoate as the carbon source and was a gift from Dr. Dale Pelletier of Oak Ridge National Laboratory. The microbial mixture consisting of Rhodopseudomonas palustrz's CGA009, Saccharomyces cerevisiae, Shewanella oneidensis MR-l and Escherichia coli K-12 (each present at approximately 25% w/w) were grown under chemoheterotrophic conditions and was a gift from Dr. Abhijeet Borole and Dr. Brian Davison of Oak Ridge National Laboratory. 3.2 Cellular Lysis 3.2.1 Rhodopseudomonas palustris Isolate Samples Two separate cellular lysis techniques utilizing either guanidine hydrochloride (HCl) or urea as the lysing agent were initially investigated in this study. The cellular 28 lysis techniques were initially performed on 1, 5 or 25 mg starting cell mass of the R. palustris isolate (aliquoted from a 0.25 g/mL stock solution of cells resuspended in 50 mM Tris/10 mM EDTA). The guanidine HCl lysis method was performed by the addition of 6 M guanidine HCl/ 10 mM DTT (prepared in 50 mM Tris/10 mM CaClz, pH 7.6 at 37 °C) to the starting cellular material followed by incubation at 37 °C overnight with gentle rocking. The urea lysis method was performed by the addition of 8 M urea/10 mM DTT (prepared in 50 mM Tris/10 mM CaClz, pH 7.6 at 37 °C) to the starting cellular material followed by incubation at 37 °C overnight with gentle rocking. 3.2.2 Microbial Mixture Samples The guanidine HCl lysis method was performed on 1, 5 or 25 mg starting cell mass (aliquoted from a 1 g/mL stock solution of cells resuspended in 50 mM Tris/ 10 mM EDTA) Of a microbial mixture consisting of approximately 25% w/w each of Rhodopseudomonas palustris CGA009, Saccharomyces cerevisiae, Shewanella oneidensis MR-l and Escherichia coli K-12. The same procedure as described above for lysis of the R. palustris isolate was used, with the exception that the microbial mixture samples were vortexed every 10 min for the first hour of incubation to ensure proper mixing. 3.3 In-Solution Proteolytic Digestion Following cellular lysis, each sample was diluted six-fold with 50 mM Tris/10 mM CaClz and trypsin was added according to the amount of starting cell mass as follows: 20 pg of trypsin for 25 mg starting cell mass; 10 pg of trypsin for 5 mg or 1 mg 29 starting cell mass. It should be noted that the protein yield following cellular lysis was not quantified, since it was estimated that the protein concentration would likely fall outside of the linear working range for assays commonly employed for protein quantitation. Thus, the amount of trypsin added for proteolytic digestion may not have been optimal. Each sample was then incubated at 37 °C for 6 hr with gentle rocking followed by the addition of a second aliquot of trypsin prior to incubation overnight at 37 °C. A final reduction step was then performed by the addition of 20 mM DTT followed by incubation at 37 °C for 2 hr. Each sample was centrifuged at 10,000 x g for 10 min to pellet unlysed cells and cellular debris and the supernatant was transferred to a clean Eppendorf tube. The resultant peptide mixtures were then desalted using either Sep-Pak Lite C13 cartridges or OMIX® C18 pipette tips (Varian, Palo Alto, CA, USA) according to the manufacturer’s instructions prior to loading the sample onto the biphasic column for LC/LC-MS/MS analysis. Alternatively, the resultant peptide mixtures were loaded directly onto the biphasic column and then desalted by washing with 95% HzO/5% CH3CN/0.l% formic acid for 20-30 min prior to LC/LC-MS/MS analysis. 3.4 Liquid Chromatography/Mass Spectrometry Analysis LC/LC-MS/MS experiments were performed using an Ultimate HPLC system (LC Packings, a division of Dionex, San Francisco, CA, USA) directly coupled to either a quadrupole ion trap mass spectrometer (Thermo model LCQ Deca; San Jose, CA, USA) or a linear quadrupole ion trap mass spectrometer (Thermo model LTQ) equipped with a nanospray ionization (nESI) source. The HPLC pump provided a nominal flow rate of 100 uL/min that was split precolumn to achieve a final flow rate of approximately 300 30 nL/min at the nanospray tip. A biphasic fused silica column (150 um inner diameter) was packed via a pressure cell as follows: approximately 3.5 cm of strong cation exchange (Luna SCX, 5 pm, 100 A; Phenomenex, Torrance, CA, USA) followed by approximately 3.5 cm of C13 reversed—phase (Aqua C13, 5 pm, 200 A; Phenomenex). Each sample was then individually loaded onto the biphasic column using a pressure cell. The loaded biphasic column was then inserted behind a 100 um inner diameter fused silica nanospray tip (pulled in-house using a model P-2000 micropipette puller; Sutter Instrument Company, Novato, CA, USA) packed with approximately 15 cm C13 reversed-phase (Jupiter C13, 5 pm, 300 A; Phenomenex) via a pressure cell. The entire column system was positioned in front of the mass spectrometer using either a nanospray ionization source constructed in-house for analyses performed on the LCQ, or a Proxeon nanospray ionization source (Odense, Denmark) for analyses performed on the LTQ as shown in Figure 3.1. Filter union 3.5 cm 3.5 cm 15 cm F*“w“ r ““rr ». ‘ ”I) (‘i Reversed Strong cation Reversed phase phase exchange Heated capillary Figure 3.1 Schematic of the column system used for two-dimensional liquid chromatography separation. (Reproduced and modified from reference 63) Each sample was individually analyzed using a 24 hr, 12-step, two-dimensional liquid chromatographic method consisting of increasing salt pulses (0-500 mM) of ammonium acetate, followed by 2 hr reverse phase gradients from 100% aqueous solvent 31 (95% H20/5% CH3CN/0.l% formic acid) to 50% organic solvent (30% H20/7O% CH3CN/0.1% formic acid). The spray voltage of the mass spectrometer was maintained at 3.6 kV and the heated capillary temperature was 200 °C for experiments performed on both the LCQ and LTQ. The LCQ and LTQ were both operated in a data dependent acquisition mode where either the four (LCQ) or five (LTQ) most abundant precursor ions identified above a preset threshold of 1.00 x 103 counts were automatically isolated and subjected to CID-MS/MS following the acquisition of a full MS scan (m/z 400- 1700). Dynamic exclusion [64, 65] was enabled with a repeat count Of 1 and the exclusion duration time set to 3 min. 3.5 Data Analysis Uninterpreted MS/MS Spectra were searched against an appropriate database as described below using the DBDigger algorithm [35] with the following parameters: Enzyme type: trypsin; Precursor mass tolerance: 3.0; Fragment ion tolerance: 0.5; Number of possible missed cleavage sites: 4; fully tryptic peptides only. For the R. palustris isolate data, searches were performed using a database of 4833 predicted proteins concatenated with 36 common contaminants (e.g., trypsin, keratin, etc.). This database was constructed from the genome annotation of R. palustris performed at Oak Ridge National Laboratory and is available on-line at http://compbio.ornl.gov/rpal_proteome/databases. For the microbial mixture data, searches were performed using a database containing the predicted proteins of R. palustris CGA009, S. cerevisiae, S. onez'a'ensis MR-l and E. coli K-12 concatenated with 36 common contaminants. The output data files from each database search were filtered 32 using the DTASelect algorithm [66] with the following parameters: tryptic peptides only, minimum delCN of 0.08 and MASPIC [67] scores of at least 25 (+1 charge state), 30 (+2 charge state) and 45 (+3 charge state). It has been previously demonstrated that these filtering criteria result in a false positive protein identification rate of 1-2% [30, 68]. The algorithm Contrast [66] was then used to compare the number of proteins identified for the different bacterial cell lysis and sample clean-up techniques investigated. 33 CHAPTER FOUR Results and Discussion 4.1 Introduction As discussed in Chapter 1, the key issues associated with the bottom-up tandem mass spectrometry (MS/MS) analysis of proteins expressed from milligram amounts of starting cellular material are bacterial cell lysis and sample clean-up. To address these challenges, alternative methods for bacterial cell lysis and sample clean-up were investigated in this study in order to develop a bottom—up MS/MS-based approach for the detection and identification of biological threat agents. The experimental approach for this study, illustrated in Figure 4.1, involved the lysis of bacterial cells followed by proteolytic digestion using trypsin, both of which were performed in a single tube. Performing all stages of sample preparation in a single tube is important, because it minimizes sample losses and cross-contamination. The resultant peptide mixtures were then desalted and analyzed by two-dimensional liquid chromatography coupled with nanoelectrospray ionization tandem mass spectrometry (LC/LC-MS/MS). The uninterpreted MS/MS spectra were then searched against an appropriate database using the DBDigger algorithm [3 5] and filtered with DTASelect [66]. 34 1. Cell lysis/Protein denaturation & reduction V 2. Digest with trypsin Desalt peptide mixture I LC/LC-MS/MS analysis Data analysis using DBDigger/DTA Select Figure 4.1 Schematic of the experimental approach used in this study. 4.2 Development of a Bacterial Cell Lysis Technique Guanidine hydrochloride (HCl) and urea were initially investigated as potential reagents for achieving lysis of milligram amounts of starting cellular material. Guanidine HCl and urea were chosen because both of these reagents are commonly used to disrupt the secondary and tertiary structure of proteins (i.e., denature) prior to proteolytic digestion. Thus, it is possible that cellular lysis and protein denaturation could be achieved in a single step. In addition, the conditions used for cellular lysis and protein denaturation are compatible with the conditions required for proteolytic digestion, 35 therefore it is feasible that all stages Of sample preparation could be carried out in a single tube prior to mass spectrometry analysis. Performing cellular lysis and proteolytic digestion in a single tube would reduce the number of surfaces that proteins/peptides come into contact with, thereby reducing sample losses and minimizing cross- contamination. TO determine whether guanidine HCl or urea could be utilized as lysing reagents, the bacterium Rhodopseudomonas palustris, which is widely distributed throughout the environment, was used as a model organism for initial studies. Cellular lysis was initially performed using 25 mg of starting cellular material. For this initial investigation, the peptide mixtures were desalted using solid-phase extraction, a technique commonly employed in large-scale proteome experiments, and the traditional LCQ three- dimensional quadrupole ion trap mass spectrometer was used for mass spectrometry analysis. Uninterpreted MS/MS spectra were searched against a database of 4833 predicted R. palustris proteins concatenated with 36 common contaminants. Lysis of 25 mg of starting cellular material using guanidine HCl and urea resulted in 794 and 667 protein identifications, respectively (Figure 4.2). This represents 16% and 14% of the proteins which have been predicted to be encoded in the R. palustris genome based on genome annotation [69]. As shown in Figure 4.2, 506 protein identifications were common to both the guanidine HCl and urea lysis methods, while 288 and 161 protein identifications were unique to the guanidine HCl and urea lysis methods, respectively. The number of proteins identified here are consistent with the numbers obtained from large-scale lysis by sonication, whereby 800-900 proteins are typically identified [30]. Thus, these preliminary results suggested that both guanidine HCl and 36 urea had the potential to be utilized as reagents for small-scale cell lysis without the loss of information. Guanidine Hydrochloride Figure 4.2. Venn diagram for the 25 mg aliquot of R. palustris subjected to lysis using guanidine HCl or urea. Samples were desalted by solid-phase extraction and analyzed using the LCQ. The numbers represent the total number of identified proteins shared between the samples and unique to each sample. A representative example of the process by which protein identification was achieved is depicted in Figure 4.3 for the 25 mg aliquot of R. palustrz's subjected to lysis using guanidine HCl. Figure 4.3A shows the base peak chromatogram following reversed-phase separation of the complex peptide mixture resulting from tryptic digestion. For this example, the 30% ammonium acetate salt step is represented. The MS spectrum obtained from time point 1 of the base peak chromatogram (63.9 minutes) is shown in Figure 4.38 and the MS/MS product ion spectrum Obtained by dissociation of the doubly protonated precursor ion at m/z 735.7 is depicted in Figure 4.3C. Following analysis using the DBDigger algorithm, the MS/MS product ion spectrum was matched to the predicted peptide HYDVIVAPWTEK, which is unique to the R. palustrz's rpIW 50S ribosomal protein L23. Each letter in the peptide sequence represents an individual amino acid residue. 37 100T 20 40 6O 80 100 120 Retention time (min) 100 |M+2H|1+ 1009.9 " 735.7 E \ ~ I «3 635.5 1469.7 2 905.4 ”88'7 3 — 595.1 1225.6 <3 3 — 1154.6 3' 0) —t ed °\° ' l" i I . ; “"huhfl-lh 400 600 800 l 000 1200 l 400 1 600 ys 100 — C Y9 YII ._ b2 b4 b5 Y7 0 Y6 b b ‘ b3 6 7 Y10 __ b3 m 82 a4 bIO 44er . nil I ...lil alumiu . -r JA . J .4. 41.12 _.i _ 1 L .-i.l L I I I I I I I I f I I I 200 400 600 800 1 000 l 200 1400 m/z Figure 4.3 Liquid chromatography-mass spectrometry analysis of the peptide mixture resulting from guanidine HCl lysis and tryptic digestion of 25 mg of R. palustris. (A) Base peak chromatogram following reversed-phase separation (30% ammonium acetate salt step). (B) Mass spectrum obtained from time point 1 of the base peak chromatogram. (C) MS/MS product ion spectrum obtained from dissociation of the doubly protonated precursor ion (m/z 735.7) of the predicted peptide HYDVIVAPVVTEK from the R. palustris rpIW 50S ribosomal protein L23. 38 4.3 Comparison of Sample Clean-up Techniques Prior to MS/MS Analysis Given the above results, 5 mg of R. palustris was then subjected to cellular lysis using either guanidine HCl or urea. The LCQ three-dimensional quadrupole ion trap mass Spectrometer was used for mass spectrometry analysis, however, two-altemative sample clean-up techniques, solid-phase microextraction and on-line desalting, were investigated. Solid-phase microextraction utilizes a reversed-phase C13 chromatographic material packed into a pipette tip. This technique is specifically designed to desalt and enrich small quantities of sample (IO-100 pg binding capacity) in order to minimize sample losses. Using on-line desalting, a sample is first loaded onto the biphasic (reversed-phase/strong cation exchange) chromatography column used for separation. Following peptide binding, the chromatography column is washed for 20-30 minutes with 95% water/5% acetonitrile/0.1% formic acid to desalt prior to LC/LC-MS/MS analysis. Lysis of 5 mg of starting cellular material using guanidine HCl and urea resulted in the identification of 273 and 265 proteins, respectively, when solid-phase microextraction was used for sample clean-up (Figure 4.4A). For both lysis methods, this represents 6% of the proteins predicted to be encoded in the R. palustris genome. 189 protein identifications were shared between the guanidine HCl and urea lysis methods, while 84 and 76 protein identifications were unique to the guanidine HCl and urea lysis methods, respectively. When on-line desalting was utilized for sample clean-up, 419 and 391 proteins were identified for the guanidine HCl and urea lysis methods (Figure 4.48), which represents 9% and 8% of the proteins predicted to be encoded in the R. palustris genome. 252 of the proteins identified were common to both lysis methods, while 167 39 and 139 protein identifications were unique to the guanidine HCl and urea lysis methods, respectively. Guanidine Hydrochloride On-line desalting Solid-phase microextraction 180 On-line desalting Guanidine Hydrochloride Solid-phase microextraction 167 208 Figure 4.4 Venn diagrams for the 5 mg aliquot of R. palustris subjected to lysis using (A) guanidine HCl or urea and desalted using solid-phase microextraction, (B) guanidine HCl or urea and desalted on-line, (C) guanidine HCl and desalted either on-line or using solid- phase microextraction, and (D) urea and desalted either on-line or using solid-phase microextraction. Samples were analyzed using the LCQ. The numbers represent the total number of identified proteins shared between the samples and unique to each sample. It is evident from the above results that on-line desalting resulted in more protein identifications than solid-phase microextraction following lysis with either guanidine HCl or urea. This is further illustrated in Figures 4.4C and 4.4D. 239 of the proteins identified following lysis using guanidine HCl were common to the analyses in which the on-line desalting and solid-phase microextraction sample clean-up techniques were employed (Figure 4.4C). However, 180 unique proteins were identified in the analysis which utilized on-line desalting, while only 34 unique proteins were identified in the analysis 40 which employed solid-phase microextraction. Similar results were also obtained when urea was used for cellular lysis. 183 of the proteins identified following lysis using urea were common to the analyses in which on-line desalting and solid-phase microextraction sample clean-up techniques were employed (Figure 4.4D). A total of 208 unique proteins were identified in the analysis utilizing on—line desalting, while only 82 unique proteins were identified in the analysis employing solid-phase microextraction. Given that more proteins were identified when on-line desalting was utilized suggests that this sample clean-up technique minimizes sample losses. This result is somewhat expected since the peptide mixture is loaded directly onto the biphasic chromatographic column fiom the same tube where cellular lysis and proteolytic digestion are performed. Thus, on-line desalting was determined to be the optimal sample clean-up technique for peptide mixtures derived from milligram amounts of starting cellular material. 4.4 Application of Small-scale Bacterial Cell Lysis and Sample Clean-up Techniques to Reduced Amounts of Starting Cellular Material Given the success of guanidine HCl and urea in achieving cell lysis of 25 mg and 5 mg of starting cellular material, each lysis technique was also applied to 1 mg of R. palustris. The peptide mixtures resulting from proteolytic digestion were desalted on—line and the LCQ three-dimensional quadrupole ion trap mass spectrometer was used for mass spectrometry analysis. A total of 651 and 468 proteins, which represents 14% and 10% of the proteins predicted to be encoded in the R. palustris genome, were identified using guanidine HCl and urea, respectively (Figure 4.5). 384 protein identifications were shared between the guanidine HCl and urea lysis methods, while 267 and 84 protein identifications were unique to the guanidine HCl and urea lysis methods, respectively. 41 Interestingly, when guanidine HCl and urea were employed as lysing reagents, more proteins were identified following analysis of the 1 mg aliquot versus the 5 mg aliquot. This observation could potentially be rationalized by taking into consideration the amount of trypsin that was added to each sample for proteolytic digestion. For the 1 mg and 5 mg aliquots of R. palustris 10 pg of trypsin was added to each sample. Thus, the trypsin/protein ratio would have been greater for the 1 mg aliquot, which could have potentially resulted in more efficient proteolytic digestion. Further evidence in support of this explanation was provided by the fact that an increase in trypsin autodigestion was Observed for the 1 mg aliquot (discussed in more detail in Section 4.8). Guanidine Hydrochloride Figure 4.5 Venn diagram for the 1 mg aliquot of R. palustris subjected to lysis using guanidine HCl or urea. Samples were desalted on-line and analyzed using the LCQ. The numbers represent the total number of identified proteins shared between the samples and unique to each sample. Based on the results described above, which demonstrated that the guanidine HCl lysis method resulted in more protein identifications than the urea lysis method for all amounts Of starting cellular material investigated, guanidine HCl was selected as the exclusive lysis reagent for all subsequent analyses. 42 4.5 Assessment of Reproducibility In order to assess the reproducibility of the guanidine HCl lysis method, additional 5 mg and 1 mg aliquots Of R. palustris were subjected to cellular lysis. Following cellular lysis and tryptic digestion, the resultant peptide mixtures were desalted on-line and then analyzed by LC/LC-MS/MS using an LCQ three-dimensional quadrupole ion trap mass spectrometer. A total of 596 proteins were identified following the lysis of 5 mg of starting cellular material, with a total Of 674 non-redundant proteins identified from the two replicate experiments (Figure 4.6A). Of the 674 total proteins identified, 341 proteins were identified in both analyses, while 78 and 255 unique proteins were identified in the first and second analysis, respectively. The fact that an additional 177 proteins were identified after performing the guanidine HCl lysis method on the second 5 mg aliquot suggests that there is variability in the lysis and/or proteolytic digestion steps. However, this is difficult to assess, as the experiment was only performed in duplicate due to time constraints. Following lysis of the 1 mg aliquot, 662 proteins were identified, with a total of 790 non-redundant proteins identified from the two replicate experiments (Figure 4.68). Of the 790 total proteins identified, 523 proteins were shared between both analyses, while 129 and 139 unique proteins were identified in the first and second analysis, respectively. Given that the total number of identified proteins was consistent following lysis of each individual 1 mg aliquot suggests that the lysis and proteolytic digestion steps may in fact be reproducible. The difference in reproducibility between the 1 mg and 5 mg aliquots could be due to more efficient proteolytic digestion of the 1 mg aliquot as a result of the greater trypsin/protein ratio. However, this is difficult to confirm as the experiment was only performed in duplicate. 43 Analysis #1 Analysis #2 Analysis #1 Analysis #2 Figure 4.6 Venn diagrams for replicate analysis of the 5 mg (panel A) and 1 mg (panel B) aliquots of R. palustris subjected to lysis using guanidine HCl. Samples were desalted on- line and analyzed using the LCQ. The numbers represent the total number of identified proteins shared between the samples and unique to each sample. 4.6 Assessment of Bias The predicted proteome of R. palustris has been divided into 16 functional categories based on the Oak Ridge National Laboratory annotation scheme for bacteria (http://genome.ornl.gov/microbialh. These categories include hypothetical, unknowns and unclassified, replication and repair, energy metabolism, carbon and carbohydrate metabolism, lipid metabolism, transcription, translation, cellular processes, amino acid metabolism, general function prediction, metabolism of cofactors and vitamins, conserved hypothetical, transport, signal transduction, and purine and pyrimidine metabolism. The total number Of non-redundant proteins identified from the 5 mg and 1 mg replicate experiments using guanidine HCl as the lysing reagent were organized according to these 16 functional categories (Figures 4.7 and 4.8, respectively). The non- bolded percentages in Figures 4.7 and 4.8 represent the number Of proteins identified for a given category divided by the total number of non-redundant proteins identified by LC/LC-MS/MS. For example, 8% of the 674 proteins identified following analysis of the 5 mg aliquot of R. palustris were found to be involved in signal transduction. The bolded 44 percentages represent the number Of proteins predicted from the R. palustris genome for a given category divided by the total number of proteins predicted from the genome. These two percentages can be compared for each individual category in order to assess whether the bottom-up tandem mass Spectrometry approach developed here is biased towards the types of proteins being identified. In general, the percentage of the total identified proteome was similar to the percentage of the total predicted proteome for each individual functional category indicating that there was minimal bias. The most notable exceptions were the categories translation and unknown and unclassified which were overrepresented, as well as the categories hypothetical, conserved hypothetical and transport which were underrepresented. It is important to note that these conclusions are based on the assumption that all of the genes are being transcribed and translated equally. However, this is not a fair assumption, because gene expression is dependent upon many factors including growth conditions and the stage of the cell cycle during which the cells are harvested. Thus, it is difficult to assess whether this approach is biased towards a particular class of proteins. Irrespective of this, it is evident that protein identifications were achieved for all 16 functional categories following lysis of both the 5 mg and 1 mg aliquots of R. palustris. This is particularly important for the analysis of biological threat agents, where a unique protein biomarker could feasibly belong to any class of proteins. It is also important to recognize that the percentage of the total identified proteome was similar for both the 5 mg and 1 mg aliquots, indicating that the bottom-up tandem mass spectrometry approach does not result in the loss of information when the amount of starting cellular material is decreased. 45 Purine & Pyrimidine Signal Metabolism Transduction (3%, 1%) Transport (3%, 5%) Conserved (3%» 15%) Hypothetical Hypothetical / (1%, 9%) Unknowns & Unclassified (1%, 11%) Metabolism of \ i q (17%, 9%) Replication & Repair / (1%, 3%) Cofactors & Enzymes En "8y (4%, 3%) Metabolism G eneral (9%“ 6%) Function Carbon 8!. Fred rctlon Carbohydrate Metabolism (7%, 9%) (4%, 2%) Amino Acid Metabolism (7%, 4%) Lipid Metabolism (6%, 3%) Transcription (3%, 6%) Cellular Processes (13%, 11%) Translation (12%, 3%) 674 Identified Proteins Figure 4.7 Total number of non-redundant proteins identified following LCQ-MS/MS analysis of the 5 mg aliquot of R. palustris subjected to lysis using guanidine HCl and desalted on-line. The identified proteins are organized according to the functional categories of R. palustris. The non-bolded and bolded percentages represent the percentage of the identified proteome and the percentage of the predicted proteome, respectively. 46 Purine & Pyrimidine Signal Metabolism Transduction (2%, 1%) “TSP? (3%, 5%) Conserved (9 A" '5 /") Hypothetical Hypothetical (1%, 9%) Unknowns & (I%, 11%;) Unclassified l9°/, 9"/) . . Metabolism of \ . ( ° " Rephcatron & Cofactors & H .. R arr Enzymes (1%, 3%) (4%, 3%) . Energy Metabolism General —/ Function (9%: 6%) Prediction Carbon & (8%, 9%) Carbohydrate Metabolism (4%, 2%) Amino Acid Metabolism L' 'd 6v.4°/ 1‘” ( ° °) Metabolism (6%, 3%) Cellular Processes (13%, 11%) Transcription Translation (3%" 6%) (11%, 3%) 790 Proteins Identified Figure 4.8 Total number of non-redundant proteins identified following LCQ-MS/MS analysis of the 1 mg aliquot of R. palustris subjected to lysis using guanidine HCl and desalted on-line. The identified proteins are organized according to the functional categories of R. palustris. The non-bolded and bolded percentages represent the percentage of the identified proteome and the percentage of the predicted proteome, respectively. 4.7 Comparison of Mass Spectrometry Instrumentation It has been previously demonstrated that the increased duty cycle of the two- dimensional linear quadrupole ion trap results in more protein identifications than the traditional three-dimensional quadrupole ion trap when employed in bottom-up tandem mass spectrometry approaches [70-72]. Therefore, a 1 mg aliquot of R. palustris was subjected to cellular lysis using guanidine HCl, desalted on-line and then subsequently analyzed by LC/LC-MS/MS using an LTQ linear quadrupole ion trap mass spectrometer. Analysis of the 1 mg aliquot of R. palustris using the LTQ resulted in a total of 2135 proteins being identified, which represents 44% of the proteins predicted to be encoded in 47 the R. palustris genome. This is approximately three times as many proteins as that identified using the LCQ. With the exception of five proteins, all of the proteins identified using the LCQ were also identified using the LTQ. Although more proteins were identified using the LTQ, the category distribution of the identified proteins was similar to that obtained for the LCQ (Figure 4.9). Unknown and unclassified was the category which was overrepresented, while hypothetical, transport and conserved hypothetical were the categories which were underrepresented. Given that more proteins were identified using the LTQ suggests that the linear quadrupole ion trap would be the most suitable instrument platform for MS/MS analysis of peptides derived from milligram amounts of starting cellular material. Purine & Pyrimr'dine Metabolism Signal (2%, 1%) Transduction o 0 ' Tmnspm (5 A. 5 A)\ f iii/3‘33]?! Unknowns & (9%, 15%) ‘ Unclassrfied Conserved _ (14%, 9%) Replication & Hypothetical (504.11%) \ Metabolism of —a Cofactors& Enzymes (5%. 3%) Energy Metabolism (8%, 6%) Carbon & Carbohydrate Metabolism (4%, 2%) General Function —/ Lipid. Prediction Metabolrsm (l0%, 9%) (5%, 3%) Amino Acid Transcription Metabolism (4%, 6%) Translation Cellular Processes (6%, 3%) (13%. 11%) (6%, 4%) 2135 Identified Proteins Figure 4.9 Total number of proteins identified following LTQ-MS/MS analysis of the 1 mg aliquot of R. palustris subjected to lysis using guanidine HCl and desalted on-line. The identified proteins are organized according to the functional categories of R. palustris. The non—bolded and bolded percentages represent the percentage of the identified proteome and the percentage of the predicted proteome, respectively. 48 4.8 Protein Quantitation It is important to note that for the analyses described above, the total protein concentration following cellular lysis was not quantified, since it was estimated that the protein concentration would likely fall outside of the linear working range for assays commonly employed for protein quantitation, typically 20-2,000 ug/mL. Total protein concentration is usually measured prior to tryptic digestion in order to determine the appropriate amount of trypsin to be added to achieve a desired trypsin/protein ratio (usually in the range of 1:50 to 1:100). When trypsin is added in excess of the protein substrate, cleavage of trypsin may dominate, which could potentially reduce the total number of endogenous proteins identified from the sample. To assess the extent to which trypsin autodigestion had occurred following proteolytic cleavage of the 25, 5 and 1 mg aliquots of the R. palustris guanidine HCl lysis samples, the percent sequence coverage and the spectral count number related to trypsin were determined. The percent sequence coverage is defined as the ratio of the number of peptides observed to the total number of peptides which could theoretically be observed. The spectral count number refers to the total number of non-redundant MS/MS spectra which identify a protein. The percent sequence coverage and the spectral count number were determined for three types of peptides which could potentially be observed, fully tryptic, semi—tryptic and non-tryptic. Fully tryptic peptides result from cleavage at lysine or arginine residues at both the N- and C-terminus of the peptide, while semi-tryptic and non-tryptic peptides result from cleavage at residues other than arginine or lysine at one end or both ends of the peptide, respectively. As shown in Table 4.1, both the percent sequence coverage and spectral count number corresponding to trypsin increased for each peptide classification as the 49 amount of starting cellular material decreased. These results suggest that the trypsin concentration is an important factor to consider when working with limited amounts of starting cellular material. Although not investigated here, it would be beneficial to determine a method for measuring the total protein concentration in small-scale analysis. Fully Tryptic Semi Tryptic Non Tryptic Cellular material Sequence Spectral Sequence Spectral Sequence Spectral (mg) coverage (%) count coverage (%) count coverage (%) count 25 18.2 27 31.6 37 31.6 34 5 24.2 17 58.4 48 68.4 47 1 51.5 116 86.6 348 90.5 388 Table 4.1 Percent sequence coverage and spectral count number corresponding to trypsin for the 25, 5 and 1 mg aliquots of R. palustris subjected to lysis using guanidine HCl, desalted on-line and analyzed using the LCQ. 4.9 Application to a Complex Microbial Mixture Given the success of guanidine HCl in achieving cell lysis of R. palustris, the lysis method was also extended to a more complex microbial mixture, which would be more representative of the type of sample likely to be encountered in biological threat agent detection and identification. The mixture chosen for analysis was readily available in the laboratory and consisted of two common soil microbes, Rhodopseudomonas palustris and Shewanella oneidensis MR—l, the model yeast species Saccharomyces cerevisiae, and Escherichia coli K-12, each present at approximately 25% w/w. The total amount of starting cellular material chosen for cellular lysis using guanidine HCl was 1, 5 and 25 mg. Following cellular lysis, the protein complement was subjected to proteolytic digestion using trypsin, and the resultant peptide mixtures were desalted on-line and analyzed by LC/LC—MS/MS using an LCQ quadrupole ion trap mass spectrometer. The uninterpreted MS/MS spectra were then searched against a database containing the 50 predicted proteins of R. palustris, S. oneidensis, S. cerevisiae and E. coli plus 36 common contaminants using the DBDigger algorithm [35] followed by filtering with DTASelect [66]. Figure 4.10 illustrates the number of proteins identified for each species present in the microbial mixture as a function of the amount of starting cellular material subjected to lysis using guanidine HCl. I E. coli 400 - 389 13"? R. palustris C] S. cerevisiae I S. oneidenris 200* l50e Number of Protein Identifications l()() - l 5 25 Total Amount of Starting Cellular Material (mg) Figure 4.10 Number of proteins identified for each species present in a four microbe mixture as a function of the amount of starting cellular material subjected to lysis using guanidine HCl. All samples were desalted on-line and analyzed using the LCQ. It can be seen that the guanidine HCl lysis method resulted in protein identifications for all species present in the microbial mixture, including S. cerevisiae, which is generally more difficult to lyse due to the presence of a tough and rigid cell wall [73]. In fact, S. cerevisiae resulted in the second highest number of protein identifications out of the four 51 microbial species present in the mixture for all amounts of starting cellular material investigated. The most number of protein identifications were achieved for E. coli, while S. oneidensis resulted in the least number of protein identifications for all amounts of starting cellular material. Interestingly, the number of proteins identified for each individual species present in the microbial mixture did not correlate with the number of proteins predicted to be encoded in each of the species respective genomes. For example, 4836 [69], 4931 [74] and 4288 [75] proteins are predicted to be encoded in the R. palustris, S. onez’densis and E. coli genomes, respectively. Given that the number of predicted proteins for these three species is approximately equal, it would have been expected that roughly the same number of proteins would have been identified for each species. However, 2-3 times as many proteins were identified for E. coli compared to R. palustris and S. oneidensis, which both had approximately the same number of protein identifications. Subtle differences in the composition of the cell membranes of these microbes, as well as differences in the transcriptome levels could account to some extent for the differences observed in the number of protein identifications. The fact that S. oneidensis has a thick and rigid cell wall helps to explain why more protein identifications were achieved for E. coli than for S. oneidensis, even though 2320 more proteins are predicted to be encoded in the S. oneia’ensis genome [76] than that predicted to be encoded in the E. coli genome [75]. Even though there were differences in the number of proteins identified for each individual species, it is important to highlight that protein identifications were achieved for all four species present within the mixture even when as little as 1 mg of starting cellular material was subjected to lysis. This demonstrates that the small-scale bottom-up tandem mass spectrometry approach 52 deve10ped here is generally applicable to the analysis of different types of microbes, which is important in the context of biological threat agent detection and identification where the potential biological threat agent present within a mixture will not be known. However, it is important to recognize that as the complexity of the mixture increases, the number of proteins identified for any given species present within the mixture will decrease. For example, 700-800 R. palustris proteins were identified when R. palustris was the only species present, while only 150-200 proteins were identified when R. palustris was analyzed as part of the four microbe mixture. It is expected that if the same analysis were to be performed using the LTQ, which has an increased duty cycle compared to that of the LCQ, more protein identifications would be achieved for each individual species present within the mixture. 4.10 Summary Overall, the small-scale bottom-up tandem mass spectrometry approach developed here has shown great potential for the detection and identification of biological threat agents, not only when present individually, but also when present as part of a complex microbial mixture. The potential limitation of this approach is the length of time required for sample preparation and mass spectrometry analysis, which would render it unsuitable for the on-site, real-time detection and identification of biological threat agents. However, this approach would find importance in the field of microbial forensics which attempts to link the strain of a biological threat agent used in a terrorist attack to the individual or group responsible. Previous studies have demonstrated the potential of the bottom-up tandem mass spectrometry approach to accurately identify strains of microorganisms [37, 77]. This approach could also be used to confirm whether a 53 “positive” identification achieved by real-time, on-site detection methods is in fact a true positive identification. In both situations the time required for analysis would not be as critical. 54 CHAPTER FIVE Conclusions and Future Directions 5.1 Conclusions Due to the increased threat of terrorism there is currently a need to develop sensitive and specific methods for the detection and positive identification of biological threat agents. The detection and identification of biological threat agents based on the presence of unique protein biomarkers is particularly attractive given that proteins are present in high abundance in all potential biological threat agents, and that a high degree of protein variability exists between species. In recent years, the bottom-up tandem mass spectrometry (MS/MS) approach has emerged as one of the dominant methods employed for identifying proteins expressed by various microorganisms. Thus, the bottom-up MS/MS approach is an attractive option for the detection and identification of biological threat agents. While the general bottom-up MS/MS approach is well established for the large-scale analysis of proteins, problems are likely to arise when analyzing limited quantities of starting cellular material, as would often be encountered in biological threat agent detection and identification. The work presented here aimed to address the challenges associated with the analysis of cellular material at the milligram scale, with emphasis on bacterial cell lysis and sample clean-up, in an effort to develop a bottom-up tandem mass spectrometry approach for biological threat agent detection and identification. Initially, guanidine hydrochloride and urea were investigated as potential reagents for achieving lysis of milligram amounts of starting cellular material using the model 55 bacterium Rhodopseudomonas palustris. It was demonstrated that the number of proteins identified following lysis with guanidine hydrochloride or urea was comparable with the number of proteins typically identified following large-scale lysis by sonication, even when as little as 1 mg of starting cellular material was subjected to lysis. For the 25, 5 and 1 mg aliquots of R. palustris investigated in this study, guanidine hydrochloride resulted in more protein identifications than that of urea. Thus, it was determined that guanidine hydrochloride would be the optimal reagent to use for bacterial cell lysis at small scales. One of the advantages to using guanidine hydrochloride is that it enables bacterial cell lysis and protein denaturation tobe achieved simultaneously. In addition, the guanidine hydrochloride lysis conditions are compatible with the conditions required for proteolytic digestion, therefore all stages of sample preparation can be performed in the same tube such that sample losses and cross-contamination can be minimized. Solid-phase microextraction and on-line desalting were both investigated as alternative techniques for achieving sample clean-up prior to liquid chromatography- tandem mass spectrometry analysis. These sample clean-up techniques were explored in an effort to minimize sample losses. Following analysis of the 5 mg aliquot of R. palustris, which had been subjected to lysis using either guanidine hydrochloride or urea, it was demonstrated that sample clean-up using on-line desalting resulted in more protein identifications than that of solid-phase microextraction. Thus, it was determined that on- line desalting would be the most appropriate method to use for sample clean-up when milligram amounts of starting material are subjected to cellular lysis. Although the reproducibility of the small-scale bottom-up tandem mass spectrometry approach was investigated in this study, it was difficult to assess since the 5 56 mg and 1 mg aliquots of R. palustris were only analyzed in duplicate. However, by organizing the non-redundant protein identifications resulting from replicate analysis of the 5 mg and 1 mg aliquots into the 16 functional categories of R. palustris it was revealed that protein identifications were achieved for all 16 functional categories with minimal bias. This is particularly important for biological threat agent detection and identification where a unique protein biomarker could feasibly belong to any class of proteins. It was also demonstrated in this study that the two-dimensional linear quadrupole ion trap significantly out-performed the traditional three-dimensional quadrupole ion trap with respect to the number of proteins identified following MS/MS analysis. The two-dimensional linear quadrupole ion trap resulted in a three-fold increase in the number of proteins identified for the 1 mg aliquot of R. palustris subjected to guanidine hydrochloride lysis. The guanidine hydrochloride lysis method was also extended to a microbial mixture consisting of Rhodopseudomonas palustris, Shewanella oneidensis MR-l, Saccharomyces cerevisiae, and Escherichia coli K-12. The complexity of this mixture is more representative of the type of sample which is likely to be encountered in biological threat agent detection and identification. For all amounts of starting cellular material investigated, it was demonstrated that protein identifications could be achieved for all species present within the mixture, although fewer proteins were identified for each species than if they were to each be present individually as expected due to current instrumental capabilities. 57 5.2 Future Directions While the small-scale bottom-up tandem mass spectrometry approach developed here has shown great potential for the detection and identification of biological threat agents, future work is still required to fully optimize this technique. It was demonstrated in this study that trypsin concentration is an important factor that must be carefully considered in order to minimize the extent to which trypsin autodigestion occurs. Thus, it would be beneficial to investigate methods for measuring the total protein concentration resulting from small-scale cell lysis. For example, Pierce has developed a Micro Bicinchoninic Acid (BCATM) Protein Assay (cat. no. 23235) which is formulated for measuring dilute protein solutions in the range of 05-20 ug/mL. In order to fully investigate the reproducibility of the small-scale bottom-up tandem mass spectrometry approach it would be useful to perform each analysis at least in triplicate. It would also be beneficial to perform a series of time course experiments to determine if efficient cell lysis can be achieved at incubation times of less than 24 hours in order to reduce the overall analysis time. Finally, it would also be useful to apply the small-scale bottom-up tandem mass spectrometry approach to the analysis of microgram amounts of starting cellular material, which might be more typical of the amount encountered in biological threat agent detection and identification. 58 10. 11. REFERENCES . Department of Health and Human Services, Centers for Disease Control and Prevention, Bioterrorism Overview. www.bt.cdc.gov/bioterrorism/overview.asp (accessed March 4, 2008). Bhalla, D.K.; Warheit, D.B. Biological agents with potential for misuse: a historical perspective and defense measures. T oxicol. Appl. Pharmacol. 2004, 199, 71-84. Torok, T.J.; Tauxe, R.V; Wise, R.P; Livengood, J.R; Sokolow, R.; Mauvais, S.; Birkness, K.A.; Skeels, M.R.; Horan, J .M.; Foster, L.R. A large community outbreak of salmonellosis caused by intentional contamination of restaurant salad bars. JAMA 1997, 278, 389-395. Higgins, J .A.; Cooper, M.; Schroeder-Tucker, L.; Black, S.; Miller, D.; Kams, J.S.; Manthey, E.; Breeze, R.; Perdue, M.L. A field investigation of Bacillus anthracis contamination of US. Department of Agriculture and other Washington DC. buildings during the antrax attack of October 2001. Appl. Environ. Microbiol. 2003, 69, 593-599. Henderson, BA. The looming threat of bioterrorism. Science 1999, 283, 1279-1289. Broussard, L.A. Biological agents: weapons of warfare and bioterrorism. Mol. Diagn. 2001, 6, 323-333. Breeze, R.; Budowle, B.; Schutzer, S. Microbial Forensics; Academic Press: Burlington, MA, 2005. Department of Health and Human Services, Centers for Disease Control and Prevention, Bioterrorism Agents/Diseases. www.bt.cdc.gov/agent/agentlist.asp (accessed February 24, 2008). Keim, P.; Smith, K.L.; Keys, C.; Takahashi, H.; Kurata, T.; Kaufinann, A. Molecular investigation of the Aum Shinrikyo anthrax release in Kameido, Japan. J. Clin. Microbiol. 2001, 39, 4566-4567. Lim, D.V.; Simpson, J.M.; Keams, E.A.; Kramer, M.F. Current and developing technologies for monitoring agents of bioterrorism and biowarfare. Clin. Microbiol. Rev. 2005, 18, 583-607. Ivnitski, D.; O’Neil, D.J.; Gattuso, A.; Schlicht, R.; Calidonna, M.; Fisher, R. Nucleic acid approaches for the detection and identification of biological warfare and infectious disease agents. Biotechniques 2003, 35, 862-869. 59 12. l3. 14. 15. l6. 17. 18. 19. 20. 21. 22. 23. Peruski, L.F.; Peruski, A.H. Rapid diagnostic assays in the genomic biology era: detection and identification of infectious disease and biological weapon agents. Biotechniques 2003, 35, 840-846. Barshick, S.A.; Wolf, D.A.; Vass, A.A. Differentiation of microorganisms based on pyrolysis-ion trap mass spectrometry using chemical ionization. Anal. Chem. 1999, 71, 633-641. Griest, W.H.; Wise, M.B.; Hart, K.J.; Lammert, S.A.; Thompson, C.V.; Vass, A.A. Biological agent detection and identification by the block 11 chemical biological mass spectrometer. Field Anal. Chem. Techno]. 2001, 5, 177-184. Peruski, A.H.; Peruski, L.F. Immunological methods for detection and identification of infectious disease and biological warfare agents. Clin. Diagn. Lab. Immunol. 2003, 10, 506-513. Cain, T.C.; Lubman, D.M.; Weber, W.J. Differentiation of bacteria using protein profiles from matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Rapid Commun. Mass Spectrom. 1994, 8, 1026-1030. Krishnamurthy, T.; Ross, P.L. Rapid identification of bacteria by direct matrix- assisted laser desorption/ionization mass spectrometric analysis of whole cells. Rapid Commun. Mass Spectrom. 1996, 10, 1992-1996. Demirev, P.A.; Ho, Y.P.; Ryzhov, V.; Fenselau, C. Microorganism identification by mass spectrometry and protein database searches. Anal. Chem. 1999, 71, 2732-2738. Fenselau, C.; Demirev, P.A. Characterization of intact microorganisms by MALDI mass spectrometry. Mass Spectrom. Rev. 2001, 20, 157-171. Kim, J .Y.; Freas, A.; Fenselau, C. Analysis of viral glycoproteins by MALDI-TOF mass spectrometry. Anal. Chem. 2001, 73, 1544-1548. Wahl, K.L.; Wunschel, S.C.; Jarman, K.H.; Valentine, N.B.; Peterson, C.E.; Kingsly, M.T.; Zartolas, K.A.; Saenz, A.J. Analysis of microbial mixtures by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Anal. Chem. 2002, 74, 6191-6199. Demirev, P.A.; Ramirez, 1.; Fenselau, C. Tandem mass spectrometry of intact proteins for characterization of biomarkers from Bacillus cereus T spores. Anal. Chem. 2001, 73, 5725-5731. Castanha, E.R.; Fox, A.; Fox, K.F. Rapid discrimination of Bacillus anthracis from other members of the B. cereus group by mass and sequence of “intact” small acid soluble proteins (SASPs) using mass spectrometry. J. Microbiol. Methods 2006, 67, 230-240. 60 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. Scherperel, G.; Reid, G.E. Emerging methods in proteomics: top-down protein characterization by multistage tandem mass spectrometry. Analyst 2007, 132, 500- 506. VerBerkmoes, N.C.; Bundy, J .L.; Hauser, L.; Asano, K.G.; Razumovskaya, J.; Larimer, F .; Hettich, R.L.; Stephenson Jr., J .L. Integrating “top-down” and “bottom- up” mass spectrometric approaches for proteomic analysis of Shewanella oneidensis. J. Proteome Res. 2002, I, 239-252. Peng, J.; Elias, J.E.; Thoreen, C.C.; Licklider, L.J.; Gygi, S.P. Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC- MS/MS for large-scale protein analysis: the yeast proteome. J. Proteome Res. 2003, 2, 43-50. Washbum, M.F.; Wolters, D.; Yates 111, JR. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 2001, 19, 242-247. Lipton, M.S.; Pasa-Tolié, L.; Anderson, G.A.; Anderson, D.J.; Auberry, D.; Battista, J.R.; Daly, M.J.; Fredrickson, J.; Hixson, K.K.; Kostandarithes, H.; Masselon, C.; Markillie, L.M.; Moore, R.J.; Romine, M.F.; Shen, Y.; Stritrnatter, E.; Tolié, N.; Udseth, H.R.; Venkateswaran, A.; Wong, K.-K.; Zhao, R.; Smith, R.D. Global analysis of the Deinoccus radiodurans proteome by using accurate mass tags. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 11049-11054. Corbin, R.W.; Paliy, 0.; Yang, F .; Shabanowitz, J .; Platt, M.; Lyons Jr., C.E.; Root, K.; McAuliffe, J .; Jordan, M.I.; Kustu, S.; Soupene, E.; Hunt, D.F. Toward a protein profile of Escherichia coli: comparison to its transcription profile. Proc. Nat]. Acad. Sci. U.S.A. 2003, 100, 9232-9237. VerBerkmoes, N.C.; Shah, M.B.; Lankford, P.K.; Pelletier, D.A.; Strader, M.B.; Tabb, D.L.; McDonald, W.H.; Barton, J .W.; Hurst, G.B.; Hauser, L.; Davison, B.H.; Beatty, .1.T.; Hardwood, C.S.; Tabita, F.R.; Hettich, R.L.; Larimer, F.W. Determination and comparison of the baseline proteomes of the versatile microbe Rhodopseudomonas palustris under its major metabolic states. J. Proteome Res. 2006, 5, 287-298. Hunt, D.F.; Yates, J .R.; Shabanowitz, J .; Winston, S.; Hauer, C.R. Protein sequencing by tandem mass spectrometry. Proc. Natl. Acad. Sci. U.S.A. 1986, 83, 6233-6237. Steen, H.; Mann, M. The abc’s (and xyz’s) of peptide sequencing Nature Rev. Mol. Cell. Biol. 2004, 5, 699-711. Eng, J .K.; McCormack, A.L.; Yates 111, J .R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 1994, 5, 976-989. 61 34. 35 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. Perkins, D.N.; Pappin, D.J.; Creasy, D.M.; Cottrell, J.S. Probability-base protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999, 20, 3551-3567. .Tabb, D.L.; Narasimhan, C.; Strader, M.B.; Hettich, R.L. DBDigger: Reorganized proteomic database identification that improves flexibility and speed. Anal. Chem. 2005, 77, 2464-2474. VerBerkmoes, N.C.; Hervey, W.J.; Shah, M.; Land, M.; Hauser, L.; Larimer, F.W.; Van Berkel, G.J.; Goeringer, D.E. Evaluation of “shotgun” proteomics for identification of biological threat agents in complex environmental matrixes: experimental simulations. Anal. Chem. 2005, 7 7, 923-932. Krishnamurthy, T.; Deshpande, S.; Hewel, J.; Liu, H.; Wick, C.H.; Yates 111, JR. Specific identification of Bacillus anthracis strains. Int. J. Mass Spectrom. 2007, 259, 140-146. Leij, L.D.; Witholt, B. Structural heterogeneity of the cytoplasmic and outer membranes of Escherichia coli. Biochemica et Biophysica Acta 1977, 4 71, 92-104. Guerlava, P.; Izac, V.; Tholozan, J .-L. Comparison of different methods of cell lysis and protein measurements in Clostridium perfringens: application to the cell volume determination. Curr. Microbiol. 1998, 36, 131-135. Coleman, S.E.; Van de Rijn, I. Bleiweis, A.S. Lysis of grouped and ungrouped streptococci by lysozyme. Infect. Immun. 1970, 2, 563-569. Ezaki, T.; Suzuki, S. Achromopeptidase for lysis of anaerobic gram-positive cocci. J. Clin. Microbiol. 1982, 16, 844-846. Fliss, 1.; Emond, E.; Simard, R.E.; Pandian, S. A rapid and efficient method of lysis of Listeria and other gram-positive bacteria using mutanolysin. Biotechniques, 1991, 11, 453-457. Niwa, T.; Kawamura, Y.; Katagiri, Y.; Ezaki, T. Lytic enzyme libase for a broad range of gram-positive bacteria and its application to analyze functional DNA/RNA. J. Microbiol. Methods 2005, 61, 251-260. Bhaduri, S.; Demchick, P.H. Simple and rapid method for disruption of bacteria for protein studies. Appl. Environ. Microbiol. 1983, 46, 941-943. Van Huynh, N.; De Backer, O.; Decleire, M.; Colson, C. A procedure for the preparation of bacterial DNA that employs dimethyl sulfoxide to induce the lysis of cells. Anal. Biochem. 1989, 176, 464-467. 62 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. Bosserhoff, A.; Wallach, J.; Frank, R.W. Micropreparative separation of peptides derived from sodium dodecyl sulphate-solubilized proteins. J. Chromatogr. 1989, 473, 71-77. Jeannot, M.A.; Zheng, J.; Li, L. Observation of sodium gel-induced protein modifications in dodecylsulfate polyacrylamide gel electrophoresis and its implications for accurate molecular weight determination of gel-separated proteins by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. J. Am. Soc. Mass Spectrom. 1999, 10, 512-520. Yu, Y.-Q.; Gilar, M.; Lee, P..l.; Bouvier, E.S.P.; Gebler, J .C. Enzyme-fiiendly, mass spectrometry-compatible surfactant for in-solution enzymatic digestion of proteins. Anal. Chem. 2003, 75, 6023-6028. Wang, H.; Qian, W.-J.; Mottaz, H.M.; Clauss, T.R.W.; Anderson, D.J.; Moore, R.J.; Camp, DO. 11; Khan, A.H.; Sforza, D.M.; Pallavicini, M.; Smith, D.J.; Smith, R.D. Development and evaluation of a micro- and nanoscale proteomic sample preparation method. J. Proteome Res. 2005, 4, 2397-2403. Fenn, J.E.; Mann, M.; Meng, C.K.; Wong, S.F.; Whitehouse, C.M. Electrospray ionization for mass spectrometry of large biomolecules. Science 1989, 246, 64-71. Wilm, M.S.; Mann, M. Electrospray and Taylor cone theory, Dole’s beam of macromolecules at last? Int. J. Mass Spectrom. Ion Process. 1994, 136, 167-180. Gomez, A.; Tang, K. Charge and fission of droplets in electrostatic sprays. Phys. Fluids 1994, 6, 404-414. Iribame, J.V.; Thomson, B.A. On the evaporation of small ions from charged droplets. J. Chem. Phys. 1976, 64, 2287-2294. Thomson, B.A.; Iribame, J .V. Field induced ion evaporation from liquid surfaces at atmospheric pressure. J. Chem. Phys. 1979, 71 , 4451-4463. Kebarle, P.A brief overview of the present status of the mechanisms involved in electrospray mass spectrometry. J. Mass Spectrom. 2000, 35, 804-817. Karas, M.; Hillencamp, F. Laser desorption ionization of proteins with molecular masses exceeding 10 000 daltons. Anal. Chem. 1988, 60, 2299-2301. Zenobi, R.; Knochenmuss, R. Ion formation in MALDI mass spectrometry. Mass Spectrom. Rev. 1998, I 7, 337-366. March, R.E. An introduction to quadrupole ion trap mass spectrometry. J. Mass Spectrom. 1997, 32, 351-369. 63 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. de Hoffinann, E.D.; Stroobant, V. Mass spectrometry principles and applications, 2nd edition, John Wiley and Sons, New York, 2002. Hager, J.W. A new linear ion trap mass spectrometer. Rapid Commun. Mass. Spectrom. 2002, 16, 512-526. Schwartz, J .C.; Senko, M.W.; Syka, J .E. A two-dimensional quadrupole ion trap mass spectrometer. J. Am. Soc. Mass Spectrom. 2002, 13, 659-669. Skoog, D.A.; Holler, F.J.; Nieman, T.A. Principles of instrumental analysis, 5‘h edition, Thomson Learning, Inc., Australia, 1998. McDonald, W.H.; Ohi, R.; Miyamoto, D.T.; Mitchison, T.J.; Yates 111, JR. Comparison of three directly coupled HPLC MS/MS strategies for identification of proteins from complex mixtures: single-dimension LC-MS/MS, 2-phase MudPIT, and 3-phase MudPIT. Int. J. Mass Spectrom. 2002, 219, 245-251. Spahr, C.S.; Davis, M.T.; McGinley, M.D.; Robinson, J.H.; Bures, E.J.; Beierle, J.; Mort, J.; Courchesne, P.L.; Chen, K.; Wahl, R.C.; Yu, W.; Luethy, R.; Patterson, S.D. Towards defining the urinary proteome using liquid chromatography-tandem mass spectrometry. Profiling an unfractionated tryptic digest. Prateamics 2001, 1, 93-107. Davis, M.T.; Spahr, C.S.; McGinley, M.D.; Robinson, J.H.; Bures, E.J.; Beierle, J.; Mort, J .; Yu, W.; Luethy, R.; Patterson, S.D. Towards defining the urinary proteome using liquid chromatography-tandem mass spectrometry. Limitations of complex mixtures analyses. Prateamics 2001, 1, 108-117. Tabb, D.L.; McDonald, W.H.; Yates III, J.R. DTASelect and Contrast: Tools for assembling and comparing protein identifications from shotgun proteomics. J. Proteome Res. 2002, I, 21-26. Narasimhan, C.; Tabb, D.L.; VerBerkmoes, N.C.; Thompson, M.R.; Hettich, R.L.; Uberbacher, E.C. MASPIC: Intensity-based tandem mass spectrometry scoring scheme that improves peptide identification at high confidence. Anal. Chem. 2005, 77, 7581-7593. Thompson, M.R.; VerBerkmoes, N.C.; Chourey, K.; Shah, M.; Thompson, D.K.; Hettich, R.L. Dosage-dependent proteome response of Shewanella oneidensis MR-l to acute chromate challenge. J. Proteome Res. 2007, 6, 1745-1757. Larimer, F.W.; Chain, P.; Hauser, L.; Lamerdin, J .; Malfatti, 8.; Do, L.; Land, M.L.; Pelletier, D.A.; Beatty, J.T.; Lang, A.S.; Tabita, F.R.; Gibson, J.L.; Hanson, T.E.; Bobst, C.; Torres y Torres, J .L.; Peres, C.; Harrison, F.H.; Gibson, J .; Harwood, C.S. Complete genome sequence of the metabolically versatile photosynthetic bacterium Rhadopseudomanas palustris. Nat. Biotechnol. 2004, 22, 55-61. 64 70. 71. 72. 73. 74. 75. 76. 77. Mayya, V.; Rezaul, K.; Cong, Y-.S.; Han, D. Systematic comparison of a two- dimensional ion trap and a three-dimensional ion trap mass spectrometer in proteomics. Mal. Cell. Proteomics 2005, 4, 214-223. Riter, L.S.; Gooding, K.M.; Hodge, B.D.; Julian Jr., R.K. Comparison of the Paul ion trap to the linear ion trap for use in global proteomics. Prateamics 2006, 6, 1735- 1740. Blackler, A.D.; Klammer, A.A.; MacCoss, M.J.; Wu, C.C. Quantitative comparison of proteomic data quality between a 2D and 3D quadrupole ion trap. Anal. Chem. 2006, 78, 1337-1344. Zhang, N.; Gardner, D.C.J.; Oliver, S.G.; Stateva, L.I. Genetically controlled cell lysis in the yeast Saccharamyces cerevisiae. Biotechnol. Bioeng. 1999, 64, 607-615. Heidelberg, J .F .; Paulsen, I.T.; Nelson, K.E.; Gaidos, E.J.; Nelson, W.C.; Read, T.D.; Eisen, J .A.; Seshadri, R.; Ward, N.; Methe, 8.; Clayton, R.A.; Meyer, T.; Tsapin, A.; Scott, J .; Beanan, M.; Brinkac, L.; Daugherty, S.; DeBoy, R.T.; Dodson, R.J.; Durkin, A.S.; Hafi, D.H.; Kolonay, J.F.; Madupu, R.; Peterson, J.D.; Umayam, L.A.; White, 0.; Wolf, A.M.; Vamathevan, J .; Weidman, J .; Impraim, M.; Lee, K.; Berry, K.; Lee, C.; Mueller, J.; Khouri, H.; Gill, 1.; Utterback, T.R.; McDonald, L.A.; Feldblyum, T.V.; Smith, H.O.; Venter, J .C.; Nealson, K.H.; Fraser, C.M. Genome sequence of the dissimilatory metal ion-reducing bacterium Shewanella aneidensis. Nat. Biotechnol. 2002, 20,1118-1123. Blattner, F.R.; Plunkett, G. III; Bloch, C.A.; Pema, N.T.; Burland, V.; Riley, M.; Collado-Vides, J .; Glasner, J .D.; Rode, C.K.; Mayhew, G.F.; Gregor, J .; Davis, N.W.; Kirkpatrick, H.A.; Goeden, M.A.; Rose, D.J.; Mau, B.; Shao, Y. The complete genome sequence of Escherichia coli K-12. Science 1997, 27 7, 1453-1462. Saccharamyces Genome Database. www.yeastgenome.org (accessed March 11, 2008) Denef, V.J.; Shah, M.B.; VerBerkmoes, N.C.; Hettich, R.L.; Banfield, J.F. Implications of strain- and species-level sequence divergence for community and isolate shotgun proteomic analysis. J. Proteome Res. 2007, 6, 3152-3161. 65 l ‘2 l l I l l I l ll IBHARIE H l 4980 ‘v‘ 1. ll l l l . [r lfll .1 ‘Al 1293 £2356 1" l l 1 ll“ l l l MICHIGAN STA 1E UNWER‘ H H l i . .r. I. : w 1.. 1. .LL. . .50....