Differential Proteomic Investigations of Normal Appearing Gray Matter in Multiple Sclerosis and Control Post-Mortem Brain Tissue
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DIFFERENTIAL PROTEOMIC INVESTIGATIONS OF NORMAL APPEARING GRAY MATTER IN MULTIPLE SCLEROSIS AND CONTROL POST-MORTEM BRAIN TISSUE. A dissertation submitted to Kent State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy by Laurie A. Broadwater May, 2013 Dissertation written by Laurie A. Broadwater B.S., Kent State University, 2002 M.S., Kent State University, 2003 Ph.D., Kent State University, 2013 Approved by ____________________________________, Co-Chair, Doctoral Dissertation Committee Roger Gregory, Ph. D. ____________________________________, Co-Chair, Doctoral Dissertation Committee Jennifer McDonough, Ph. D. _____________________________________, Member, Doctoral Dissertation Committee Soumitra Basu, Ph. D. _____________________________________, Member, Doctoral Dissertation Committee Nicola Brasch, Ph. D. _____________________________________, Member, Doctoral Dissertation Committee Edgar Koojiman, Ph. D. Accepted by _____________________________________, Chair, Department of Chemistry & Biochemistry Michael Tubergen, Ph. D. _____________________________________, Dean, College of Arts and Sciences Raymond Craig, Ph. D. ii TABLE OF CONTENTS LIST OF FIGURES………………………………………………………………………iv LIST OF TABLES……………………………………………………………………...viii ACKNOWLEDGMENTS……………………………………………………...………...ix INTRODUCTION…..…………………………………………………………………….1 DIFFERENTIAL PROTEOMIC INVESTIGASTIONS………………………………...32 Methods……………………………………………………………….…….32 Results……………………………………………………………….……....43 Discussion………………………………………………………….…….….73 METHOD DEVELOPMENT…..…………………………………………………….….93 Optimization of Matrix Formulation for Mass Spectral Analysis……...…...93 Synthesis of Novel Matrix Molecule…………………………………..…..103 Affinity Capture for the Identification Verification……………….………113 CONCLUSIONS……………………………………………………………....…….....133 BIBLIOGRAPHY……………………………………………………………..……….137 iii LIST OF FIGURES Figure 1: Geographical Distribution of Multiple Sclerosis………………………………..2 Figure 2: The role of mitochondria in axonal degeneration………………………………7 Figure 3: Panel A illustrates the MALDI process while Panel B illustrates the ESI process……………………………………………………………………………………10 Figure 4: Mass Analyzers for the Proteomic Laboratory………………………………...14 Figure 5: Types of surfaces available for Proteinchip® interaction studies……………..17 Figure 6: The key to the selectivity of SELDI…………………………………………...18 Figure 7: Schematic of the SELDI system……………………………………………….21 Figure 8: The correlating time of flight with m/z ratios in laser desorption mass spectrometry……………………………………………………………………………...22 Figure 9: Graphical representation of the transformation of the coordinate system during PCA………………………………………………………………………………………25 Figure 10: A scree plot summarizes the total variability accounted for in the PCA…….28 Figure 11: Scatter plot of scores aid in the visualization of data segregation……….…...30 Figure 12: Hierarchical clustering analysis shows similar objects……………….……...31 Figure 13: Proteomic Work Flow employed in this research……………………………34 Figure 14: Tissue Characterization of representative MS NAGM tissue section using PLP staining…………………………………………………………………………………...44 Figure 15: PLP staining of MS and Control Motor Cortex……………………………...45 Figure 16: Representative western blot demonstrating the relative purity of the cellular fractionation……………………………………………………………………………...46 iv Figure 17: Scheme outlining the data analysis strategy for identification of differentially expressed proteins………………………………………………………………………..47 Figure 18: Representative SELDI-TOF spectra from a single patient…………………...48 Figure 19: A box and whiskers plot visualizes the distribution of data points…………..51 Figure 20: Multivariate analysis of cohort 1, fraction 3 using hierarchical clustering analysis…………………………………………………………………………………...55 Figure 21: Multivariate analysis of cohort 1, fraction 6 using hierarchical clustering analysis…………………………………………………………………………………...56 Figure 22: Multivariate analysis of cohort 2, fraction 6 using hierarchical clustering analysis…………………………………………………………………………………...57 Figure 23: Principal Component Analysis of Cohort 1 Fraction 3………………………58 Figure 24: Principal Component Analysis of Cohort 1 Fraction 6………………………59 Figure 25: Principal Component Analysis of Cohort 2 Fraction 6………………………60 Figure 26: Decision Tree used in the selection of differentially expressed to submit for peptide fingerprint mapping (PFM)……………………………………………………..63 Figure 27: SELDI-TOF mass spectra of differentially expressed proteins identified PFM……………………………………………………………………………………...68 Figure 28: SELDI-TOF mass spectra of differentially expressed proteins identified by MS/MS at 9.7 and 9.8 kDa and 15.9 kDa…………………………………………..........69 Figure 29: SELDI-TOF mass spectra of differentially expressed proteins identified by MS/MS at 16.7 and 17.2 kDa.……………………………………………………………70 Figure 30: SELDI-TOF mass spectra of differentially expressed proteins identified by MS/MS at 22.7 kDa……………………………………………………………………...71 v Figure 31: Affinity pull down assay for the confirmation of protein identification…......73 Figure 32: Cytochrome c Oxidase, subunits I, II, II, 5a and 5b (pdb 2EIJ file)….….…..82 Figure 33 Creatine Kinase Reactions. ……………………………………………………86 Figure 34: The CK Shuttle- connecting the utilization and production of energy……….90 Figure 35: Comparison of SELDI TOF mass spectra of mitochondrially-enriched protein samples from human brain tissue with CHCA and SPA matrix solutions………………98 Figure 36: Comparison of SELDI TOF mass spectra of mitochondrially-enriched protein samples from human brain tissue obtained with formic acid (FA) and OGP……………99 Figure 37: Representative SELDI TOF mass spectra of mitochondrially-enriched protein samples from human brain tissue obtained with OGP alone…………………………...101 Figure 38: Structure of α-cyanocinnamic acid and chloro-cyanocinnamic acid……….104 Figure 39: 1H and 13C NMR spectra of the purified chloro-cyanocinnamic acid……..107 Figure 40: Representative SELDI spectra acquired using both CHCA and Cl-CCA…..108 Figure 41: Signal to Noise ratio comparison of peaks detected using CHCA and Cl- CHCA…………………………………………………………………………………..109 Figure 42: SELDI spectra acquired using stored Cl-CHCA matrix……………………111 Figure 43 : 13C and 1H NMR spectra acquired using stored Cl-CHCA matrix……….112 Figure 44: SELDI spectra of nitrated ribonuclease A (1) and hen egg white lysozyme (2)……………………………………………………………………………………….121 Figure 45: Nitrated Ribonuclease A peak intensity is significantly changed after exposure to anti-nitrotyrosine beads………………………………………………………………122 Figure 46: Reproducibility of normalized ribonuclease A intensity before and after exposure to anti-nitrotyrosine affinity beads…………………………………………...126 vi Figure 47: Mining nitrated proteins from a complex brain mitochondrially enriched sample…………………………………………………………………………………..128 Figure 48: Identifiy verification of the 10.6 kDa SELDI mass spectral peak with anti- COX5b affinity beads…………………………………………………………………..130 Figure 49: The verification of COX5b identity by affinity capture…………………….131 vii LIST OF TABLES Table 1: Performance Characteristics of Typical Proteomic Mass Analyzers ……..….. 13 Table 2: Donor Demographics……………………………………….....…….…..……...33 Table 3: Differentially Expressed Spectral Peaks…..........................................................50 Table 4: Differentially Expressed Proteins Identified by PMF………………………….67 Table 5: Confident Candidates for MS/MS Identifications of Differentially Expressed Peaks……………………………………………………………………………………..67 Table 6: The effect of OGP concentration and sample:matrix dilution factor on protein ionization, desorption and detection……………………………………..……………..102 Table 7: p values generated by Homoscedastic two-tailed Student’s t-tests comparing CHCA and Cl-CCA……………………………………………….……………………110 Table 8: Mean peak intensities before and after affinity bead exposure……………….125 viii ACKNOWLEDGEMENTS I would like to thank Professor Roger Gregory for his guidance, skillful censure and valuable mentorship during my graduate years. I would like to express my gratitude to Dr. Jennifer McDonough for the most amazing scientific project and the freedom to explore those aspects of this project. ix Chapter 1 Introduction 1.1 Multiple Sclerosis and its molecular pathology Multiple sclerosis (MS) is an inflammatory neurodegenerative disorder of the central nervous system which results in physical and cognitive disability. It is the most common chronic neurological disease among young adults, with a mean onset age of approximately 30 years and a prevalence of 1.3/1000 people in the developed world (Ziemann, 2011). Geographical distribution (Figure 1, Multiple Sclerosis Research Center, UK) is not uniform with the greatest incidence in the extreme latitudes and women are two to three times more likely to develop MS than men (Hassan-Smith, 2011). Symptoms include numbness in the arms or legs, pain, loss of vision, muscle weakness or tremors, paralysis, vertigo, fatigue, speech difficulties and depression. Existing pharmaceutical treatments slow the disease progression by targeting immune components of MS; however, no cure is currently available (National Multiple Sclerosis Society online publication, 2012). 1 2 Figure 1: Geographical Distribution of Multiple Sclerosis Attempts to characterize the genetic cause of MS have revealed that familial reoccurrence is approximately 20% (Compston, 2008). The human leukocyte antigen (HLA) on chromosome 6 is a major histocompatibility complex (MHC) and alleles DR15 and Dq6 are shown