Metabolomics and Chemical Library Screening for Antibacterial

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METABOLOMICS AND CHEMICAL LIBRARY SCREENING FOR ANTIBACTERIAL

DRUG DISCOVERY

A DISSERTATION IN Pharmaceutical Sciences and Chemistry

Presented to the Faculty of University of Missouri-Kansas City in partial fulfillment of the requirements for the degree

DOCTOR OF PHILOSOPHY

NAVID JUBAER AYON

MS, University of Missouri-Kansas City, 2019 MPharm, University of Dhaka, 2013 BPharm, University of Dhaka, 2012

Kansas City, Missouri, USA 2020

METABOLOMICS AND CHEMICAL LIBRARY SCREENING FOR ANTIBACTERIAL

DRUG DISCOVERY

Navid Jubaer Ayon, Candidate for the Doctor of Philosophy Degree

University of Missouri–Kansas City, 2020

ABSTRACT

There are twenty standard amino acids which are proteinogenic and can exist in two stereoisomeric forms, D- and L-enantiomers, except for glycine, which is achiral. The D-amino acids are not incorporated in proteins; however, they play important roles in many biological systems and processes. Amino acids are polar analytes and are difficult to separate on traditional silica based stationary phases in reverse phase chromatography to allow accurate quantification in their natural form. In part I of this dissertation, we investigated the use of a chiral derivatizing reagent known as Marfey’s reagent, coupled with liquid chromatography tandem mass spectrometry (LC-MS/MS) based separation and quantification for the common proteinogenic amino acid D- and L- stereoisomers. We compared standard formic acid and ammonium acetate based solvent systems in both positive and negative modes; and established baseline separation with superior sensitivity for all nineteen amino acid pairs and glycine with an analytical C8 column in reverse phase chromatography in negative ionization mode using an ammonium acetate based neutral solvent system. We optimized the derivatization reaction time for reaction completion and various parameters including linearity, matrix effect, stability, lower limit of detection and quantification to validate the analytical method. Finally, we showed the application of the developed method by determining the levels of proteinogenic

iii amino acids DL-stereoisomers in mid-log phase cellular extracts of Methicillin-Resistant

Staphylococcus aureus (MRSA F–182). This approach enables chromatographic resolution and MS/MS-based quantification of all nineteen common proteinogenic D- and L- amino acids and glycine in complex matrices. We also used LC-MS/MS to separate and quantify deoxyribonucleic acid (DNA)/nucleosides to assess the activity of Ten-Eleven Translocation

2 (TET2) enzyme. Methylation of one of the DNA bases, cytosine at the C-5 position (5- methylcytosine, 5mC) plays a crucial role in epigenetic transcriptional regulation. TET- family dioxygenases (TET1, TET2 and TET3) initiate active demethylation of 5mC. TET2 oxidizes 5mC into 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5- carboxylcytosine (5caC) by iterative oxidation. Mutations in the TET2 gene are frequently detected in myeloid malignancies. Despite these critical emerging roles of TET dioxygenases in health and diseases, in vitro characterization of these enzymes and their mutants is still rudimentary. We compared both positive and negative ion based detection mode mass spectrometry and developed an ion-switching LC-MS/MS method for separation and quantification of four normal nucleosides (deoxyadenosine, A; deoxyguanosine, G; deoxycytidine, C; thymidine, T) and four cytosine modifications (5mC, 5hmC, 5fC, and 5caC).

This approach enables analysis of these DNA nucleosides, specially 5hmC, 5fC and 5caC with the best sensitivity possible among different optimization efforts undertaken to elucidate the function of TET2 enzymes using analytical LC-MS/MS technology. With modification, this method can also be used to study effects of drugs on biosynthesis of DNA and can help in understanding the cellular biochemistry when organisms, such as bacteria are exposed to exogenous molecules interfering with DNA biosynthesis machinery.

Part II of this dissertation describes the development and optimization of a high throughput whole cell-based screening workflow that utilizes automated sample preparation and liquid chromatography tandem mass spectrometry technology. The newly established workflow was applied to screening of chemical compound drug libraries to identify novel antibacterial activities against MRSA by increasing the dimensionality of the screening effort.

One dimension involved generating and screening a human liver microsome metabolized drug library in parallel with the original library to identify intrinsically active drugs and drugs with antibacterial metabolites. Several drugs demonstrated improved antibacterial activities against

MRSA after microsomal metabolism e.g. capecitabine, balofloxacin, gatifloxacin. A second dimension involved screening in the presence and absence of cefoxitin to identify synergistic agents. Several drugs exhibited synergy with cefoxitin against MRSA that includes floxuridine, gemcitabine, novobiocin, rifaximin and 4-quinazolinediamine. This study demonstrates how a dimensionally enhanced comparative screening effort can identify new antibacterial agents and contribute to strategies for countering antibacterial resistance.

APPROVAL PAGE

The faculty listed below, appointed by the Dean of School of Graduate Studies have examined the dissertation titled, “Metabolomics and Chemical Library Screening for Antibacterial Drug

Discovery” presented by Navid Jubaer Ayon, candidate for the Doctor of Philosophy degree, and certify that in their opinion it is worthy of acceptance.

Supervisory Committee

William Gutheil, Ph.D., Committee Chair

Division of Pharmacology and Pharmaceutical Sciences

Zhonghua Peng, Ph.D.

Department of Chemistry

Russell Melchert, Ph.D.

Division of Pharmacology and Pharmaceutical Sciences

Simon Friedman, Ph.D.

Division of Pharmacology and Pharmaceutical Sciences

Joanna Barbara, Ph.D.

Division of Pharmacology and Pharmaceutical Sciences

TABLE OF CONTENTS

ABSTRACT ...... iii

LIST OF ILLUSTRATIONS ...... xiii

LIST OF TABLES ...... xix

PART I: LIQUID CHROMATOGRAPHY TANDEM MASS SPECTROMETRY BASED

SEPARATION AND QUANTIFICATION OF PROTEINOGENIC AMINO ACID

STEREOISOMERS AND DNA NUCLEOSIDES ...... 1

1. FEATURES, ROLES AND CHIRAL ANALYSES OF PROTEINOGENIC AMINO ACID

STEREOSIOMERS ...... 2

1.1 Introduction ...... 2

1.2 Structural features and stereochemistry ...... 4

1.3 Physicochemical properties of proteinogenic amino acids ...... 5

1.4 Functions of proteinogenic amino acids in biology ...... 6

1.5 Industrial application of proteinogenic amino acids ...... 8

1.6 Stereospecific roles of proteinogenic amino acids...... 9

1.7 Role of proteinogenic amino acids in microbial world ...... 13

1.8 Analytical methods for separation and quantification of proteinogenic amino acids ...... 16

1.8.1 Analysis of proteinogenic amino acids in underivatized form ...... 17

1.8.2 Analysis of proteinogenic amino acids in derivatized form ...... 18

1.9 Conclusion ...... 19

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2. LC-MS/MS-BASED SEPARATION AND QUANTIFICATION OF MARFEY’S

REAGENT DERIVATIZED PROTEINOGENIC AMINO ACID DL-STEREOISOMERS . 21

2.1 Introduction ...... 21

2.2 Materials and methods ...... 23

2.2.1 General ...... 23

2.2.2 Marfey’s derivative preparation for method development ...... 24

2.2.3 Marfey’s derivative purification for method development ...... 24

2.2.4 LC-MS/MS method development ...... 25

2.2.5 Reaction kinetics of Marfey’s derivatization reactions ...... 33

2.2.6 Analytical derivatization procedure ...... 35

2.2.7 Sensitivity and linearity features of the analytical method ...... 36

2.2.8 Biological matrix preparation ...... 36

2.2.9 Matrix effect assessment and determination of proteinogenic DL-amino acid levels in

MRSA extract ...... 37

2.3 Results and discussion ...... 37

2.3.1 Use of D-Mar to provide L-Mar-D-AA standard surrogates ...... 37

2.3.2 LC-MS/MS method development and optimization ...... 38

2.3.3 Marfey’s derivatization reaction kinetics...... 40

2.3.4 Linearity and sensitivity characteristics ...... 44

2.3.5 Assessment of matrix and storage effects ...... 49

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2.3.6 Analysis of DL-amino acid levels in MRSA ...... 49

2.4 Conclusion ...... 50

3. OXIDATION OF DEOXYRIBONUCELIC ACID: BIOLOGICAL IMPLICATIONS AND

ANALYTICAL APPROACHES ...... 52

3.1 Introduction ...... 52

3.2 Mutation and DNA damage ...... 55

3.3 DNA Oxidation and its biological implications...... 55

3.4 TET family of enzymes and their biological implications ...... 56

3.5 Analytical approaches to study DNA oxidation ...... 58

3.6 Conclusion ...... 59

4. LC-MS/MS-BASED SEPARATION AND QUANTIFICATION OF REGULAR AND

TEN-ELEVEN TRANSLOCATION-2 (TET2) 5-METHYLCYTOSINE DIOXYGENASE

MEDIATED MODIFIED DNA NUCLEOSIDES ...... 60

4.1 Introduction ...... 60

4.2 Materials and methods ...... 61

4.2.1 General ...... 61

4.2.2 Optimization of mass spectrometry parameters ...... 62

4.2.3 Optimization of chromatographic condition ...... 62

4.2.4 LC-MS/MS analytical method validation ...... 63

4.3 Results and discussions ...... 64

4.3.1 LC-MS/MS based analysis of DNA nucleosides in positive mode ...... 64

4.3.2 LC-MS/MS based analysis of DNA nucleosides in negative mode ...... 67

4.3.3 LC-MS/MS based analysis of DNA nucleosides in positive/negative ion-switching mode

...... 70

4.4 Conclusion ...... 75

PART II: CHEMICAL LIBRARY SCREENING FOR ANTIBACTERIAL ACTIVITIES . 76

5. WHOLE CELL BASED CHEMICAL LIBRARY SCREENING FOR ANTIBACTERIAL

DRUG DISCOVERY ...... 77

5.1 Introduction ...... 77

5.2 Chemical library screening ...... 78

5.3 In vitro drug metabolism ...... 78

5.4 Drug metabolite identification ...... 81

5.5 Synergy and antagonism ...... 81

5.6 Spectrum of activity ...... 84

5.7 Frequency of resistance...... 85

5.8 Mechanism of action study ...... 86

5.9 Conclusion ...... 89

6. DIMENSIONALLY ENHANCED CHEMICAL LIBRARY SCREENING FOR

ANTIBACTERIAL ACTIVITY ...... 90

6.1 Introduction ...... 90

6.2 Materials and methods ...... 92

6.2.1 General ...... 92

6.2.2 Library replication, addition of metabolism and antibacterial control compounds ...... 94

6.2.3 Initial library screening and observation of contamination ...... 94

6.2.4 Diagnosis of MRSA and microsome stock ...... 95

6.2.5 Identification of contaminant bacteria from microsome using MALDI-TOF/TOF ...... 96

6.2.6 Approaches to decontaminate microsomes ...... 97

6.2.7 LC-MS/MS based analytical method development for control CYP substrates ...... 97

6.2.8 Kinetics analysis of drug metabolism ...... 100

6.2.9 Selection of panel of control antibiotics ...... 101

6.2.10 In vitro microsomal metabolism to provide the working PM library ...... 101

6.2.11 UM/PM vs –/+ Cef library screen against MRSA ...... 103

6.2.12 Hit picking and minimum inhibitory concentration (MIC) determination ...... 103

6.2.13 Scaled-up metabolism reaction and active metabolite purification ...... 105

6.2.14 Identification of active metabolites ...... 106

6.2.15 Spectrum of activity determination ...... 107

6.2.16 Checkerboard assays to confirm synergy with cefoxitin ...... 108

6.2.17 Frequency of resistance determination ...... 109

6.3 Results and discussion ...... 109

6.3.1 Initial observation of contamination in PM library plates ...... 109

6.3.3 MALDI-TOF/TOF identification of contaminant bacteria from microsomes ...... 113

6.3.4 Decontamination of microsome ...... 114

6.3.5 Evaluation of microsomal drug metabolism ...... 119

6.3.6 Library pre-metabolism and optimization of library screening workflow ...... 121

6.3.7 Activity of control antibiotics ...... 122

6.3.8 Evaluation of preliminary library screening result ...... 123

6.3.9 Two-dimensional library screening to increase informational value ...... 124

6.3.10 Analysis of hits ...... 126

6.3.11 Identification of novel antibacterial drug metabolites ...... 135

6.3.12 Spectrum of activity of capecitabine metabolites and related agents ...... 139

6.3.13 Synergistic activity of library hits with cefoxitin ...... 141

6.3.14 Frequency of resistance of top FDA library hits ...... 144

6.3.15 Conclusion ...... 145

7. SUMMARY AND RECOMMENDATIONS...... 146

7.1 Summary ...... 146

7.2 Recommendations ...... 150

APPENDIX ...... 152

REFERENCES ...... 164

VITA ...... 210

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LIST OF ILLUSTRATIONS

Figure Page

Figure 1. Structures of non-superimposable stereoisomers of α-amino acid...... 5

Figure 2. Steps involved in bacterial peptidoglycan biosynthesis during all three phases and their associated location in gram positive bacteria109. Most gram negative bacteria use meso- diaminopimelic acid in place of L-Lys110. PEP – phosphoenolpyruvate; AEK – L-Ala-γ-D-Glu-

L-Lys (© American Society for Microbiology, reprinted with permission)...... 14

Figure 3. Reaction scheme of DL-amino acids with L- and D-Marfey’s reagent...... 22

Figure 4. LC-MS/MS chromatograms of Marfey’s derivatives of the 19 common DL-amino acids and glycine using the standard formic acid (pH 2) based solvent system on a C8 column and MS/MS detection in positive mode. Each panel represents a single Q1/Q3 channel, and is associated with a particular Mar-AA. Since Leu and Ile have the same molecular weights

(isobars), their Marfey’s derivatives both show up in the Q1/Q3=384.2/338.2 channel...... 29

Figure 5. Multiple reaction monitoring (MRM) chromatogram of Marfey’s derivatives of the

19 common DL-amino acids and glycine using the ammonium acetate (pH 6.5) based neutral solvent system on a C8 column and MS/MS detection in negative mode...... 31

Figure 6. LC-MS/MS chromatograms of Marfey’s derivatives of the 19 common DL-amino acids and glycine using the ammonium acetate (pH 6.5) based neutral solvent system on a C8 column and MS/MS detection in negative mode. Each panel represents a single Q1/Q3 channel, and is associated with a particular Mar-AA (amino acid). Since Leu and Ile have the same molecular weights (isobars), their Marfey’s derivatives both show up in the two Q1=382.2 panels. The Q1/Q3 = 382.2/263.2 channel is selective for Ile (two higher peaks in the Mar-

Ile/Mar-Leu panel), and the Q1/Q3 = 382.2/288.2 channel is selective for Leu (two higher

xiii peaks in the Mar-Leu/Mar-Ile panel). Note that Mar-Leu/Ile also shows some signal intensity in the Mar-Asn MS/MS channel due to the Mar-Leu/Ile M+1 isotopomers, but since there is good chromatographic resolution of all these L-Mar-DL-AA species they can be individually quantified...... 33

Figure 7. Reaction rate constants for and correlation between D- and L-amino acids (except

Gly, His and Tyr)...... 42

Figure 8. Reaction kinetics of L-Mar with L- and D-His and L- and D-Tyr...... 43

Figure 9. Linearity of L-Mar-L-AA derivatives using the acidic solvent system (pH 2) and positive mode MS/MS detection...... 45

Figure 10. Linearity of L-Mar-L-AA derivatives using the neutral solvent system (pH 6.5) and negative mode MS/MS detection...... 47

Figure 11. Chemical structure of double helix DNA showing phosphodiester bonds between deoxyribose sugars and hydrogen bonds between nitrogen bases...... 53

Figure 12. DNA methylation and demethylation cycle by DNMTs and TET enzymes201

(reprinted with permission)...... 57

Figure 13. DNA digestion workflow to obtain nucleotides and nucleosides...... 59

Figure 14. MRM chromatogram of eight DNA nucleosides using 10 mM ammonium acetate/acetonitrile solvent system (pH 4) on a C18 column and MS/MS detection in positive mode...... 65

Figure 15. Standard curves of eight DNA nucleosides using 10 mM ammonium acetate/acetonitrile solvent system (pH 4) and MS/MS detection in positive mode...... 66

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Figure 16. MRM chromatogram of eight DNA nucleosides using 10 mM ammonium acetate/acetonitrile solvent system (pH 6.5) on a C18 column and MS/MS detection in negative mode...... 68

Figure 17. Standard curves of eight DNA nucleosides using 10 mM ammonium acetate/acetonitrile solvent system (pH 6.5) and negative mode MS/MS detection...... 69

Figure 18. Mixed mode MRM chromatogram of eight DNA nucleosides using water/methanol solvent system (pH 3.5) on a C8 column (a) period 1 – positive mode (b) period 2 – negative mode, (c) Period 3 – positive mode...... 72

Figure 19. Standard curves of eight DNA nucleosides in positive/negative ion-switching mode using water/methanol solvent system (pH 3.5)...... 73

Figure 20. Extraction of various liver components (© Sekisui XenoTech, reprinted with permission)...... 80

Figure 21. Checkerboard assay setup for synergy determination...... 82

Figure 22. Representation of checkerboard experiment plates showing synergistic action, no interaction and antagonistic action...... 83

Figure 23. Isobolograms showing synergistic (red), additive (black) and antagonistic (blue) effects...... 84

Figure 24. Schematics of frequency of resistance (FoR) experiment (© Emery Pharma, reprinted with permission)...... 86

Figure 25. Cytoplasmic intermediates and the enzymes involved in S. aureus cell wall biosynthesis pathway showing stepwise formation of functional units leading to peptidoglycan formation111...... 87

Figure 26. Cytoplasmic intermediates and the enzymes involved in S. aureus DNA biosynthesis pathway showing stepwise formation of functional units leading to DNA formation...... 88

Figure 27. Schematics of identification of microorganisms using MALDI-TOF/TOF...... 97

Figure 28. MRM chromatograms of control CYP substrates (a) Positive mode (b) Negative mode...... 99

Figure 29. Normalized histogram of library screening results demonstrating the separation of known actives from known inactives and the distribution of unknowns...... 104

Figure 30. Schematics of observation of contamination in PM library plates...... 110

Figure 31. Assessment of MRSA and microsomes using non-selective and selective solid nutrient media. (a), (e) – CAMH; (b), (f) – SBA; (c), (g) – Spectra MRSA; (d), (h) – MSA plates for (a – d): MRSA; (e – h): U–1 and U–2. U – Contaminant microorganisms from microsome (unknowns)...... 112

Figure 32. (a) Reference for catalase (+) ve and (–) ve test (b) Results of catalase test for microsomal contaminant unknown microorganism (U–1 and U–2)...... 113

Figure 33. Effect of selective agents on MRSA and microsomal contaminants...... 116

Figure 34. Effect of DMSO on microsomal contaminants...... 117

Figure 35. Assessment of microsome from additional vendors using CAMH-Agar solid media.

(a) Corning (b) Gibco...... 118

Figure 36. Kinetics study of control CYP substrates. Blue line indicates disappearance of parent drug with respect to microsomal incubation time, red line indicates formation of major drug metabolite with respect to time...... 120

Figure 37. Resazurin (Alamar Blue) based fluorescence assay...... 122

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Figure 38. Heat map of control antibiotics. Blue zone – activity/inhibition, yellow zone – no activity/no inhibition (UM = Un-metabolized, PM = Pre-metabolized)...... 123

Figure 39. Venn analysis of library screening results...... 124

Figure 40. UM/PM vs –/+Cef library screening workflow. (a) The PM library is generated by in vitro microsomal metabolism and working UM and PM libraries prepared at 1 mM. (b) UM and PM libraries are screened for antibacterial activity in the presence and absence of cefoxitin

(8 µg mL-1) to generate a merged hit list. (c) MICs are determined for each hit in the merged hit list under all four screening conditions (UM/PM vs –/+Cef)...... 125

Figure 41. Pictorial list of library compounds with a minimum of 1.5 log2 fold change in activity due to microsomal metabolism and presence of cefoxitin. UM better – compounds showing reduced activity due to metabolism, PM Better – compounds showing increased activity due to metabolism, +Cef Better – compounds showing increased activity (synergy) due to presence of cefoxitin (8 µg mL–1) and –Cef Better – compounds showing reduced activity (antagonism) due to presence of cefoxitin (8 µg mL– 1)...... 127

Figure 42. Overlaid UM and PM capecitabine enhanced mass scanning chromatograms. .. 135

Figure 43. Resolution of capecitabine active metabolites by semi-preparative HPLC and identification of active metabolites by high resolution mass spectrometry (HRMS)...... 136

Figure 44. Comparison of triple quadrupole MS chromatogram for purified capecitabine metabolites and commercial standards...... 137

Figure 45. Representative product ion spectrum for C1 (m/z 404, 5.04 min) detected in active fraction collected from the balofloxacin incubation with human liver microsomes...... 138

Figure 46. Representative product ion spectrum for C2 (m/z 392, 5.41 min) detected in active fraction collected from the gatifloxacin incubation with human liver microsomes...... 139

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Figure 47. Structures of capecitabine and other structurally similar compounds sharing pyrimidine backbone...... 141

Figure 48. Checkerboard assay results for combinations of cefoxitin with floxuridine, gemcitabine, novobiocin and rifaximin (FDA library hits). Isobolograms for combinations of cefoxitin (y–axes) with (x–axes): (a) floxuridine, (b) gemcitabine, (c) novobiocin, (d) rafiximin. Minimum FIC indices are given in the isobolograms. The dashed line in the isobolograms correspond to the expected no interaction (additive MICs) curve. Cef = cefoxitin,

Flox = floxuridine, Gemc = gemcitabine, Novo = novobiocin, Rifa = rifaximin...... 142

Figure 49. Checkerboard assay results for combinations of cefoxitin with 4- quinazolinediamine, celastrol and teniposide (NCI library hits). Isobolograms for combinations of cefoxitin (y–axes) with (x–axes): (a) 4-quinazolinediamine, (b) celastrol, (c) teniposide. Minimum FIC indices are given in the isobolograms. The dashed line in the isobolograms corresponds to the expected no interaction (additive MICs) curve. Cef = cefoxitin, 4QDA = 4-quinazolinediamine, Cela = celastrol, Teni = teniposide...... 143

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LIST OF TABLES

Table Page

Table 1. List of 20 common proteinogenic essential and non-essential amino acids5, 6...... 3

Table 2. Important bioactive molecules, their amino acid precursors and biological functions.

...... 7

Table 3. List of various D-amino acids in higher organisms, their location and associated roles.

...... 11

Table 4. Compound dependent parameters, retention times (tR) and MS/MS detection characteristics for the three most intense MS/MS transitions of the 20 common amino acids using positive mode detectiona...... 25

Table 5. Compound dependent parameters, retention times (tR) and MS/MS detection characteristics for the three most intense MS/MS transitions of the 20 common amino acids using negative mode detectiona...... 29

Table 6. Compound dependent parameters and LC-MS/MS characteristics for the most intense

MS/MS transitions of the 20 common amino acids in negative mode detectiona...... 39

Table 7. Kinetics features of Marfey’s derivatization reaction for 18 amino acids that show apparent first order reaction kineticsa ...... 41

Table 8. Squared correlation coefficients for linearity (0-800 pmol) of L-Mar-L-AA derivatives using the acidic solvent system (pH 2) and positive mode MS/MS detection...... 46

Table 9. Linearity (0-800 pmol), matrix effect analysis and 24 hour stability of L-Mar-L-AA derivatives using the neutral solvent system (pH 6.5) and negative mode MS/MS detection.48

Table 10. DL-amino acid profile of MRSAa...... 50

Table 11. DNA codon table...... 54

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Table 12. Compound dependent parameters and sensitivity characteristics for the most intense

MS/MS transitions of the eight DNA nucleosides in positive modea...... 67

Table 13. Compound dependent parameters and sensitivity characteristics for the most intense

MS/MS transitions of the eight DNA nucleosides in negative modea...... 70

Table 14. Details of periods, compound dependent parameters and sensitivity characteristics for the most intense MS/MS transitions of the eight DNA nucleosides in positive/negative ion- switching modea...... 74

Table 15. Compound and source dependent parameters for control CYP substrate analytical method developmenta...... 98

Table 16. MICs of various control antibiotics against different bacteria (© American Society for Microbiology)...... 101

Table 17. Extent and rate of metabolism for the control CYP substrates...... 121

Table 18. List of active compounds from FDA library screening (validated MIC≤100 µM) from library screening against MRSA F-182 (ATCC 43300) (UM/PM vs –/+ 8 µg mL–1) ranked by lowest minimum MIC...... 128

Table 19. List of active compounds from NCI library screening (validated MIC≤100 µM) from library screening against MRSA F-182 (ATCC 43300) (UM/PM vs –/+ 8 µg mL–1) ranked by lowest minimum MIC...... 130

Table 20. FDA library anti-MRSA hit MICs sorted by greatest average increase in activity after metabolism (AL2 ≥1.5)...... 133

Table 21. NCI library anti-MRSA hit MICs sorted by greatest average increase in activity after metabolism (AL2 ≥1.5)...... 134

Table 22. FDA library anti-MRSA hit MICs sorted by greatest difference in activity between

–/+ cefoxitin (Abs(AL2) ≥2)...... 134

Table 23. NCI library anti-MRSA hit MICs sorted by greatest difference in activity between –

/+ cefoxitin (Abs(AL2) ≥2)...... 134

Table 24. Balofloxacin and gatifloxacin anti-MRSA metabolite profiling and characterization.

...... 138

Table 25. Spectrum of activity of capecitabine metabolites and related compounds (MIC, µM).

...... 140

Table 26. Frequency of resistance for different FDA library hits...... 144

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ACKNOWLEDGEMENTS

I am thankful to Dr. William Gutheil for assigning me cool projects, giving me research directions and teaching me various scientific methodologies and data analysis workflows. His training helped me to develop as a scientist. He introduced me to mass spectrometry and separation science, and I am thankful to him for that.

I am sincerely grateful to Dr. Zhonghua Peng for always being there with his tremendous support, guidance, academic counselling and inspiration. Dr. Peng has been the rock for me and just knowing that he is there always increased my bandwidth to move forward.

I am blessed to have him as a mentor and will always cherish his guidance and mentorship.

I am indebted to Dr. Russell Melchert for his availability, invaluable guidance and support, and for his faith on me. Dr. Melchert’s high expectation always helped me to go the extra miles to improve the quality of everything I do. I will forever cherish his honest efforts and sincere well wishes.

I am thankful to Dr. Simon Friedman for his excellent teaching which has helped me to strengthen my fundamentals of medicinal chemistry.

I am truly grateful to Dr. Joanna Barbara for her constant support, guidance and feedback on my research and her constant motivation throughout my graduate studies. Dr.

Barbara has been a strong inspiration for me and I look up to her as a role model. Without her generous support, I will not be able to complete some of the projects.

It has been an honor to have a panel of such extraordinary scientists and researchers as part of my PhD supervisory committee.

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I would like to take this opportunity to express my sincere gratitude to Dr. Matthew

Sobansky, my exclusive mentor for his extraordinary teachings, mentorship and support which have been a great source of strength and motivation during my graduate studies. Dr. Sobansky enhanced my scientific value and morale every step of the way and taught me how to be an efficient scientist with high degree of productivity. He taught me proteomics and some other cool applications of mass spectrometry including microbial identification using MALDI-TOF mass spectrometer. The support and guidance I received from Dr. Sobansky, I cherish that forever. Doing the internship under his mentorship was the highlight of my PhD life and I am very fortunate to be able to learn from him. I aspire to be a human being and a scientist like

Dr. Sobansky.

My sincere thanks to Drs. Mridul Mukherji and Celestin Bi-Botti Youan for their instrumental support and collaboration. Working on different projects with Drs. Mukherji and

Youan helped me to enhance my scientific acumen and critical thinking. I am very thankful to

Dr. Andrew Keightley form the School of Biological and Chemical Sciences for his patience, constant support with Orbitrap analysis and his teachings of proteomics data processing. I sincerely appreciate the time, efforts and teachings that Dr. Priyanka Chitranshi (Shimadzu

Corporation) provided with proteomics workflow establishment with the Q-ToF LCMS-9030.

I am extremely thankful to Drs. Nadya Galeva and Seema Muranjan from Sekisui XenoTech,

LLC for their kind assistance with drug Met ID experiments. My sincere gratitude to Dr.

Charles Wurrey for his high quality teaching, and care to foster a young scientist like me. I am very fortunate to be his student and I am proud to find him as a mentor. I am sincerely thankful to Drs. David Vanhorn and Keith Buszek for their outstanding teaching which have helped me to learn important aspects of inorganic and organic chemistry, and instrumental data analysis.

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I am sincerely thankful to my senior lab mate, Dr. Harika Vemula for teaching me bacterial metabolomics and giving me hands on training on mass spectrometry and HPLC, which were critical to be successful in my research projects. I am very thankful to Amar Deep

Sharma for assisting me with the amino acid project and with the Q-ToF, and to Gioia Heravi for assisting me with the library screening project. I would like to extend my thanks to Shivani

Gragvanshi, Nitish Mishra and Vidit Minda for their assistance with different projects and their cordial friendship.

I would like to take this opportunity to express my sincere thankfulness to my colleagues, Drs. Fohona Coulibaly, Byeongtaek Oh, Albert Ngo, Meshal Alshamrani,

Abdullah Alsalhi, Chayan Bhattacharya and Aninda Sundar Dey with whom I had the pleasure to collaborate. I sincerely appreciate the constant support of Sharon Self, Joyce Johnson,

Norma Aguirre, Sarah Beach, Miranda Rheuport, Gwen Huke, Tamica Lige, Shana

Eisentrager, Jane Poe, Jeannie Westmoreland, Nancy Bahner, Jana Boschert and Casey

Ramsey in administrative assistance throughout my graduate study. I am very much thankful to Nancy Hoover and Michelle Heiman from the UMKC School of Graduate Studies, and Asia

Williams from the UMKC School of Biological and Chemical Sciences for their help and support. A very special thanks to Joyce Ward from the UMKC International Student Affairs

Office for her continuous help and kind support throughout my graduate studies.

A very special thanks to the Chair of the division, Dr. Gerald Wyckoff for his constant support, availability, patience and the financial support that he extended on multiple occasions.

I am thankful to thank Dr. Candace Schlein from the School of Education for giving me the opportunity to be a Preparing Future Faculty Fellow, which has been a rewarding journey during my graduate study. A special thanks to Drs. Jennifer Lundgren, Chirs Liu and Joseph

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Parisi for their strong leadership. I would also like to thank National Institute of Health, UMKC

Pharmacy Foundation and UMKC School of Graduate Studies for financial support.

My special gratitude to my parents, Dr. M. A. Mazid and Mrs. Nasima Mazid for their blessings, sacrifice, patience, constant support, encouragement and understanding throughout my PhD. Without their blessings, faith, love, nurture and care, this will never be possible. I am eternally thankful to the love of my life, my dear friend and confidant, my wife, Dr. Tasmim

Hossain. She has put me in eternal debt by pausing her career to stay beside me while I pursue my PhD. What my parents and my wife have done while I pursue my PhD were priceless and without their support, encouragement and patience, I will never be able to complete my degree.

I am looking forward to the future so that I can bring millions times more happiness and contentment to them as they sacrificed so many things for me while I pursue my PhD. I am grateful to my in laws, Drs. M. A. Hossain and Nahid Sultana for their constant support and inspiration. I thank my brothers Irfan Mazid Adib, Hashibur Rahman, Jahid Kamal and my sisters Maliha Naureen Moon, Tanjim Hossain Disha and Mithila Hossain Riea for their love and support.

Finally, I am grateful to Almighty Allah for enabling me successfully completing the

PhD, teaching me patience, helping me treading on this extremely hard journey of life while I pursue my PhD.

xxv

Dedicated to My Loving Family M A Mazid and Nasima Mazid (Parents) Tasmim Hossain (Wife)

xxvi

PART I: LIQUID CHROMATOGRAPHY TANDEM MASS SPECTROMETRY BASED

SEPARATION AND QUANTIFICATION OF PROTEINOGENIC AMINO ACID

STEREOISOMERS AND DNA NUCLEOSIDES

CHAPTER 1

1. FEATURES, ROLES AND CHIRAL ANALYSES OF PROTEINOGENIC AMINO

ACID STEREOSIOMERS

1.1 Introduction

Proteins are integral components of living organisms and play critical roles in numerous biological processes. In living organisms, protein synthesis occurs on the ribosomes, which link amino acids in an order dictated by messenger RNA (mRNA). Transfer RNA

(tRNA) carries the amino acids specified by the three nucleotides RNA codon1. Amino acids are small molecule metabolites containing an amine (–NH2) and a carboxyl (–COOH) functional groups and are present in all living things2. To date, 22 naturally-occurring amino acids are found to be present in proteins, among which 20 are encoded from the genetic code and known as proteinogenic amino acids that play a central role in cellular metabolism3. The remaining two amino acids, selenocysteine and pyrrolysine can be incorporated into proteins by a special translational mechanism4. In eukaryotes, besides 11 non-essential amino acids, which the body can synthesize, there are 9 amino acids which are known as essential amino acids as they can only be obtained from exogenous sources. These proteinogenic amino acids, both essential and non-essential, connect to each other through peptide bonds to form proteins.

The structures, polarity features, acid-base properties and stereochemistry of the 20 common proteinogenic amino acids are listed in Table 1.

Table 1. List of 20 common proteinogenic essential and non-essential amino acids5, 6.

Essential Amino Acids

O O O H N OH OH OH N NH2 NH2 Histidine (His) – P (Basic) Isoleucine (Ile) – NP Leucine (Leu) - NP O O O H N S 2 OH OH OH NH NH2 NH2 2 Lysine (Lys) – P (Basic) Methionine (Met) – NP Phenylalanine (Phe) – NP H OH O N O

OH NH2 OH NH OH NH 2 O 2 Threonine (Thr) – P (Neutral) Tryptophan (Trp) – NP Valine (Val) – NP

Non-Essential Amino Acids

O NH O O

H2N OH H2N N OH OH H NH2 NH2 O NH2 Alanine (Ala) – NP Arginine (Arg) – P (Basic) Asparagine (Asn) – P (Neutral) O O O O HO OH HS OH H2N OH O NH2 NH2 NH2 Aspartic acid (Asp) – P (Acidic) Cysteine (Cys) – P (Neutral) Glutamine (Gln) – P (Neutral) O O O O HO OH H N 2 OH N OH NH2 H Glycine (Gly) – P (Neutral) Glutamic acid (Glu) – P (Acidic) Proline (Pro) – NP O O

HO OH OH

NH NH2 2 HO Serine (Ser) – P (Neutral) Tyrosine (Tyr) – P (Neutral) P – polar, NP -non-polar.

All proteinogenic amino acids (except Gly) exist in two stereoisomeric forms (D- and

L-). All ribosomally synthesized proteins are primarily made up of L-AAs. However, D-AAs have found to have important biological functions in both prokaryotic and eukaryotic systems.

Therefore, it is imperative to accurately distinguish and identify the specific amino acid stereoisomer in a given system to understand their role correctly. Due to similarities in polarity and acid-base characteristics, it has been a challenge to analytically resolve and quantify all proteinogenic amino acid D- and L-stereoisomers (39 species, 38 isomeric D- and L-AAs and

Gly) in a single analytical run. Recent advancements in the field of stationary phases7, 8, availability of numerous derivatizing reagents9 and instrumentation technology10-13 have made chiral analysis of amino acids more efficient and less time consuming14.

1.2 Structural features and stereochemistry

Structural features play a direct role in regulating the physicochemical properties displayed by different amino acids. Based on the location of the –NH2 group, amino acids can be classified into four major types: alpha- (α-), beta- (β-), gamma- (γ-) or delta- (δ-) amino acids. Various other properties such as polarity (polar and non-polar), pH characteristics and charge (positively charged, neutral, negatively charged), and side chain attributes (aliphatic, acyclic, aromatic, hydroxyl or sulfur containing etc.) also play crucial role in determining the reactivity and functions of the proteinogenic amino acids. The α-carbon of the common proteinogenic amino acids (except Gly) contain a chiral center which leads to existence of two non-superimposable stereoisomers as shown in Figure 1.

Figure 1. Structures of non-superimposable stereoisomers of α-amino acid.

Due to presence of the carboxylic acid and amino functional groups, amino acids are amphiprotic in nature and can exist as zwitterions at various pH. Therefore, the charge distribution on amino acid molecules largely depends on the pH of the environment, which can be exploited to ionize them in mass spectrometry-based detection.

1.3 Physicochemical properties of proteinogenic amino acids

Amino acids are colorless, non-volatile crystalline solids, which have a melting point above 200 ºC in their inorganic salt forms15. Several factors determine the nature and physicochemical properties of these amino acids including charge, polarity, size and functional groups. Properties of the amino acids ultimately contribute to the properties and functions of proteins they form. For example, proteins that will bind to negatively charged molecules will have surfaces rich with positively charged amino acids such as Lys, Arg and His. Conversely, proteins binding to positively charged molecules will have surfaces rich with negatively charged amino acids such as Glu and Asp. Similarly, water-soluble proteins tend to have the hydrophobic amino acids buried in the core, which is inaccessible to water, whereas the hydrophilic amino acids remain accessible. Water insoluble or limited water-soluble proteins,

5 on the other hand, have their hydrophobic amino acid residues exposed, for example, lipid membrane bound proteins.

Depending on the environmental pH, amino acids can have a predominating charged form, which determines their movement in an electric field. The pH at which amino acids do not migrate in an electrical field is called their isoelectric point; it can be manipulated to improve the solubility and stability of the proteins they form16. It has been reported that, non- proteinogenic amino acids, such as cyano and fluoro Trp and Phe are relatively more stable than the corresponding proteinogenic amino acids (Trp and Phe), when exposed to heat, UV and gamma radiation, which also helps explaining the presence of non-proteinogenic amino acids in meteorites and other extraterrestrial components whereas proteinogenic amino acids as part of life biochemistry17, 18.

1.4 Functions of proteinogenic amino acids in biology

Proteinogenic amino acids are integral part of almost all biological systems as proteins are ubiquitous and responsible for a plethora of important biological roles. They play a critical role in various pathways of human metabolism such as urea cycle, citric acid cycle and gluconeogenesis19, 20. Several amino acids also act as precursors of important bioactive molecules as listed in Table 2.

Table 2. Important bioactive molecules, their amino acid precursors and biological functions.

Precursor Amino Acids Bioactive Compounds Biological Functions Arg Nitric oxide Signaling molecule21, vasodilation22 Asp, Gly and Glu Nucleotides DNA biosynthesis23 Gly Porphyrins (heme) Cofactor of hemoglobin which is responsible to supply oxygen to tissue24, CNS inhibition25 Glu Glutathione Antioxidant26 GABA Inhibition of nerve transmission27 His Tetrahydrofolic acid Carbon metabolism28, oxidative DNA degradation29 Histamine Gastric acid secretion30, vasodilation31, allergic reaction32 Met S-adenosyl Energy metabolism33 methionine/Choline Pro Hydroxyproline Stability of collagen34 Phe and Tyr Catecholamine Motivational salience35, fight or neurotransmitters flight response36 (dopamine, epinephrine and norepinephrine) Phenylethylamine (human) CNS stimulant (human)37, and phenylpropanoids protection from UV light and (plants) pathogens (plants) 38 Tyr Melanin Albinism39 Trp Serotonin Neurotransmitter modulating cognition, reward, learning, memory and numerous physiological processes40 Niacin Carbohydrate metabolism41

Several important therapeutic applications of proteinogenic amino acids have been reported in respiratory physiology, cardiology, nephrology, neurology, immunology, reproductive system, cell signaling, growth and differentiation, gene expression, acid-base balance, systemic circulation, metabolic, bone and congenital disorders42, 43. Role of non- essential amino acids in cancer biology has also been comprehensively discussed44. Free essential amino acids contribute to glycolytic and Krebs cycle and are used as a source of energy during depletion of carbohydrate and fat45. Muscle can catabolize the branched chain 7 amino acids i.e. Val, Leu and Ile46. Imbalance in amino acid levels is reported in various diseases due to changes in metabolic capacity including cancer47, liver cirrhosis48, neurological49-52 and metabolic disorder53. The major caveat of using amino acids as biomarkers is that they are vital to many biological processes and their pool might be dysregulated for various other reasons, which need to be carefully considered before using them diagnostically as biomarkers. However, the presence of D-amino acids can be a good indication of bacterial infection. In plant biology, amino acids are reported to play important roles in production of chlorophyll, hormones and growth factors; stimulation of vitamin and enzyme synthesis, mineral chelation, and increased pest and pathogen resistance54, 55. D-amino acids can exhibit both inhibitory effect (D-Ser, D-Ala and D-Arg) and positive effect (D-Ile, D-

Val) on Arabidopsis growth56, whereas D-Lys can promote growth of both Arabidopsis and tobacco57.

Roles of amino acids as precursors of important bioactive molecules, biomarkers and clinically important agents emphasize why robust analytical methods are required to accurately identify and quantify their levels in different biological matrices.

1.5 Industrial application of proteinogenic amino acids

Proteinogenic amino acids have several industrial applications including use in food, agriculture and pharmaceutical industry, human nutrition and dietary supplements, animal feed58 etc. For example, Glu and Asp are used as flavor enhancer59 and artificial sweetener60 in food industry. In agriculture industry, metal chelating amino acids are used in case of mineral deficiencies and help improving plant health61 by playing a role in soil remediation.

Thr, Asp, His and Cys have been reported to extract cadmium, copper, nickel and zinc

8 comparably to EDTA62. Besides, proteinogenic amino acids are regularly used in pharmaceutical and cosmetic industries for various applications such as environmentally friendly packaging, in drug delivery and in prosthetic implants due to biodegradable characteristics63,64. Due to its water solubility, biodegradable features and metal chelating ability, polyaspartate is used in disposable diapers65, as an anti-scaling agent and corrosion inhibitor66, 67. Tyr, on the other hand is reported to be used as an alternative to phenol in polycarbonate synthesis which has various construction and engineering applications68.

1.6 Stereospecific roles of proteinogenic amino acids

L-amino acids are primarily present in various prokaryotic and eukaryotic proteins.

Participation of D-amino acids in protein synthesis is very limited, although some D-amino acids are detected in bacteria, some vertebrates, marine invertebrates and plants in composition of proteins, peptide antibiotics, hormones, neuropeptides, opioids and toxins69. Proteins have ubiquitous roles in the animal and plant kingdom including structural integrity, metabolic functions and transport of necessary entities, among others. The role of L-amino acids is limitless as they make proteins, which are responsible for performing numerous functions in living organisms to maintain the cellular function, hence are related to the evolution of life.

Reasons for the selection of only one isomer of amino acids are not well established, but it can be understood that a mix of both will hinder proper folding of the proteins and might render them inactive. In general, D-amino acids cannot be digested as well as L-amino acids since peptide bonds containing D-L, L-D and D-D configurations offer resistance towards proteolytic enzymes69, hence homochirality is essential for life.

Conversion of L- to D-amino acids is primarily carried out by D-amino acid racemase via formation of α-iminoacid. L-amino acid oxidase can also participate in this conversion through α-ketoacid formation. In mammals, oxidase predominates over the racemase pathway; the latter is more common in bacteria. Heat and alkaline treatment can also cause L- to D-amino acid conversion. As D-amino acids are less frequently found in nature and biology, curiosity developed in studying this stereoisomeric version of amino acids. Besides prokaryotes, several

D-amino acids have been identified in human, cattle, mouse, rat, frog, snail and spider in free form or part of peptides and proteins and thought to be the result of racemization during development and aging70. Table 3 lists the presence of several D-amino acids in different higher organisms and their biological roles in different diseases.

Table 3. List of various D-amino acids in higher organisms, their location and associated roles.

D-Amino Acids Proteins/Peptides/ Locations Associated diseases/Functions Free AAs D-Asp Elastin Aorta and skin Arteriosclerosis (H)71, 72 Myelin, Brain (H)73,74 Alzheimer’s β-amyloid Free AA Brain (H, R, C)75 Nueromodulatory effect Testis, adrenal Inhibit secretion of melatonin and pineal Increase testosterone glands (R)76, 77 production D -Asp, D -Asn, α-crystallin Lens (H)78 Cataract D -Ser and D -Thr D -Ala Dermorphine Skin (F)79, 80 1000 times more analgesic Deltorphine than morphine due to presence of D-Ala D -Met Dermenkephalin Skin (F)81 Analgesia D -Phe Achatin I Ganglia and Enhances cardiac activity atrium (S)82 Excitatory action on muscles Hyperglycemic Sinus gland Increase glucose concentration hormone (L)83, 84 in response to stress D -Asn Fulicin Ganglia (S)85 Enhance contraction of penis retractor muscle D -Trp Contryphan Venom (CS)86 Paralysis of fish prey by snails D -Ser ω-agatoxin Venom (SP)87 Calcium channel blocker Free AA Brain (M)75 N-methyl D-aspartate (NMDA) receptor agonist H – Human, R – Rat, C – Chicken, F – Frog, S – Snail, L – Lobster, CS – Cone Snail, SP – Spider, M – Mammals

The biggest source of D-amino acids is the food industry and it has been reported that in general we consume more than 100 mg of D-amino acids everyday9. Marcone, G. L. et al discussed elaborately different D-amino acids that are present in different food products either naturally or due to processing or contamination9. Naturally, fruits and vegetable contain a number of D-amino acids. Previously, various D-amino acids such as D-Asn, D-Asp, D-Ala, D-

Arg, D-Gln, D-Glu, D-Val, D-Leu, D -Lys, D-Ser have been reported in milk, apple, pineapple, papaya, mango, clementine, orange, pear, lemon, passion fruit, grapefruit, cabbage,

11 watermelon, tomato, carrot, celery, rice, meat, fish, liquid spice, peanuts and honey9, 88.

Fermented dairy products such as yogurt and cheese, and alcoholic beverages are reported to contain several D-amino acids such as D-Phe and D-Pro due to lactic, acetic or bacterial fermentation89. Coffee is another major source of D-amino acids primarily containing D-Asp,

D-Glu, D-Pro and D-Phe90. Soy and corn based meals are reported to contain D-Met in addition to other commonly present D-amino acids in food. Finally, temperature and pH induced racemization can also cause formation of certain D-amino acids in processed food items, so they can be used as a marker for such treatment. It should be noted that abnormal or higher than normally reported amount of D-amino acids in food items warrants a quality check as it is often indicative of microbial contamination, especially increased level of D-Asp, D-Glu and D-

Ala91 and indicate the necessity of a sensitive and accurate bioanalytical tool in quality control and assurance of food items and beverages.

In addition to serving as an indicator of bacterial infection, several free and bound D- amino acids have been reported as biomarkers, e.g. D-Ser, D-Pro and D-Asn for kidney disease92-94, D-amino acid oxidase for post-stroke dementia95 and cellular senescence96, D- amino acid peptide for better cellular retention on monoclonal antibody for tumor targeting97 and D-Glu in heart diseases98. Other important D-amino acid containing enzymes include: bacterial dehydrogenase, fungal N-acetyltransferase, D-Pro reductase, D-Glu cyclase, D-Cys desulfhydrase, D-arginase, D -Ser dehydrase and D-Met aminotransferase99. There are also reports where the presence of D-amino acid is found to be important for the biological activity, both of therapeutic and adverse origin e.g. D-Ala for analgesic activity of demorphine79, D-Phe for antibacterial activity of gramicidin S100, D-Ser for calcium channel blocking activity101, D-

Met for toxicity of defensin like peptide102, D-Val for cancer therapy103, D-Tyr for hypertension104, and D-Trp for growth promoting effect of Auxin in plants105.

1.7 Role of proteinogenic amino acids in microbial world

Prokaryotes, especially bacteria, are a great source of D-amino acids. D-amino acids are present in peptidoglycan (PG) layer of the cell wall, and participate in cell wall biosynthesis and remodeling, spore germination, and biofilm formation106,107. Studying bacterial D-amino acid contents can help in better understanding of their cellular adaptation and behavior under stress condition, and can further aid efforts to understand development of resistance mechanisms, pharmacological action of antibiotics and in the discovery of antibacterial drugs and treatment approaches. The PG layer contributes to the morphology, strength and resistance to stressors, which is primarily composed of linear glycan strands cross-linked by short peptides. The D-amino acids are primarily present in those short-peptides connecting the sugar chains and play important roles in the biosynthesis of the PG and are an imperative part of bacterial survival. The linear glycan chain is made up of either N-acetyl glucosamine (GlcNAc,

NAG) or N-acetylmuramic acid (MurNAc, NAM). Bacterial PG synthesis occurs in three phases, each of which consists of multiple steps108 (Figure 2).

Glu-L-Lys (© American Society for Microbiology, reprinted with permission).

D-amino acids participate in the very early stage i.e. phase I of bacterial cell wall biosynthesis where several D-amino acids such as D-Ala, D-Ala- D-Ala and D-Glu along with

L-Ala, L-Lys and L-Glu take part in synthesizing the core uridine diphosphate (UDP) linked intermediates: UDP-GlcNAc (UDP-NAG), UDP-NAG-enolpyruvate (UDP-EP), UDP-

MurNAc (UDP-NAM), UDP-NAM-L-Ala (UDP-Mono), UDP-NAM-L-Ala-D-Glu (UDP-Di),

UDP-NAM- L-Ala-γ-D-Glu-L-Lys (UDP-Tri), and UDP-NAM-L-Ala-γ-D-Glu-L-Lys-D-Ala-D-

Ala (UDP-Penta)111. All these steps take place in the bacterial cytoplasm during phase I leading to synthesis of UDP-Penta and phosphor-NAM-pentapeptide, which are then transferred to lipid carrier for further elaboration during phase II on the inner membrane to form lipid I and then ultimately to lipid II by incorporation of NAG from UDP-NAG intermediate. Finally,

14 during phase III, lipid II undergo transglycosylation and transpeptidation by penicillin-binding proteins (PBPs) and are assembled into mature peptidoglycan on the outer membrane.

D-amino acids also participate in PG remodeling and metabolism, such as D-Met, D-

Leu, D-Val and D-Ile in rod-to-sphere shape transition of mrcA type Vibrio cholera112, D-Arg in negative regulation of PG synthesis in V. cholerae and B. subtilis113, incorporation of D-Cys by several bacteria from the nutrient media supplemented with D-Cys114. A very recent study discussed the role of D-Met and D-Phe in maintaining cell wall integrity during acid stress in

Lactococcus lactis strain F44115. The exact mechanism by which D-amino acids participate in bacterial PG remodeling is yet to be determined, but it is hypothesized that D-amino acids regulate the periplasmic enzymes responsible for synthesis and modification of PG polymer and modulate PBPs activity106. In addition, Cava, P. et al discussed the possibility that the presence of D-amino acid in a nutrient-depleted bacterial culture might be indicative of downregulation of PG synthesis i.e. biomarker of stress conditions for bacterial population106.

In addition, D-Ala and D-His are reported to act as germination inhibitors for Bacillus species during high spore population and can alter kinetics of germination to enhance infection116. On the other hand, D-amino acids containing peptides (D-Ala-D-Lac and D-Ala-D-Met) can also help bacteria to develop drug resistance (e.g. resistance against vancomycin) by preventing binding of drug to the PG precursor leading to cell wall inhibition or by making the cell wall resistant to various protease enzymes117.

D-amino acids are also involved in biofilm formation and provide protection to the bacteria against antimicrobial agents and host’s immune system which makes treatment of associated infections challenging118. Bacterial biofilm is primarily composed of polysaccharides, proteins and/or DNA119. Several D-amino acids such as D-Leu, D-Met, D-Trp,

D-Tyr, D-Pro and D-Phe are reported to induce dissolution of biofilm produced by B. subtilis,

S. aureus, S. epidermidis and P. aeruginosa, an effect which was not observed with L-amino acids120-122. Interestingly these roles of D-amino acids such as negative metabolism effect on

PG synthesis and biofilm dispersal effect indicate potentials for combinatory effects of these

D-amino acids to potentiate the activity of other antibiotics or antibiotic like molecules123. For example, D-amino acids are reported to enhance the activity and stability of several peptide antibiotics such as penicillin, cephalosporin, cycloserine, gramicidin, actinomycin, bacitracin and polymyxin124. D-amino acids are also present in staphylopine, a metal scavenging agent synthesized by S. aureus to trap various metals including nickel, zinc, cobalt, copper and iron125.

1.8 Analytical methods for separation and quantification of proteinogenic amino acids

Myriad roles and functions of different proteinogenic amino acid D- and L- stereoisomers warrant robust analytical tools to accurately distinguish and quantify them in a system of interest and to be able to study, predict, explore and explain important biological phenomena. Amino acid analysis is also used for quantification and characterization of peptides and proteins126. There are various analytical tools available to separate, detect and quantify the amino acid stereoisomers including chromatographic, spectrometric, fluidics, electrophoretic, microbiological or enzymatic approaches. At any given time, complete separation and quantification can be challenging, as racemization can occur due to various physical and chemical factors127. Frequency of spontaneous racemization has been reported to decrease in the following order128:

Cys>Asp>Pro>Glu>Met>His>Leu>Lys>Phe>Ala>Tyr>Arg>Trp>Val>Ile>Ser>Thr.

Chromatographic techniques, including simple thin layer chromatography129, 130, conventional liquid or gas chromatography or the advanced multidimensional chromatography131, are the most common platforms for amino acid analysis and with the advent of mass spectrometry based detection, this has remained the popular method for quite sometime132-134. A carefully developed and optimized chromatography approach coupled with a mass spectrometry based analytical method can provide baseline separation, superior sensitivity and high throughput analysis of all proteinogenic amino acid stereoisomers135.

Microfluidics and nanofluidics based devices e.g. lab-on-a-chip also enables chiral separation, and offers portability and faster analysis136. Capillary electrophoresis on the front end for separation has been recently used due to its reliability, automation and robustness137 and showed successful chiral analysis of amino acids (non-proteinogenic and proteinogenic) in extraterrestrial138 as well as biological139 samples.

1.8.1 Analysis of proteinogenic amino acids in underivatized form

Direct analysis of amino acids in their native form does not involve any type of derivatization. Polarity characteristics of the proteinogenic amino acids in underivatized form offer a chromatographic challenge making separation difficult when using a single chromatographic technique (stationary phase and solvent system). For example, a traditional

C18 analytical column (non-polar stationary phase) will not retain the polar amino acids such as Arg, His, Lys, Asp and Glu; on the other hand a HILIC column (polar-stationary phase) will not retain the non-polar amino acids such as Ala, Val, Ile, Leu, Met, Phe, Pro and Trp well.

Even if a variety of amino acids is retained well in either column, the relative difference between retention times might not be large enough for baseline separation of all the

17 proteinogenic amino acid stereoisomers. Most of them do not have a chromophore and cannot be detected using UV-Vis detection. However, α-cyclodextrin, crown ether, zwitterionic, and teicoplanin based chiral stationary phases (CSPs) can enable separation of underivatized proteinogenic amino acids. In addition, smaller molecules are difficult to fragment using mass spectrometer and hence are difficult to analyze in tandem mass spectrometry mode (MS/MS).

Despite these challenges, due to advancements in stationary phases, chromatographic strategies and mass spectrometers, some facile and direct routes for underivatized amino acid analysis have been reported to date140-145. It should be noted that, amino acids cannot be analyzed by gas chromatography (GC) without derivatization; on the other hand, direct analysis using chiral ligand exchange chromatography and RP-HPLC require non-volatile solvent and ion-pairing reagents respectively, which are not recommended for MS detection.

1.8.2 Analysis of proteinogenic amino acids in derivatized form

Indirect analysis of amino acid utilizes various chemical reagents, which react with the amino acids and form derivatives that can offer potential advantages such as the introduction of a chromophore for UV-Vis detection or changes in polarity hence desired binding and elution in traditional stationary phase with reverse phase chromatography etc., which can ultimately improve resolution and detection in a feasible manner. There are pre-column derivatization and post-column derivatization techniques. The former is more popular as post column derivatization cannot be controlled and is prone to inconsistent quantification between analytical runs146, 147. Pre-column derivatization using chemical derivatizing reagents also has some drawbacks including variation in reaction kinetics among amino acids and their isomers with the derivatizing reagents, stability of the derivatives, removal of excess derivatizing

18 reagents, introduction of racemization due to temperature and pH required for certain derivatization reactions, derivatization time etc. However, this approach allows using the traditional, most reliable and most economic reverse-phase chromatography approach coupled to mass spectrometry based detection. Additionally, this offers flexibility in choosing a suitable derivatizing reagent from a plethora of different commercially available reagents. Upon stringent validation in terms of robustness, reproducibility and performance, this approach can be one of the best approaches for targeted amino acid analysis and amine metabolomics study.

Extensive list of different derivatizing reagents for chiral amino acid analysis can be found in

Bhushan, R. et al. 2004148, Ilisz, I. et al. 2008149, Tanwar, S. et al. 2015150, Szoko, E. et al.

2016151 and Marcone, G. L. et al. 20209.

1.9 Conclusion

Amino acids are important biomolecules, which either bear important biochemical roles in different biological systems or indicate occurrence of different biological phenomena.

Due to stereochemistry, it is crucial to accurately distinguish the specific stereoisomer responsible for a certain event, for which, a robust, sensitive, fast and reproducible analytical method is a must. Significant advancement in the development of stationary phase, chromatographic and mass spectrometric platforms has made direct analysis of amino acids in their native form achievable. However, direct analysis requires specialized stationary phases, such as chiral columns, or chiral mobile phase-achiral stationary phase that suffer from lack of control and selectivity of interaction between chiral selector and amino acids. Lack of a chromophore and existence of isomeric and isobaric amino acids make analysis of these amino acids in their native form with traditional UV-Vis or MS detectors very challenging as well.

Indirect analysis with an ion-pairing reagent-achiral stationary phase requires non-volatile ion- pairing reagents, which can contaminate the mass spectrometer. On the other hand, indirect analysis with chiral derivatizing reagents allows use of traditional stationary phases, chromatography and mass spectrometry platforms. Although, indirect analysis offers flexibility, it needs to be carefully optimized and routinely monitored to obtain accurate results, as the derivatives are prone to variation due to changes in temperature, pH, reaction and storage condition. Both direct and indirect approaches have proven advantages and pitfalls, and the right approach needs to be carefully selected based on the aim of the study, type of samples and the available resources. In lights of these considerations, direct analysis with a chiral stationary phase and indirect analysis with a chiral derivatizing reagent can provide feasible route to analyze and study proteinogenic amino acid DL-stereoisomers.

CHAPTER 2

2. LC-MS/MS-BASED SEPARATION AND QUANTIFICATION OF MARFEY’S

REAGENT DERIVATIZED PROTEINOGENIC AMINO ACID DL-STEREOISOMERS

2.1 Introduction

There are 20 proteinogenic amino acids (AAs), which are incorporated as their L- enantiomers (except for glycine, which is achiral) in proteins. Their corresponding D- enantiomers also play important roles in many biological systems and processes152. The most important D-amino acids in humans are D-Ala and D-Ser. D-Ser is found in the brain153, where it serves as a co-agonist of the N-methyl-D-aspartate (NMDA) receptor154, 155. It has been implicated in a number of diseases, with increased levels observed in Alzheimer’s156, 157 and amyotrophic lateral sclerosis158 and with decreased levels observed in schizophrenia159, 160. A number of other D-amino acids, including D-Asp, D-Cys, D-Leu, D-Pro and D-Glu, have been found in rat brain and pituitary glands161, 162. D-Amino acids associated with other diseases include increased D-Ser, D-Pro, and D-Asn with kidney disease and diabetes mellitus163 increased D-Ala with restricted feeding in rats164 and decreased D-Glu in heart disease98.

Presence of proteinogenic D-amino acids in different food items and their role in the biosynthesis of bacterial cell wall is discussed before. Given the importance of amino acid chirality to the existence of life on earth, and that amino acids racemize at very slow rates, chiral amino acid analysis is also used to study fossils, archeological artifacts and meteorites165,

166.

Given their biological significance, there has been considerable interest in the development and application of analytical methods for D-amino acid quantification167-169.

Regardless of detection method, DL-amino acid enantiomers must be separated chromatographically to allow separate quantification. Several approaches to this problem have been developed. One approach is to use chiral stationary phases to separate DL-amino acid enantiomers170. GC-based analyses using this approach are well established171. The alternative approach is to derivatize amino acids with a chiral derivatizing agent, followed by separation of the resulting diastereomers on standard achiral chromatography media172, 173. Marfey’s reagent, 1-fluoro-2,4-dinitrophenyl-5-L-alanine amide (L-Mar) is a popular chiral amino acid derivatizing agent174,167, 175 (Figure 3).

Figure 3. Reaction scheme of DL-amino acids with L- and D-Marfey’s reagent.

This reagent provides good separation of most DL-amino acid enantiomers as their L-

Mar derivative diastereomers on standard reverse phase media with acidic solvent systems. In prior studies using such an approach, we have used Marfey’s reagent and this general approach for the LC-MS/MS based quantification of several key intermediates in the bacterial cell wall peptidoglycan biosynthesis pathway, including D-Ala, L-Ala, D-Ala-D-Ala176, D-Glu and L-

Glu111 and D-Ala-D-Lac109. Marfey’s reagent derivatization combined with multichannel LC-

MS/MS detection has also been demonstrated for the resolution and quantification of all 8

22 aminobutyric acid isomers177, demonstrating the potential of Marfey’s reagent combined with data intensive MS/MS detection and spectral deconvolution for the analysis of complex analyte mixtures.

The present study aims to evaluate and optimize the use of Marfey’s reagent combined with MS/MS detection for the separation and quantification of Gly and the 19 proteinogenic

DL-amino acids. This study demonstrates that separation under neutral solvent conditions using negative mode MS/MS detection is an effective approach. Derivatization kinetics for amino acids that can form bis-Marfey’s adducts (Tyr and His) is the principal complicating factor for this approach.

2.2 Materials and methods

2.2.1 General

Individual amino acids and amino acid standard mixtures were from Sigma-Aldrich

(St. Louis, MO). L-Marfey’s reagent (1-fluoro-2,4-dinitrophenyl-5-L-alanine amide) and D-

Marfey’s reagent (1-fluoro-2,4-dinitrophenyl-5-D-alanine amide) were from Novobiochem (a division of EMD Chemicals, Gibbstown, NJ). C18 silica gel was from Waters Sep-Pak

Cartridges (Milford, MA). Other reagents were obtained from standard sources and were reagent grade or better. LC-MS/MS was performed on a Sciex QTrap 3200 triple quadrupole mass spectrometer (Foster City, CA) coupled to a Shimadzu UFLC LC-20 series HPLC system

(Columbia, MD) using electrospray ionization (ESI) and run with Analyst v. 1.6.2 software.

2.2.2 Marfey’s derivative preparation for method development

An equimolar standard mixture of all 20 L-amino acids was prepared by mixing 400

µL of a 2.5 mM L-amino acid standard solution (catalog # AAS18) from Sigma-Aldrich (which contains 17 amino acids each at 2.5 mM) with 1300 µL water, and 100 µL each of 10 mM stock solutions of L-Gln, L-Asn and L-Trp. This provided a 2 mL solution with each amino acid at 0.5 mM. To a 100 µL aliquot of this mixture was added 200 µL of 20 mM L-Marfey’s reagent in acetone, followed by 50 µL of 0.5 M triethylamine (TEA) to initiate the reaction. The contents were mixed and kept in the dark at 37 ºC overnight. The derivatization reaction was quenched next day by adding 50 µL of 0.5 M HCl, and the sample diluted to 1000 µL with

20% acetonitrile/0.1% formic acid to obtain a mixture of amino acids as L-Mar- L-AA derivatives. Marfey’s adducts of individual amino acids, particularly for isobaric Leu and Ile, were prepared similarly. A parallel effort was performed using D-Marfey’s reagent in place of

L-Marfey’s reagent, which provided D-Mar-L-AA derivatives as surrogates (physically identical) to L-Mar-D-AA derivatives (Figure 3).

2.2.3 Marfey’s derivative purification for method development

To obtain purified desalted samples of individual Mar-AA isomers for MS/MS method optimization, 200 mL samples of the standard mixtures (L-Mar-L-AAs or D-Mar-L-AAs) were fractionated by semi-preparative HPLC on a 4.6 × 250 mm Nucleodur C8 column using a gradient of 5% to 90% of solvent B (30% water/70% acetonitrile/0.1% formic acid) in solvent

A (water/0.1% formic acid) over 90 min at a flow rate of 1 mL min-1. Alternatively, individual

Marfey’s reagent derivatized amino acids were cartridge purified over C18-silica to provide desalted samples178.

2.2.4 LC-MS/MS method development

Initial efforts were based on the use of standard water/acetonitrile/formic acid gradients with MS/MS detection in positive mode. MS/MS detection was optimized for individual Mar-

AAs by infusion of purified desalted fractions into the mass spectrometer and using the Analyst quantitative optimization wizard. Various compounds dependent parameters such as declustering potential (DP), entrance potential (EP), collision cell entrance potential (CEP), collision energy (CE), and collision cell exit potential (CXP) and various source dependent parameters such as curtain gas (CUR), temperature (TEM), gas flows (GS1 and GS2), collision-associated-dissociation (CAD) and ion-spray voltage (IS) were optimized using the automated quantitative optimization feature of the Analyst software. For each analyte, the three most intense fragments observed in Q3 and the associated optimum parameters were used in building the final MS/MS method (Table 4).

tR (min) AU/fmol DP EP CEP CE L-Mar-AAb Q1 Q3 L-AA D-AA L-AA D-AA (V) (V) (V) (V) Gly 328.2 119.1 51 6 14 53 10.23 0.75 238.1 21 2.70 283.2 17 2.89 Ala 342.2 251.2 61 6 18 25 11.10 12.24 0.55 0.79 296.2 15 1.26 1.44 297.2 19 1.26 1.93 Serc 358.2 134.1 36 10 14 51 8.78 0.39 268.2 21 1.07 313.2 19 2.55 Pro 368.2 277.2 61 7 18 19 11.47 11.99 1.33 1.48 322.2 21 2.97 3.56

323.2 23 2.23 2.95 Val 370.2 280.2 81 5 20 25 14.45 16.61 1.16 0.85 324.2 17 2.11 2.37 325.2 19 1.10 1.21 Thr 372.2 237.1 41 8.5 16 21 9.12 10.11 0.24 0.31 281.2 23 0.36 0.64 327.2 19 0.73 1.15 Ile 384.2 205.1 51 8.5 16 29 16.71 19.16 0.45 0.15 294.2 23 0.90 0.53 338.2 15 2.88 2.34 Leu 384.2 118.1 46 10.0 16 77 17.91 19.57 0.28 0.26 294.2 25 0.96 0.73 338.2 15 2.30 5.75 Asnc 385.2 277.1 36 9.5 18 23 8.26 0.12 295.2 23 0.09 340.2 19 0.24 Asp 386.2 277.3 51 5 24 27 9.25 9.48 0.32 0.45 296.3 19 0.51 1.78 341.2 19 1.21 1.62 Glnc 399.2 246.2 46 9.0 16 39 8.54 0.66 292.2 23 1.58 354.2 20 0.46 Glu 400.3 292.2 56 7 16 21 9.77 10.13 0.46 0.36 310.2 23 0.39 0.44 355.2 19 0.80 1.14 Met 402.3 133.1 31 9.5 18 29 14.09 15.92 0.32 0.39 312.2 21 0.71 0.65 357.3 20 0.44 0.65 Hisc 408.3 81.0 51 9 16 77 6.60 1.68 274.2 27 5.00 363.3 22 0.55 His (Bis) 660.4 508.3 126 10 22 33 11.56 12.48 2.89 3.44 526.3 33 1.10 1.30 553.4 31 1.51 1.80 Phe 418.3 164.1 36 8.5 18 45 17.37 18.98 0.16 0.13 281.2 21 0.71 0.48 372.2 17 2.02 2.05 Argc 427.3 70.0 66 9.5 18 63 6.75 6.80 130.1 37 7.15 382.3 30 0.07 Tyr 434.3 165.1 41 7.0 16 19 12.59 13.16 0.30 0.30 237.1 23 0.45 0.45 388.2 17 0.97 1.46 Tyr (Bis) 686.4 219.1 106 8.0 24 29 21.06 22.80 0.14 0.20

237.1 25 0.35 0.52 534.3 25 0.78 1.58 Trp 457.3 130.1 41 8.5 18 59 16.70 17.75 0.002 0.07 146.1 41 0.23 0.35 188.1 19 0.42 0.63 Lys (Bis) 651.5 291.2 56 8 24 37 16.96 17.46 0.07 0.06 302.2 33 0.11 0.10 561.4 27 0.94 0.48

Cys2 (Bis) 745.5 205.1 56 10 28 37 16.83 17.88 0.34 0.72 13 C3-D-Ala 345.2 300.2 60 4.5 19 14 12.24 0.82 aGlobal method parameters: TEM – 300 oC; IS – 5500 V; GS1 & GS2 – 50 (arbitrary units); CAD – Medium. bAll AAs listed were detected as their mono-Marfey’s adducts except for those indicated as bis-adducts AA(Bis). cL and D not resolved.

For HPLC method development, a mixture of all 20 L- and D-Mar derivatized L-amino acids was used. The optimized acidic pH solvent system consisted of solvent A (water/0.1 % formic acid) and solvent B (30% water/70% acetonitrile/0.1% formic acid). The optimized gradient was 10-40% B (0-1 min), 40-80% B (1-21 min), 80-100% B (21-22 min), 100-10%

B (22-23 min). The flow rate of the solvent system was 0.3 mL min-1. However, using this gradient, or even shallower gradients, only 15 of 19 Mar-DL-amino acid pairs could be adequately resolved (Figure 4).

(isobars), their Marfey’s derivatives both show up in the Q1/Q3=384.2/338.2 channel.

A neutral pH gradient was then tested to see if it could provide improved resolution.

This neutral gradient consisted of solvent A (10 mM ammonium acetate pH 6.5 in water) and solvent B (30% A/70% acetonitrile). The optimized neutral pH gradient was 5% B (0-1 min),

5-55% B (1-21 min), 55-100% B (21-22 min), and 100-5% B (22-23 min). The flow rate of the solvent system was 0.3 mL min-1. This neutral pH gradient was able to separate all enantiomeric Mar-DL-amino acid pairs. However, low sensitivity was observed for these analytes with positive mode MS/MS detection at neutral pH. All Mar-DL-amino acids were then optimized for negative mode MS/MS detection at pH 6.5, and the three most sensitive Q3 fragments for each amino acid (Table 5) used to build a final neutral pH/negative mode detected LC-MS/MS method (Figure 5 and 6).

tR (min) AU/fmol DP EP CEP CE L-Mar-AAb Q1 Q3 L-AA D-AA L-AA D-AA (V) (V) (V) (V) Gly 326.2 162.1 -35 -4.5 -22 -28 13.38 4.62 192.1 -16 1.50 217.1 -16 0.93 Ala 340.2 176.1 -45 -4 -18 -34 13.30 15.39 1.15 1.71 189.1 -34 1.33 1.95 261.2 -22 3.10 3.98 29

Ser 356.2 162.1 -45 -5.5 -18 -42 11.30 12.65 2.70 3.84 192.1 -20 2.32 2.89 264.2 -14 2.05 2.67 Pro 366.2 176.1 -30 -6.5 -18 -40 13.78 15.37 1.30 1.68 232.1 -26 3.10 3.94 248.1 -24 2.06 2.63 Val 368.2 191.1 -40 -6.5 -18 -30 15.53 18.09 4.26 5.32 263.2 -18 8.80 10.98 306.2 -16 5.68 6.99 Thr 370.2 162.1 -40 -7 -16 -38 11.96 14.56 8.18 9.65 192.1 -24 3.81 5.56 264.2 -18 3.56 5.00 Ile 382.2 191.1 -40 -3.5 -16 -32 17.07 19.56 4.84 5.19 263.2 -18 13.79 15.60 320.2 -14 7.93 7.78 Leu 382.2 216.1 -40 -3.5 -16 -44 17.59 19.87 4.25 4.02 248.1 -20 4.21 3.80 288.2 -28 11.10 14.58 Asn 383.2 176.1 -55 -7.5 -14 -34 11.35 12.98 0.65 0.66 232.1 -38 0.45 0.53 321.2 -14 0.75 0.86 Asp 384.2 162.1 -50 -3.5 -54 -40 4.19 6.09 0.16 0.21 190.1 -38 0.41 0.55 268.2 -28 1.05 1.45 Gln 397.2 202.1 -70 -6.5 -20 -42 12.35 13.40 0.27 0.45 274.2 -24 0.58 0.92 353.2 -18 1.46 2.38 Glu 398.3 172.1 -50 -5 -18 -44 7.94 8.50 0.91 1.02 184.1 -38 0.81 1.00 202.1 -38 2.28 2.91 Met 400.3 202.1 -40 -6 -16 -44 16.04 18.27 3.24 4.37 274.2 -24 9.68 12.99 338.2 -16 4.54 5.84 His 406.3 162.1 -55 -5 -18 -40 13.84 14.82 0.96 0.84 176.1 -34 0.53 0.51 264.2 -20 2.78 2.30 His (Bis) 658.4 506.3 -90 -7.5 -22 -40 20.04 20.95 0.62 0.72 549.3 -30 1.79 1.24 596.4 -20 0.41 0.50 Phe 416.3 116.1 -45 -5 -18 -70 18.50 20.45 2.95 4.12 265.2 -38 6.76 8.87 337.2 -20 14.48 20.67 Arg 425.3 185.1 -60 -5 -16 -56 16.70 17.48 0.32 0.61 202.1 -44 0.66 1.24

274.2 -26 2.25 4.50 Tyr (Bis) 684.4 263.2 -80 -8.5 -26 -76 23.65 25.21 4.34 5.28 280.2 -64 6.72 8.51 352.2 -38 9.15 18.6 Trp 455.3 162.1 -55 -7.5 -20 -42 18.75 20.25 1.03 1.56 264.2 -18 9.64 14.92 376.2 -20 1.47 2.01 Lys (Bis) 649.5 186.1 -85 -7.5 -24 -64 20.27 21.36 0.93 1.09 191.1 -58 2.03 3.38 216.1 -54 1.16 1.39 Cys2 (Bis) 743.5 216.1 -70 -4.5 -34 -66 15.12 16.43 0.61 0.91 294.2 -40 3.14 6.48 338.2 -38 3.26 4.77 13 C3-D-Ala 343.2 263.2 -55 -5.5 -14 -20 15.35 2.46 aGlobal method parameters: TEM – 300 oC; IS – -4500 V; GS1 & GS2 – 50 (arbitrary units); CAD – Medium. bAll Aas listed were detected as their mono-Marfey’s adducts except for those indicated as bis-adducts AA(Bis).

8.E+05

Gly Ala Ser Pro Val -

- Thr Ile Leu Asn Asp - 6.E+05 Gln Glu Met His (Bis) Phe Arg Tyr (Bis) Lys (Bis) Trp Cys2

4.E+05

2.E+05

Area Area (countsper second)

- -

0.E+00 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 - - - Time (min) - - -

Figure 5. Multiple reaction monitoring (MRM) chromatogram of Marfey’s derivatives of the

19 common DL-amino acids and glycine using the ammonium acetate (pH 6.5) based neutral solvent system on a C8 column and MS/MS detection in negative mode.

Q1=382.2 panels. The Q1/Q3 = 382.2/263.2 channel is selective for Ile (two higher peaks in the Mar-Ile/Mar-Leu panel), and the Q1/Q3 = 382.2/288.2 channel is selective for Leu (two higher peaks in the Mar-Leu/Mar-Ile panel). Note that Mar-Leu/Ile also shows some signal intensity in the Mar-Asn MS/MS channel due to the Mar-Leu/Ile M+1 isotopomers, but since there is good chromatographic resolution of all these L-Mar-DL-AA species they can be individually quantified.

2.2.5 Reaction kinetics of Marfey’s derivatization reactions

Preliminary studies indicated that most amino acids reacted with Marfey’s reagents relatively quickly, but some – particularly Thr, reacted slowly. Also, some amino acids [His,

Tyr, Lys, and Cys2 (the S-S linked Cys dimer)] can form bis-Marfey’s adducts and their derivatization kinetics to mono- and bis-adducts needed to be determined to allow proper design of quantitative derivatization conditions. Marfey’s reagent derivatization reaction kinetics were therefore assessed. To 200 µL of the standard amino acid mix of all 20 L-AAs at

0.5 mM was added 400 µL of 20 mM of L- or D-Marfey’s reagent in acetone, followed by 100

µL of 0.5 M TEA in water. Samples of 20 µL were removed at different times and quenched with 180 µL 20% acetonitrile/0.1% formic acid. These samples were then analyzed by neutral pH/negative mode LC-MS/MS. Except for His and Tyr, all amino acids underwent

33 derivatization following apparent first order kinetics (including Lys and Cys2). Under the conditions of excess Marfey’s reagent in reaction mixtures (~4-fold), as used in this study, the derivatization reactions for His and Tyr will be pseudo-first order. For those species which followed apparent first order derivatization kinetics (in the presence of excess Marfey’s reagent), their derivatization kinetics were analyzed by fitting to first order kinetic expressions with the following equations in SPSS statistical software.

A simple first order reaction is described by:

k A → B … … … (Eq. 2.1)

Where A represents the underivatized amino acid, and B represents the derivatized product.

The time dependence for the formation of the product is given by:

−푘푡 퐵푡 = 퐴0 (1 − 푒 ) … … … (Eq. 2.2)

Where A0 indicates the initial concentration of A, k is the rate constant, t is the time of reaction and Bt is the concentration of the reaction product B at time t. The observed area for the MS/MS detected Marfey’s adduct is equal to Bt times the sensitivity factor for its MS/MS detection in area units/fmol (AU/fmol), which gives the equation used for fitting the data:

−푘푡 퐴푈퐵 = 푆푒푛푠퐵 퐴0 (1 − 푒 ) … … … (Eq. 2.3)

Where SensB is the sensitivity constant for the detection of the reaction product B, and AUB is the detected area units for B. The results of this analysis for those amino acids showing apparent first order kinetics are summarized in Table 7.

His and Tyr demonstrated more complex two-step reaction kinetics due to the sequential formation of mono- and bis-Marfey’s derivatives. For the following two-step sequential reaction:

k1 k2 A → B1 → B2 … … … (Eq 2.4)

The time dependence of the disappearance of A is described by:

−푘1푡 퐴푡 = 퐴0 푒 … … … (Eq 2.5)

The time dependence of B1, the mono-Marfey’s adduct, is described by the following integrated rate equation179:

푘 1 −푘1푡 −푘2푡 퐵1,푡 = 퐴0 ( ) (푒 − 푒 ) … … … (Eq 2.6) 푘2−푘1

The observed MS/MS signal for B1 (the mono-Marfey’s adduct) is equal to B1,t × SensB1, where

SensB1 is the sensitivity factor for B1 (AU/fmol). The expression for B2, the bis-Marfey’s adduct, is:

퐵2,푡 = 퐴0 − 퐴푡 − 퐵1,푡 … … … (Eq. 2.7)

The observed MS/MS signal for B2 (the bis-Marfey’s adduct) is equal to B2,t × SensB2, where

SensB2 is the MS/MS sensitivity factor for B2 (AU/fmol).

2.2.6 Analytical derivatization procedure

For analytical sample preparation, 40 µL of 20 mM Marfey’s reagent was added to 20

13 µL of sample spiked to 100 µM with C3-D-Ala as internal standard, followed by 10 µL of 0.5

M TEA. Reaction mixtures were incubated at 37 ºC for 24 hours, then quenched with 10 µL

0.5 M HCl, and diluted to 200 µL with 20% acetonitrile/0.1% formic acid. For unknown samples, UV-vis (340 nm) monitored HPLC was used to confirm a substantial (at least 3-4 fold) excess of Marfey’s reagent. If no excess was confirmed, then samples were diluted prior to derivatization.

2.2.7 Sensitivity and linearity features of the analytical method

To check the sensitivity and linearity of the developed method, a 13 step serial dilution

(steps of two) of the 20 L-amino acid mixture was prepared in triplicate in 0.1 N HCl with 100

13 µM C3-D-Ala as internal standard. These serially diluted samples were derivatized with 20 mM Marfey’s reagent as described above and LC-MS/MS analysis performed on 20 µL injections using both the acidic gradient/positive mode detection method and the neutral pH/negative mode detection method. For each sample, Marfey’s reagent was at least 3-fold in excess of amino acids.

2.2.8 Biological matrix preparation

MRSA extract was prepared from a mid-log secondary culture. Briefly, 20 mL of liquid nutrient media (cation adjusted Mueller Hinton, CAMH) was inoculated with MRSA and was grown overnight. Next day, a fresh 100 mL secondary bacterial culture was started diluting the overnight culture with liquid nutrient media (CAMH) in 1:100 ratio and the culture was grown up to 0.6 optical density (OD600). The MRSA extract was then prepared from this secondary culture using centrifugation based cell intermediate extraction approach using methanol/water111. The collected methanol/water extract was dried under vacuum, and resuspended in 5 mL of water+0.1% formic acid to make a stock matrix solution. A 1:25 fold dilution of this stock solution was made in water+0.1% formic acid to prepare a working matrix solution.

2.2.9 Matrix effect assessment and determination of proteinogenic DL-amino acid levels in

MRSA extract

Matrix effects on analyte quantification were assessed by diluting L-amino acid standards in water and in matrix, at levels corresponding to 0, 25, 100 and 400 pmol of injected analyte. These samples were derivatized with L-Marfey’s reagent and analyzed by LC-MS/MS using the negative mode/neutral solvent system protocol described above in quadruplicate.

Matrix effect for each analyte was calculated from the slopes (S) of the linear regression curves as:

%푀퐸 = 100 ∗ (푠푚푎푡푟𝑖푥⁄푠푤푎푡푒푟 − 1)… … … (Eq 2.8)

2.3 Results and discussion

2.3.1 Use of D-Mar to provide L-Mar-D-AA standard surrogates

The goal of this study was to develop a Marfey’s derivatization-based LC-MS/MS analytical approach for the resolution and quantification of the 19 common L- and D-amino acids and Gly. While validated L-amino acid standards are commercially available, similar D- amino acids standards are not. The standard version of Marfey’s reagent is based on L-alanine amide (L-Ala-NH2) (L-Mar). Its D-Ala-NH2 homolog is also commercially available (D-Mar).

The approach used in this study was to derivatize an L-AA standard mix with L-Mar to provide an L-Mar-L-AA standard mix, and with D-Mar to provide a D-Mar-L-AA standard mix. Since a D-Mar-L-AA isomer is the mirror image of an L-Mar-D-AA isomer (Figure 3) – and therefore indistinguishable on achiral stationary phase, chromatography media and by MS/MS detection

– D-Mar-L-AA standards were used as surrogates for L-Mar-D-AA standards to study the D-

AAs.

2.3.2 LC-MS/MS method development and optimization

Our initial effort focused on developing an LC-MS/MS method using a standard acidic

(formic acid based) solvent system and MS/MS detection in positive mode (Table 4, Figure 4).

However, 5 DL-amino acid pairs – Ser, Asn, Gln, His, and Arg – could not be adequately resolved chromatographically using this acidic water/acetonitrile solvent system. Longer gradients and incorporation of methanol or isopropanol into the solvent system, also failed to resolve those analytes. We then tested HPLC separation using a neutral (pH 6.5) solvent system using ammonium acetate, which allowed for separation of all DL-amino acid pairs including the 5 pairs that were not previously resolved with the standard acidic solvent system. However, positive mode detection at pH 6.5 was poor, especially for early eluting amino acids (Asp, Glu,

Ser). MS/MS optimization for negative mode detection was then performed (Table 5).

Negative mode detection with HPLC separation at pH 6.5, provided an optimum method for the resolution and quantification of all 20 proteinogenic DL-amino acids (Figure 5, 6; Table 4,

5). In the final MRM method, the most intense Q3 fragment for each amino acid was used in the MRM channel for superior sensitivity (Table 6).

Table 6. Compound dependent parameters and LC-MS/MS characteristics for the most intense

MS/MS transitions of the 20 common amino acids in negative mode detectiona.

c tR (min) AU/fmol LLOQ (pmol) DP EP CEP CE L-Mar-AAb Q1 Q3 L-AA D-AA R d L-AA D-AA L-AA D-AA (V) (V) (V) (V) S Gly 326.2 162.1 -35 -4.5 -22 -28 13.38 4.62 2.7 Ala 340.2 261.2 -45 -4 -18 -22 13.30 15.39 1.82 3.1 3.98 7.7 6.1 Ser 356.2 162.1 -45 -5.5 -18 -42 11.30 12.65 1.20 2.7 3.84 3.6 2.5 Pro 366.2 232.1 -30 -6.5 -18 -26 13.78 15.37 1.46 3.1 3.94 3.8 3.1 Val 368.2 263.2 -40 -6.5 -18 -18 15.53 18.09 1.82 8.8 10.98 2.3 1.9 Thr 370.2 162.1 -40 -7 -16 -38 11.96 14.56 2.44 8.18 9.65 3.3 2.7 Ile 382.2 263.2 -40 -3.5 -16 -18 17.07 19.56 4.42 13.79 15.6 2.8 2.4 Leu 382.2 288.2 -40 -3.5 -16 -28 17.59 19.87 2.98 11.1 14.58 3.8 2.9 Asn 383.2 176.1 -55 -7.5 -14 -34 11.35 12.98 1.62 0.65 0.66 5.6 5.4 Asp 384.2 268.2 -50 -3.5 -54 -28 4.19 6.09 1.50 1.045 1.45 6.7 4.7 Gln 397.2 353.2 -70 -6.5 -20 -18 12.35 13.40 1.21 1.46 2.38 4.5 2.8 Glu 398.3 202.1 -50 -5 -18 -38 7.94 8.50 0.82 2.28 2.91 7.1 5.5 Met 400.3 274.2 -40 -6 -16 -24 16.04 18.27 2.01 9.68 12.99 3.2 2.4 His (Bis) 658.4 549.3 -90 -7.5 -22 -30 20.04 20.95 1.00 14.48 20.67 5.5 7.9 Phe 416.3 337.2 -45 -5 -18 -20 18.50 20.45 1.79 2.25 4.5 3.3 2.2 Arg 425.3 274.2 -60 -5 -16 -26 16.70 17.48 0.92 9.15 18.6 3.5 1.7 Tyr (Bis) 684.4 352.2 -80 -8.5 -26 -38 23.65 25.21 1.52 9.64 14.92 5.7 2.8 Trp 455.3 264.2 -55 -7.5 -20 -18 18.75 20.25 1.52 2.03 3.38 3.3 2.1 Lys (Bis) 649.5 191.1 -85 -7.5 -24 -58 20.27 21.36 1.04 3.14 6.48 3.5 2.1

Cys2 (Bis) 743.5 294.2 -70 -4.5 -34 -40 15.12 16.43 1.33 3.1 3.98 4.1 1.9 aGlobal method parameters: TEM – 300 oC; IS – -4500 V; GS1 & GS2 – 50 (arbitrary units); CAD – Medium . bAll AAs listed were detected as their mono-Marfey’s adducts except for those indicated as bis-adducts AA(Mar). Cys2 was also detected as its bis-adduct. cThe average % coefficient of variation (CV) for retention times was 0.12%. dThe average % coefficient of variation (CV) for resolution was 2.4%.

Ile and Leu are isobaric (identical molecular weight) amino acids with similar structures. Their Marfey’s derivatives have identical Q1 masses and give similar fragmentation patterns, resulting in both Ile and Leu showing up in each other’s MS/MS channels (Figure 4 and 6). The sequence of elution of these two amino acids in both the neutral (Table 5) and acidic (Table 4) solvent systems was L-Ile, L-Leu, D-Ile, D-Leu (Figure 6 and 4), as determined using individual standards of each. Two different Q3 masses, 263.2 and 288.2 selected in

39 negative mode, provide some selectivity for Ile and Leu, respectively. The M+1 isotopomers of Mar-Ile/Leu also showed up in the Mar-Asn channel. However, all of these L-Mar-DL-AAs were well resolved chromatographically (Figure 6), which allows all of these amino acid isomers to be individually quantified. DL-Glu was the least well resolved of all the DL-amino acids, but it was sufficiently well resolved for individual isomer quantification as long as their ratio was not too extreme.

2.3.3 Marfey’s derivatization reaction kinetics

It was evident in preliminary studies that some amino acids react with Marfey’s reagent slowly, or with complex kinetics, because of their ability to form both mono- and bis-adducts

(e.g. Tyr, His, Lys, and Cys2). The kinetics of derivatization of all 19 DL-amino acids and Gly were therefore analyzed (Table 7), including for bis-adducts for those capable of bis-adduct formation (Figure 8).

Table 7. Kinetics features of Marfey’s derivatization reaction for 18 amino acids that show apparent first order reaction kineticsa

L-AA D-AA -1 b -1 b b L-Mar-AA k (hr ) 4×t1/2 (hr) k (hr ) 4×t1/2 (hr) Max (4×t1/2) (hr) Log2 (kD/kL) Gly 2.92 0.95 0.95 Ala 0.99 2.81 1.40 1.98 2.81 0.50 Ser 0.46 6.02 0.90 3.08 6.02 0.97 Pro 40 0.07 22 0.12 0.29 -0.86 Val 1.90 1.46 2.92 0.95 1.46 0.62 Thr 0.17 16.31 0.29 9.56 16.72 0.77 Ile 2.55 1.09 3.01 0.92 1.09 0.24 Leu 1.88 1.48 2.05 1.35 1.48 0.12 Asn 0.84 3.30 0.92 3.02 9.86 0.13 Asp 0.31 8.94 0.51 5.44 8.97 0.72 Gln 1.11 2.49 2.11 1.32 2.49 0.93 Glu 0.36 7.70 0.56 4.95 7.69 0.64 Met 1.27 2.18 1.35 2.05 2.18 0.09 Phe 4.41 0.63 3.03 0.92 0.92 -0.54 Arg 1.25 2.22 1.49 1.86 2.9 0.25 Trp 7.35 0.38 4.49 0.62 0.62 -0.71 Lys (Bis) 0.93 2.98 0.92 3.01 3.02 -0.02

Cys2 (Bis) 0.78 3.56 0.61 4.55 4.57 -0.35 aFor Tyr and His, which show multistep kinetics, see Figure 8. b 4×t1/2 is used as indicator of near quantitative (95%) completion of the derivatization reaction.

For the majority of mono-adduct forming amino acids, reaction kinetics were rapid under the conditions used here (Table 7). 4×t½ is used to indicate an incubation time for 95% completion of the derivatization reaction. Pro yield the fastest Marfey’s derivatization reaction kinetics, with a 4×t½ of 30 minutes. The reaction kinetics of some amino acids were relatively slow, with L-Thr being the slowest reacting of the simple amino acids with a 4×t½ of 17 hours

– likely due to the electron withdrawing effect of the adjacent –OH group. Gly, not included in Figure 8, reacted with a rate similar to D-Ile (Table 7). Given that the reaction of Marfey’s reagent with a given DL-amino acid pair gives different diastereomeric products and will therefore proceed through different transition-states, there was some potential for different

41 reaction rates for Marfey’s derivatization of D- vs. L-amino acids. A strong correlation between

L- and D-AA reaction kinetics was observed (Figure 7), with D-amino acids reacting only slightly faster than L-amino acids.

Figure 7. Reaction rate constants for and correlation between D- and L-amino acids (except

Gly, His and Tyr).

For bis-adduct forming amino acids, both Lys and Cys2 formed their corresponding bis-adducts quickly, following apparent first order reaction kinetics. In contrast, His and Tyr both demonstrated clear two-step reaction kinetics via the mono-Marfey’s derivative to the ultimate bis-Marfey’s derivatives (Figure 8). For both His and Tyr, formation of bis-adducts was much slower than for mono-adducts.

Figure 8. Reaction kinetics of L-Mar with L- and D-His and L- and D-Tyr.

In consideration of these reaction kinetics, a 3 hour incubation would be sufficient for quantitative analysis (>95% conversion) for many DL-amino acid pairs, with the exception of

Thr, Asn, Asp, Glu, Cys2, His, and Tyr. A 24 hour incubation would be sufficient for all amino acids except His. DL-His can be determined by using a very long incubation of >78 hours for formation and quantification of the bis-adduct, or by using isotopically labeled His internal standards with quantification of either the mono- or bis-adduct, or as its mono-adduct using the midpoint time between the mono-adduct maximum for D- and L-His (80 minutes). Or both the mono- and bis-adduct can be quantified separately and added, to determine the total amount of D- and L-His in a sample.

2.3.4 Linearity and sensitivity characteristics

To assess linearity, a serially diluted L-amino acid standard mix in water was derivatized with L-Marfey’s reagent. These samples were then analyzed using both the acidic gradient/positive mode detection method (Figure 9, Table 8) and the neutral gradient/negative mode detection method (Figure 10, Table 9). The neutral gradient/negative mode detection method provided good linearity up to 800 pmol (the highest level tested) for all 20 amino acids.

Sensitivity fell off slightly at higher analyte concentrations in the acidic gradient/positive mode detection method (Figure 9, Table 8).

Figure 9. Linearity of L-Mar-L-AA derivatives using the acidic solvent system (pH 2) and positive mode MS/MS detection.

Table 8. Squared correlation coefficients for linearity (0-800 pmol) of L-Mar-L-AA derivatives using the acidic solvent system (pH 2) and positive mode MS/MS detection.

L-Mar-L-AA Correlation Coefficient (R2) Gly 0.998 Ala 0.997 Sera 0.991 Proa 0.991 Val 0.998 Thr 0.998 Ile 0.999 Leu 0.998 Asna 0.999 Aspa 0.995 Glna 0.996 Glua 0.988 Met 0.997 His 0.995 His (Bis) 0.993 Phe 0.993 Arg 0.998 Tyr (Bis)a 0.997 Trpa 0.995 Lys (Bis)b 0.984 a Cys2 (Bis) 0.996 aRange of linearity was 0-400 pmol bRange of linearity was 0-200pmol

Figure 10. Linearity of L-Mar-L-AA derivatives using the neutral solvent system (pH

6.5) and negative mode MS/MS detection.

Table 9. Linearity (0-800 pmol), matrix effect analysis and 24 hour stability of L-Mar-L-AA derivatives using the neutral solvent system (pH 6.5) and negative mode MS/MS detection.

L-Mar-L-AA Linearity (R2) Matrix Effect (%ME) 24 hr Stability (± SE) Gly 0.998 -2.2 95.6 ± 6.4 Ala 0.996 -7.9 96.5 ± 3.7 Ser 0.996 -2.8 100.8 ± 4.7 Pro 0.999 -6.1 103.6 ± 2.8 Val 0.997 -2.6 97.9 ± 3.2 Thr 0.998 -3.7 96.8 ± 4.0 Ile 0.998 -4.3 93.7 ± 2.4 Leu 0.999 5.4 98.0 ± 1.3 Asn 0.993 2.2 91.2 ± 8.3 Asp 0.997 -6.0 98.7 ± 5.4 Gln 0.998 0.3 98.7 ± 5.9 Glu 0.995 5.6 107.9 ± 2.9 Met 0.998 -4.7 100.7 ± 1.7 His (Bis) 0.999 -8.4 102.6 ± 4.8 Phe 0.996 -0.3 93.5 ± 3.5 Arg 0.996 2.0 101.2 ± 2.5 Tyr (Bis) 0.994 -7.2 102.1 ± 3.0 Trp 0.999 -8.4 94.6 ± 2.0 Lys (Bis) 0.999 -4.9 103.8 ± 3.1 Cys2 (Bis) 0.988 -1.6 105.2 ± 1.6

13 C3-D-Ala was used as an internal standard in this study. A lag in response to low

13 analyte levels in the absence of C3-D-Ala (data not shown) was observed in both the neutral gradient/negative mode detection method and acidic gradient/positive mode detection method,

13 which was not observed in the presence of C3-D-Ala. This lag is likely due to the presence of traces of 1,5-difluoro-2,4-dinitrobenzene (the precursor to Marfey’ reagent174) in commercial

Marfey’s reagent, which can react with amino acids to give undetectable (in our MRM

13 methods) derivatives. Inclusion of C3-D-Ala (or any other suitable unnatural amino acid) as an internal standard competes for reaction with this impurity and restores linearity. The neutral gradient/negative mode detection method was more sensitive (lower LLOQ) than the acidic

48 gradient/positive mode detection method (Table 4, 5 and 6). These observations clearly establish the neutral gradient with negative mode detection approach as optimal for these analytes.

2.3.5 Assessment of matrix and storage effects

Our own research interest is primarily in the area of bacterial cell wall biosynthesis, which involves several D-amino acids109, 111, 176, 180, 181 as described before. To determine the effect of matrix on the analysis of DL-amino acid levels in bacterial extracts, a pool of bacterial extract was prepared from a 100 mL MRSA culture. Quantification in quadruplicate of serially diluted amino acids in water vs matrix demonstrated that matrix effects were minimal, with an average %ME of -2.8 ± 4.4 (SD) (Table 9). Samples rerun after 24 hours at room temperature showed negligible changes in analyte levels (Table 9). After storage at -20 oC for 30 days, most amino acids were at >85% of the originally measured level except for Met (84%), Cys2 (60%) and Tyr (Bis) (13%).

2.3.6 Analysis of DL-amino acid levels in MRSA

This method was then applied to the determination of these analytes in MRSA (Table

10). Several of these amino acids (D- and L-Ala, D- and L-Glu, L-Lys) have been measured previously in MRSA using an acidic solvent system with positive mode detection109, 111, with results consistent with those reported in Table 10. L-Pro and L-Glu were the two most abundant amino acids with concentrations of 129 mM and 119 mM, respectively. The most abundant D- amino acids were D-Glu and D-Ala, consistent with their important roles as components of

49 bacterial cell wall peptidoglycan. Significant levels of D-Asp, D-Tyr, D-Pro and D-Ser were also observed.

Table 10. DL-amino acid profile of MRSAa.

Concentration (mM) ± SEb L-AAs D-AAs %D/L Gly 15.3 ± 0.4 Ala 67.2 ± 1.7 18.5 ± 0.3 28 Ser 15.1 ± 0.3 1.26 ± 0.03 8.3 Pro 128.9 ± 1.3 1.42 ± 0.03 1.1 Val 34.3 ± 0.5 0.51 ± 0.03 1.5 Thr 14.9 ± 0.4 ND Ile 20.1 ± 0.3 0.46 ± 0.03 2.3 Leu 22.8 ± 0.1 0.56 ± 0.02 2.5 Asn 0.61 ± 0.02 ND Asp 40 ± 4 3.93 ± 0.08 10 Gln 24.3 ± 0.5 ND Glu 119 ± 3 25.2 ± 0.3 21 Met 21.3 ± 0.2 0.29 ± 0.01 1.4 His (Bis) 2.74 ± 0.11 0.28 ± 0.03 10 Phe 17.6 ± 0.2 0.218 ± 0.004 1.2 Arg 6.79 ± 0.13 0.16 ± 0.02 2.4 Tyr (Bis) 14.1 ± 0.9 2.93 ± 0.06 21 Trp 0.198 ± 0.005 ND Lys (Bis) 14.4 ± 0.1 ND Cys2 0.045 ± 0.003 ND aIntracellular concentration determined from measured analyte quantity as described previously109, 111. bSE = Standard error, ND = Not detected (< 0.1 mM).

2.4 Conclusion

Analytical methods for stereospecific quantification of D- and L-amino acids are of interest in many areas. Marfey’s reagent is an effective and commonly used chiral derivatizing agent for the chromatographic resolution of D- and L- amino acids167, 174, 175. Despite its popularity, a systematic study of its application for the resolution and quantification of all 20

50 common DL-amino acids has not been reported to our knowledge. An LC-MS/MS based approach is ideal for such an analysis given the chromatographic complexity of such a group of analytes (19 DL-amino acid pairs + Gly = 39 species), and the ability of tandem MS to specifically detect and quantify analytes in complex biological samples.

Somewhat surprisingly, although Marfey’s reagent based separations are traditionally performed using acidic solvent systems, a neutral (pH 6.5) solvent system was found superior in our study in that it allowed all diastereomer pairs to be resolved, as well as the Mar-Leu and

Mar-Ile isobars (Figure 6). Sensitivity in positive mode at neutral pH was poor (Table 4), but sensitivity at neutral pH in negative mode (Table 5 and 6) was found to be superior to positive mode detection at acidic pH. These observations clearly identified the neutral pH solvent system with negative mode detection as optimal for this application. A kinetics study using this method was then performed (Table 7, Figure 7 and 8), which demonstrated that a 24 hour derivatization is sufficient for all amino acids except for His (Figure 8). This method was then validated in terms of linearity, sensitivity, matrix effect and stability. Finally, this method was demonstrated for DL-amino acid analysis in a complex matrix by the analysis of the DL-amino acid composition of an MRSA extract. This study provides a viable method for optimal resolution, and selective and stereospecific quantification of all proteinogenic DL-amino acids by LC-MS/MS technology.

CHAPTER 3

3. OXIDATION OF DEOXYRIBONUCELIC ACID: BIOLOGICAL IMPLICATIONS

AND ANALYTICAL APPROACHES

3.1 Introduction

Nucleic acids are essential biomacromolecules that carry genetic instructions for coding, regulation and expression of genes which are critical for the growth, development and functioning of all living organism. Deoxyribonucleic acids (DNAs) are double helix molecules where two chains of polynucleotides coil around each other due to hydrogen bonding between purine (adenine and guanine) and pyrimidine (cytosine and thymine) nitrogen bases (Figure

11). The sequence of the nitrogen bases encodes the genetic information. During transcription,

RNA is synthesized using DNA as a template, which in turn specifies the amino acid sequence and directs protein translation on the ribosomes. With some exceptions, a three-nucleotide codon in a nucleic acid sequence specifies a single amino acid (Table 11).

Figure 11. Chemical structure of double helix DNA showing phosphodiester bonds between deoxyribose sugars and hydrogen bonds between nitrogen bases.

Table 11. DNA codon table.

Amino acid biochemical properties Nonpolar Polar Basic Acidic Termination: stop codon Standard genetic code nd 1st 2 base 3rd base T C A G base TTT TCT TAT TGT T Phe Tyr Cys TTC TCC TAC TGC C T Ser TTA TCA TAA Stopb TGA Stopb A TTG TCG TAG Stopb TGG Trp G CTT CCT CAT CGT T Leu His CTC CCC CAC CGC C C Pro Arg CTA CCA CAA CGA A Gln CTG CCG CAG CGG G ATT ACT AAT AGT T Asn Ser ATC Ile ACC AAC AGC C A Thr ATA ACA AAA AGA A Lys Arg ATGa Met ACG AAG AGG G GTT GCT GAT GGT T Asp GTC GCC GAC GGC C G Val Ala Gly GTA GCA GAA GGA A Glu GTG GCG GAG GGG G aATG codon codes for Met and acts as an initiation codon where protein synthesis begins. bTAA, TAG and TGA act as the stop codons.

Besides the standard nitrogen bases (adenine – A, thymine – T, guanine – G and cytosine – C), modified nitrogen bases can also occur in DNA. The first modified nitrogen base reported was 5-methylcytosine (5mC) which has roles in providing immune protection to bacteria against viruses182 and in the epigenetic control of gene expression in animals and plants183. Besides 5mC, methyl adenosine (mA) and 5-hydroxymethylcytosine (5hmC) were found in bacteria and mouse brain184. Methylation of cytosine can also cause low or no gene expression and can be induced by packaging of DNA molecules or interaction with histone proteins in chromatin185. The existence of these modified DNA bases can be indicative of

54 important biological phenomena, hence analytical methods to accurately differentiate and quantify these biomolecules are very important.

3.2 Mutation and DNA damage

For proper functioning of an organism, the genotypic composition needs to be correct so that the proper genetic codes can be translated to required proteins to keep the biological machinery in desired operation. Damaged or severely mutated DNA can lead to error in phenotypic reactions and leads to unwanted function or complete loss of function186. DNA can be damaged naturally or by various mutagens such as oxidizing agents, alkylating agents, electromagnetic radiation, ultra-violet radiation and X-rays. Often times, the mutation can be repaired by various DNA repairing mechanisms which can revert back the mutated sequence to its original state and restores normal function187. There are various checkpoints in cells which look for damages during the cell growth and multiplication, such as a break in the strand of a DNA, missing base from the backbone or a chemically modified base etc. If the damaged

DNA cannot be repaired, it starts accumulating in dividing cells, give rise to epigenetic alterations and contributing to development of cancers188. Oxidative DNA damage is reported to produce more than 20 types of altered DNA bases and single strand breaks189. Small scale mutation can sometimes be difficult to detect, whereas large scale mutation almost always leads to alterations in phenotypic reactions, as the later has more chances to mutate coding regions of the genes and hence will affect the translation of proteins.

3.3 DNA Oxidation and its biological implications

When the nitrogen bases of the DNA molecules are severely oxidized, it can often indicate various abnormal cellular condition and diseases such as cancers190. However normal

55 level of oxidized DNA bases plays a role in memory and learning processes191. So, it is very important to be able to differentiate between normal and abnormal level of oxidation. For example, increased levels of oxidized guanine, 8-oxo-2’-deoxyguanosine has been reported as a biomarker for oxidative stress and found to be increased in colon cancer, Alzheimer’s disease, systemic lupus erythematosus and other major psychiatric diseases such as bipolar disorder and Schizophrenia192-194. Increased level of 5mC is reported to play a critical role in tissue- specific gene expression, X-chromosome inactivation, gene imprinting and nuclear reprogramming195-197. Cytosine methylation is catalyzed by a family of DNA methyltransferases (DNMTs), and deficiencies in these enzymes result in profound developmental defects198. Demethylation of 5mC by Ten-eleven translocation (TET) enzyme mediated oxidation lead to formation of 5-hydroxymethylcytosine (5hmC), 5-formylcytosine

(5fC) and 5-carboxylcytosine (5caC), and are considered important biomarkers for TET mediated biological processes including zygotic epigenetic reprogramming, pluripotent stem cell differentiation, hematopoiesis and development of leukemia199.

3.4 TET family of enzymes and their biological implications

TET is a family of dioxygenase enzymes that oxidize methylated cytosine and this family consists of three enzymes namely TET1, TET2 and TET3. All three TET enzymes are involved in subsequent oxidation of 5mC to 5hmC, 5fC and 5caC in Fe(II)- and 2-oxoglutarate

(2-OG)-dependent reactions as mentioned before200 (Figure 12).

Figure 12. DNA methylation and demethylation cycle by DNMTs and TET enzymes201

(reprinted with permission).

TET gene expression has been primarily found in different metazoan physiological systems. For example, TET1 is usually expressed in fetal heart, lung, brain tissue and in adult skeletal muscle, thymus and ovary; expression of TET1 in adult brain and heart is marked with brain cancer202 and acute leukemias203. TET2 is highly expressed in hematopoietic cells and is marked with many myelodysplastic syndromes204. TET3 is highly expressed in zygotes and is involved in epigenetic chromatin reprogramming during fertilization205. Although TET mediated oxidation can be linked with certain diseases, it is implicated in regulation of embryonic development, stem cell functions and lineage specification; whereas the dysregulation of these TET proteins is marked in a wide range of cancers206. Besides, TET

57 enzymes are also involved in histone modification and interactions with other metabolic enzymes to control transcriptional regulation. Therefore, studying these oxidation products of

5mC can help to understand the roles and function of TET enzymes in regulating biochemical pathways in different neurologic, oncologic and immune disorders and can help with management of these diseases.

3.5 Analytical approaches to study DNA oxidation

Various analytical methods are available for studying DNA bases that include liquid chromatography tandem mass spectrometry (LC/MS/MS), capillary electrophoresis, fluorescence post-labelling, antibody-based immunoassays, comet assay, alkaline elution assay, 32P and 3H-post-labelling207. In the last two decades, LC-MS/MS based analysis of DNA bases (both nucleosides and nucleotides) has become a pioneering approach due to their superior sensitivity, specificity, dynamic range and the ability to study numerous normal and modified DNA bases simultaneously208-211. Double stranded DNA molecules can be digested to nucleotides, using an enzyme cocktail containing DNAse I and nuclease I, which then can be further digested to nucleosides using alkaline phosphatases (Figure 13) for LC-MS/MS analysis. DNA nucleotides contain phosphate groups and can be analyzed by LC-MS/MS using an ion-paring (IP) reagent, but is not desirable due to their involatile nature212. Without IP reagents, these phosphate containing polar nucleotides can also be analyzed using hydrophilic- interaction liquid chromatography-mass spectrometry213. On the other hand, the DNA nucleosides can be analyzed using traditional reverse-phase chromatography without any IP reagents and without any type of chemical derivatization techniques214.

Figure 13. DNA digestion workflow to obtain nucleotides and nucleosides.

3.6 Conclusion

DNAs are biomacromolecules that are essential for growth, development and functioning of all living organisms. The sequence of nitrogen base in DNA constitute the genetic code and an alteration in desired sequence can give rise to mutations. Excessive mutation or damage to DNA has serious biological consequences and needs to be accurately identified. Highly sensitive, accurate and robust analytical methods are therefore essential to confidently study these DNA bases that bear critical information about health and diseases.

Due to superior sensitivity, resolution and throughput, the LC-MS/MS based analytical platform remains the most popular approach to study DNA bases. Optimum DNA digestion conditions and sample clean up protocols coupled with appropriate chromatographic and mass spectrometry technique can lead to high performance analytical methods by which regular and modified DNA bases can be accurately analyzed.

CHAPTER 4

4. LC-MS/MS-BASED SEPARATION AND QUANTIFICATION OF REGULAR AND

TEN-ELEVEN TRANSLOCATION-2 (TET2) 5-METHYLCYTOSINE DIOXYGENASE

MEDIATED MODIFIED DNA NUCLEOSIDES

4.1 Introduction

Although the C5 position of cytosine within CpG dinucleotides is a predominant methylation site (5mCpG) in mammalian genomes215, extensive cytosine methylation (5mC) in non-CpG sites (5mCpH, where H = A, T, or C) has been reported as well216. DNMTs catalyzed methylation of cytosine has important biological functions such as immune- protection, epigenetic control of gene expression, memory preservation etc. as discussed earlier, and mutations in these enzymes cause significant developmental defects198. In mammalian cells, up to 3 – 6 % of the cytosine residues are 5mC217. Cytosine methylations upstream of a promoter region cause transcriptional repression of the downstream coding region and removal of the methylation marks restores transcription of the gene. As previously discussed, the removal of 5mC marks are initiated by TET1-3 5mC dioxygenases218 that oxidize 5mC into 5hmC, 5fC and 5caC in a stepwise fashion (Figure 13). Finally, thymine-

DNA glycosylase (TDG) replaces 5fC and 5caC to unmodified cytosine residues using the base-excision repair (BER) pathway200.

Due to its critical role in epigenetic transcription regulation, 5mC and 5hmC are often termed as the fifth and sixth DNA bases (in addition to the four normal bases adenine, guanine, cytosine, and thymine). The 5mC modification generally serves as a transcriptional silencer, gene promoters219 with additional roles in X chromosome inactivation, gene imprinting,

60 nuclear reprogramming and tissue-specific gene expression220. Unregulated 5mC modification is one of the fundamental features observed in many types of cancer221 and several neurological disorders222 as discussed before. Reduction in the 5hmC levels is also associated with the impaired self-renewal of embryonic stem (ES) cells223. A recent study demonstrated that 5fC and 5caC have functional roles in epigenetics224 that affect the rate of nucleotide incorporation and specificity of RNA polymerase II (RNAPII)225.

Due to their continuously emerging important roles in epigenetic regulation and their relationship with serious immune, neurologic and oncologic disorders, substantial efforts have been made in developing advanced and highly sensitive analytical methods to accurately study these nucleic acid modifications. LC-MS/MS techniques have been widely used to study cytosine modifications due to superior sensitivity and resolution, broad dynamic range, and ability for simultaneous quantification of these modifications in a time and cost-effective way.

We evaluated both positive and negative mode electrospray ionization mass spectrometry with substantial optimization of chromatography, solvent composition, pH, stationary phase and sample preparation approaches to achieve the optimum resolution and sensitivity possible for eight DNA nucleosides (four regular and four modified cytosine derivatives). This highly optimized and validated LC-MS/MS based analytical method can aid understanding of the roles of cytosine derivatives in health and diseases.

4.2 Materials and methods

4.2.1 General

All the chemicals were from Thermo Fisher Scientific (Waltham, MA) or Sigma-

Aldrich (St. Louis, MO), unless otherwise stated, and were either analytical grade or better.

Nucleoside standards were from Carbosynth (now Biosynth Carbosynth, Oakbrook Terrace,

IL).

4.2.2 Optimization of mass spectrometry parameters

LC-MS/MS analysis was performed on a Sciex QTrap 3200 triple quadrupole mass spectrometer (Foster City, CA) coupled to a Shimadzu UFLC LC-20 series HPLC system

(Columbia, MD) using electrospray ionization (ESI) and run with Analyst v. 1.6.2 software.

The C18 columns (100×3 mm, particle size 5µM) were from Keystone Scientific (now Thermo

Fisher Scientific). Standard stock solutions of all eight nucleosides were prepared at a concentration of 100 µM in HPLC grade water with 0.1% formic acid and infused into the mass spectrometer at a flow rate of 10 µL min-1 using a 4.6 mm Hamilton syringe (Franklin,

MA). Initial studies were performed in an enhanced mass scan mode to detect the parent ion peak, followed by generation of product ion peaks in enhanced product ion mode. Finally, the generated product ions were confirmed using enhanced precursor ion mode. Various compound and source dependent parameters as discussed in section 2.2.4 were optimized using the automated quantitative optimization feature of the Analyst software. These optimum parameters and the most intense fragment ions per nucleoside in the positive, negative and ion- switching modes in terms of sensitivity and resolution, were used to build the final MRM method.

4.2.3 Optimization of chromatographic condition

A number of different solvent systems at different modifier and mobile phase composition, pH and gradient slope were evaluated for positive, negative and ion-switching

62 methods for the separation of the eight nucleosides for optimum performance. These include water, ammonium acetate and ammonium formate as aqueous phases, and acetonitrile or methanol as organic phases. For the positive mode analysis, the gradient used was 0% B (0-2 min), 0-20% B (2-5 min), 20-60% B (5-9 min), 60-0% B (9-10 min) and then a 5 minute post- equilibration with solvent A (where solvent A was 10 mM ammonium acetate pH 4.0 and solvent B was 20% acetonitrile/80% 10 mM ammonium acetate pH 4.0).

For the negative mode analysis, an ammonium acetate based neutral solvent system was used where solvent A was 10 mM ammonium acetate pH 6.5 and solvent B was of 80% acetonitrile/20% 10 mM ammonium acetate pH 6.5. The gradient used was 0% B (0-2 minute),

0-20% B (2-5 minute), 20-60% B (5-9 minute), 60-0% B (9-10 minute) and then a 5-minute post-equilibration with solvent A.

For the ion-switching mode, a water/methanol based solvent system pH 3 was used, where solvent A was water/0.1% formic acid and solvent B was methanol/0.1% formic acid.

The gradient used was 0% B (0-1 minute), 0-2% B (1-12 minute), 2-30% B (12-17 minute),

30% B (17-18 minute), 30-0% B (18-18.5 minute), followed by a 4.5-minute equilibration with solvent A. For all three analytical methods, the flow rate was kept at 0.3 mL min-1.

4.2.4 LC-MS/MS analytical method validation

In order to construct the standard curves (and to determine the linearity) for all eight nucleosides in the positive, negative and ion-switching modes, 25 µM standard stock solutions of each nucleoside were serially diluted in HPLC grade water with 0.1% formic acid. Diluted samples were analyzed with the developed LC-MS/MS methods (described above). Standard curves were constructed by plotting peak areas at different concentrations. For each nucleoside,

63 the limit of detection (LOD) and lower limit of quantification (LLOQ) was calculated using the following equations:

푠푡푎푛푑푎푟푑 푑푒푣𝑖푎푡𝑖표푛 표푓 푡ℎ푒 푙표푤푒푠푡 푐표푛푒푐푛푡푟푎푡𝑖표푛 퐿푂퐷 = 3.3 × … … … (Eq. 4.1) 푠푙표푝푒 표푓 푡ℎ푒 푠푡푎푛푑푎푟푑 푐푢푟푣푒

푠푡푎푛푑푎푟푑 푑푒푣𝑖푎푡𝑖표푛 표푓 푡ℎ푒 푙표푤푒푠푡 푐표푛푒푐푛푡푟푎푡𝑖표푛 퐿퐿푂푄 = 10 × … … … (Eq. 4.2) 푠푙표푝푒 표푓 푡ℎ푒 푠푡푎푛푑푎푟푑 푐푢푟푣푒

To analyze the matrix effect (%ME), the peak areas (cps) for all eight nucleosides were plotted at three different concentrations (0.2, 0.78, and 12.5 µM) and the slope of regression lines were calculated for both solvent and matrix using equation 2.8. The matrix used for this study comprised all co-factors and TET2 enzyme in 2-[4-(2-hydroxyethyl)piperazin-1- yl]ethanesulfonic acid (HEPES) buffer without the substrate DNA.

4.3 Results and discussions

4.3.1 LC-MS/MS based analysis of DNA nucleosides in positive mode

The positive LC-MS/MS based MRM method provided baseline separation of all eight

DNA nucleosides within a 15 minute analytical run (Figure 14). For all eight nucleosides, standard curves were linear up to 12.5-25 µM (Figure 15). Optimized MS/MS parameters,

LOD and LLOQ are listed in Table 12.

mC 3.0E+04 m: 241.1 m/z: 242.1 A m: 251.2 2.3E+04 C m/z: 252.2 fC m: 227.1 hmC m: 251.1 m/z: 228.1 m: 257.2 m/z: 252.1 1.5E+04 m/z: 258.2 G m: 267.3 T Counts per second 7.5E+03 m/z: 268.2 caC m: 242.1 m: 271.2 m/z: 243.1 m/z: 272.2 0.0E+00 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Time (min)

Figure 14. MRM chromatogram of eight DNA nucleosides using 10 mM ammonium acetate/acetonitrile solvent system (pH 4) on a C18 column and MS/MS detection in positive mode.

Figure 15. Standard curves of eight DNA nucleosides using 10 mM ammonium acetate/acetonitrile solvent system (pH 4) and MS/MS detection in positive mode. 66

Table 12. Compound dependent parameters and sensitivity characteristics for the most intense

MS/MS transitions of the eight DNA nucleosides in positive modea.

DP EP CEP CE LOD LLOQ Nucleosides Q1 Q3 (V) (V) (V) (V) (µM) (µM) A 252.2 136.1 41 9 14 17 0.06 0.20 T 243.2 117.1 16 8 14 15 1.8 5.94 G 268.2 152.1 21 7 14 37 7.8 25.74 C 228.1 112.1 21 7 14 15 0.1 0.33 mC 242.2 126.1 31 6.5 24 13 0.03 0.10 hmC 258.2 142.1 16 6 14 13 0.6 1.98 fC 256.2 140.1 11 6 14 15 0.2 0.66 caC 272.2 156.1 6 7 94 23 3.9 12.87 aGlobal method parameters: CXP – 2V, CUR – 50 (arbitrary unit), TEM – 500 oC; IS – 4500 V; GS1 & GS2 – 50 (arbitrary units); CAD – Medium.

4.3.2 LC-MS/MS based analysis of DNA nucleosides in negative mode

Due to a major structural difference among caC and the rest of the seven DNA nucleosides (presence of a –COOH group that is difficult to protonate and more easily loses a proton to generate a negatively charged –COO– ion), LC-MS/MS based analysis in positive ionization mode offers poor sensitivity for caC analysis. Our previously developed LC-

MS/MS-MRM method in positive mode (discussed in section 4.3.1)214, did not provide adequate sensitivity for 5caC quantification in biological samples irrespective of different sample preparation, gradient and solvent conditions evaluated. To address this issue, we tuned different mass spectrometry parameters for all eight nucleosides in the negative mode. Using a slightly modified neutral solvent system pH 6.5 (solvent A: 10 mM ammonium acetate and solvent B: 80% acetonitrile/20% 10 mM ammonium acetate), we were able to achieve improved sensitivity for 5caC, possibly because neutral to slightly basic pH is better for the generation and detection of negatively charged species135. This negative mode MRM method with ammonium acetate based neutral solvent system allowed baseline separation and

67 quantitation of these eight nucleosides (Figure 16) while preserving the elution order of all eight DNA nucleosides as that observed in positive mode MRM method (Figure 14).

5.0E+04 fC 4.0E+04 m:255.2 m/z:254.2 G 3.0E+04 caC m:267.2 m: 271.2 m/z:266.2 T 2.0E+04 m/z: 270.2 mC m:242.1 hmC m:241.1 C m/z: 241.1 m: 257.2 m/z:240.1 1.0E+04 m: 227.1 A Counts per second m/z: 256.2 m/z: 226.1 m: 251.2 m/z: 250.2 0.0E+00 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Time (mins)

Figure 16. MRM chromatogram of eight DNA nucleosides using 10 mM ammonium acetate/acetonitrile solvent system (pH 6.5) on a C18 column and MS/MS detection in negative mode.

For all analytes, standard curves were linear up to 12.5 µM (Figure 17). Optimized MS parameters along with LOD and LLOQ of each analytes are listed in Table 13. Taken together, our new chromatographic condition in negative mode resulted in 80x increased sensitivity for

5caC compared to a previously published paper226. Although the negative mode markedly improved the sensitivity for 5caC (and G), this approach resulted in lower sensitivities for some of the other nucleosides compared to the positive mode, especially A and mC.

Figure 17. Standard curves of eight DNA nucleosides using 10 mM ammonium acetate/acetonitrile solvent system (pH 6.5) and negative mode MS/MS detection. 69

Table 13. Compound dependent parameters and sensitivity characteristics for the most intense

MS/MS transitions of the eight DNA nucleosides in negative modea.

DP EP CEP CE LOD LLOQ Nucleosides Q1 Q3 (V) (V) (V) (V) (µM) (µM) A 250.2 134.1 -35 -10 -10 -22 0.10 0.32 T 241.2 125.1 -40 -8 -14 -12 0.20 0.35 G 266.2 150.1 -60 -7.5 -14 -26 0.003 0.01 C 226.1 93.0 -35 -9 -10 -24 0.10 0.32 mC 240.1 124.1 -40 -10.5 -10 -16 0.20 0.65 hmC 256.2 123.1 -50 -9 -14 -22 0.20 0.65 fC 254.2 121.1 -50 -2 -10 -24 0.01 0.02 caC 270.2 110.1 -50 -4 -12 -24 0.05 0.16 aGlobal method parameters: CXP – -3V, CUR – 40 (arbitrary unit), TEM – 500 oC; IS – - 4500 V; GS1 & GS2 – 50 (arbitrary units); CAD – Medium.

4.3.3 LC-MS/MS based analysis of DNA nucleosides in positive/negative ion-switching mode

Our optimization process has demonstrated greater sensitivity for 5caC and G in the negative mode, while positive mode demonstrated higher sensitivities for other nucleosides.

This suggested that incorporating a period of preferred mode of ionization for each specific nucleoside could lead to a highly sensitive LC-MS/MS analytical method. However, there were several challenges that needed to be resolved to develop a mass spectrometry method with positive/negative polarity switching. Since mass spectrometry in the positive and negative modes typically require slightly different pH and composition of the mobile phase, finding a common solvent system was a challenge. Further, we observed that a minimum of 1-2 minutes is required for the Sciex QTrap 3200 triple quadrupole mass spectrometer which we used in the study to switch and operate in the desired ionization mode to provide optimum sensitivity and accommodate proper detection of the nucleosides within a specific time window of the analytical runtime. Thus, the separation of all the nucleosides must be done in such a way that the elution of 5caC occurs in the negative mode period while others elute in the positive mode

70 period. This was challenging, given the similar properties of 5hmC and 5caC on a C18 column in reverse phase chromatography using most solvent systems227.

To mitigate these issues, several solvent systems, pH and chromatographic conditions were evaluated. Finally, a water/methanol-based solvent system (described earlier) at pH 3.5 provided appropriate elution of seven nucleosides in the determined time-windows and in their preferred mode, except for 5mC. Under these chromatographic conditions, C and 5hmC eluted in the first positive mode period followed by elution of 5caC and 5mC in the second period operating in negative mode. Remaining nucleosides (A, T, G, and 5fC) eluted in the final positive mode period (Figure 18). For all analytes, the standard curves were linear up to 12.5

µM (Figure 19). Optimized MS parameters along with LOD and LLOQ of each analyte are listed in Table 14.

Figure 19. Standard curves of eight DNA nucleosides in positive/negative ion-switching mode using water/methanol solvent system (pH 3.5). 73

Period Mode Nucleosides Q1 Q3 DP EP CE CE CXP LOD LLOQ (Duration, (V) (V) P (V) (V) (µM) (µM) min) (V) 1 (6 min) (+) ve C 228.1 112.1 21 7 14 15 4 0.36 1.09 hmC 258.2 142.1 36 3.5 14 17 4 0.25 0.75 2 (5 min) (–) ve mC 240.1 124.1 -40 -10.5 -10 -16 -3 0.49 1.49 caC 270.2 110.1 -50 -4 -12 -24 -3 0.12 0.37 3 (12 min) (+) ve T 243.2 117.1 16 8 14 15 4 0.16 0.48 A 252.2 136.1 41 9 14 17 4 0.33 1.00 fC 256.2 140.1 11 6 14 15 4 0.24 0.72 G 268.2 152.1 21 7 14 37 4 0.25 0.76 aGlobal method parameters: CUR – 40 (arbitrary unit), TEM – 500 ºC, GS1 and GS2 – 50 (arbitrary unit), CAD – Medium, IS – 4500 V for positive mode and -4500 V for negative mode.

Although the sensitivity of 5caC at pH 3.5 (in water/methanol-based solvent system) was little less compared to pH 6.5 (in ammonium acetate/acetonitrile-based solvent system), possibly due to the reason described earlier135, sensitivities for other nucleosides (particularly

5hmC and 5fC) in the positive mode increased several folds compared to ammonium acetate or ammonium formate based solvent systems. In comparison to our previously reported paper, sensitivity of 5hmC and 5fC improved by 6-8 fold, whereas a 35 fold improvement in sensitivity was achieved in case of 5caC. However, the sensitivity for 5mC is comparatively less in this ion-switching mode where the MRM channel for 5mC had to be kept in the second period of negative ionization mode due to its elution feature. Overall, the developed positive- negative ion switching method while does not provide optimum sensitivity for 5mC, offers superior sensitivity for the three oxidation products of 5mC i.e. 5hmC, 5fC and 5caC, whose formation is catalysed by TET2 dioxygenase enzymes228. Hence this approach can be used to evaluate the catalytic activity of TET family of enzymes.

4.4 Conclusion

Our effort was primarily focused on achieving superior resolution and quantitation of the three primary oxidation products of methylcytosine that are regulated by TET2 dioxygenases. To achieve optimal analytical performance, we evaluated different chromatographic (solvents, modifier, pH, gradient) and mass spectrometry (ionization mode, period-time) parameters. Among different methods that we developed, the positive-negative ion-switching LC-MS/MS based method provided the best sensitivity and resolution for the three modified TET2 mediated oxidation products of 5mC, along with the four regular DNA nucleosides. This method can be used to characterize the activity of wild type TET enzymes and its clinically relevant mutants. This method with a slight modification to include an MRM channel for deoxyuridine (U), can also help to study all five common DNA nucleosides and can help in studying the effect of exogenous stress on nucleic acid biosynthesis and identifying the enzymes regulating the associated pathways.

PART II: CHEMICAL LIBRARY SCREENING FOR ANTIBACTERIAL ACTIVITIES

CHAPTER 5

5. WHOLE CELL BASED CHEMICAL LIBRARY SCREENING FOR ANTIBACTERIAL

DRUG DISCOVERY

5.1 Introduction

Infectious diseases cause millions of deaths worldwide and the most common type of infections are caused by bacterial pathogens. The biggest challenge for the treatment and prevention of bacterial infection is the development of resistance to existing antibacterial agents. New resistance to effective antimicrobial agents is rising, and the problem is exacerbated by an extremely dry and slow pipeline of new antibacterial agents229-231. This clearly indicates the dire need for strategic planning and careful scientific venture to discover new antibacterial agents or treatment approaches to provide better healthcare and to improve the quality of human life. Among different drug discovery approaches, high throughput screening (HTS) has remained a popular one, which enables screening of thousands of drugs and/or xenobiotics for therapeutic activity232. HTS of chemical libraries has the potential to repurpose drugs that are indicated for other diseases including bacterial infections233-235 and can help speeding up the pipeline of antibacterial drug development. There are two general approaches to identify novel antibacterial agents using chemical compound libraries; target

(enzyme) specific screening – which often provides hits without antibacterial activity in whole cell assays, and whole cell screening – which often provides hits for which identification of targets can be challenging or which are toxic in vivo236, 237. However, a whole cell screening approach can interrogate all possible targets simultaneously and selects for agents with intrinsic antibacterial activity. FDA approved drug libraries have been screened previously for

77 antibacterial activity238, 239 and hold potential for discovery of novel antibiotics or combination therapies to combat difficult to treat infections caused by multidrug resistant bacteria.

5.2 Chemical library screening

Chemical library screening (CLS) is an automated drug discovery process that allows testing of large number of chemical compounds for a specific biological target or a specific therapeutic activity. State of the art robotics and automation technologies have made CLS a fast and more cost effective approach to modern drug discovery initiatives. Generally, the throughput of a chemical library screening depends on the number of samples tested per day, referred to low throughput (1-500 compounds), medium throughput (500-10,000 compounds), high throughput (10,000-100,000 compounds) and ultra-high throughout (>100,000 compounds)240. CLS is one of the most commonly used technologies in the pharmaceutical and biotechnology industries, due to its potential to derive information-rich results. CLS started as an empirical drug discovery approach but with the advent of automation and system biology approaches, CLS is now also being used for different downstream application for lead optimization241.

5.3 In vitro drug metabolism

Drugs or xenobiotics when introduced into the body, undergo various biotransformation processes, collectively known as metabolism. There are different enzymes that can act on drugs or xenobiotics to either enhance their water solubility to promote excretion (through addition of small groups like hydroxyl groups or through conjugation with larger chemical groups such as glucuronic acid), or to activate or deactivate them by

78 introducing reactive group(s) or unreactive moieties242. Rate and extent of drug metabolism are highly variable between drugs243 and also depend on the presence of different enzymes which can vary between different organisms, sex, race and age groups244. Besides, patients with reduced hepatic function or liver diseases can also show variability in drug metabolism245,

246. Primarily the purpose of the metabolism is to reduce the lipophilicity or increase the hydrophilicity of the drugs so that after eliciting the therapeutic activity they can be excreted from the body. For prodrugs however, metabolism actually activates them so that they can produce the pharmacological effect that leads to the pharmacodynamic response247. Several prodrugs which have no intrinsic activity before metabolism, are metabolized into active compound(s) that yield intended pharmacodynamic responses e.g. diamorphine, codeine, enalapril and levodopa248. Liver is the primary site of drug metabolism249 where metabolism occurs in two phases250. In phase I, smaller polar groups are introduced into the drug predominantly by different cytochrome (CYP) P450 enzymes through various reactions such as oxidation, reduction, hydrolysis etc. During phase II, large polar moieties such as glutathione, glucuronic acid, acetate, sulfonate etc. are conjugated to the drugs.

Performing drug metabolism in vitro has been of keen interest to scientists for a variety of reasons, including deciphering metabolic routes, identifying metabolites, studying differences in inter-species metabolism, toxicity study, assessment for clinical potential etc. In vitro drug metabolism can be performed using whole cell test systems such as liver slices or hepatocytes, subcellular fractions such as S9, cytosolic or microsomal fractions and recombinant or purified metabolizing enzymes such as CYP P450s251. Liver slices and hepatocytes mimic in vivo conditions the best among the different approaches available for drug metabolism, and contain both phase I and phase II drug metabolizing enzymes (DMEs)252.

From liver slices or hepatocytes, three different fractions can be obtained through differential centrifugation: S9, cytosolic and microsomal fractions (Figure 20).

Figure 20. Extraction of various liver components (© Sekisui XenoTech, reprinted with permission).

S9 fractions contain DMEs both for phase I and II metabolism, but microsome can be manipulated to only perform phase I metabolism by selectively adding the cofactors for phase

I reactions251. In general, as phase I DMEs activate prodrugs, they can be used for drug metabolite profiling for activity analysis, and can aid in drug repurposing through identification of biological activity of drug metabolites.

5.4 Drug metabolite identification

Purification and identification of drug metabolites can be performed by various chromatographic and spectroscopic techniques. Due to increased sensitivity and the ability to obtain structural information on metabolites, mass spectrometry (MS) based detection is a widely used technique for drug metabolite identification. MS based approaches are information rich and also utilize various cheminformatic software for example, Meteor, MetaDrug,

MetaSite, StarDrop, BioTransformer with different data mining features such as mass defect filtering, isotope pattern filtering and background subtraction253-255. Despite availability of various analytical platforms, cheminformatic and bioinformatics software, drug metabolite identification often still requires tedious data analysis and critical inspection. Structure elucidation of potentially toxic or active drug metabolites is crucial to study the structure- activity relationships, safety and stability profile256.

5.5 Synergy and antagonism

Combination effect of drugs is an important phenomena that should be meticulously studied in order to provide safe and effective treatment for patients while avoid having unwanted side effects. When coadministration of drugs leads to greater pharmacological activity than administration of the drugs individually, that is commonly known as an additive effect. Any deviation from the additive effect is characterized as either synergy or antagonism.

Synergistic activity in managing bacterial infection needs to be carefully evaluated because of the potential of resistance development due to polypharmacology. Polymicrobial infection, multidrug resistance, high mutation rate of causative bacteria and dose related toxicity are some of the situations which could benefit by synergistic activity of antibiotics257. One of the

81 most common in vitro techniques to confirm synergy is known as the ‘checkerboard experiment’ (Figure 21), in which two serially diluted drugs are tested at varying ratios against the microbe of interest to calculate the fractional inhibitory concentration index (FICI). FICI expresses the extent of interaction between two drugs that contextualizes the reduction of bacterial growth in presence of combination of two drugs when compared to bacterial growth associated with each individual drug.

Figure 21. Checkerboard assay setup for synergy determination.

FIC index (FICI) can be calculated from the following formula:

푀퐼퐶 푀퐼퐶 퐹퐼퐶 𝑖푛푑푒푥 = ( 퐶표푚푏푖푛푎푡푖표푛 표푓 푑푟푢푔 1 푎푛푑 2) + ( 퐶표푚푏푖푛푎푡푖표푛 표푓 푑푟푢푔 1 푎푛푑 2) … … … 푀퐼퐶퐷푟푢푔1 푀퐼퐶퐷푟푢푔2

(Eq. 5.1)

A FICI less than 0.5 indicates synergy, whereas greater than 4.0 indicates antagonism; a FICI within 0.5-1.0 is indicative of additive effect, whereas a value in the range of 1.0-4.0 indicates negligible synergistic effect258. Checkerboard experiment also provides characteristics visual patterns (Figure 22) that can be rapidly interpreted to make general inferences about the combination effects exhibited, which can then be validated with the FIC indices.

Figure 22. Representation of checkerboard experiment plates showing synergistic action, no interaction and antagonistic action.

Isobologram analysis further confirms synergistic or antagonistic action by characteristic non-linear isoboles, while additive action is associated with a linear response259

(Figure 23). Isobolograms are formed by plotting the fractional minimum inhibitory

83 concentrations of the drugs in combination for each drug, one on each axis, calculated using the following formula:

푀퐼퐶 표푓 푐표푚푏𝑖푛푎푡𝑖표푛 표푓 푑푟푢푔푠 퐹푟푎푐푡𝑖표푛푎푙 푚𝑖푛𝑖푚푢푚 𝑖푛ℎ𝑖푏𝑖푡표푟푦 푐표푛푐푒푛푡푟푎푡𝑖표푛 = ( ) … … … 푀퐼퐶 표푓 푑푟푢푔 푎푙표푛푒

(Eq. 5.2)

Figure 23. Isobolograms showing synergistic (red), additive (black) and antagonistic (blue) effects.

5.6 Spectrum of activity

Spectrum of activity (SoA) for antibiotics refers to the different types and strains of bacteria against which the antibiotic is effective. Based on SoA, antibiotics can be divided into three categories: broad-spectrum (affect growth of wide range of bacteria), extended-spectrum

(affect some gram-positive and gram-negative bacteria) and narrow-spectrum (affect only limited number of bacteria)260. It is almost always preferable to use narrow-spectrum

84 antibiotics to avoid development of resistance of unknown origins. However, broad-spectrum antibiotics are preferred during severe infection, especially when the causative agents are unknown or are multidrug resistant. In antibiotic drug discovery, it is common practice to determine the spectrum of activity of newly identified lead compounds to obtain a preliminary idea about the potential effectiveness of the agents.

5.7 Frequency of resistance

Frequency of resistance (FoR) is another important parameter in the antibacterial drug discovery effort and is used to calculate the mutation rate, which is widely used for the prognosis of the emergence of antibiotic-resistant bacteria. FoR is the frequency at which mutant colonies grow in the presence an antibiotic at a given concentration261. In practice, FoR is calculated by dividing the number of confirmed resistant colonies grown on antibiotic containing solid nutrient media (prepared with at least 4x MICs of the antibiotics effective against the wild strain) by the total number of colony forming units (CFU) plated262 (Figure

24). Based on the frequency of resistance, a preliminary idea of the potency of the agents and mutation rate of the target organism against the agents can be obtained which can help predict the potential success for lead agents to be effective antibacterial agents. In general, there are four categories of FoR characteristics, that bacteria demonstrate based on their FoR values: hypomutable (FoR ≤ 8E–9), normomutable (8E–9 < FoR < 4E–8), weak mutator (4E–8 ≤ FoR<

4E–7) and strong mutator (FoR ≥ 4E–7)263.

Figure 24. Schematics of frequency of resistance (FoR) experiment (© Emery Pharma, reprinted with permission).

5.8 Mechanism of action study

Antibiotics exhibit their action by altering the normal biochemical operation of bacterial cellular machinery. Studying mechanism of action of antibiotics reveals the biochemical changes occur inside the target organism when exposed to drugs or any other external stressors and can help to identify new drug targets, aid in drug discovery and assist in understanding how resistance might develop. Different antibiotics target different sites of bacterial cells, triggering changes in complex biochemical pathways which can be studied by different bioanalytical and molecular biology techniques. Often, multiple approaches are required to draw useful conclusions from mechanistic studies and confirm the findings. Mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy based metabolomics and proteomics approaches are powerful tools to explore antibiotic effects on bacterial cells264, 265.

Untargeted approaches can be a good starting point to identify the primary dysregulated pathways266, 267, and once confirmed can be followed by a more targeted approach focusing specifically on the molecules of interest of the dysregulated pathways, for example the cell wall biosynthesis pathway (Figure 25) or the DNA biosynthesis pathway (Figure 26).

Figure 25. Cytoplasmic intermediates and the enzymes involved in S. aureus cell wall biosynthesis pathway showing stepwise formation of functional units leading to peptidoglycan formation111.

Figure 26. Cytoplasmic intermediates and the enzymes involved in S. aureus DNA biosynthesis pathway showing stepwise formation of functional units leading to DNA formation.

Molecular biology-based techniques can certainly be employed for these applications, but often suffer from a lack of sensitivity and challenging steps of target protein isolation and purification. On the other hand, mass spectrometry-based proteomics and metabolomics can provide high throughput and a comprehensive study of the proteome and metabolome268-270, but can be very complex and require sophisticated instrumentation and data analytics workflows. Whole genome sequencing (WGS), on the other hand can be employed to identify the key mutations to confirm the molecular target of an antibacterial agent and to confirm the resistance pathways associated with various antibacterial agents271-277, which has become an important component of antibacterial drug screening and characterization271-277. For example, the molecular target of the quinolones was only identified using bacterial genomics278, 279.

5.9 Conclusion

Whole cell based chemical library screening enables discovery of lead agents irrespective of their molecular target. Although, determining the molecular target can be a challenging task for whole cell based screening, it at least provides diverse compounds with biological activity compared to a target-based screening; the later often fails to provide hits.

Incorporation of pre-metabolized compound libraries can expand the chemical space evaluated in one study, and screening in the presence of a known antibiotic offers identification of synergistic agents, which holds potential for identification of new antibacterial agents or treatment strategies. Chemical libraries that consist of FDA approved drugs are desirable as they have been tested for safety and efficacy, hence identified synergistic agents are amenable to being developed into therapeutic drugs. State of the art instrumentation and systems biology approaches have enabled genomics, proteomics and metabolomics based studies, which if performed in an integrated way can help to identify, understand and confirm the dysregulated biochemical pathways, responsible genes, proteins and associated network of intracellular metabolites targeted by novel hits from chemical library screening and can help in identifying the molecular target(s). A carefully designed chemical library screening workflow represents a highly promising antibacterial drug discovery approach contingent upon validation, robustness, affordability, feasibility and cost-effectiveness.

CHAPTER 6

6. DIMENSIONALLY ENHANCED CHEMICAL LIBRARY SCREENING FOR

ANTIBACTERIAL ACTIVITY

6.1 Introduction

Pathogenic bacteria are becoming increasingly drug resistant, with some now virtually untreatable280, 281. There has concurrently been a lack of new antibacterial agents identified over the last thirty years to counter this threat229-231, 282. Methicillin-resistant Staphylococcus aureus (MRSA) is one of the ESKAPE (Enterococcus faecium, Staphylococcus aureus,

Klebsiella penumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and

Enterobacter species) pathogens283, 284, and is a common cause of both nosocomial and community-acquired infections285, 286. It is characterized by resistance to methicillin, oxacillin, most other commonly used β-lactam antibiotics, and to many other antibiotic classes and agents287. Chemical compound library (CLS) screening is a foundational approach for the discovery of new bioactive agents, including antibacterial agents. However, large whole cell and target-based library screening efforts for new antibacterial agents have generally yielded disappointing results237, 288. An alternative to large library screens are smaller scale efforts such as FDA approved drug library screening, which has become a popular strategy for “drug repurposing”289. Additionally, screening chemical libraries with diverse pharmacophore containing molecules greatly enhances the possibility of finding lead agents. Among different approaches of drug discovery, drug repurposing is an expanding area which has become commonplace to explore and identify novel therapeutic activity for neglected diseases due to reduced cost, effort and time289-293. Drug repurposing can also facilitate identification of

90 combinatory effects, mechanism of action and new drug classes. This strategy can also reveal novel activities of FDA approved drugs, which provides a potential greatly shortened path to clinical application.

The use of human liver microsomes for drug metabolism studies is also commonplace in drug discovery294. Nearly all drugs and xenobiotics are transformed into at least one metabolite, and generally more. Such metabolites frequently have distinct biological activities295, 296. It seemed likely that in-situ microsomally generated metabolites of an FDA approved drug library could identify drug metabolites as novel antibacterial agents, the demonstration of which was the initial major objective of this study. In addition to the FDA library, an NCI chemical library (Diversity set V) was screened for antibacterial activity against MRSA. This requires a comparative screening approach, in which the un-metabolized

(UM) and pre-metabolized (PM) libraries are screened in parallel. Microsomal metabolism was performed to obtain phase I drug metabolites and to assess them for improved antibacterial activity against MRSA in a whole cell based assay. The microsomes used in this study were pooled from 50 donors, yielding a good representation of the types of metabolic variability expected between patients due to age, gender, ethnicity and physiological condition. Another strategy to counter antimicrobial resistance is to identify agents that can act synergistically with another antibiotic, or restore the activity of an antibiotic against which bacteria are resistant. To further enhance the proposed UM vs PM library screen, it was combined in this study with a –/+ resistant to antibiotic (cefoxitin; Cef) screen. This provided a 2x2 experimental design (UM/PM vs –/+ Cef), which provided both a degree of redundancy (replication) in this screening effort, and two extra dimensions of information for use in filtering and assessing the screening results. This study demonstrates the ability of such a multidimensional screening

91 approach to identify library compounds with active metabolites, as well as agents with synergistic activity.

6.2 Materials and methods

6.2.1 General

The FDA approved drug library of 978 compounds was from Selleckchem (Houston,

TX) and the NCI library of 1593 compounds was from National Cancer Institute (NCI,

Bethesda, MD). Human liver microsomes were from Sekisui XenoTech LLC (Kansas City,

KS) (catalog # H0630, pooled human liver microsomes from 50 donors of mixed sex, age and race) and were stored and used according to the manufacturer’s instructions. Control CYP substrates included a panel of six standard drugs: phenacetin, tolbutamide, dextromethorphan, coumarin, chlorzoxazone and diclofenac, all of which were from Sigma-Aldrich (St. Louis,

MO). Cation adjusted Mueller-Hinton broth (CAMH) and Leucosnostoc mesenteroides glucose-6-phosphate dehydrogenase (G6PDH) were from Sigma-Aldrich (St. Louis, MO).

Glucose-6-phosphate (G6P) and β-nicotinamide adenine dinucleotide phosphate (NADP+) were from Alfa Aesar (a division of Thermo Fisher Scientific, Waltham, MA). Sheep Blood

Agar (SBA) plates (catalog # R01198) and SpectraTM MRSA plates (catalog # R01821) were from Thermo Fisher Scientific (Waltham, MA); Mannitol Salt Agar (MSA) plates (catalog #

M9052) were from Sigma-Aldrich (St. Louis, MO). Multiwell polypropylene cluster tube plates (96 wells) were from Corning (catalog # 4411; Corning, NY) and 96 well U-bottom polypropylene storage plates were from Becton Dickinson (catalog # 351190; Franklin Lakes,

NJ). Sterile 384 well microtiter plates were from Corning (catalog # 3680; Corning, NY) and

Griener Bio-one (catalog # 781186; Monroe, NC); sterile 96 well microtiter plates were from

MidSci (catalog # TP92096; Valley Park, MO). Petri dishes were from USA Scientific (catalog

# 8609; Ocala, FL). Other reagents were from standard sources and were reagent grade or better. Bacterial strains were obtained from American Type Culture Collection (ATCC)

(Manassas, VA) and the Biodefense and Emerging Infections Research Resources Repository

(BEI) (Manassas, VA). The bacterial strain used for library screening was MRSA strain F-182

(ATCC 43300). Other bacterial strains used in this study were MRSA strain N315 (BEI NR-

45898), HI022 (BEI NR-30550), MN8 (BEI NR-45918), TCH70 (BEI HM-139), RN1 (BEI

NR-45904), COL (BEI NR-45906); a clinical strain of vancomycin-resistant Enterococcus faecium (VRE, VanA type), and Escherichia coli strain Seattle 1946 (ATCC 25922).

Proteomics based microbial identification was performed on a Bruker ultrafleXtreme MALDI-

TOF/TOF mass spectrometer (Billerica, MA) and LC-MS/MS based microsomal metabolic activity assessment was performed on a Sciex QTrap 3200 triple quadrupole mass spectrometer

(Foster City, CA) mass spectrometer coupled to a Shimadzu UFLC LC-20 series HPLC system

(Columbia, MD) using electrospray ionization (ESI) source. Drug metabolite identification was performed on a Thermo Fisher Scientific Q-Exactive HF Orbitrap mass spectrometer

(Waltham, MA) and a Waters Xevo G2 XS HDMS QToF coupled with a Waters Acquity

UPLC system (Milford, MA). The liquid handling workflow including library replication, microsomal metabolism and working library preparation were performed on a Beckman

Coulter Biomek 3000 liquid handling workstation (Brea, CA). Serial dilution of hits, addition of bacteria and dye were performed on an Integra VIAFLO Assist platform (Plainsboro

Township, NJ).

6.2.2 Library replication, addition of metabolism and antibacterial control compounds

The FDA approved drug library was delivered in columns 1–11 in 96 deep well plates,

12 plates in total, with each sample well containing 100 µL of a 10 mM solution of compound dissolved in dimethyl sulfoxide (DMSO). Antibiotic controls (20 µL of 10 mM stock solutions of vancomycin, ampicillin, doxycycline, kanamycin and chloramphenicol) were added to column 12 of odd numbered plates and microsomal (CYP) substrate controls (20 µL of 10 mM stock solutions of phenacetin, tolbutamide, dextromethorphan, coumarin, chlorzoxazone and diclofenac) were added to column 12 of even numbered plates. The NCI drug library was delivered in columns 2-10 in U-bottom 96 well plate, with each sample well containing 20 µL of a 10 mM solution of a compound dissolved in DMSO. For the NCI library, 20 µL of 10 mM stock solutions of control antibiotics were added to column 1 and 20 µL of 10 mM stock solutions of microsomal (CYP) substrate controls were added to column 12 of every plate.

Aliquots of library samples were transferred to multiwell plates using a liquid handling workstation (Biomek 3000) for performing microsomal metabolism. DMSO, which can interfere with microsomal drug metabolism reactions, was removed by freezing the plates at –

80 oC and drying the replicated library plates under strong vacuum (<50 µmHg) in a Genevac

Quatro centrifugal concentrator. To prepare the UM working library, both FDA and NCI library was diluted with DMSO to obtain a 0.5 mM library concentration and stored in 96 well

U-bottom polypropylene storage plates (Becton Dickinson, catalog # 351190).

6.2.3 Initial library screening and observation of contamination

In our initial effort, microsomal metabolism was done fresh each time before screening at a library concentration of 0.2 mM with a microsomal reaction mixture comprising 0.5 mg

-1 mL of human liver microsomes, 50 mM potassium phosphate pH 7.4, 3 mM MgCl2, 5 mM glucose-6-phosphate, 1 unit mL–1 glucose-6-phosphate dehydrogenase and 1 mM NADP+ in sterile 384 well microtiter plates (Corning, catalog # 3680). Plates were frozen at –80 °C and dried as described above. To each well in each set was added 20 µL cation-adjusted Mueller-

Hinton (CAMH) broth containing 4000 CFU MRSA (ATCC 43300). Optical density was measured at 590 nm using a Tecan Spectra Fluor Plus multimode microplate reader (Chapel

Hill, NC) and differences in OD between wells were compared. Visual observation and comparison of the UM and PM library plates exhibited growth of more than one type of organism as determined by differences in turbidity between the UM and PM library plates.

6.2.4 Diagnosis of MRSA and microsome stock

The MRSA stock was prepared from a single colony of MRSA F–182 (ATCC 43300) grown in CAMH–Agar plate containing 16 µg mL-1 cefoxitin. Briefly, a single colony was carefully picked with a sterile loop and grown in 20 mL CAMH media overnight at 35 ºC with shaking. Next day, the overnight culture was used to start a secondary culture with freshly prepared CAMH media. The secondary culture was grown until 0.6 OD, diluted 100 times and

500 µL of aliquots were prepared and stored at –80 ºC. The culture was thawed and 50 µL was plated in CAMH-Agar plate, Sheep Blood Agar plate, Spectra MRSA plates and Mannitol Salt

Agar plates in duplicates. Similarly, 50 µL of microsomes were plated in those four different types of agar plates. The plates were incubated in an inverted position overnight at 35 ºC and next day checked for growth, and characteristics shape, morphology and patterns according to manufacturer instructions. Gram staining and catalase testing were performed according to the standard protocol from American Society for Microbiology297-299.

6.2.5 Identification of contaminant bacteria from microsome using MALDI-TOF/TOF

Identification of unknown contaminant microorganisms from microsomes was performed on a MALDI-TOF/TOF mass spectrometer (Bruker ultrafleXtreme). Before analysis, the MALDI target plate was cleaned according to manufacturer’s instructions.

Bacterial standard solution (BTS) containing Escherichia coli was prepared by adding 1000

µL of 1:1 of water/acetonitrile with 0.25% trifluoroacetic acid, and was mixed with a pipette.

The BTS solution was then centrifuged at 13,000 rpm for 2 minutes, 1000 µL were aliquoted, and kept at –20 ºC until used. The microbial sample was prepared according to manufacturer instructions300-303. Briefly, a single microbial colony for each organism was picked up using a sterile 1 µL inoculation loop and were transferred to sterile Eppendorf tubes, each containing

300 µL sterile water. To the Eppendorf tubes were then added 900 µL 100% ethanol, and the tubes were mixed well using a vortex mixer. The tubes were then centrifuged at 13,000 rpm for 2 minutes and the supernatants were discarded. To the tubes were then added 900 µL 100% ethanol again and the step was repeated for a second extraction. The tubes were then air-dried at room temperature for 30 minutes inside a biosafety hood. To the dried tubes were then added

25 µL of 70% aqueous formic acid, resuspended and then 25 µL 100% acetonitrile. The sample were vortexed well and centrifuged at 13,000 rpm for 2 minutes. From the sample, 1 µL supernatant was removed and deposited to the MALDI target plate along with 1 µL of BTS.

The samples were air-dried to which was added 1 µL MALDI-matrix, α-cyano-4- hydroxycinnamic acid (HCCA). Upon drying, the target plate was inserted into the MALDI-

TOF/TOF mass spectrometer for spectral recording. Once acquired, the BioTyper 3.1 software matched the acquired spectral data against a database to provide the final identification result

(Figure 27).

Figure 27. Schematics of identification of microorganisms using MALDI-TOF/TOF.

6.2.6 Approaches to decontaminate microsomes

In order to sterilize the microsomes, several approaches were attempted; these included

UV exposure304, thermal pasteurisation305, selective agents for MRSA including oxacillin, nalidixic acid, ketoconazole, polymyxin B, NaCl and cefoxitin. Finally, we evaluated organic extraction and reconstitution with 100% DMSO to decontaminate microsomes after overnight incubation of the library plates.

6.2.7 LC-MS/MS based analytical method development for control CYP substrates

Stock solutions of six standard CYP substrates at a concentration of 100 µM were prepared in 3% acetonitrile/97% water with 0.1% formic acid and were used to tune various compound and source dependent parameters (Table 15) using the automated quantitative

97 optimization wizard of Analyst v. 1.6.2, on a Sciex QTrap 3200 triple quadrupole mass spectrometer coupled with a Shimadzu UFLC LC-20 series HPLC system.

Table 15. Compound and source dependent parameters for control CYP substrate analytical method developmenta.

Control CYP Substrates Q1 Q3 DP (V) EP (V) CEP (V) CE (V) Phenacetin 180.1 110.1 46 9.5 12 27 Tolbutamide 271.2 172.1 41 6.5 16 18 Dextromethorphan 272.2 213.1 61 7.5 16 35 Coumarin 147.1 103.1 46 3.0 13 23 Chlorzoxazone 168.1 132.1 -50 -11.5 -10 -28 Diclofenac 294.2 250.1 -25 -7.0 -14 -16 aGlobal method parameters: CUR – 20 (arbitrary unit), CAD – high, TEM – 300 ºC, GS1 and GS2 – 50 (arbitrary unit), IS – 5500 for positive mode and -4500 for negative mode, CXP – 4 V for positive mode and -2 V for negative mode.

The most intense Q3 fragments were selected for the final MRM method. Four of the control CYP substrates, phenacetin, tolbutamide, dextromethorphan and coumarin were detected in positive MRM mode using a standard formic acid based solvent system (pH 2)

(Figure 28a) and the remaining two control CYP substrates, chlorzoxazone and diclofenac were detected in negative MRM mode using a 10 mM ammonium acetate based neutral solvent system (pH 6.5) (Figure 28b).

(a) (MRM Positive Mode) 2000

Dextromethorphan 1500 Phenacetin 1000 Coumarin Tolbutamide

500 Area (cps, 1E3) (cps, Area 0 5 6 7 8 9

Time (mins)

(b) 600 (MRM Negative Mode)

450 Chlorzoxazone

300 Diclofenac

150 Area (cps, 1E3) (cps, Area 0 5 6 7 8 9 Time (mins)

Figure 28. MRM chromatograms of control CYP substrates (a) Positive mode (b) Negative mode.

For tracking the major metabolite formation for each control CYP substrate, MRM channels were created based on the parameters for their parent drug and were included in the developed positive and negative MRM methods. Precursor ion (Q1) masses for the metabolites were added based on the major CYP reaction they undergo. For example, tolbutamide, coumarin, chlorzoxazone and diclofenac, all undergo CYP mediated hydroxylation306 and their major metabolite channel was included as Q1 of parent drug + m/z of a hydroxyl group - proton

(Q1 of parent+16). Phenacetin and dextromethorphan, on the other hand undergo CYP mediated deethylation and demethylation306, respectively; and their major metabolite channel

99 was included as Q1 of parent drug - m/z of an ethyl group + proton (Q1 of parent drug-28) and

Q1 of parent drug - m/z of a methyl group + proton (Q1 of parent drug -14), respectively.

Selection of fragment ion (Q3) masses for each of these metabolites was based on MS/MS fragmentation of the parent drugs. A simple linear reverse-phase gradient of acetonitrile (3-

100% acetonitrile with 0.1% formic acid in 15 minutes) was used for both positive and negative mode detection with a 0.3 mL min-1 flow rate and a Kromasil C18 column (100 × 3.0 mm, 5 micron particle size).

6.2.8 Kinetics analysis of drug metabolism

A standard mix of control CYP substrates containing phenacetin, tolbutamide, dextromethorphan, coumarin, chlorzoxazone and diclofenac at a concentration of 100 µM of each was incubated with the microsomal reaction buffer containing 50 mM potassium

–1 phosphate pH 7.4, 3 mM MgCl2, 5 mM glucose-6-phosphate, 1 unit mL glucose-6-phosphate dehydrogenase, 1 mM NADP+ and 0.5 mg mL–1 total microsomal protein307. At different time points, 10 µL of sample from the microsomal CYP substrate mix was taken out to which was added 90 µL 3% acetonitrile/97% water with 0.1% formic acid. The samples were analyzed in quadruplicates using the developed analytical methods in both positive and negative MRM mode. Rate and extent of metabolism were calculated using following formula as discussed in

Knights, K. M. et al 2016308:

푝푒푎푘 푎푟푒푎 푎푡 푡 푚𝑖푛 % 푃푎푟푒푛푡 푐표푚푝표푢푛푑 푑푒푝푙푒푡𝑖표푛 = 100 − ( × 100) … … … (Eq. 6.1) 푝푒푎푘 푎푟푒푎 푎푡 0 푚𝑖푛

(∆퐶 ×1000) 푅푎푡푒 표푓 푑푒푝푙푒푡𝑖표푛 (푝푚표푙/푚𝑖푛/푚푔) = … … … (Eq. 6.2) 퐵×푇

100

Where, ΔC = [peak area at 0 min] – [peak area at t min], t = the time at each observation is made, B = microsome concentration (mg mL-1), T = incubation time (min) and 1000 is the conversion factor from nmol to pmol.

6.2.9 Selection of panel of control antibiotics

A panel of antibiotics was chosen considering their activity against different gram- positive and gram-negative bacteria especially MRSA F-182, VRE (clinical) and E. coli

(Seattle 1946). Minimum inhibitory concentrations (MIC) of these control antibiotics against these bacteria are given in Table 16.

Table 16. MICs of various control antibiotics against different bacteria (© American Society for Microbiology).

MICs (µg mL-1) Antibiotics MRSA (F – 182) VRE (Clinical) E. Coli (Seattle 1946) Vancomycin 0.5 – 1 64 – 128 64 – 256 Ampicillin 32 – 64 64 – 128 4 – 8 Doxycycline 0.125 – 0.25 4 – 8 1 – 4 Kanamycin – – 4 – 8 Chloramphenicol 2 – 4 4 – 8 32 – 64

6.2.10 In vitro microsomal metabolism to provide the working PM library

Cluster tube well plates containing 20 µL FDA library compounds and deep well plates containing 4 µL NCI library compounds in each well were in DMSO, which can interfere with microsomal drug metabolism reactions, and so was removed by freezing the plates at –80 oC and drying the replicated library plates under strong vacuum (<50 µmHg) in a Genevac Quatro centrifugal concentrator. To the dried FDA library samples was added 20 µL acetonitrile/water

(20/80%, v/v) and to the dried NCI library samples was added 4 µL acetonitrile/water (20/80%,

101 v/v) to resolubilize samples. Plates were incubated for 2 h at 35 °C on a Cole-Parmer tilt shaker table, followed by the addition of 980 µL of freshly prepared (on ice) microsomal reaction mixture for the FDA library metabolism and 96 µL of the same microsomal buffer for the NCI library metabolism containing 50 mM potassium phosphate pH 7.4, 3 mM MgCl2, 5 mM glucose-6-phosphate, 1 unit mL–1 glucose-6-phosphate dehydrogenase, 1 mM NADP+ and 0.5 mg mL–1 total microsomal protein309. Reaction mixtures were incubated for 24 hours at 35 °C with gentle rocking on a Cole-Parmer tilt shaker table. Library plates were then centrifuged at

4000g for 30 minutes at 4 °C in a Sorvall RT6000 refrigerated centrifuge and supernatants

(800 µL for FDA library and 80 µL for NCI library) were transferred to sterile 96 deep well cluster tube plates (FDA) and 96 deep well plates (NCI). To the residues was added 200 µL

DMSO on the FDA plates and 20 µL DMSO on the NCI plates, and the samples were mixed thoroughly. Library plates were centrifuged again at 4000g for 30 minutes, and the supernatants (300 µL for FDA library and 30 µL for NCI library) were removed and combined with the first extracts. The resulting extracts were frozen at –80 °C and dried under strong vacuum (<50 µmHg) in a Genevac Quatro centrifugal concentrator. These FDA and the NCI

PM library samples were then reconstituted in 200 µL and 40 µL DMSO, respectively to provide a 1 mM PM FDA and NCI working library. The original FDA and the NCI library were similarly replicated and diluted with DMSO to obtain a 1 mM UM FDA and NCI working library. Both UM and PM working libraries were stored in U-bottom polypropylene storage plates (Becton Dickinson, catalog # 351190) at –80 °C. Samples of wells containing microsomally metabolized drug controls from both UM and PM plates were analyzed by LC-

MS/MS to provide a relative measure of metabolism.

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6.2.11 UM/PM vs –/+ Cef library screen against MRSA

Four sets of library screening plates were prepared for the following screens: UM–Cef,

UM+Cef, PM–Cef, PM+Cef. Two sets of UM plates and two sets of PM plates were first prepared from working library samples (4 µL @ 1 mM) in 384 well microtiter plates (Corning, catalog # 3680) using a Biomek 3000 liquid handing workstation. Plates were frozen at –80

°C and dried as described above. To each well in each set was added 20 µL cation-adjusted

Mueller-Hinton (CAMH) broth containing 4000 CFU MRSA (ATCC 43300), and containing either no Cef for –Cef screens or +8 µg mL–1 Cef (equal to ¼ to ½ x MIC) for +Cef screens.

These additions were performed using an Integra Viaflo Assist automated multichannel pipetter in a Labconco BSL-2 biosafety cabinet. Plates were incubated for 48 hours at 35 °C.

Fresh CAMH broth (10 µL) was added to the wells of these four sets of plates, followed by incubation for 2 hours at 35 °C to restart active cell growth. To the wells of these plates was then added 6 µL of 100 µg mL–1 resazurin (sodium salt)310-312. The plates were incubated for another 2 hours at 35 °C, and the A610/A500 absorbance ratio (Promega Technical Bulletin

TB317) was measured in a Molecular Devices SpectraMax M5 multimode microplate reader

(San Jose, CA).

6.2.12 Hit picking and minimum inhibitory concentration (MIC) determination

Library screening data was processed and analyzed using Matlab scripts (The

Mathworks, Natick, MA). Based on the values for known active and inactive antibacterial agent controls (Figure 29), a cut-off value between active and inactive compounds was selected, and lists of active wells in each screening set (UM–Cef, UM+Cef, PM–Cef, PM+Cef) was generated.

103

0.5

0.4 Actives Inactives

Known inactives 0.3 Known actives

N Unknowns Σ 0.2

N / / N Z'-Factor = 0.66

0.1

0.0 0 0.4 0.8 1.2 1.6 2

A610/A500

Figure 29. Normalized histogram of library screening results demonstrating the separation of known actives from known inactives and the distribution of unknowns.

These lists were merged to give a pooled hit list. Rows were added to this pooled hit list to include known active and inactive antibiotic as controls. MICs were determined by hit picking 2 µL samples from both UM and PM working plates (two sets from each) into the first columns of 384 well plates (four sets total, for UM–Cef, UM+Cef, PM–Cef and PM+Cef MIC determinations). These samples were then serially diluted in steps of two across the plates with

DMSO using an Integra Viaflo Assist automated multichannel pipettor. The last column was left blank (DMSO only). These plates were frozen at –80 °C, and dried under strong vacuum as described above. To each well in each set was added 20 µL cation-adjusted Mueller-Hinton

(CAMH) broth containing 4000 CFU MRSA (ATCC 43300), and containing either no Cef for

–Cef MICs or 8 µg mL–1 Cef for +Cef MICs. This provided MIC plates with 100 µM as the

104 highest test agent concentration. Plates were incubated for 48 hours at 35 °C. Fresh CAMH broth (10 µL) was added to the wells of these four sets of plates, followed by incubation for 2 hours at 35 °C to restart active cell growth. To the wells of these plates was then added 6 µL of 100 µg mL–1 resazurin. The plates were incubated for another 2 hours at 35 °C, and the

A610/A500 absorbance ratio measured as described above. MICs were determined using a cutoff midway between known active and inactive samples. All MICs were determined at least in triplicate, and at least in quadruplicate for minimum MICs less than 25 µM, to ensure reproducibility.

6.2.13 Scaled-up metabolism reaction and active metabolite purification

Among library hits showing better activity after microsomal metabolism, capecitabine demonstrated significant antibacterial activity only after metabolism (Table 20). To identify its active metabolite(s), a scaled-up metabolism reaction was performed on 1 mL of 400 μM capecitabine using the reaction conditions described above for PM library preparation for 24 hours. To this reaction mixture was then added 4 mL 80% ice-cold isopropanol with 200 mM acetic acid, the sample was mixed well, and microsome debris was pelleted at 4000g for 30 minutes at 4 °C. The supernatant was collected, and the pellet re-extracted with 2 mL 67% isopropanol with 200 mM acetic acid. The combined extracts were frozen at –80 °C and dried in a Genevac Quatro centrifugal concentrator. The residue was dissolved in 0.4 mL of 3% acetonitrile/97% 10 mM ammonium acetate and syringe filtered to make a sample for semi- preparative HPLC. The sample was purified by semi-preparative HPLC on a Kromasil C18 column (150 × 3.0, 5 µm particle size, catalog # K08670646). Fractions were collected at 1 minute intervals in a 96 well microtiter plate (MidSci, catalog # TP92096). The flow rate was

105

250 µL min–1 and the purification gradient was: 3% B for 0–5 min, 3–13% B for 5–65 min,

13–100% B for 65–75 min; solvent A = 10 mM ammonium acetate pH 6.5, solvent B = 30% solvent A with 70% acetonitrile.

Three fluoroquinolone antibiotics, balofloxacin, gatifloxacin and marbofloxacin also demonstrated better antibacterial activity post microsomal metabolism (Table 20). To identify their active metabolites, scaled-up metabolism reactions for each of these antibiotics were carried out as described before with slight modifications. Due to minimal microsomal conversion of the parent drugs and trace formation of the metabolites, each of these three drugs was metabolized at 2 mg mL-1 (approximately 5 mM) concentration with 2 mg mL-1 of human liver microsome for 24 hours. The microsomal reaction was extracted and the sample was prepared as described before with 3% acetonitrile/97% water with formic acid and was purified on a Macherey-Nagel NUCLEODUR C18 column (250 × 21 mm, 5 µm particle size, catalog

# 762022.210). Fractions were collected at 1 minute intervals in glass test tubes at a flow rate of 5 mL min-1. The purification gradient was: 3% B for 0–5 min, 3–50% B for 5–65 min, 50–

100% B for 65–75 min; solvent A = water with 0.1% formic acid pH 2.0, solvent B = 30% solvent A with 70% acetonitrile.

6.2.14 Identification of active metabolites

Purified HPLC fractions (4 µL each for capecitabine and 100 µL each for the fluoroquinolones) were dispensed into a fresh sterile 96 well plates (MidSci, catalog #

TP92096). These plates were frozen at –80 °C, and dried in a Genevac Quatro centrifugal concentrator. To each well was added 100 µL CAMH broth containing 4000 CFU MRSA, and plates were incubated for 48 hours at 35 °C. Fresh CAMH broth (50 µL) was added, plates

106 were incubated at 35 °C for 2 hours, 30 µL of 100 µg mL–1 resazurin was added, and plates were incubated an additional 2 hours, and the A610/A500 ratio was used to identify active fractions. Active capecitabine fractions were analyzed by liquid-chromatography tandem mass spectrometry (LC-MS/MS) on an Sciex QTrap 3200 triple quadrupole mass spectrometer coupled to a Shimadzu UPLC LC-20 series HPLC system in negative mode with electrospray ionization incorporating in-line UV detection, and high-resolution mass spectrometry (HRMS) on a Thermo Q Exactive Plus Orbitrap mass spectrometer in positive mode with electrospray ionization for compound identification and were confirmed by comparison with authentic commercial samples.

Active fractions of balofloxacin, gatifloxacin and marbofloxacin were analyzed by ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS,

Waters Acquity UPLC system) with a quadrupole time of flight mass spectrometer (Waters

Xevo G2 XS HDMS), for acquisition of high-resolution accurate mass data, in positive mode with electrospray ionization incorporating in-line UV detection with a photodiode array detector on a Waters Acquity HSS T3 column (100 × 2.1 mm, 1.8 µm particle size). The flow rate was 400 µL min–1 and the gradient was: 0% B for 0–1 min, 0–95% B for 1–12 min, 95%

B for 12–13 min, with 2 minutes post-equilibration at 0%B; solvent A = water/0.1% formic acid, solvent B = acetonitrile/0.1% formic acid.

6.2.15 Spectrum of activity determination

In order to determine the spectrum of activity of hits and related compounds, a customized library was generated by dispensing 2 µL of the selected hits (100 µM) using

Biomek 3000, and serially diluted with Integra Viaflo Assist automated multichannel pipettor.

107

Plates were dried and MICs were determined as described above for capecitabine, its microsomal metabolites, 5’-deoxy-5-fluorocytidine (DFCR) and 5’-deoxy-5-fluorouridine

(DFUR); it’s ultimate in vivo active anti-cancer metabolite (5-fluorouracil), and several related compounds (floxuridine, 5-fluorocytidine, 5-fluorouridine, carmofur, and gemcitabine) against a panel of bacterial strains (Table 25, Figure 47). The strains tested were MRSA strains F-182

(ATCC 43300), N315 (BEI NR-45898), HI022 (BEI NR-30550), MN8 (BEI NR-45918),

TCH70 (BEI HM-139), RN1 (BEI MR-45904), COL (BEI NR-45906), one strain of VRE

(clinical), and one strain of E.coli Seattle 1946 (ATCC 25922).

6.2.16 Checkerboard assays to confirm synergy with cefoxitin

A number of agents demonstrated apparent synergistic activity with cefoxitin (upper entries in Table 22 and 23). Checkerboard assays313 were performed to confirm synergy for the top ranked agents; floxuridine, gemcitabine, novobiocin, rifaximin, 4-quinazolinediamine, celastrol and teniposide. Checkerboard assays were performed in 96 well plates. The drugs were serially diluted in 96 well cluster tube plates at 32x of MIC from top to bottom (cefoxitin) and left to right (floxuridine, gemcitabine, novobiocin, rifaximin, 4-quinazolinediamine, celastrol and teniposide) on 300 µL scale using a multi-channel pipettor. From the cluster tube plates, 25 µL were then transferred to a fresh sterile 96 well plate on two different dimensions: cefoxitin (top to bottom) and test agent (left to right) leaving the last well of each column and row empty. The last column of each checkerboard plates was left blank intentionally as a growth control (positive control). The plates were then dried under vacuum as described before. To these plates was added 100 µL CAMH broth containing 4000 CFU MRSA to each well, and plates were incubated for 48 hours at 35 °C. Fresh CAMH broth (50 µL) was added,

108 plates incubated at 35 °C for 2 hours, 30 µL of 100 µg mL-1 resazurin added, plates incubated an additional 2 hours, and resazurin absorbance ratio was measured as described above. All checkerboard assays were performed in triplicate and the average of these three datasets were used to calculate the fractional MIC of the drugs to construct the isobolograms and the fractional inhibitory concentration index (FICI), in order to determine the synergistic effects.

6.2.17 Frequency of resistance determination

Frequency of resistance was determined for the capecitabine active metabolites and the top 4 synergistic agents to determine their mutation rates. Briefly, MRSA was grown overnight and diluted 1:100 times next day. To a solid nutrient (CAMH-Agar) plates containing different concentration of the test drug (1 to 4x MIC) were then plated 200 µL of 1:100 diluted culture of MRSA, and incubated on inverted position at 35 °C for 2-3 days to allow resistant MRSA colonies grow. At the same time, the MRSA culture was diluted and plated similarly in CAMH-

Agar plate to determine the number of colonies plated. After 2-3 days the number of colonies grown in the presence of a test drug was divided by the total number of colonies plated to calculate the frequency of resistance.

6.3 Results and discussion

6.3.1 Initial observation of contamination in PM library plates

Colonies of different bacteria can take on various colors when grown on nutrient media due to production and release of various pigments or metabolites. In our initial screening, we observed different shades of yellow to white turbid suspension in several wells of the PM library plates (Figure 30) which was also apparent from the optical density data at 590 nm (data

109 not shown) indicating probable presence of more than one type of bacteria other than MRSA

F-182.

Before After

Control Antibiotics Growth (MRSA) Control CYP Substrates Growth (Unknown) UM Library Compounds Inhibition PM Library Compounds

Figure 30. Schematics of observation of contamination in PM library plates.

The panel of control antibiotics comprising vancomycin, ampicillin, doxycycline, and chloramphenicol showed activity against both the contaminating bacteria and MRSA F-182

(except for kanamycin, negative control for MRSA F-182); however the z-score values were below 0.5 and the separation between the control antibiotics (known actives) and the control

CYP substrates (known inactives) was small314 (data not shown), indicating poor screening results.

110

6.3.2 Microbiological and biochemical assessment of MRSA and microsome stock

Diagnosis of MRSA F-182 stock used in the screening showed circular, convex and golden-yellow colored colonies on CAMH–Agar plate (Figure 31a), which is consistent with the desired size, shape, color and morphology of MRSA. However, microsome stock exhibited presence of two different types of colonies (U–1 and U–2) both of which were visually different from the MRSA (Figure 31e). MRSA exhibited what appears to be β–hemolysis in

SBA plates (Figure 31b) represented by breakdown of hemoglobin of the red blood cells in the vicinity of a bacterial colony which is characteristic of some strains of MRSA315. MRSA also exhibited blue denim colored colonies on Spectra MRSA plates (Figure 31c) and a change in color of MSA plates from red to yellow (Figure 31d) both of which are characteristic and selective patterns consistent with MRSA growth316, 317. The results from the non-selective and selective plates confirmed the integrity and quality of the MRSA stock used in the library screening. The two unknown microsomal contaminant, U–1 and U–2 exhibited β– and α– hemolysis315 respectively on SBA plates (Figure 31f). Both U–1 and U–2 showed negative results on Spectra MRSA (Figure 31g) and MSA (Figure 31h) plates confirming that the two unknown organisms were not MRSA316, 317. Gram staining confirmed U–1 and U–2 as gram negative bacteria as they appeared pink along with the control (Escherichia coli) whereas gram positive bacteria (MRSA) appeared violet298, 318 (data not shown). The catalase test indicated negative for U–1 and positive for U–2 (Figure 32).

111

112

U-1

U-2

Figure 32. (a) Reference for catalase (+) ve and (–) ve test (b) Results of catalase test for microsomal contaminant unknown microorganism (U–1 and U–2).

6.3.3 MALDI-TOF/TOF identification of contaminant bacteria from microsomes

Ethanolic extracts of U–1 and U–2 were identified as Stenotrophomonas maltophilia and Chryseobacterium inodologenes with BioTyper ID score of 2.395 and 2.556, respectively from MALDI-TOF/TOF analysis. Escherichia coli (BTS) was used as a control and was identified with a score of 2.303. An ID score in between 2.300-3.000 indicates secure genus and highly probable species identification. Stenotrophomonas maltophilia (U–1) is an aerobic, motile, non-fermentative gram-negative bacterium319. Although aerobic, it has been reported

113 to grow in the absence of oxygen, using nitrate as a terminal electron acceptor320.

Chryseobacterium indologenes is a gram-negative, anaerobic, non-motile and filamentous bacterium321, which also explains survival during the processing, storage and handling of the microsomes. Both these organisms are reported to be present in soil, aqueous environments, plants and most importantly in catheters and endoscopes used in hospitals321-323. Both bacteria have the ability to form biofilm on plastic and metal surfaces and have resistance to a variety of antibiotics and heavy metals324, 325, which might explain their presence in microsomes. As discussed earlier, the microsomes used in this study was pooled from 50 different patients of different age, gender and ethnicity after their demise, many of whom had different underlying physical conditions as reported on the material safety datasheet. Contamination of the liver microsomes with these two bacteria might come from an infected liver, or may be of nosocomial nature. Both these bacteria have low pathogenesis but exhibit resistance to a number of antibiotics320, 326, necessitating caution during handling to prevent infection and further resistance development. The observed contamination highlights the necessity to find an effective way of decontaminating the microsomes without affecting their ability to metabolize drugs, especially if the downstream applications for the materials involve antibacterial activity assessment.

6.3.4 Decontamination of microsome

UV sterilization of the microsomes with 0-120 minutes exposure did not result in much improvement in decontaminating microsomes from Stenotrophomonas maltophilia and

Chryseobacterium indologenes, but degraded the library compounds as indicated by activity reduction and higher than expected MICs of known control antibiotics (data not shown).

114

Pasteurization, on the other hand, resulted in removal of the contaminating bacteria from microsomes, probably due to the temperature used (75 °C). But pasteurization was inconvenient to perform as it required to expose the library plates to a high temperature in water bath. Keeping the library plates on water bath caused water to enter into the plates creating chances of cross-contamination. Additionally, large water bath was not available to accommodate the whole library for heat exposure in a time effective way. Use of a panel of selective antibacterial agents (NaCl, cefoxitin, polymyxin and ketoconazole) also helped to remove Stenotrophomonas maltophilia and Chryseobacterium indologenes from the microsomes (Figure 33), but were expensive and displaying complex combination effects against MRSA.

115

Figure 33. Effect of selective agents on MRSA and microsomal contaminants.

As observed in Figure 33, the lower three triangular part of all four CAMH plates established that the presence of the selective antibacterial agents had no negative effect on the growth of MRSA (A1-1, A1-2, A1-3 and A2-1, A2-2, A2-3). In contrast, the upper three triangular part on the two CAMH plates containing NaCl, cefoxitin, polymyxin and ketoconazole, on the right established that the selective antibacterial agents were able to inhibit the growth of the two contaminating bacteria detected in the microsomes (U1-1, U1-2, U1-3

116 and U2-1, U2-2, U2-3). The use of 100% DMSO to decontaminate the microsomes resulted in complete elimination of both contaminants from the microsome (Figure 34).

Figure 34. Effect of DMSO on microsomal contaminants.

This step was performed after overnight incubation of the library plates with microsomes to allow sufficient time for metabolism to take place. In addition, using DMSO ensured the highest extraction of the potential metabolites of the drugs present in the library due to their solubility characteristics. Like many other organic solvents, DMSO has the ability to inhibit the growth of bacteria and/or kill them. Various studies suggest that a solution of

20% DMSO can completely kill different gram-positive and gram-negative bacteria including

Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Bacillus megaterium,

Bacillus subtilis, Saccharomyces cerevisiae and Micrococcus species327-329. In addition,

DMSO provides better long term stability of the compounds when compared to other organic solvents while ensuring solubility of a wide variety of compounds up to a specific time and number of freeze-thaw cycle330. Finally, using DMSO enabled maintaining the same condition in which the original library (UM) was prepared and delivered, which not only helped to achieve long-term stability of the library compounds but also ensured similar solvent condition for both libraries (UM and PM) during screening against bacteria.

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Human liver microsomes from two other vendors, Corning (catalog # 452117, Lot #

38291) and Gibco (catalog # HMMCPL, Lot # PL050C-B) (Figure 35) were also tested for presence of unknown contaminant microorganisms. Both lots were pooled from 50+ donors and found to have presence of contaminant microorganisms (Figure 35). Microsome from

Sekisui XenoTech LLC was used in metabolizing the libraries as they were provided as a generous gift.

Figure 35. Assessment of microsome from additional vendors using CAMH-Agar solid media.

(a) Corning (b) Gibco.

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6.3.5 Evaluation of microsomal drug metabolism

The kinetics study as determined by the analytical LC-MS/MS method showed almost complete metabolism of coumarin and diclofenac within 10 hours (Figure 36, blue line). For all six control CYP substrates, the percentage of parent depletion i.e. the extent of metabolism was above 50% with tolbutamide showing the least metabolism (51.55% loss of parent over the incubation period). LC-MS/MS analysis also demonstrated formation of the major metabolites for each control CYP substrate (Figure 36, red line). As expected, for tolbutamide, coumarin, chlorzoxazone and diclofenac, the major metabolites were the hydroxylated version of the corresponding parent drug. For phenacetin, we observed the deethylation of the parent leading to the formation of acetaminophen, whereas for dextromethorphan, the major metabolite was the demethylated dextromethorphan. The percent of drug metabolized and the corresponding rate of drug metabolism were calculated according to equation 6.1 and 6.2 as mentioned in section 6.2.8. The results are listed in Table 17 and were comparable to the reported values from the microsome manufacturer.

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Table 17. Extent and rate of metabolism for the control CYP substrates.

Rate (pmol/mg/min) per µM of substrate Control CYP Substrates % Metabolized In our system XenoTech Phenacetin 65.98% 6.4 5.8 Tolbutamide 51.55% 4.2 NR Dextromethorphan 54.59% 2.6 3.4 Coumarin 100% 6.7 NR Chlorzoxazone 60.05% 2.9 2.5 Diclofenac 95.42% 25.9 29.1 NR – not reported on XenoTech material safety datasheet for HLMs (catalog # H0630)

6.3.6 Library pre-metabolism and optimization of library screening workflow

In our initial attempts, microsomal metabolism was performed fresh each time prior to screening against MRSA which led exploration of contaminant bacteria in microsomes. After identification and removal of these microsomal contaminants, microsomal metabolism was performed on larger scale. After microsomal metabolism, the library was reconstituted in

DMSO that provided a sterile PM working library at the same nominal concentration as the diluted UM working library (isomolar mixture of the parent drug and its metabolites). Initial anti-MRSA screens were performed using optical density (OD600) to measure wells for growth/no-growth, but this gave poor results in 384 well plates. However, a resazurin-based bacterial viability assay310 gave very good separation of known actives from known inactives, with Z’–factors331 in the range of 0.6–0.7 (Figure 29 and 37).

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Figure 37. Resazurin (Alamar Blue) based fluorescence assay.

6.3.7 Activity of control antibiotics

The positive antibiotic controls, vancomycin, ampicillin, doxycycline and chloramphenicol showed activity against MRSA F-182 whereas the negative control, kanamycin did not show any activity against MRSA F-182 (Figure 38). Except for ampicillin, the remaining positive control retained activity post metabolism as seen in Figure 38 heat map on the right side.

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Figure 38. Heat map of control antibiotics. Blue zone – activity/inhibition, yellow zone – no activity/no inhibition (UM = Un-metabolized, PM = Pre-metabolized).

6.3.8 Evaluation of preliminary library screening result

Venn analysis of both the FDA and the NCI library screening results showed an overall increase of 22.56% and 11.79% increase in the total number of hits due to human liver microsomal metabolism respectively (Figure 39) calculated using the following formula:

푈푛𝑖푞푢푒 푃푀 퐻𝑖푡푠 Metabolism increased number of hits (%) = × 100 … … … (Eq. 6.3) 푇표푡푎푙 푈푀 퐻𝑖푡푠

30 % increase in number of hits in FDA library screening = × 100 = 22.56% 133

25 % increase in number of hits in NCI library screening = × 100 = 11.79% 212

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Figure 39. Venn analysis of library screening results.

6.3.9 Two-dimensional library screening to increase informational value

It seems reasonable to repeat a library screen at least once to ensure reproducibility.

However, identical replication has low information content over the initial screen. A strategy of performing replication under somewhat different but informative conditions seemed optimal, which led to the two-dimensional (UM/PM vs –/+Cef) screening strategy described here (Figure 40). Initial library screens were performed at relatively high library compound concentrations of 200 µM, and in the absence or presence of 8 µg mL–1 cefoxitin (to which

MRSA F-182 is resistant). The rationale for using high library compound concentrations in the initial screen (200 µM) was: 1) To not miss any prospective actives; 2) To identify agents with possible active trace metabolites; and 3) To identify agents with weak intrinsic activity but potentially interesting synergistic or antagonistic interactions with cefoxitin. In view of the results summarized below, an initial screen at 50 or 25 µM would provide comparable results in future efforts with this approach (i.e., no high interest compounds had minimum MICs above

25 µM).

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(8 µg mL-1) to generate a merged hit list. (c) MICs are determined for each hit in the merged hit list under all four screening conditions (UM/PM vs –/+Cef).

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6.3.10 Analysis of hits

Following library screening, a pooled hit list was made (i.e. any compound that gave a hit (was active) in any of the four UM/PM vs –/+Cef screens was added to the list) for follow- up MIC determinations. MICs for all the compounds in this pooled hit list were then determined under all four screening conditions (UM–Cef, UM+Cef, PM–Cef, and PM+Cef) to give a final table of MICs. The results from these MIC determinations, sorted by minimum

MIC and for minimum MICs of ≤100 μM, are summarized in Tables 18 and 19 for the FDA and the NCI library screening, respectively. In general, four major observations were apparent for various compounds: several parent compounds gained activity against MRSA after microsomal metabolism (PM Better – Figure 41, 1st quadrant) and in the presence of cefoxitin

(+Cef Better – Figure 41, 2nd quadrant); conversely several parent compounds lost activity after microsomal metabolism (UM Better – Figure 41, 4th quadrant) and in the presence of cefoxitin

(–Cef Better – Figure 41, 3rd quadrant). Figure 41 lists all the compounds from both the FDA and the NCI library that exhibited at least a 1.5 log2 fold change in activity due to multidimensional screening strategy, among which capecitabine, floxuridine, chlorobiocin, hitachimycin and methacycline were the hits showing the maximum change in anti-MRSA activity due to microsomal metabolism and the presence of cefoxitin. It was interesting to note that, chlorobiocin showed increased activity due to metabolism, but showed antagonism with cefoxitin. Ceftiofur, on the other end of the spectrum, lost its activity both after metabolism and in the presence of cefoxitin.

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8.0 UM Better PM Better

Hitachimycin Rifampin

Rifabutin Streptovaricin Methacycline NSC207895 6.0 Closantel Ethyl Violet Fidaxomicin Capecitabine Rifaximin Celastrol Domiphen Tetracycline NSC53275 4.0 Gemcitabine NSC309401 Albacarcin NSC308848 Balofloxacin Rifapentine Cefdinir Marbofloxacin Retapamulin Novobiocin Mupirocin Carmofur 2.0 Gatifloxacin Chaetochromin NSC37187 NSC260594 Porfiromycin Bactobolin Chlorobiocin Sitafloxacin Cetrimonium Ceftiofur Naphtanilide LB 0.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 Teniposide Ceftiofur Dicloxacillin NSC654260 Celastrol -2.0 Oxytetracycline Novobiocin

Nafcillin Tebipenem pivoxil Rifaximin Porfiromycin Cloxacillin -4.0 Gemcitabine Chlorobiocin Hitachimycin 4-quinazolinediamine Floxuridine –Cef Better + Cef Better -6.0

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UM MICs (µM) PM MICs (µM) Compound CAS # –CEF +Cef –Cef +Cef Min_MIC Novobiocin 1476-53-5 0.2 1.5E–3 9.8E–2 0.2 1.5E–3 Floxuridine 50-91-9 3.1 3.1E–3 3.1 6.25 3.1E–3 Gemcitabine 95058-81-4 2.4E–2 3.1E–3 0.78 4.9E–2 3.1E–3 Rifaximin 80621-81-4 0.39 1.2E–2 6.25 3.1 1.2E–2 Sitafloxacin 163253-35-8 4.9E–2 1.2E–2 0.1 4.9E–2 1.2E–2 Nadifloxacin 124858-35-1 2.4E–2 9.8E–2 9.8E–2 4.9E–2 2.4E–2 Rifabutin 72559-06-9 4.9E–2 4.9E–2 3.3 3.3 4.9E–2 Rifampin 13292-46-1 4.9E–2 4.9E–2 3.3 3.3 4.9E–2 Rifapentine 61379-65-5 0.2 4.9E–2 1.6 1.6 4.9E–2 Balofloxacin 127294-70-6 0.78 0.78 9.8E–2 9.8E–2 9.8E–2 Gatifloxacin 112811-59-3 0.78 0.39 9.8E–2 0.39 9.8E–2 Valnemulin 133868-46-9 0.2 9.8E–2 0.2 0.39 9.8E–2 Moxifloxacin 186826-86-8 0.2 0.39 0.39 0.39 0.2 Sparfloxacin 110871-86-8 1.6 0.2 0.39 0.78 0.2 Levofloxacin 100986-85-4 0.78 0.78 0.39 1.6 0.39 Mupirocin 12650-69-0 0.78 0.39 0.39 0.78 0.39 Retapamulin 224452-66-8 0.78 0.39 6.25 3.1 0.39 Dicloxacillin 13412-64-1 3.1 6.25 0.78 6.25 0.78 Difloxacin 91296-86-5 0.78 0.78 0.78 0.78 0.78 Marbofloxacin 115550-35-1 6.25 6.25 0.78 0.78 0.78 Closantel 57808-65-8 1.6 6.25 12.5 12.5 1.6 Doxifluridine 436349 1.6 3.1 3.1 3.1 1.6 Fidaxomicin 873857-62-6 6.25 1.6 100 100 1.6 Methacycline 3963-95-9 1.6 3.1 100 100 1.6 Nafcillin 7177-50-6 1.6 50 6.25 50 1.6 Tetracycline 64-75-5 1.6 6.25 100 100 1.6 5-Fluorouracil 51-21-8 3.1 6.25 6.25 6.25 3.1 Closantel 61438-64-0 3.1 3.1 12.5 12.5 3.1 Cloxacillin 7081-44-9 3.1 25 3.1 25 3.1 Pefloxacin 70458-95-6 6.25 3.1 3.1 6.25 3.1 Trimethoprim 738-70-5 6.3 3.1 6.3 3.1 3.1 Carmofur 61422-45-5 12.5 50 6.25 6.25 6.25 Ceftiofur 103980-44-5 6.25 100 100 100 6.25 Linezolid 165800-03-3 12.5 25 6.25 12.5 6.25 Capecitabine 154361-50-9 12.5 25 12.5 Cefdinir 91832-40-5 50 12.5 100 100 12.5 Epirubicin 56390-09-1 25 12.5 12.5 128

Idarubicin 57852-57-0 12.5 25 12.5 Oxytetracycline 6153-64-6 12.5 25 100 100 12.5 Penicillin 69-57-8 50 100 50 12.5 12.5 Tebipenem pivoxil 161715-24-8 25 100 12.5 100 12.5 Azlocillin 37091-65-9 100 25 100 25 25 Bacitracin 1405-87-4 100 25 25 Cefditoren pivoxil 117467-28-4 25 25 Cefoperazone 62893-19-0 100 25 100 25 Clomifene 50-41-9 25 25 Daunorubicin 23541-50-6 25 25 25 Domiphen 538-71-6 25 25 100 100 25 Doxorubicin 25316-40-9 50 25 25 Florfenicol 73231-34-2 50 50 50 25 25 Isoconazole 24168-96-5 50 25 100 100 25 Ivacaftor 873054-44-5 50 25 100 25 Mezlocillin 42057-22-7 100 25 50 25 Miconazole 22832-87-7 50 25 25 Penfluridol 26864-56-2 25 25 100 25 Pyrithione 13463-41-7 25 25 25 Tamoxifen 54965-24-1 100 25 25 Toremifene 89778-27-8 100 25 25 Ampicillin 69-52-3 50 100 50 Butoconazole 64872-77-1 50 50 100 50 50 Candesartan 145040-37-5 50 50 50 Dronedarone 141625-93-6 100 50 50 Econazole 24169-02-6 50 50 100 100 50 Eltrombopag 496775-62-3 100 50 50 Etoposide 33419-42-0 50 100 100 50 Flumequine 42835-25-6 100 100 50 100 50 Miconazole 22916-47-8 50 100 50 Moxalactam 64953-12-4 50 50 100 100 50 Otilonium 26095-59-0 50 100 100 100 50 Sulconazole 82382-23-8 50 50 100 50 Teniposide 29767-20-2 50 100 50 50 Terfenadine 50679-08-8 100 50 100 100 50 Tioconazole 65899-73-2 100 100 100 50 50 Zafirlukast 107753-78-6 100 50 100 100 50 AMG-073 364782-34-3 100 100 100 Amoxicillin 34642-77-8 100 100 Amoxicillin 26787-78-0 100 100 Aprepitant 170729-80-3 100 100 Bifonazole 60628-96-8 100 100 100 Camptothecin 7689-034 100 100 Caspofungin 179463-17-3 100 100 100 Celecoxib 169590-42-5 100 100 129

Clotrimazole 23593-75-1 100 100 100 Crizotinib 877399-52-5 100 100 Diclazuril 101831-37-2 100 100 100 Ezetimibe 163222-33-1 100 100 Ftorafur 17902-23-7 100 100 100 100 Furaltadone 3759-92-0 100 100 Idebenone 58186-27-9 100 100 Ifosfamide 3778-73-2 100 100 100 Indacaterol 753498-25-8 100 100 Nebivolol 152520-56-4 100 100 Nifuroxazide 965-52-6 100 100 Nithiamide 140-40-9 100 100 100 100 Oxethazaine 126-27-2 100 100 100 Piperacillin 59703-84-3 100 100 100 Sorafenib 475207-59-1 100 100 100 Sulfamethoxazole 723-46-6 100 100 100 Sulfathiazole 72-14-0 100 100 100 100 Sulfisoxazole 127-69-5 100 100 100 Sulphadimethoxine 122-11-2 100 100 100 Thiamphenicol 15318-45-3 100 100 100 Thioridazine 130-61-0 100 100 Ticagrelor 274693-27-5 100 100 100 Tolcapone 134308-13-7 100 100 100 Trifluridine 70-00-8 100 100

UM MICs (µM) PM MICs (µM) Compound CAS # -CEF +CEF -CEF +CEF Min_MIC Chlorobiocin 39868-96-7 3.8E-4 3.1E-3 4.8E-5 3.1E-3 4.8E-5 Hitachimycin 77642-19-4 1.5E-3 0.10 0.78 1.6 1.5E-3 4-quinazolinediamine 0.39 1.2E-2 0.39 4.9E-2 4.9E-2 Basic Violet 4 2390-59-2 0.78 0.10 25 12.5 0.10 NSC309401 77681-42-6 0.39 0.39 3.13 3.13 0.39 Porphyromycin 801-52-5 12.5 3.1 6.25 0.78 0.78 Streptovaricin 23344-17-4 6.25 1.6 25 12.5 1.6 Bactobolin 72615-20-4 3.13 1.6 12.5 6.25 1.6 Teniposide 29767-20-2 25 3.1 25 12.5 3.1 Chaetochromin 75514-37-3 6.25 3.1 50 25 3.1 MLS002667576 12.5 6.25 50 25 6.25

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Albacarcin 92841-46-8 6.25 3.1 100 100 6.25 Napthanilide LB 132-61-6 6.25 6.25 25 12.5 6.25 NSC207895 58131-57-0 6.25 6.25 200 200 6.25 NSC369066 12.5 12.5 200 200 12.5 NSC654260 100 12.5 100 50 12.5 NSC204262 12.5 12.5 12.5 25 12.5 NSC367428 65905-96-6 50 50 50 25 25 Ellipticine 519-23-3 25 25 25 25 25 Celastrol 34157-83-0 200 50 200 50 50 NSC332670 24293-67-2 200 200 200 50 50 NSC344494 200 50 200 200 50 Niazo 617-19-6 200 50 200 200 50 CDDO-Im 443104-02-7 200 50 200 200 50 NSC11668 200 200 100 50 50 NSC11667 200 200 100 50 50 NSC341196 200 200 100 50 50 NSC522131 400877-42-1 100 100 100 50 50 NSC177407 58885-11-3 100 100 100 50 50 NSC38090 6339-64-6 100 50 100 100 50 NSC260594 50 50 200 100 50 NSC46492 6343-35-7 50 50 100 100 50 NSC373981 95455-02-0 50 50 100 100 50 NSC277184 55078-51-8 50 50 100 100 50 NSC308848 69408-82-8 50 50 200 200 50 NSC228155 50 100 200 200 50 Naphthol AS-KB 135-63-7 50 200 200 200 50 Sulfaquinoxaline 59-40-5 100 100 100 100 100 ZINC100367399 3557-92-4 200 100 200 100 100 NSC159566 200 100 200 100 100 Naphthol AS-OL 135-62-6 200 200 200 100 100 NSC137399 94300-82-0 200 200 200 100 100 Malonoben 10537-47-0 200 200 200 100 100 NSC33353 200 100 100 100 100 NSC329249 200 200 200 100 100 Vacquinol-1 5428-80-8 200 200 200 100 100 Benzbromarone 3562-84-3 200 200 200 100 100 Methyl Streptonigrin 3398-48-9 200 200 200 100 100 NSC33005 6265-56-1 200 100 200 200 100 o- 74037-43-7 200 100 200 200 100 Chlorophenazopyridine NSC149286 3939-04-6 200 100 200 200 100 NSC138389 200 100 200 200 100 NSC302584 66474-53-1 200 100 200 200 100 NSC147358 38053-02-0 200 100 200 200 100 Mequitazine 29216-28-2 200 100 200 200 100 NSC407628 7499-60-7 200 100 200 200 100

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Tricinolone 3092-82-8 200 100 200 200 100 acetophenonide Redoxal 52962-95-5 100 100 200 100 100 NSC116339 5059-58-5 200 100 200 200 100 Methiothepin maleate 19728-88-2 200 100 200 200 100 NSC133071 200 100 200 200 100 Daunomycin 3-oxime 34610-60-1 200 100 200 200 100 NSC601359 100 200 200 100 100 NSC317003 80568-29-2 100 100 100 100 100 NSC 311727 75464-90-3 100 100 200 200 100 NSC30260 100 100 200 200 100 Unii-pqq8L7Z138 497181-30-3 100 100 200 200 100 NSC622689 100 100 200 200 100 Enpiroline 100 100 200 200 100 Wander 100 100 200 200 100 NSC13156 5433-92-1 100 200 100 100 100 NSC215721 200 200 100 200 100 NSC369070 100 200 200 200 100 NSC 177365 63345-17-5 100 200 200 200 100 5'-O- 25030-31-3 100 200 200 200 100 sulfamoyladenosine

The Table 18 list is dominated by known antibacterial agents, as expected. Table 19, on the other hand, suggests activity of numerous experimental compounds with their IUPAC names some of which have yet not assigned a CAS number. A number of “non-antibacterials” in the UM–Cef library screen were identified as having anti-MRSA activity, most of which have previously been identified in FDA approved drug library antibacterial activity screens332.

These previously identified anti-MRSA “non-antibacterials” include floxuridine239, 333, 334, gemcitabine335, 336, closantel337-339, carmofur239, 340, 5-fluorouracil239, 341, 342, doxifluridine239 and daunorubicin239, 343 (Table 18). The UM–Cef library screen also identified epirubicin (MIC

12.5 µM) and idarubicin (MIC 12.5 µM) as having anti-MRSA activity (Table 18). NCI library screening exhibited anti-MRSA activity for various experimental chemical compounds. An interesting observation was the anti-MRSA activity of chaetochromin (anti-diabetic)344 and ellipticine345 (Table 19). Among all compounds tested, chlorobiocin and novobiocin exhibited

132 the most potent activity with a MIC of 3.8E-4 and 0.2 µM, respectively. They are aminocoumarin antibiotics and showed synergy with cefoxitin against MRSA F-182.

Two comparisons are possible using the MIC data in Table 18 and 19; one highlights those compounds with significant increases in activity after metabolism (Table 20 and 21), and the other highlights those compounds showing the greatest differences in activity between the

–Cef and +Cef library screens (Table 22 and 23). The upper sections of Table 22 and 23 show those agents with substantially enhanced activity in the presence of 8 µg mL-1 cefoxitin. The lower sections of Table 22 and 23 show those compounds with substantially reduced activity in the presence of cefoxitin. These comparisons were made using an average log2 (AL2) function as determined by the following two formulas.

For the UM vs PM comparison:

UM−Cef UM+Cef AL2(UM/PM) = Avg(log2 ( ) , log2 ( )) … … … (Eq. 6.4) PM−Cef PM+Cef

For the –/+Cef comparison:

UM−Cef PM−Cef AL2(–Cef/+Cef) = Avg(log2 ( ) , log2 ( )) … … … (Eq. 6.5) UM+Cef PM+Cef

Table 20. FDA library anti-MRSA hit MICs sorted by greatest average increase in activity after metabolism (AL2 ≥1.5).

UM MICs (µM) PM MICs (µM)

Compound –Cef +Cef –Cef +Cef Min_MIC AL2(UM/PM) Capecitabine NAa NAa 12.5 25 12.5 ≥4.5 Balofloxacin 0.78 0.78 9.8E–2 9.8E–2 9.8E–2 3.0 Marbofloxacin 6.25 6.25 0.78 0.78 0.78 3.0 Carmofur 12.5 50 6.25 6.25 6.25 2.0 Gatifloxacin 0.78 0.39 9.8E–2 0.39 9.8E–2 1.5 aNA – no activity (MIC ≥200 µM, counted as 400 µM).

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Table 21. NCI library anti-MRSA hit MICs sorted by greatest average increase in activity after metabolism (AL2 ≥1.5).

UM MICs (µM) PM MICs (µM) a Compound –Cef +Cef –Cef +Cef Min_MIC AL2(UM/PM) Porfiromycin 12.5 3.1 6.25 0.78 0.78 1.5 Chlorobiocin 3.8E-4 3.1E-3 4.8E-05 3.1E-3 4.8E-5 1.5

Table 22. FDA library anti-MRSA hit MICs sorted by greatest difference in activity between

–/+ cefoxitin (Abs(AL2) ≥2).

UM MICs (µM) PM MICs (µM) a Compound –Cef +Cef –Cef +Cef Min_MIC AL2(–Cef/+Cef) Floxuridine 3.1 3.1E–3 3.1 6.25 3.1E–3 4.5 Gemcitabine 2.4E–2 1.5E–3 0.78 4.9E–2 1.2E–2 3.5 Novobiocin 0.20 1.5E–3 9.8E–2 0.20 1.5E–3 3.0 Rifaximin 0.39 1.2E–2 6.25 3.1 1.2E–2 3.0 ...... Dicloxacillin 3.1 6.25 0.78 6.25 0.78 –2.0 Ceftiofur 6.25 100 100 100 6.25 –2.0 Tebipenem pivoxil 25 100 12.5 100 12.5 –2.5 Cloxacillin 3.1 25 3.1 25 3.1 –3.0 Nafcillin 1.6 50 6.25 50 1.6 –4.0

Table 23. NCI library anti-MRSA hit MICs sorted by greatest difference in activity between –

/+ cefoxitin (Abs(AL2) ≥2).

UM MICs (µM) PM MICs (µM) a Compound –Cef +Cef –Cef +Cef Min_MIC AL2(–Cef/+Cef) 4-quinazolinediamine 0.39 1.2E-2 0.39 4.9E-2 1.2E-2 3 Celastrol NAa 50 NAa 50 50 2 Teniposide 25 3.1 25 12.5 3.1 2 ...... Hitachimycin 1.5E-3 9.8E-2 0.78 1.56 1.5E-3 -3.5 Chlorobiocin 3.8E-4 3.1E-3 4.8E-5 3.1E-3 4.8E-5 -4.5 aNA – no activity (MIC ≥200 µM, counted as 400 µM).

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6.3.11 Identification of novel antibacterial drug metabolites

A number of active agents, not surprisingly, lost substantial activity after microsomal metabolism (Tables 18 and 19). Microsomal metabolism reactions possibly lead to the removal of the functional group(s) (i.e. pharmacophore) responsible for the therapeutic activity.

However, this analysis for UM vs PM MICs (Table 18 and 19) revealed a number of compounds with increased activity after microsomal metabolism. These include several fluoroquinolones, several other antibiotics and anti-cancer drugs. Capecitabine demonstrated a dramatic increase in activity after metabolism (Table 18 and 20), indicating at least one active metabolite. Comparative analysis of the parent and the microsomal mix of capecitabine by LC-

MS in positive mode revealed existence of two new peaks (M1 and M2) indicating the presence of at least two metabolites (Figure 42).

2.0E+3 Capecitabine Cap_UM (Rt - 11.47) Cap_PM 1.5E+3

1.0E+3 M1

Area (cps) Area (Rt - 7.06) 5.0E+2 M2 (Rt - 7.49)

0.0E+0 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (min)

Figure 42. Overlaid UM and PM capecitabine enhanced mass scanning chromatograms.

Active fractions isolated from preparative HPLC purification of the scaled up capecitabine microsomal reaction were identified as 5’-deoxy-5-fluorocytidine (DFCR) (MIC 135

= 3.1 µM), and doxifluridine (5’-deoxy-5-fluorouracil; DFUR) (MIC = 1.6 µM) by HRMS analysis (Figure 43) and comparison with commercially available standards by LC-MS/MS

(Figure 44).

Figure 43. Resolution of capecitabine active metabolites by semi-preparative HPLC and identification of active metabolites by high resolution mass spectrometry (HRMS).

136

Figure 44. Comparison of triple quadrupole MS chromatogram for purified capecitabine metabolites and commercial standards.

DFUR is further converted to 5-fluorouracil (5-FU), the active anti-cancer agent, in tissues through the action of pyrimidine nucleoside phosphorylase346. The anti-MRSA activity of DFUR and 5-FU was also found in this (Table 18) and in prior FDA library screening studies239, 341, 342. The discovery of the anti-MRSA activity of DFCR, which is not in the FDA approved drug library, demonstrates the potential of this approach to identify novel antibacterial metabolites of library agents, which was further demonstrated by the identification of active metabolites of balofloxacin and gatifloxacin. Anti-MRSA metabolites of these two fluoroquinolone antibiotics were identified as N-methylated balofloxacin (C1) and hydroxylated gatifloxacin (C2) (Table 24, Figure 45 and 46)

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Table 24. Balofloxacin and gatifloxacin anti-MRSA metabolite profiling and characterization.

Mass Retention Experimental Theoretical Mass Elemental Proposed Component error time (min) m/z m/z shift composition biotransformation (ppm) N-methylation of C1 5.04 404.1986 404.1986 0.0 14.0157 C H FN O 21 26 3 4 balofloxacin Hydroxylation of C2 5.41 392.1623 392.1622 0.3 15.9950 C H FN O 19 22 3 5 gatifloxacin

O O 359 F OH

N N O H3C N H3C CH3 315

Figure 45. Representative product ion spectrum for C1 (m/z 404, 5.04 min) detected in active fraction collected from the balofloxacin incubation with human liver microsomes.

138

374 (-H2O) O O 330 (-H2O) F OH

H3C HO N N HN O H3C 319, 275 (-CO2)

Figure 46. Representative product ion spectrum for C2 (m/z 392, 5.41 min) detected in active fraction collected from the gatifloxacin incubation with human liver microsomes.

6.3.12 Spectrum of activity of capecitabine metabolites and related agents

A number of other 5-fluorouracil related anticancer agents were observed in this and other studies to be active against MRSA, as cited above (Table 18). To assess the potential of this group of agents that share the pyrimidine scaffold in their structure as antibacterial agents, their spectrum-of-activity was assessed against several MRSA strains, 1 VRE (faecium) strain, and 1 E. coli strain (Table 25 and Figure 47). Two control antibiotics (vancomycin and doxycycline) were also included in this study. None of these compounds demonstrated activity against E. coli, which is a gram-negative bacteria and contains an outer membrane as part of cell wall composition (absent in cell wall of gram-positive bacteria). Capecitabine was inactive

139 against all the tested strains, both gram-positive and gram-negative bacteria. DFCR was active against 5/7 MRSA strains but inactive against the VRE strain. DFUR was active against all of the MRSA strains, but also inactive against the VRE strain, indicating that DFUR has broader anti-MRSA coverage. 5-fluorocytidine and floxuridine were active against all the MRSA strains, and weakly active against the VRE strain. 5-fluorouracil, 5-fluorouridine, gemcitabine, and carmofur (another prodrug form of 5-fluorouracil with intrinsic antibacterial activity) were active against all the MRSA and VRE strains. Gemcitabine in particular appears to have the potential for further study against gram positive organisms.

Table 25. Spectrum of activity of capecitabine metabolites and related compounds (MIC, µM).

VRE E. coli MRSA MRSA MRSA MRSA MRSA MRSA MRSA Compounda (faecium, (Seattle (F-182)b (N315) (HI022) (MN8) (TCH70) (RN1) (COL) clinical) 1946) Capecitabine NAc NAc NAc NAc NAc NAc NAc NAc NAc DFCR 3.1 NAc NAc 6.3 25 12.5 3.1 NAc NAc DFUR 1.6 6.3 3.1 6.3 6.3 1.6 3.1 NAc NAc Floxuridine 3.1 3.1 1.6 6.3 1.6 1.6 1.6 50 NAc 5-Fluorocytidine 3.1 3.1 3.1 6.3 3.1 1.6 3.1 50 NAc 5-Fluorouracil 3.1 12.5 6.3 12.5 12.5 3.1 3.1 12.5 NAc 5-Fluorouridine 3.1 6.3 6.3 12.5 12.5 3.1 6.3 12.5 NAc Carmofur 12.5 25 6.3 12.5 12.5 6.3 6.3 6.3 NAc Gemcitabine 2.4E–2 2.4E–2 9.8E–2 4.9E–2 9.8E–2 4.9E–2 4.9E–2 6.1E–3 NAc Vancomycind 0.39 0.20 0.39 0.39 0.39 0.20 0.39 NAc NAc Doxycyclined 0.39 0.39 0.39 0.39 0.39 0.39 50 3.1 1.6 aStructures shown in Figure 43 below bATCC 43300 strain used for library screening. Other vendor IDs given in the text. cNA – Not active at 50 µM, the highest concentration used in these MIC determinations. dControl antibiotic.

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O NH NH2 O O F F F F N N NH NH

N O N O N O N O H CH CH CH 3 O 3 O 3 O

OH OH OH OH OH OH Capecitabine 5'-Deoxy-5-fluorocytidine 5 '-Deoxy-5-fluorouridine 5 -Fluorouracil (DFCR) (DFUR)

NH2 NH2 O O F F F O N N NH NH F NH N O N O N O N O HO HO HO HO N O O O O O F O N OH OH OH F OH OH OH H 5-Fluorocytidine Gemcitabine 5-Fluorouridine Floxuridine Carmofur

Figure 47. Structures of capecitabine and other structurally similar compounds sharing pyrimidine backbone.

6.3.13 Synergistic activity of library hits with cefoxitin

Several compounds showed apparent synergy with cefoxitin (Table 22 and 23). In particular, floxuridine demonstrated an apparent strong synergistic activity with cefoxitin

(AL2(UM/PM) = 4.5, a 23-fold average change in the MIC of floxuridine –/+Cef). Celastrol, gemcitabine, novobiocin, rifaximin, 4-quinazolinediamine and teniposide also demonstrated significant apparent synergy. To confirm and further assess this synergy, checkerboard assays347 were performed with cefoxitin (Figure 48 and 49).

141

Flox = floxuridine, Gemc = gemcitabine, Novo = novobiocin, Rifa = rifaximin.

142

Among these seven compounds, floxuridine showed the highest synergy with an FIC index348 of 0.14. This value is comparable to the FIC index of 0.15 for ampicillin/sulbactam synergy349. Gemcitabine, novobiocin, rifaximin and 4-quinazolinediamine also exhibited appreciable synergistic activity with cefoxitin, with FIC indices of 0.38 each. Although, celastrol showed significant synergy with cefoxitin, with a FIC index of 0.25, the MIC of

143 celastrol itself was high (>200 µM); teniposide on the other hand falls within the borderline of synergy and additive effect with a FIC index of 0.5. The biochemical bases of these synergistic interactions is presently unknown, but the synergy could provide a useful approach to countering β-lactam resistance in MRSA or other organisms. This is currently under further investigation using multiomics approaches. A number of antagonistic interactions were also revealed (Table 22 and 23, bottom). Of particular note was the observation that several β- lactams demonstrated significant anti-MRSA activity in the absence of cefoxitin, which was lost in the presence of cefoxitin (nafcillin, cloxacillin, dicloxacillin and ceftiofur). This observation suggests that these four β-lactam antibiotics are unable to induce β-lactam resistance. Cefoxitin, however is able to induce resistance, which then provides β-lactam resistance to these sensitive-to β-lactam antibiotics as well. Further study of these antagonistic interactions is underway.

6.3.14 Frequency of resistance of top FDA library hits

Frequency of resistance values calculated for different agents indicated that all these agents show hypomutable behavior at 1x – 4x MICs except for gemcitabine, which showed normomutable behavior263 (Table 26). The lowest frequency of resistance was exhibited by the two floxuridine metabolites, DFCR and DFUR at 4x MIC, indicating their potential as low resistance anti-MRSA agents.

Table 26. Frequency of resistance for different FDA library hits.

xMIC DFCR DFUR 5-FU Novobiocin Gemcitabine Floxuridine Rifaximin 1x 1.89E-08 7.24E-10 7.96E-10 6.12E-10 2.33E-08 5.81E-09 1.51E-10 2x 4.34E-09 7.16E-10 1.93E-10 2.60E-10 1.60E-08 3.08E-10 1.35E-10 4x 8.04E-12 3.22E-11 No FoR 1.57E-10 1.16E-08 3.21E-10 1.09E-10

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6.3.15 Conclusion

This study initially aimed to evaluate the potential of in-situ metabolism of chemical libraries to enhance a drug repurposing and screening effort by allowing active metabolites to be identified. To further enhance the ability of this approach to reveal novel and interesting activities, it was combined with a –/+ resistant to antibiotic (cefoxitin) screen. This provided a

2-dimensional screening approach (2x2 experimental design). The results from this approach exceeded our expectations. Comparison of UM vs PM screens (Table 20 and 21) revealed a number of agents with potential active metabolites. Capecitabine was one such agent, and its anti-MRSA metabolites were fractionated to identify DFCR and DFUR. Several fluoroquinolones such as gatifloxacin, balofloxacin and marbofloxacin, and antineoplastic antibiotics such as porfiromycin and chlorobiocin demonstrated substantially enhanced activity after metabolism. Fractionation of metabolized fluoroquinolones reveals very low levels (<1%) of one or more potent metabolites. Active metabolites of balofloxacin and gatifloxacin were identified as N-methylated balofloxacin and hydroxylated gatifloxacin.

Comparison of –Cef vs +Cef screens identified a number of potentially synergistic agents with cefoxitin (Table 22 and 23), which was confirmed for seven of these by checkerboard analysis

(Figure 48 and 49). Floxuridine in particular demonstrated a high degree of synergy with cefoxitin. The mechanism of synergy of these agents with cefoxitin is unknown and is currently being investigated. Such synergistic activities could provide a means of restoring an antibacterial agent for which resistance is known besides identifying new synergistic agents for therapeutic use. This study illustrates the utility of a comparative library screening approach to identify new antibacterial agents and approaches.

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CHAPTER 7

7. SUMMARY AND RECOMMENDATIONS

7.1 Summary

Amino acids are important biomolecules both for prokaryotic and eukaryotic systems.

Prokaryotic cells can synthesize all twenty common proteinogenic amino acids whereas eukaryotic cells can only make eleven of them; the non-essential amino acids. Essential amino acids are required to be delivered exogenously for different biological roles. Amino acid pools can be dysregulated by various disease conditions hence amino acid profiling remains an area of interest for scientists throughout the world. Classically, D-amino acids were only considered part of prokaryotic systems, but due to advancements in analytical techniques, various D-amino acids have now been reported in different eukaryotic systems, such as in different mammalian organisms including humans. Due to stereochemistry and existence of isobars, analysis of amino acids can be a challenging task. With the advent of state of the art analytical platforms including various separation and detection technologies, trace amount of amino acids can be accurately quantified today. Marfey’s reagent is a chiral derivatizing reagent commonly used for chromatographic separation of D- and L-amino acids. Despite its popularity, a systematic study of its application for resolution and quantification of all twenty common proteinogenic amino acid DL-stereoisomers has not been reported to the best of our knowledge.

The first part of this dissertation was aimed at addressing this and offering a systematic study of Marfey’s derivatization, chiral separation and quantification of nineteen common proteinogenic amino acid DL-stereoisomers and glycine. We developed and validated an LC-

MS/MS based analytical method for chromatographic separation of 19 DL-amino acid

146 stereoisomeric pairs and glycine with tandem MS to detect and quantify these analytes in

MRSA extract. To the best of our knowledge, negative mode detection of amino acid diastereomers using ammonium acetate based neutral solvent system (pH 6.5) as well as isobaric Ile and Leu using Marfey’s derivatization technique have not been reported before.

We also showed the application of surrogacy using D-Marfey’s reagent to synthesize the D-

Mar-L-AAs, which showed identical chromatographic retention on C8 analytical column in reverse phase and enabled detection of all 19 proteinogenic D-amino acids using their L- isomers with D-Marfey’s reagent. We compared both positive and negative mode detection using both acidic and neutral solvent systems, and found that negative mode detection with neutral solvent system offers the optimal sensitivity and resolution. We performed a kinetics study for the Marfey’s derivatization reaction and determined that a 24 hour derivatization is sufficient for all amino acid stereoisomers and glycine except for His, which requires a 3 day derivatization for complete bis-Marfey’s adduct formation. The developed method was validated in terms of linearity, sensitivity, matrix effect and then used to analyze these amino acid stereoisomers in MRSA extract. This study provides a feasible method for indirect but selective and stereospecific separation and quantification of all proteinogenic DL-amino acid stereoisomers by liquid chromatography tandem mass spectrometry (LC-MS/MS) technology.

Deoxyribonucleic acids (DNAs) are important biomacromolecules and are the genetic materials of all living organisms that direct protein synthesis, and control the flow of information within cells. Besides four standard DNA nucleosides, several modified DNA nucleosides have been reported and have important biological implications. DNA methyl transferases primarily induce methylation of cytosine, which has important epigenetic function besides having roles in growth and development. The ten-eleven translocation (TET) family

147 of enzymes can sequentially oxidize 5-methylcytosine (5mC) to form 5- hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC), all of which have important epigenetic roles in different diseases, including cancer, immune and neurologic disorders. Thus, accurate quantification of these epigenetic markers is very important and requires robust analytical methodology. Following the development of a high performance LC-MS/MS based analytical method for proteinogenic amino acid stereoisomers, we developed a positive/negative ion-switching LC-MS/MS based method which can resolve and quantify four regular DNA bases (A, T, G, C) and four modified cytosine DNA bases

(5mC, 5hmC, 5fC, 5caC) using C18 analytical column in reverse phase chromatography with formic acid based water/methanol solvent system. We incorporated three periods of positive/negative/positive ionization modes to allow the elution of all these DNA bases in the respective mode, providing the optimum intensity for all the DNA nucleosides except for 5mC.

This method can help to study the catalytic activity of TET dioxygenase enzymes and their role in human health and diseases. This method upon slight modification, can also help in studying the effect of external factors and exogenous molecules on bacterial DNA biosynthesis, and can aid identification of important enzymes and biochemical steps that can help in understanding the mechanism of action of various drugs and stimuli with potential applications in drug discovery.

Methicillin-resistant Staphylococcus aureus is a serious resistant bacteria responsible for infection of skin, soft tissue and respiratory tract. It is one of the most common bacteria responsible for nosocomial infection and poses serious health risks, which aggravated due to lack of new antibiotics or new therapeutic approaches. In last few decades, no new classes of antibacterial agents have been discovered and it is high time to address these issues and to be

148 better prepared in any potential outbreak caused by this known yet harmful superbug. In the second part of this dissertation, we evaluated the potential of in-situ metabolism using human liver microsomes of two chemical libraries to enhance a drug repurposing effort through increasing chemical space and diversity, which was further enhanced by comparative screening in the presence and absence of a known antibiotic (cefoxitin) against which the MRSA F-182 strain is resistant. This strategy of library screening for antibacterial activity enabled identification of drug metabolites with improved anti-MRSA activity from comparison of UM vs PM library screening. DFCR, DFUR, N-methyl balofloxacin and hydroxy gatifloxacin demonstrated improved antibacterial acivity compared to their parent drugs, capecitabine, balofloxacin and gatifloxacin respectively. We discovered that drugs sharing the pyrimidine scaffold in their structure showed an overall potent anti-MRSA activity but were not active against VRE or E. coli as determined by the spectrum of activity experiment. We also identified several drugs with potential synergistic activity with cefoxitin including floxuridine, gemcitabine, novobiocin, rifaximin, 4-quinazolinediamine. Floxuridine showed a high degree of synergy with cefoxitin. To the best of our knowledge, these synergies against MRSA F-182 have not been previously reported. Identified lead agents from both comparative UM vs PM and –/+ cefoxitin FDA library screening, showed hypomutable characteristics consistent with very low mutation rates except for gemcitabine. The mechanism of synergy of the identified drugs with cefoxitin is not known yet. Such synergistic activity could help in restoring activity of an antibiotic against which the bacteria has developed resistance, or in enhancing an antibiotic’s activity by lowering the chances of resistance development. Our strategy illustrates the utility of a dimensionally enhanced chemical library screening approach to identify new

149 antibacterial agents and approaches which can aid in combatting infectious diseases caused by

MRSA.

7.2 Recommendations

These projects were intended to develop and validate comprehensive workflows for separation and quantification of proteinogenic amino acid stereoisomers, DNA nucleosides and chemical library screening for antibacterial activity exploration. The established efforts resulted in two high performance LC-MS/MS based analytical methods for separation and quantification of all proteinogenic amino acids (38 DL-stereoisomers and glycine) and eight

DNA nucleosides (four regular: A, T, G, C and four modified: 5mC, 5hmC, 5fC, fcaC). The efforts also resulted in a high throughout chemical library screening workflow to identify new antibacterial drug metabolites and synergistic agents.

The developed LC-MS/MS methods enabled baseline separation and accurate detection of 39 amino acid species within 30 minutes and eight DNA nucleotides within 23 minutes using microflow reverse phase chromatography tandem mass spectrometry. Analytical run times can be shortened if nanoLC is used in the front end. Also, ion mobility spectrometry can be used for faster chiral analysis of the proteinogenic amino acid stereoisomers. Similar approaches can also be explored in facile analysis of DNA nucleosides and nucloetides.

Lead agents obtained from chemical library screening look promising in treating

MRSA infections, but their mechanism of action is not determined yet. Screening chemical libraries in presence of an antibiotic that works against the bacteria instead of an antibiotic against which the target organism is resistant, can aid identification of synergistic agents that can lead to powerful combination therapy. Such combination therapy can potentially lead to treat infection caused by multidrug resistant bacteria. The efforts and the workflows that we

150 developed and optimized laid the foundation to explore such clinically important combination therapy against notorious bacterial infection.

Several approaches such as genomics, proteomics and metabolomics can be used and if explored in parallel might help to understand the mechanistic action of active anti-MRSA drug metabolites or synergistic agents in detail. Untargeted proteomics and metabolomics can help to identify enzymatic target and specific biochemical steps on a pathway responsible for eliciting observed antibacterial activity. Whole genome sequencing of resistant MRSA grown in the presence of the lead agents can help to identify the responsible gene and to confirm the observation from proteomics and metabolomics data, leading to identification of the molecular target(s). Such an approach can then be verified using a targeted proteomics or metabolomics approach in conjunction with ELISA or Western Blotting based molecular biology techniques.

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APPENDIX

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RE: Biology Methods & Protocols - BMP-2020-025.R1 VENTIKOS, Joanna Mon 10/26/2020 3:49 AM To: • Ayon, Navid Jubaer (UMKC-Student) ; • Biology Methods Editorial Office Cc: • [email protected]

Dear Navid,

Please see info below regarding your query, also available at https://academic.oup.com/journals/pages/authors/production_and_publication/online_licensing.

If you have any questions, let us know and we’ll do our best to help.

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********************** Joanna Ventikos | Publisher Academic Division | Oxford University Press Great Clarendon Street | Oxford | OX2 6DP [email protected]

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VITA

Navid Jubaer Ayon was born on December 31, 1990 in Mymensingh, Bangladesh. He completed Secondary and Higher Secondary School from Agriculture University High School

(Mymensingh, Bangladesh) and Notre Dame College (Dhaka, Bangladesh) respectively. Later he obtained his Bachelor of Pharmacy and Master of Pharmacy degree, both from the

University of Dhaka (Dhaka, Bangladesh). He then received his Master of Science in

Chemistry degree from the University of Missouri-Kansas City.

Navid joined University of Missouri-Kansas City in Summer 2014 as a graduate student of interdisciplinary Doctor of Philosophy program to pursue his Ph.D. in

Pharmaceutical Sciences and Chemistry, where he worked on liquid chromatography and mass spectrometry based analytical method development for various endogenous and exogenous molecules and chemical library screening for antibacterial drug discovery. He received numerous awards during his tenure at UMKC including prestigious School of Graduate Studies

Research Grant Award (2019-20), Preparing Future Faculty Scholar Award Fellowship (2019-

20), William J. Rost Memorial Graduate Scholarship (2017 and 2018) and UMKC Pharmacy

Foundation Student Professional Development Grant (2017, 2018 and 2020). He completed his doctoral studies in November 2020.

Navid did a summer internship at Streck Inc. Laboratories (La Vista, NE, USA) and participated in UMKC leadership program. He has authored/co-authored ten peer-reviewed articles in reputed international journals till date from his tenure of doctoral studies at UMKC.

He presented his research in several international and national conferences and won best poster award at the 48th Pharmaceutics Graduate Student Research Meeting (PGSRM) in 2016 and best abstract awards at the PharmSci 360 – American Association of Pharmaceutical Scientists

210

(AAPS) annual conference in 2020. He is also an active member of American Association of

Pharmaceutical Scientists. He has been graduate student member of American Society for

Mass Spectrometry, American Chemical Society, American Society for Microbiology,

Pharmaceutical Science Graduate Student Association and Controlled Release Society.

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