Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item.Includes bibliographical references (leaves 12-13).In this post-genomic era, researchers are striving to find new ways to use the enormous amounts of data that have been collected.  One obvious way is with proteomics, the large-scale identification of expressed proteins.  We have developed a novel method for identifying proteins using two dimensions of non-denaturing high performance liquid chromatography (HPLC) and matrix assisted laser desorption ionization time of flight (MALDI-TOF) mass spectrometry.  The first dimension of separation uses an anion exchange column and each of those fractions is run through the second dimension, a hydrophobic interaction column.  The proteins were then dialyzed, denatured, and digested with trypsin before being subjected to mass spectrometry.  Identifications were made based on the peptide masses.  Using this method we have made 2012 protein identifications, 310 of which are unique.  These numbers are comparable to other forms of proteomics such as 2-D gels

Campbell, Christopher S

Texas A&amp;M Repository

7+US And' U/4IVEf'~!TV USFiARY PROTEOMIC ANALYSIS OF E. COL1 USING 2D HPLC AND MALDI-TOF MASS SPECTROMETRY A Senior Thesis By CHRISTOPHER S. CAMPBELL Submitted to the Office of Honors Programs & Academic Scholarships Texas A&M University In partial fulfillment of the requirements of the UNIVERSITY UNDERGRADUATE RESEARCH FELLOWS April 2002 Group: Life Sciences I PROTEOMIC ANALYSIS OF E. COLI USING 2D HPLC AND MALDI-TOF MASS SPECTROMETRY A Senior Thesis By CHRISTOPHER S. CAMPBELL Submitted to the Office of Honors Programs k Academic Scholarships Texas A&M University In partial fulfillment of the requirements of the UNIVERSITY UNDERGRADUATE RESEARCH FELLOWS Approved as to style and content by: Ia'mes C. Hu Edward A. Funkhouser April 2002 Group: Life Sciences I ABSTRACT Proteomic Analysis of E. coli Using 2D HPLC and MALDI-TOF Mass Spectrometry. Christopher S. Campbell Department of Biochemisty/Biophysics Texac AkM University Fellows Advisor; Dr. James C. Hu Department of Biochemisty/Biophysics In this post-genomic era, researchers are striving to find new ways to use the enormous amounts of data that have been collected. One obvious way is with proteomics, the large-scale identification of expressed proteins. We have developed a novel method for identifying proteins using two dimensions of non-denaturing high performance liquid chromatography (HPLC) and matrix assisted laser desorption ionization time of flight (MALDI-TOF) mass spectrometry. The first dimension of separation uses an anion exchange column and each of those fractions is run through the second dimension, a hydrophobic interaction column. The proteins were then dialyzed, denatured, and digested with trypsin before being subjected to mass spectrometry. Identifications were made based on the peptide masses. Using this method we have made 2012 protein identifications, 310 of which are unique. These numbers are comparable to other forms of proteomics such as 2-D gels. This thesis is dedicated to Jimmy. I' ll always remember you Jimmy. ACKNOWLEDGEMENTS I would like to thank Dr. Hu and Matthew Champion for their guidance. In addition, I would like to thank Matt for help with the figures. TABLE OF CONTENTS ABSTRACT Page DEDICATION. . ACKNOWLEDGEMENTS. TABLE OF CONTENTS. . . . V1 LIST OF FIGURES. . . . . V11 LIST OF TABLES. . CHAPTER V111 I INTRODUCTION. 11 MATERIALS AND METHODS. m RESULTS AND CONCLUSIONS. . . . . REFERENCES. . . . 12 vn LIST OF FIGURES FIGURE Page I Flow Chart. 2 Venn Diagram. . 3 Distributions of E(g) Numbers. . . 4 Average E(g) Numbers. 5 pI vs. MW Graphs. . . LIST OF TABLES TABLE Page I Functional Classifications of Proteins. . . 2 Pair-wise Interaction Candidates. . . . . . 10 INTRODUCTION Proteomics is one if the most rapidly developing fields in the biological sciences. As such, a quick and efficient method for performing proteomtc analyses is essential. The traditional method for proteomics involves the use of 2-D gels to visualize the proteins. 2-D gels first separate proteins based on their pI using isoelectric focusing. The second mode of separation is sodium dodecyl sulfate polyacrylamide gel electrophoresis. The spots are then excised and identified with mass spectrometry. However, there have been numerous complaints brought up against 2-D gels. Low abundance proteins are difficult to identify on 2-D gels. Many proteins have similar pIs, making separations difficult. 2-D gels also have an apparent bias towards proteins in the lower pl ranges. An alternate method that has been gaining popularity is the use of various forms of liquid chromatography for separating the proteins. ' zs Often affinity or reverse phase chromatography is used, These methods also use mass spectrometry to make identifications. There are problems with these methods as well. They are very costly and labor intensive. They also have an excessive false positive rate upwards of 30 percent. My thesis research involved developing an alternate method. Our method uses two forms of non-denaturing liquid chromatography in series; anion exchange and hydrophobic interaction. This separates the proteins into 380 fractions each containing 0-7 identifiable proteins. The fractions are each dialyzed, denatured and digested with trypsin before being subjected to analysis by MALDI-TOP mass spectrometry. Identtftcations are made using Protein Prospector MS-Fit software. Our method can be done with very low cost and only one or two people. We did the complete experiment four times. Proteomics involves more than just identifications. Ideally, we would like to know what the proteins are interacting with. Since the mode of separation is non- denaturing, protein activity and complexes are preserved. We have proven P- galactosidase activity is maintained through both chromatography steps. Many, if not most proteins are believed to exist in complexes. Experiments such as those done by Ho et al, and Eisenberg et al. have been able to provide some evidence for the existence of specific complexes. However, multiple types of experiments are needed to get the whole picture. We hope to be able to identify protein complex candidates based on co-fractionation. The entire proteomic analysis has been done with 2 different pHs at which the anion exchange is run. Changing the pH changes which fractions the proteins elute into. By observmg which proteins continue to co- fractionate after the shift, we have been be able to accumulate more circumstantial evidence for the existence of complexes. MATERIALS AND METHODS One liter of E. coli (MG1655) cells were grown in minimal glucose (M9) media. Cells were pelleted at 4000g for 20 minutes and resuspended in 200ml of 20mM Tris- HCI, 20mM NaCI, lmM EDTA, pH 8. 75. They were then centrifuged again and resuspended in 6ml of the same buffer. The cells were lysed via French press and half of the lysate was loaded onto a lml Waters column packed with SOURCE 15 Q resin. A gradient from 20mM to IM NaCI was used. The pH set for the run was either 7. 5 or 8. 75. Five ml fractions were collected and each of them was loaded onto a lml Waters column packed with SOURCE 15 Phe resin. The gradient used went from 1. 5M to OM ammonium sulfate. Each 500)tl fraction was collected directly into a Slide-A-Lyzer MINI Dialysis unit (Pierce 3, 500 MWCO). The samples were dialyzed for 24 hours in 25mM ammonium bicarbonate. They were then denatured with heat (95'F for 20') and digested with Iltg of modified trypsm (Promega) each for 5 hours. The MALDI was done in a similar fashion to that previously described by Park et al. . Identifications from the peptide mass data were made with Protein Prospector MS-Fit software (prospector. ucsf. edu). Factors looked at for making the identifications include MOWSE score, sequence coverage, number of peptides 8 matched, and trends in peptide error. All of the identified proteins were checked to make sure that they are present in the genomic DNA sequence of E. coli strain MG1655. E. coif clarified lysate 2D GE ~ ~ ~ ~ ~ ~ AIX HIC MALDI-MS Protein ID's Figure 1. Flow chart describing the path of the proteins for identification by MALDI and for 2D gel analysis. RESULTS AND CONCLUSIONS A total of 2012 identifications have been inade which include 310 different proteins identified. This is slightly more than ihe 271 different proteins identified by thc Swiss 2-D project. ' Figure 2 shows the overlap betwccn the two projects. 10. 11 This Study 194 --:116 Tonella et al. SWISS 2D-PAGE Figure 2. Venn Diagram showmg the number of proteins f'ound m only our study, only the SV'lag 2D-PA(iE prtgect, and those tound in both. One thing that we v;anted to determine was whcthcr or not v;e had any biases in our identifications towards things with high abundance or high/low pl or molecular weight. To measure the abundance of the protctns v, c identified, we used E(g) numbers. E(g) numbers predict protein abundance based on the codon usage of their genes. The higher the number, the more abundant the protein is predicted to he. On it average, our E(g) numbers were significantly higher than those ol' the entire predicted protcomc of E. coll, indicating that we do have a bias towards proteins of higher abundance. Figure 3 shoivs a comparison between thc percentage ol' proteins in dilferent E(g) ranges for our data and the entire protcome. Our Data i 'IIIII 0-04 0. 41- 061- 0. 81- 1 01- 1. 21- 1 41- 1. 61- 1. 81- )20 06 08 10 12 14 16 18 20 sag) Range Genome 2000 I 1500 1000 50D D 0-40 4(. 60 61-80 81100 101120 121140 141160 16M BO 16120 a0 8(0) 8 0 Figure 3. Dtstr(bunon of proteins for a range of E(g) numbers for A) the proteins that we have identified and B) the predicted proreome. However, our numbers were still lower than those of other proteome projects. Figure 4 compares our E(g) numbers to those of other projects. 0. 9 0. 8 07 X & 0. 5 Q. 0. 5 t: . 2 04 Pg 0. 3 tt- 0. 2 0. 1 E. coli Genome Champion Opiteck et al. SWISS-PROT Source Proteome Figure 4. Comparison of average E(g) number for a variety of protein sets, including the E, cali genome and three proteomics projects. To check for pI and molecular weight biases, we simply compared the theoretical pls and molecular weights for the proteins we identified and the entire genome. These were calculated using the prediction tools found on the SWISS-PROT website (www. expasy. org). There did not appear to be any significant dtfference between our pI and molecular weight distribution and that of the proteome. Figure 5 shows graphs of pl vs. molecular weight for our data as well as the proteome. To identify candidates for protein complexes, we found all pairs of proteins that were found in the same fraction for both pH 7. 5 and 8. 75. Using this method, 125 candidate interactions were identified (Table 1). In addition, some known complexes were found to co-fractionate. For example, phenylalanine tRNA synthetase ct and j) subunits were found together. RNA polymerase subunits u, P, and P' were also seen in the same fractions. We believe that this data in conjunction with other experiments similar to those done by Ho et al. and Eisenberg et al. could result in fairly confident identification of novel complexes. 4 0 0 50000 100000 150000 200000 250000 B 14) 10 I . . 8 C 4 0 50000 100000 150000 200000 250000 Figure 5. Graphs of pl versus molecular weight for A) the predicted proteome and B) the proteins that we identified. ACEA ACKA ACKA ADK AHPC AHPC ALAS ARGO ARGG ARGG ARGH ARGH ARGI AROA AROK AROK ASNS ASNS ASNS ASNS ASNS ASNS ASNS ASNS ASNS ASNS ASNS ASPC ASPC ASPC ASPS ASPS ASPS BGLA CLPP CLPP CYSK CYSK CYSK CYSK CYSK CYSK DAPA DAPA DAPA DAPA DAPD DAPD DAPD DAPD PNP FABI TSF GAPA GLNS TRPC YADF FUSA I SOS PURF CLPP FUSA GCVT DAPD CYSK PGI DAPA FUSA GLTA KDGK RFBB RPLJ RPSA SERB TIG TUFA VALS DAPD ILES THRC GOVT GND KBL YFBU ARGH I SOS AROK DAPD GOVT PG I PYKF SUCC ASNS GUAA KDGK PPA AROA ASPC CYSK NDK DAPD DAPD DNAK DNAK DUT ENO ENO FABI FABI FABI FABI FABI FDX FUSA FUSA FUSA FUSA FUSA FUSA GAPA GAPA GAPA GCVT GOVT GOVT GCVT GOVT GLNS GLNS GLNS GLTA GLTA GLTA GLTA GLTA GLTX GLTX GLYA GLYA GLYA GLYA GND GND GND GND GND GND GND GOR GPMA PURT SSPA LYSS TYPA GND GND SERG ACKA PROS PURH TSF YADF LPDA ARGD ARGH ASNS RPSA SPEE VALS ADK GLYA GPMA ARGI ASPS CYSK NDK PYKF AHPC PROA TRPC ASNS PURA TKTA TSF TUFA GND PPA GAPA SERO TPI A Y IF E ASPS DUT ENO GLTX GOR KBL PPA GND GAPA GREA GREA GROS GUAA GUAA GUAA GUAA GUAB H ISO ILES INFB ISCS I SOS ISCS ISCS I SOS KBL KBL KBL KDGK KDGK LPDA LYSS LYSS NDK NDK NDK NUSA NUSA NUSA NUSA PGI PGI PHES PHES PHET PHET PNP PNP PNP PNP PNP PNP PNP PPA PPA PPA PPA PPA PPI B GUAA PPA TIG DAPA GREA PPA YCHF TIG YADF ASPC LYSS ARGG CLPP PNP PURF SLYD ASPS GND PURA ASNS DAPA FDX DNAK INFB DAPD GOVT PYKF PNP SLYD SPEB YICC AROK CYSK PHET TIG PHES TIG ACEA I SOS NUSA PURF SLYD TYPA YICC DAPA GLTX GND GREA GUAA TSF PROA PROS PROS PROS PROS PURA PURA PURA PURF PURF PURF PURF PURH PURH PURH PURH PURH PURN PURT PYKF PYKF PYKF PYRH RFBB RFBB RFBB RPLI RPLJ RPLJ RPSA RPSA RPSA RPSA RPSA RPSA RSUA SERG SERG SERB SERB SLYD SLYD SLYD SLYD SLYD SPEB SPEB SPEE SSPA SSPA GLNS FABI PURH TSF TUFA G LTA KBL TKTA ARGG ISCS PNP TYPA FAB I PROS TSF TUFA YADF SSPA DAPD CYSK GOVT NDK TALB ASNS RPSA TIG TSF ASNS TIG ASNS FUSA RFBB SERS TIG VALS VALS ENO GLYA ASNS RPSA I SOS NUSA PNP SPEB YICC NUSA SLYD FUSA DAPD PURN SUCC TALB THRC TIG TIG TIG TIG TIG TIG TIG TIG TKTA TKTA TKTA TKTA TPIA TRPC TRPC TSF TSF TSF TSF TSF TSF TSF TSF TSF TUFA TUFA TUFA TUFA TUFA TUFA TYPA TYPA TYPA VALS VALS VALS VALS YADF YADF YADF YADF YCHF YFBU YICC YICC YICC Y IF E CYSK PYRH ASPC ASNS GROS GUAB PHES PHET RFBB RPLJ RPSA GLTA PURA TSF TUFA GLYA AHPC GLNS ACKA FAB I G LTA PP I B PROS PURH RPLI TKTA TUFA ASNS GLTA PROS PURH TKTA TSF DNAK PNP PURF ASNS FUSA RPSA RSUA ALAS FAB I H ISO PURH GUAA BGLA NUSA PNP SLYD GLYA Table 1. Proteins that co&actionate at both pH7. 5 and pH8. 75. The 125 pairs are shown as 250 entries;each pair is listed with each partner first to aid finding proteins of interest. 10 To find out what kinds of proteins we identified, we looked at their functional classifications. Table 2 compares the percentages of functional classifications for our data as well as the entire genome. The lack of membrane proteins was expected because the membranes are pelleted after the cells are lysed. We identified a higher proportion of protein biosynthesis and nucleotide metabolism proteins most likely because of their high abundance. Category: Protein Biosynthesis/ Chaperonin Glycolysis TCA Carbon Utilization NT Metabolism AA Synthesis Enzymatic Activities Biosynthetic Genes FA, DAP, LPS Cofactors Transcription Replication Membrane, Xport Hypothetical/ Unknown/ Putative Total % of Total % of Genome 19% 4. 5% 15% 13. 0 1 1% 1. 4% 10% 3. 0% 10% 11. 9 10% 8. 7% 4% 1. 3% 1% 2. 7% 0% 10. 3 19% 43. 2 100 100 Table 2. Percentage of proteins in each functional classification for the proteins identified in this study and the E. coli genome. Classification come from Opiteck et al. To help confirm our identifications, 2D PAGE was performed on all of the first dimension fractions run at pH 7. 5. Our identifications for each fraction were compared to the spots found on the gels. Both of the predicted molecular weight and pI as well as known migration patterns for previously identified proteins were looked at. The proteins identified by us had a much higher chance of correlating with a spot on these gels than proteins selected at random. Spots were assigned to 109 of the 219 proteins we identified at pH 7. 5. Forty-one of these have not been identified by the SWISS-2D project. At least one false positive was confirmed. UDP-glucose dehydrogenase from the K5 strain of E. coli was identified. To make sure that there was not something wrong with either our strain of E, coli or the MG1655 UDP-glucose dehydrogenase sequence, we sequenced the gene. The results showed that our strain does indeed have the MG1655 version of UDP-glucose dehydrogenase, and not that of K5. All other identified proteins came from MG1655. However, some Ids may still be false positives. Future work on this project will likely involve proving that the system can be used to identify differences in protein expression under altered conditions. Repeating the experiment with cells that have been infected with lambda phage and looking for dkfferences from the previous experiments is one possible way of doing this. Another would be to look at protein expression in outgrowth after starvation. 12 REFERENCES 1. Gygi, S. P. , Corthals, G. L. , Zhang, Y. , Rochon, Y. , and Aebersold, R. (2000) Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc Natl Acad Sci U SA 97: 9390-5. 2. Washburn, M. P. , Wolters, D. , and Yates, J. R. , 3rd (2001) Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol 19: 242-7. 3. Wolters, D. A. , Washburn, M. P. , and Yates, J. R. , 3rd (2001) An automated multidimensional protein identification technology for shotgun proteomics. Anal Chem 73: 5683-90. 4. Eriksson, J, , Chait, B. , Fenyo, D. (200) A statistical basis for testing the significance of mass spectrometric protein identification results. Anal. 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A. , Bairoch, A. , Hochstrasser, D. F. , and Appel, R. D. (2000) The 1999 SWISS-2DPAGE database update. Nucleic Acids Res 28: 286-288. 12. Karlin, S. , Mrazek, J. , Campbell, A. , and Kaiser, D. (2001) Characterizations of highly expressed genes of four fast-growing bacteria. J Bacteriol 183: 5025-40. 13. Opiteck, G. J. , Ramirez, S. M. , Jorgenson, J. W. , and Moseley, M. A. , 3rd (1998) Comprehensive two-dimensional high-performance liquid chromatography for the isolation of overexpressed proteins and proteome mapping. Anal Biochem 258: 349- 61. l ~7 l )g&~/ l55515NIlllll%5%$55%lll5 A1LI829 7LI1492 

Proteomic analysis of E. coli using 2D HPLC and MALDI-TOF mass spectrometry

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Proteomic analysis of E. coli using 2D HPLC and MALDI-TOF mass spectrometry

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Proteomic analysis of E. coli using 2D HPLC and MALDI-TOF mass spectrometry

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