DISCOVERY OF HOST LIPID BIOMARKERS FOR TUBERCULOSIS INFECTION IN MICE MARTIN W BRATSCHI BSc (Cellular, Molecular and Microbial Biology) & BA (Economics), University of Calgary, Canada A THESIS SUBMITTED FOR THE DEGREE OF A MASTER OF SCIENCE IN INFECTIOUS DISEASE, VACCINOLOGY AND DRUG DISCOVERY YONG LOO LIN SCHOOL OF MEDICINE, NATIONAL UNIVERSITY OF SINGAPORE & SWISS TROPICAL INSTITUTE, UNIVERSITY OF BASEL 2009 Acknowledgments I would like to thank Dr Markus Wenk for giving me the opportunity to my masters project in his laboratory, to work on a project that is part of a collaboration with NITD and for allowing me to present my work at two international conferences I would also like to thank Markus, the coordinator of the Joint MSc program, for taking me into the program, which has been a great learning experience I also would like to thank Dr Anne Bendt for guiding me through the day-to-day affairs of my project and allowing me to participate in many more aspect of the lab’s tuberculosis research then what is included in my project Further, I would like to thank Anne for her helpful discussions about my project and for reviewing this manuscript and providing useful suggestions At NITD, I would like to thank Veronique Dartoise and Maxime Herve for critically reviewing any of the data we presented to them I would also like to thank them as well as the BSL-3 staff, for coordinating and conducing the animal experiment for us In the lab at NUS, I would like to thank everyone for taking me into the group and for giving me a great experience in Singapore In particular I am thankful to Dr Guangho Shui, Robin Chan, Xueli Guan and Weifun Cheong for helping me with all aspects of mass spectrometry I would also like to thank Sarah Patel for her great help with any administrative matter I would further like to thank Bowen Li for helping me with the implementation of data analysis tools and for having useful suggestions for any II bioinformatics questions Also, I would like to thank Lukas Tanner for the many coffee breaks and discussions and for critically reading this manuscript and providing truly very helpful comments I further wish to thank Dr Anja Gassner for providing me with the necessary insight into statistics and critically discussing which data analysis approaches would be most appropriate Finally I am grateful to my parents and my friends in Switzerland, Calgary and Singapore for always being only a phone call away or ready to go for dinner, without them I would not be where I am now Most importantly, one very special friend has greatly supported me in every aspect and made the last year and a half very nice Thank you very, very much and I look forward to the exciting time ahead III Table of Contents Acknowledgment .II Table of Contents IV
 Summary VII
 List of Table VIII
 List of Figures IX
 Publications XI
 1.
 Introduction 1
 1.1.
 Tuberculosis 2
 1.1.1.
 Global Burden 2
 1.1.2.
 Active and Latent Disease 3
 1.1.3.
 Eliciting Mostly a Cellular Immune Response 4
 1.1.4.
 Diagnosis 5
 1.2.
 TB Drug Discovery and Development 6
 1.2.1.
 Available Chemotherapy 6
 1.2.2.
 Discovery of Novel Therapeutics 7
 1.2.3.
 Use of Animals in TB Drug Discovery 9
 1.3.
 Lipidomics – Systems Scale Analysis of Lipids 10
 1.3.1.
 Lipids: Great Chemical and Functional Diversity 10
 1.3.1.1.
 Diverse Range of Molecular Species .10
 1.3.1.2.
 Range of Functions 11
 1.3.2.
 Lipidomics: Systems Scale Analysis of Lipids 12
 1.3.3.
 Lipid Biomarkers 13
 IV 1.4.
 Aim of Study 14
 2.
 Materials and Methods .16
 2.1.
 Animal and BSL3 Work 17
 2.1.1.
 Mouse Experiment 17
 2.1.1.1.
 Intranasal Infection 17
 2.1.1.2.
 Drug Treatment 17
 2.1.1.3.
 Sample Collection 18
 2.1.2.
 Sample Inactivation 19
 2.2.
 Mass Spectrometry of Whole Blood Lipids 19
 2.2.1.
 Lipid Extraction 19
 2.2.2.
 Mass Spectrometry 20
 2.2.2.1.
 Use of MS in Lipidomics 20
 2.2.2.2.
 Biased Analysis by Multiple Reaction Monitoring (MRM) 21
 2.2.2.3.
 Molecular Species Characterization by MS/MS 24
 2.3.
 Data Analysis 24
 2.3.1.
 Raw Data Analysis 24
 2.3.1.1.
 Statistical Terminology 24
 2.3.1.2.
 Identifying Unreliable Transitions 25
 2.3.1.3.
 Identifying Outliers and Assessing Normality 25
 2.3.2.
 Normalizing Data 27
 2.3.3.
 Identifying Potential Biomarker Lipids 27
 2.3.3.1.
 Comparing Means of Three Groups .27
 2.3.3.2.
 Homogeneous Subsets: “Reverting” Lipids .29
 2.3.4.
 Building a Diagnostic Model 30
 2.3.4.1.
 Statistical Differences Between Diseased and Non-Diseased 30
 2.3.4.2.
 Recursive – Support Vector Machines 30
 2.3.4.2.1.
 Use of Support Vector Machines in Data Mining 30
 2.3.4.2.2.
 SVM Parameter Optimization 32
 2.3.4.2.3.
 Feature Reduction by R-SVM 32
 V 3.
 Results 35
 3.1.
 Rifampicin can clear M tuberculosis Beijing W4 infection in mice 36
 3.2.
 Lipid profiles vary between TB infected, cured and healthy mice 37
 3.2.1.
 Analyzing Raw Data Identifies Unreliable Features and Observations 37
 3.2.2.
 Several Observations May be Outliers 39
 3.2.3.
 Observations for Some Features Distributed Non-Normally 40
 3.2.4.
 Statistically Significant Differences Exist Between All Groups 41
 3.3.
 Levels of Potential Biomarker Lipids “Revert” to Healthy State Upon Drug Treatment 46
 3.4.
 Detailed Characterization of Reverting Lipids 48
 3.5.
 Differences Between Diseased and Non-Diseased 50
 3.6.
 Support Vector Machines (SVM) Can Differentiate Between Diseased and nonDiseased Animals 52
 4.
 Discussion 57
 4.1.
 Technical Aspects of Lipid Biomarker Discovery 58
 4.2.
 Mechanistic considerations of Observed Changes in Lipids 62
 4.3.
 Value of Developed Method in Drug Discovery 64
 4.4.
 Conclusion 65
 5.
 Bibliography 66
 6.
 Appendix 72
 6.1.
 Data Analysis Using R 73
 6.1.1.
 R script to Asses Normality: 73
 6.1.2.
 R Script to Perform R-SVM 73
 VI Summary Given the continued global burden of tuberculosis (TB), with an estimated 9.1 million new infections and 1.5 million TB related deaths in 2006, there is a pressing need to develop novel and more effective anti-TB drugs Experience of the last few years shows that even with the availability of the genome sequence of the disease causing organism, Mycobacterium tuberculosis, and new technologies in drug discovery, it remains difficult to identify novel TB drug targets and compounds based solely on genetic validation Therefore, in vivo testing of tool compounds needs to be incorporated early in the drug discovery process Here we set out to identify potential host lipid biomarkers in mice Such biomarkers might be used as a non-lethal drug efficacy read-out for the TB mouse models, to replace the currently used methods, which are slow, lethal and laborious For this purpose, we conducted a mouse experiment with three groups (healthy, infected, cured) Lipids were extracted from whole blood and analyzed using a systems scale approach called lipidomics, which is based on electrospray ionization mass spectrometry Using conventional statistics, we were able to identify twelve lipid biomarkers, which were up or down regulated during infection and reverted to the healthy state upon rifampicin treatment Interestingly, the list of these biomarker lipids included mainly phosphatidylserines and phosphatidylcholines We also successfully used our lipid data to train support vector machine (SVM) based models, which were then able to differentiate between diseased and non-diseased mice VII List of Table Table 2.1 Number of Lipids in each Class Studied 23
 Table 2.2 Settings of MS in MRM Mode .23
 Table 2.3 Collision Energies for MS/MS 24
 Table 3.1 Transitions Removed Based on Raw Data Analysis 39
 Table 3.2 Features Identified as Showing the “Reverting” of Interest 48
 Table 3.3 Characterization of Ions by MS/MM 50
 Table 3.4 Lipids Used to in Best Performing SVM 56
 VIII List of Figures Figure 1.1 Number of Estimated New Tuberculosis Cases in 2006 3
 Figure 1.2 Target of First Line TB Drugs 6
 Figure 1.3 Drug Discovery Though Time 8
 Figure 1.4 Structures of Lipids Herein Studied 11
 Figure 2.1 Study Design to Identify Host Lipid Based Tuberculosis Biomarker 18
 Figure 2.2 Analytical Approaches in MS/MS 22
 Figure 2.3 Schematic Depiction of Hyperplane Computed by Support Vector Machines (SVM) 32
 Figure 3.1 Colony Forming Units (CFU) in MTB Infected and Treated Mice 36
 Figure 3.2 Raw Data Analysis: High Mock Counts and Low Lipid-Extract Counts .38
 Figure 3.3 Heat Map of Outliers .40
 Figure 3.4 Evaluating Normality of Raw Data .41
 Figure 3.5 Heat Map of All Normalized Data 42
 Figure 3.6 Significant Differences Between Host Lipid Profiles of TB Infected Animals 45
 IX Figure 3.7 The relative Abundance of Lipids of Interest which Revert to the Healthy Level Upon Treatment 47
 Figure 3.8 Fragmentation Spectra of Ion at m/z 786 49
 Figure 3.9 Significant Differences Between Lipid Profiles of Diseased and nonDiseased Animals .51
 Figure 3.10 R-SVM Parameter Optimization and Error During Feature Reduction 53
 Figure 3.11 SVM can Differentiate Between Disease and non-Disease Mice Based on Blood Lipid Profiles 56
 X blood from healthy, infected and cured animals, we were able to identify twelve lipids having the same relative abundance in healthy and cured animals, but increased or decreased levels in infected animals Since rifampicin has been reported to have an influence on rat lipid profiles (Perrin et al., 2007), we were particularly interested in lipids which were the same in non-infected and drug treated animals, but did differ between the diseased and non-diseased groups These lipids are indicators for disease state and not of host response to the drug We refer to these lipids as ‘reverting’ lipids Interestingly, we found that several phosphatidylserines (PS) and phosphatidylcholines (PC) showed the reverting trend described above We also identified groups of closely related lipids which only differ by one double bond, which could imply biological relevance: if for example a particular pathway which connects those closely related lipids was affected by the TB infection, it would be expected that several lipids in this pathway respond in a similar fashion Furthermore, there is ample evidence that PS is implicated in the host immune response to TB This class of lipids has been shown to be exported to the cell surface during apoptosis (Wu et al., 2006) This process of programmed cell death is increased during TB infection and causes a two-fold increase in PS production (Lopez et al., 2003; Perskvist et al., 2002; Vance, 2008) PC on the other hand has been suggested to potentially play a role in the balance between pro- and anti-inflammatory responses during TB infection (Treede et al., 2007) These implications of PS and PC in the anti-TB immune response may explain why these lipid were identified here In agreement with the discussed involvement of arachidonic acid (AA) in inflammatory processes, we detected reduced level of AA containing lyso PE in infected animals This might indicate a role of AA release during TB infection 63 4.3 Value of Developed Method in Drug Discovery In TB drug discovery, mouse models are most commonly used to evaluate potential novel drugs This evaluation is most often done by counting CFU in the lungs of the animals It has recently been suggested that additional features such as damage to the lungs and the immune state of the animals should be considered It is expected that these read-outs would provide additional information on the rate of bacterial clearance as well as the relapse rate (Lenaerts et al., 2008) Here we set out to identify lipid biomarkers of TB infection which revert to the healthy level upon successful drug treatment, and to build machine learning tools that can differentiate between TB diseased and non-diseased animals Given the substantial involvement of lipids in the TB immune response we hypothesize that the lipids identified here may actually reflect the immune status of the animals (see above) Further studies analyzing the immune response, e.g by interferon gamma release assay, are needed to complement the findings of this study Furthermore, the specificity of the response to TB infection would need to be further investigated Applying a proteomics approach, markers that appear to be TB specific were identified in human patients (Agranoff et al., 2006) Within the well defined conditions of pre-clinical drug testing in an animal model though, this disease specificity is not required since animals will only be infected with a single disease However, the effect of any drug alone on a potential host based marker needs to be considered Most importantly for the current study, rifampicin has been shown to effect the lipid profile of rats (Pal et al., 2008) In a current follow-up study, a fourth group of uninfected but rifampicin treated animals has been included This 64 control group will help to differentiate between the host response to the drug itself and to the combined effect of infection and drug treatment 4.4 Conclusion With the global re-emergence of tuberculosis (TB), the 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lysophosphatidylcholine levels: potential biomarkers for colorectal cancer J Clin Oncol 25, 2696-2701 71 Appendix 72 6.1 Data Analysis Using R 6.1.1 R script to Asses Normality: # R_normality version: 13.11.2008 # This script will allow you to compute p-values for the Shapiro-Wilk test # # Data should have features (e.g relative abundance of lipids measured) arranged vertically (i.e in columns) and samples horizontally (i.e in rows) # # Steps: # Make the following changes: start.column (column of first feature); end.column (column of last feature); start.row and end.row (fisrt and last row of data to be included for computation of normality test) # Copy / past entire script into R command line # To run the script type narmality(inputfile, outputfile, test): inputfile: name off data file; outputfile: name of the file you want the output to be saved in also in “” and with csv extension; test: shapiro.test (for the Shapiro-Wilk test) # # HERE IS THE SCRIPT: normality