Identification of Chemical Probes from Macleaya Cordata (Willd.) R.Br

Identification of Chemical Probes from Macleaya cordata (Willd.) R.Br.

Author Nguyen, Duy Thanh

Published 2019-10-28

Thesis Type Thesis (Masters)

School School of Environment and Sc

DOI https://doi.org/10.25904/1912/3871

Downloaded from http://hdl.handle.net/10072/389089

Griffith Research Online https://research-repository.griffith.edu.au

Identification of Chemical Probes from

Macleaya cordata (Willd.) R.Br.

Duy Thanh Nguyen B. Sc.

School of Environment and Science

Griffith Institute for Drug Discovery

Griffith University

Submitted in fulfilment of the requirements of the degree of

Master of Science

July 2019

Statement of Originality

This work has not been previously submitted for a degree or diploma in any university.

To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made in the thesis itself.

03/07/2019

Duy Thanh Nguyen Date

Acknowledgement

First and foremost, I would like to express my sincere gratitude to my principal supervisor Assoc. Prof. Yunjiang Feng for offering me the opportunity to be her student.

Without your helps I would not have the chance to undertake my studies in modern facilities like Griffith University. Thank you for your time, support, patience and knowledge that you have given me since the day I stepped into your lab. Thank you for always giving me invaluable feedback and directions without which I could not complete this thesis.

My appreciation is also with Prof. George D. Mellick and Assoc. Prof. Stephen

A. Wood who have provided me with very precious advice and instructions on the biology part of my project. I sincerely would like to say a heartfelt thank you to Prof. Ronald J.

Quinn and Prof. Ian Jenkins for being great mentors and always being willing to share their wealth of wisdom which inspired me a lot throughout my journey.

I sincerely appreciate Dr. Jamila Iqbal for her extremely helpful input on the cytological profiling cohorts, Mr. Jianying Han for his help on density functional theory,

Ms. Minh Tam Tran Thi for her training on tissue culture, and Dr. Wendy Loa-Kum-

Cheung for her training on NMR spectroscopy and mass spectrometry.

I also want to acknowledge all members in the Feng group and Davis group for their advice and assistance at all time. My special thanks are with Dr. Mario Wibowo, future doctor Kah Yean Lum and William Gang Miao for being very good friends. I truly appreciate your friendship, your great advice and the time you have spent chatting and going out with me. Thank you for organising very relaxing trips to explore and enjoy

Australian nature.

I am grateful to all past and present members within GRIDD and the School of

Environment and Science whom I had a chance to work with. I truthfully appreciate

Assoc. Prof. Rohan Davis and Assoc. Prof. Mark Coster for their precious lessons on

NMR spectroscopy and synthetic chemistry; Assoc. Prof. Sarah Cresswell, Mr. Alan

White and his team, for sharing their knowledge on analytical chemistry; and Prof.

Rebecca Ford for showing me the importance of research methodology and integrity. I am truly thankful to Dr. Sue Boyd for offering me opportunities to obtain valued experience in academic teaching. It is also my pleasure to thank Dr. Ian Hayward, Mrs.

Sheila Twilley and Ms. Maria Nguyen for their administrative supports.

Finally, I cannot find the words to describe my wholehearted gratitude to my family members, especially my mother; you have been working tirelessly during these years to support me, and I am deeply indebted to you for your great sacrifice. I cannot thank you enough for the unconditional love and care of a mother in preparing me for my future. To my brother, his partner and the Gan family, thank you for always lending me a hand whenever I need. I am truly grateful for your advice, concern and encouragements.

This degree would not be achievable without your companionship. Lastly, in loving memory of my aunt, I sincerely thank her for everything she did for me.

Thank you all, as always.

iii

Abstract

The lack of our understanding about Parkinson’s Disease (PD), the second most common and incurable neurodegenerative disorder, has caused a great impact in both treatment and research for this disease. Although not a few intensive studies have been conducted with the hope of gaining more knowledge about the complex biology underlying this debilitating condition, it seems that our efforts have not completely uncovered the molecular mechanisms related to the neurodegeneration, which is critical in PD.

Phenotypic assay, particularly cytological profiling (CP), has recently emerged as a powerful unbiased strategy to study the mechanisms of action in many biological systems. CP is a cell-based and image-based approach that evaluates the effects of small molecules on several organelles within the cell, enabling the assessment of multidimensional phenotypic functions instead of a single interaction that is usually seen in target-based approaches. Previous research in our institute utilizing CP has identified quite a few compounds that interacted with PD patient-derived cells. It is therefore worth believing that CP could be a future approach for combating PD.

This project was conducted as part of our ongoing research intending to discover molecules from nature to study the biology of PD. Our aim was to use 1H NMR guidance to discover natural products from a traditional Chinese medicinal plant. Cytological profiling on a patient-derived cell line (human olfactory neurosphere-derived (hONS) cells) was performed for the isolated compounds to determine if they are potentially qualified as chemical probes to serve the research of PD. This thesis will demonstrate the study that integrates chemistry and biology to identify small molecules and the effects

they influenced to the cell model, revealing their potential value as molecular tools for a better interrogation of the disease mechanism.

The thesis was initiated with an introduction that covered several concepts, including traditional Chinese medicine (TCM), PD, CP, hONS cell model and chemical probes. Starting with TCM – a multi-century health care system in China, the introduction acknowledged that TCM herbal plants are invaluable sources of bioactive small molecules with respect to the traditional experience accumulated for thousands of years.

It then moved on to a brief discussion of Parkinson’s Disease ‒ an irremediable neural degenerative disorder, and some current therapeutic treatments for this disease, as well as giving rationale as to why this condition should be researched. The introduction went on with a review on CP and its application in drug discovery, followed by a description of current disease models for PD, as well as the hONS cell model used in this project. It was noted that hONS cells derived from PD patients can recapitulate some functional aspects of this disease, and thus coupling CP with hONS cell model could shed light on the biological studies of PD. The chapter continued with a description of chemical probes, their importance in medicinal research and that these small molecules are valuable tools for interrogating the pathways of PD. Finally, the durability of NMR fingerprinting in identify compounds was demonstrated in the last part of this chapter.

Chapter 2 provided details of the equipment and procedures involved in this project to assist the chemical purification and biological characterization of the compounds isolated from the selected herbal plant. Detailed spectroscopic data were also present in this chapter.

Chapter 3 first gave in introduction on the selected biota, Macleaya cordata

(Willd.) R.Br., including how it was initially chosen in the previous PhD project and its traditional medicinal use. The chapter then moved on to the targeted isolation of natural

product from this species by utilizing the robustness of 1H NMR spectroscopy and mass spectrometry (MS), which resulted in the isolation of two new compounds and fourteen known metabolites. The in-depth structural elucidation of the two new compounds, (6R)-

10-methoxybocconoline and 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine, were also mentioned in this chapter, followed by the use of density functional theory

(DFT) to assign the absolute configuration (AC) of one new molecule. The chapter concluded with discussion on the validity of NMR fingerprinting and future directions on the remaining work which involved AC assignment for the other new compound.

Chapter 4, the final chapter, presented the intensive data analysis for the CP screening of the isolated compounds. It was revealed that four out of sixteen metabolites showed significant perturbations to the hONS cellular parameters. Those compounds included bocconoline which impacted EEA1- and mitochondria-associated features, 6-

(1-hydroxyethyl)-5,6-dihydrochelerythrine, 3-O-feruloylquinic acid and ferulic acid 4-O- glucoside which influenced LC3b-related features. These compounds can potentially be used as molecular tools to probe the biological pathways of PD. The last part of the chapter was the discussion on the CP platform used to identify the above potential probes, as well as future directions on the biology cohort. It was concluded that more justifications should be made in order to verify the potency of the compounds being qualified as useful probes. Dose dependence, mechanisms of action and drug-like properties for the candidate probes were among the future follow-up research to have a better understanding in the exploration of complex molecular mechanisms underlying PD.

Table of Contents

Statement of Originality ...... i Acknowledgement ...... ii Abstract ...... iv List of Figures ...... ix List of Tables ...... xiv Abbreviations ...... xv

CHAPTER 1. INTRODUCTION ...... 1

1.1 Traditional Chinese Medicine ...... 1 1.2 Parkinson’s Disease ...... 4 1.3 Cytological Profiling ...... 7 1.4 Human Olfactory Neurosphere-derived Cell Model ...... 11 1.5 Chemical Probes ...... 14 1.6 NMR fingerprinting ...... 18 1.7 Research objectives ...... 22 1.7.1 Aims ...... 22 1.7.2 Expected outcomes and significance...... 22 1.8 Project outline ...... 23 1.9 References ...... 24

CHAPTER 2. EXPERIMENTAL METHODS ...... 32

2.1 General Experimental Procedures ...... 32 2.2 Extraction and Isolation of Macleaya cordata ...... 33 2.2.1 Plant Material ...... 33 2.2.2 Extraction and Isolation of Compounds ...... 33 2.2.3 Spectroscopic Data of Isolated Compounds ...... 38 2.3 Biological Experimental Procedures ...... 45 2.3.1 Cell Culture ...... 45 2.3.2 Cytological Profiling ...... 46 2.3.3 Cell Imaging ...... 49

vii

2.3.4 Data Analysis ...... 49 2.4 References ...... 50

CHAPTER 3 NMR-FINGERPRINTING ISOLATION OF Macleaya cordata (Willd) R. Br...... 52

3.1 Introduction ...... 52 3.2 Macleaya cordata (Willd) R.Br...... 59 3.2.1 Taxonomy ...... 60 3.2.2 Medicinal uses of Macleaya cordata ...... 60 3.3 NMR-Fingerprinting-guided Isolation of Compounds...... 62 3.3.1 Polyamide CC6 Chromatography ...... 62 3.3.2 Sephadex LH-20 Chromatography ...... 65 3.3.3 Purification of Compounds ...... 69 3.3.3.1 Isolation and Structural Elucidation of 10-Methoxybocconoline (2.1)…… ...... 70 3.3.3.2 Isolation and Structural elucidation of 6-(1-Hydroxyethyl)-10- methoxy-5,6-dihydrochelerythrine (2.2) ...... 83 3.3.3.3 Isolation of The Known Compounds ...... 90 3.4 Conclusions and Future Directions ...... 95 3.5 References ...... 97

CHAPTER 4. CYTOLOGICAL PROFILING ASSAY ...... 101

4.1 Removal of Cytotoxic Fractions and Compounds ...... 103 4.2 Control Validation ...... 103 4.3 Correlation Analysis ...... 105 4.4 Hierarchical Clustering and Heatmap ...... 108 4.5 Hit Identification ...... 114 4.6 Conclusions and Future Directions ...... 120 4.7 References ...... 122 Supporting Information for Chapter 3 ...... 123

viii

List of Figures

Figure 1.1 Yin (black) and Yang (white) as two opposite forces in TCM system...... 2

Figure 1.2 Chinese herbal medicine...... 2

Figure 1.3 Discovery of Artemisinin from Artemisia annua...... 3

Figure 1.4 Pathophysiology of Parkinson’s Disease...... 4

Figure 1.5 Schematic of various methods for PD treatment...... 5

Figure 1.6 Structures of some approved substances in PD treatment...... 6

Figure 1.7 Discovery of the antimitotic compound XR334...... 10

Figure 1.8 The olfactory epithelium ...... 13

Figure 1.9 hONS cells...... 14

Figure 1.10 Structure of Diazonamide A...... 15

Figure 1.11 Chemical structures of some probes in PD study...... 17

Figure 1.12 Bioassay-guided isolation of 5 new compounds...... 19

Figure 1.13 1H NMR fingerprint analysis in the identification of venuloside A...... 21

Figure 2.1 Overview of the isolation process ...... 34

Figure 2.2 Overview of the cytological profiling process ...... 46

Figure 2.3 Overview of the data analysis process...... 50

Figure 3.1 Flow chart demonstrating the prioritization process...... 53

1 Figure 3.2 H NMR (600 MHz, DMSO-d6) comparison between Sample A and Sample

B...... 54

Figure 3.3 Cytological profiling of the MeOH and EtOAc extract of M. cordata against hONS cells...... 55

1 Figure 3.4 H NMR data (800 MHz, CD3OD) of the EtOH extract of Macleaya cordata..

...... 57

Figure 3.5 The benzo[c]phenanthridine scaffold with numbering system and some examples of this type of alkaloid...... 58

Figure 3.6 Image of the leaves of Macleaya cordata (Willd.) R.Br...... 59

Figure 3.7 Six fractions obtained from the Polyamide CC6 chromatography of the M. cordata crude ethanol extract...... 62

1 Figure 3.8 H NMR spectra (800 MHz, DMSO-d6) of the six Polyamide CC6 chromatography fractions ...... 63

1 Figure 3.9 A close-up view of the H NMR data (800 MHz, DMSO-d6) of fraction B and fraction C...... 64

1 Figure 3.10 H NMR data (800 MHz, DMSO-d6) of fraction D and fraction E...... 65

Figure 3.11 Sephadex LH-20 chromatography result of fraction B + C with 8 fractions obtained following TLC analysis...... 66

1 Figure 3.12 H NMR spectra (800 MHz, CD3OD) of the 8 fractions (BC1 ‒ 8) from the

Sephadex LH-20 chromatography...... 66

Figure 3.13 Six smaller fractions were obtained following Sephadex LH-20 chromatography of mixture of D and E...... 67

1 Figure 3.14 H NMR spectroscopy (800MHz, CD3OD) of fractions from Sephadex LH-

20 chromatography of mixture of D and E...... 68

Figure 3.15 Compounds isolated from Macleaya cordata in this project...... 69

Figure 3.16 HPLC chromatogram of the purification of bocconoline (2.3)...... 70

Figure 3.17 Spectroscopic data for the initial fraction containing compound 2.1...... 71

1 Figure 3.18 H NMR (800 MHz, DMSO-d6) spectrum comparisons between bocconoline

(2.3) (top) and the selected HPLC fraction at retention time of 39 min (bottom)...... 72

Figure 3.19 MS-guided detection of compound 2.1...... 73

Figure 3.20 Downstream HPLC purification of 2.1, guided by MS...... 74

Figure 3.21 TLC separation of compound 2.1, performed on Merck Aluminium oxide 60

F254 basic glass plates...... 75

1 Figure 3.22 H NMR spectrum (800 MHz, CDCl3) comparison of compound 2.1 (A) and the plain aluminium oxide from Merck Aluminium oxide 60 F254 basic glass plate (B)..

...... 76

Figure 3.23 The 1,3-benzodioxole fragment ...... 77

Figure 3.24 Further assignment of the 1,3-benzodioxole fragment...... 77

Figure 3.25 The 2,3-methylenedioxynaphthalene fragment with key ROESY correlation

...... 78

Figure 3.26 Structural elucidation revealing the N-methylethanolamine fragment ...... 78

Figure 3.27 Linkage of the 2,3-methylenedioxynaphthalene and the N- methylethanolamine fragment by ROESY correlation ...... 79

Figure 3.28 Further structural elucidation of compound 2.1 by HMBC data revealing the benzo[c]phenanthridine alkaloid scaffold ...... 79

Figure 3.29 Assignments of the methoxy groups to the benzo[c[phenanthridine scaffold by ROESY (—) and HMBC (—) correlations...... 80

Figure 3.30 Key 1H-1H COSY (—), HMBC (→) and ROESY (↔) correlations of 10-

Methoxybocconoline (2.1) ...... 81

Figure 3.31 Two possible absolute configurations of 10-Methoxybocconoline ...... 82

Figure 3.32 Calculated ECD data of 10-Methoxybocconoline (blue and red line) and experimental ECD spectrum obtained for compound 2.1 (black line)...... 82

Figure 3.33 MS-guided detection of compound 2.2...... 83

1 Figure 3.34 Comparisons of the H NMR data (800 MHz, CDCl3) of compound 2.2 (top) and the plain aluminium oxide stationary phase in the TLC plate (bottom)...... 85

1 Figure 3.35 Comparisons between the H NMR data (800 MHz, CDCl3) of 10-

Methoxybocconoline (2.1) (top) and those of 2.2 (bottom)...... 86

Figure 3.36 The benzo[c]phenanthridine fragment of compound 2.2...... 86

1 Figure 3.37 Key differences in the H NMR data (800 MHz, CDCl3) of 2.1 and 2.2 from

δH 2.50 to δH 4.80...... 87

Figure 3.38 The 2-hydroxypropane-1,1-diyl fragment...... 88

Figure 3.39 Linkage of the two fragments by HMBC (—) correlations, forming the full structure of 2.2...... 88

Figure 3.40 Key 1H-1H COSY (—), HMBC (→) and ROESY (↔) correlations of 6-(1-

Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2)...... 89

Figure 3.41 HPLC purification of chelerythrine (2.5) and chelilutine (2.6) from fraction

BC2...... 90

Figure 3.42 HPLC characterization of BC3...... 91

Figure 3.43 HPLC purification on subfraction 25 to 29...... 91

1 Figure 3.44 H NMR spectra (800 MHz, CD3OD) of fractions 19 to 24...... 92

Figure 3.45 Aromatic region of the 1H NMR spectra of the fraction containing principle alkaloids of M. cordata (top) and the fraction containing coumaroyl-type compounds

(bottom)...... 93

Figure 3.46 HPLC purification of BC4...... 94

1 Figure 3.47 H NMR spectra (800 MHz, CD3OD) of subfractions 13 to 23 from BC4 separation...... 94

1 Figure 3.48 H NMR comparisons (800 MHz, CD3OD) between chelirubine (2.7) (top) and subfractions 44 (bottom)...... 95

Figure 4.1 Fractions and pure compounds subjected to cytological profiling ...... 101

Figure 4.2 Images of cells treated with positive and negative control...... 104

xii

Figure 4.3 PCA clusters of Chloroquine, Rotenone and DMSO...... 105

Figure 4.4 Spearman’s correlation of the 38 parameters...... 106

Figure 4.5 Heat map depicting the cytological profiles of fractions (30 µg/mL) and compounds (30 µM) with hierarchical clustering...... 109

Figure 4.6 Bio-cluster 1...... 110

Figure 4.7 Bar plots showing biological profiles of fractions D and E, and compound

2.3...... 110

Figure 4.8 Bar plots showing biological profiles of fractions DE2, DE4 and DE5.. .. 111

Figure 4.9 Bio-cluster 2, extracted from Figure 4.5...... 112

Figure 4.10 Bar plots showing biological profiles of compounds in bio-cluster 2. .... 112

Figure 4.11 Heat maps and bar plots for other bio-clusters...... 113

Figure 4.12 Bar charts displaying the perturbations of biological profiles for fractions

(top) and pure compounds (bottom)...... 115

Figure 4.13 Structure and effects of 2.3 on hONS cell staining (Mitotracker, LC3b,

CellMask, Dapi)...... 118

Figure 4.14 Structure and effects of 2.4 on hONS cell staining (LC3b)...... 118

Figure 4.15 Structure and effects of 2.14 on hONS cell staining (LC3b)...... 119

Figure 4.16 Structure and effects of 2.16 on hONS cell staining (LC3b)...... 119

xiii

List of Tables

Table 2.1 Complete list of 38 cytological parameters generated after quantification process...... 47

Table 3.1 Number of herbal plants in each group, extracted from the previous PhD study.

...... 52

1 13 Table 3.2 NMR spectroscopic data (800 MHz for H and 200 MHz for C, CDCl3) of

10-Methoxybocconoline (2.1)...... 81

1 13 Table 3.3 NMR spectroscopic data (800 MHz for H and 200 MHz for C, CDCl3) of 6-

(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2)...... 89

Table 4.1 Cytological features generated and their associated stains and antibodies. . 102

Table 4.2 List of 16 parameters highly correlated with others with coefficients rs greater than 0.9...... 107

Table 4.3 Cellular parameters retained after Spearman’s correlation analysis...... 108

Table 4.4 List of fractions and compounds having significant effects to tested parameters, generated from Figure 4.12...... 116

xiv

Abbreviations

[α]D specific rotation

°C degrees Celsius

µg microgram

µL microliter

µm micrometer

µm2 square micrometer

µM micromolar

1D one dimensional

2D two dimensional

AC absolute configuration

AD anno Domini/Alzheimer’s Disease

C18 octadecyl bonded silica

calcd calculated

br broad

6-OHDA 6-hydroxydopamine

CDCl3 deuterated chloroform

CO2 carbon dioxide

COSY correlation spectroscopy

cmpd. compound

CP cytological profiling

d doublet

Dapi 4',6-diamidino-2-phenylindole

DCM dichloromethane

dd doublet of doublets

DMEM/F12 Dulbecco’s modified Eagle’s medium/nutrient mixture F12

DMSO dimethylsulfoxide

DMSO-d6 deuterated DMSO

DNP dictionary of natural products dq doublet of quartets

ECD electronic circular dichroism

EEA1 early endosomal antigen 1

ESI electronspray ionization

EtOAc ethylacetate

FA formic acid

FBS fetal bovine serum

FDA food and drug administration

FDR false discovery rate

Frtn. fraction g gram(s) h hour(s)

H2O water

HMBC heteronuclear multiple-bond correlation spectroscopy hONS human olfactory neurosphere-derived

HPLC high-pressure liquid chromatography

HRESIMS high-resolution electron spray ionization mass spectrometry or

spectrum

HSQC heteronuclear single-quantum coherence spectroscopy

Hz Hertz

xvi

IR infrared

J coupling constant

LC3 microtubule-associated protein light chain 3

LC-MS liquid chromatography/mass spectrometry

LRESIMS low-resolution electron spray ionization mass spectrometry or

spectrum m multiplet

M. cordata Macleaya cordata m/z mass to charge ratio

Med Median

MeOH methanol

CD3OD deuterated methanol mg milligram

MHz mega Hertz

MIC minimum inhibitory concentration min minute(s) mL millilitre

MOA mechanism of action/mode of action

MPTP 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine

MS mass spectrometry

MW molecular weight (g/mol) nm nanometer nM nanomolar

NMR nuclear magnetic resonance

PBS phosphate-buffered saline

xvii

PCA principle component analysis

PD Parkinson’s disease ppm parts per million pTLC preparative thin-layer chromatography q quartet

ROESY rotational nuclear Overhauser effect spectroscopy rt room temperature

Rf retention factor s singlet t triplet

TCM traditional Chinese medicine

TD-DFT time-dependent density functional theory

TFA trifluoroacetic acid or trifluoroacetate

TLC thin-layer chromatography

TOF time of flight

UV Ultraviolet

Vis Visible wt weight

chemical shift

xviii

CHAPTER 1. INTRODUCTION

1.1 Traditional Chinese Medicine

Having been taking the significant role in the healthcare system of China for thousands of years, Traditional Chinese Medicine (TCM) is an enormous accumulation of knowledge in regard to the treatment of various diseases, especially complex chronic disorders. Like Western medicine, TCM has its own theoretical basis and practical application.1 TCM is governed by eight principles, namely Yin, Yang, interior, exterior, cold, heat, deficiency and excess.2 The harmony between Yin and Yang, the two opposite essential forces in the body, is considered to be the centre in the TCM system3 (Figure

1.1). It has long been believed that disease occurrences and manifestations originate from the imbalance between these forces,2, 3 which can be caused by multiple factors; and the goal for TCM practitioners is to rehabilitate the stability of the two elements.3 The balance of Yin and Yang can be restored by taking various remedies including acupuncture, dietary therapies, Tui na4, 5 and Shiatsu6, 7 massage, in addition to the well-known herbal medicines. In term of disease diagnosis, TCM practitioners generally rely on four methods, which include inspection, listening and smelling, inquiring and palpation to guide the diagnostic process.8 However, in fact, there is a considerable variability of diagnosis across different practitioners,9, 10 which puts weigh on the consistency and reliability of TCM treatment. Constituently, TCM can be considered as an experience- based approach rather than a targeted and systematic strategy in Western medicine. In comparison with Western medicine, the two forces in TCM can be referred to the homeostatic state.3 It was thought that herbs which rejuvenate the Yang element have a tendency to increase wellbeing, potentially through the enhancement of mitochondrial

oxidative processes,11 while Zhu and Woerdenbag12 suggested that Yin–nourishing herbs could relate to the maintenance of mitochondrial ATP generation.

Figure 1.1 Yin (black) and Yang (white) as two opposite forces in TCM system. Adapted from https://svgsilh.com/image/152420.html. Licensed by CC0 1.0 https://creativecommons.org/publicdomain/zero/1.0/?ref=ccsearch&atype=html

Herbal medicines (as illustrated in Figure 1.2), an important branch of the TCM structure, have held a significant role in this system owing to their tremendously frequent appearance in various ancient texts. For example, about 58% of drugs documented in the

Compendium of Materia Medica, one of the most famous Chinese herbology literature written in the Ming dynasty (⁓1590 AD), were herbal medicines.1 The Pharmacopoeia of the People's Republic of China has also held records of several herbal medicines, along with animal and mineral medicines, with the number increasing dramatically from only

78 in the first version to 1146 in the 2005 edition.1

Figure 1.2 Chinese herbal medicine. Adapted from https://pixabay.com/photos/chinese-medicine- donguibogam-2178253/. Licensed by Pixabay License https://pixabay.com/service/license/

Practically, TCM herbal medicines can be prescribed as a single portion or as a formula which consists of different herbs, minerals or animals, each of which contains a considerable number of chemical constituents. Based on this fact, it can be proposed that

TCM recipes are multi-component and multi-target agents that behave the same way as the combination therapy of multi-component drugs.13 Besides, since TCM practice is based on centuries of experience, particularly in focusing on the symptoms of a specific condition, drug discovery from TCM would be likely to have a higher chance of identifying biologically active small molecules compared to random collection and screening.1 The discovery of artemisinin from the plant Artemisia annua,14 (Figure 1.3) an effective anti-malaria compound which saves millions of lives around the globe, is a successful story of TCM integrating with drug discovery.

B A

Figure 1.3 Discovery of Artemisinin from Artemisia annua. (A) The Artemisia annua herb, adapted from https://www.flickr.com/photos/lcroth/5455231543/in/photolist-9ja3vx-9j7zfo-9je8Y9-9jdjHs- 9je5x9-9jdeuW-9j857w-9jdLcS-9j9TTK-9jbAgi-9jdPBW-9jaLxK-9j89hs-9jak9B-9jeW85-9j4uhg- 9j56Gv-9jbART-9jahRi-nMyfRB-9jbtxx-9j4f3P-9j7A2h-9jbQMr. Licensed CC BY 2.0 https://creativecommons.org/licenses/by/2.0/. Image was generated from cropping the original. (B). Structure of Artemisinin.

TCM delivers a great challenge for researchers due to its complexity, but at the same time, it presents a great opportunity to search for new therapies for complex and uncured conditions such as cancers or neurodegenerative diseases.15 Acknowledging the long-lasting history of TCM and its potential ability to cope with current health issue, this project was designed to investigate the constituents in a TCM plant, which can be used

as a source of chemical probes, leading to novel therapeutic opportunities for the neurodegenerative disorder Parkinson’s Disease.

1.2 Parkinson’s Disease

Parkinson’s Disease (PD) is the second most common neurodegenerative disorder only after Alzheimer’s Disease and affects roughly 1% of the global population over the age of 65.16, 17

PD is pathologically characterized by the progressive degeneration of dopaminergic neurons in the substantia nigra located in the midbrain and the associated loss of dopamine input to the striatum of the forebrain (Figure 1.4).16, 17 Another hallmark of PD is the presence of Lewy bodies, the intracellular inclusion bodies containing the protein alpha-synuclein, found in the surviving dopaminergic neurons.16, 18 The gradual loss of these dopamine producing neurons not only results in the impairments of motor functions such as resting tremors, muscular rigidity, bradykinesia and postural instability16 but also induces some non-motor symptoms which consist of mood changes,

Figure 1.4 Pathophysiology of Parkinson’s Disease by Blausen.com staff, https://en.wikipedia.org/wiki/Pathophysiology_of_Parkinson%27s_disease#/media/File:Blausen_0704_P arkinsonsDisease.png. Licensed CC BY 3.0 https://creativecommons.org/licenses/by/3.0/. Panel depicts normal pigmentation of the substantia nigra pars compacta produced by the dopaminergic neurons versus that in PD where there is a significant depletion of the neurons.

depressions, olfactory problems and sleep disorders.19 These symptoms can lead to severe disability, impaired quality of life, and shortened life expectancy.19

Despite quite a few intensive studies having been carried out with the hope to have a comprehensive knowledge about PD pathogenesis, it seems that this disorder yet remains mysterious. It is suggested that the development of PD could be contributed by many factors, originating from the environment, genetic or complex environment–gene interactions.16

Because of the limitation of knowledge about PD, current treatments of this disorder mainly target the dopaminergic system, with the aim to restore dopamine signalling to decrease the severity of the motor symptoms.17 They involve the use of dopamine precursors,20 dopamine agonists,21 dopamine reuptake inhibotors,22, 23 etc. as shown in Figure 1.5.

Figure 1.5 Schematic of various methods for PD treatment. Adapted from reference 23. Permission granted with license number 4616201220451 from Copyright Clearance Center http://www.copyright.com

For example, the monoamine oxidase B (MAO-B) is an enzyme functioning in the dopamine metabolism to form 3,4-dihydroxyphenylacetic acid, which is subsequently converted to homovanillic acid by catechol-O-methyl transferase (COMT).24 Inhibition

of MAO-B has been integrated into the therapeutic treatments for PD to decrease the metabolism of dopamine, resulting in increased concentrations of dopamine in the brain, which leads to a reduction in motor symptoms.23 Selegiline (1.3 in Figure 1.6) is an approved MAO-B inhibitor. Similarly, catechol-O-methyl transferase (COMT) is an enzyme that works in a parallel metabolic pathway to transform dopamine into 3- methoxytyramine. This metabolite is eventually oxidized by MAO-B to produce homovanillic acid.25 COMT inhibitors therefore also induce an increase in brain dopamine levels, relieving motor symptoms, and have been used as a combination therapy for PD treatment.23 One of the approved COMT inhibitors is Tolcapone (1.4 in Figure

1.6).

Figure 1.6 Structures of some approved substances in PD treatment. Levodopa (1.1) and carbidopa (1.2) are used as dopamine precursors. Selegiline (1.3) is an irreversible MAO-B inhibitor. Tolcapone (1.4) is a COMT inhibitor. Apomorphine (1.5) and pramipexole (1.6) are dopamine agonists binding to the dopamine receptors in the brain.

Although these therapies could diminish the motor symptoms, they are usually accompanied by many side effects such as upset stomach, nausea, orthostatic hypertension, dyskinesia and hallucinations.26 Regrettably, these medications cannot reduce, stop or reverse the progress of neuronal loss.18 In other words, it is not known what causes PD and it is unclear what to target.27

Recently, there has been an expansion in the chemical biology area, particularly in the field of utilizing chemical probes, especially those derived from natural products, to explore the biological processes,18, 28 providing many important outcomes in the field of drug discovery.28 These notions indicate that natural products are very promising and may cope with the current problems, holding the key to the fight against PD.

1.3 Cytological Profiling

Natural products discovery is undoubtedly a promising approach in studying of devastating diseases such as neurodegenerative disorders or cancers, yet there exists a difficult challenge, being how these diseases should be researched in an effective way and how the substances or the compounds should be evaluated properly. Most chemical screens to date have been performed against a specific target or a predefined phenotype.18

For example, targeted anticancer discovery from natural products is usually carried out by screening crude extracts or pure compounds in high-throughput screens using reporter assays such as enzyme-linked immunosorbent assays (ELISAs)29 or reporter gene assays

(RGAs).30-33 It is evident that target-based approaches have been a success in drug discovery with nearly 70% of FDA-approved first-in-class drugs from 1999 to 2013 coming from these strategies.34 However, despite the fact that target-based assays are generally fast and easy to develop and operate;35 and most of them can be performed in the form of high-throughput screening, they mainly focus on only a single molecular target or pathway, which means that discovery is limited to a specific set of biological targets.36 The critical disadvantage of this approach is that it cannot reveal potent effects that are not being specifically assessed in the screen;18 therefore, hits in these screens are unable to account for off-target effects, and false-positive results may even be generated in case of anticancer screens where cytotoxic compounds are present.30

The story of Alzheimer’s Disease (AD) drug discovery can be seen as an illustration for the limitation of target-directed strategies. AD, like PD, is also a devastating neurodegenerative disorder whose neuropathological hallmarks are the extracellular plaques containing the protein amyloid beta (Aβ) and the intracellular neurofibrillary tangles containing the tau protein.37, 38 It has been more than a decade since the disease was thought to be driven by the deposit of the amyloid plaques (Aβ) in the brain tissue;37-39 thus treatments that slow down the production of Aβ or that increase the

Aβ clearance have been believed to decelerate the progression of AD.37 Taking this view, many studies have been performed to alternate the Aβ formation, leading to the discovery of several anti-amyloid agents such as Bapineuzumab, Solanezumab or Tarenflurbil.

Unfortunately, although these drugs could lower the biomarker Aβ in the brain, they have failed in clinical trials as there were no significant differences in term of improving thinking ability between patients administering the drugs and placebo.38 This in turn brings doubt to the amyloid cascade hypothesis in AD.

To overcome the prejudice in target-based screening, huge efforts have been made recently to develop a broader screen for biological functions, resulting in the evolution of a multiparametric phenotypic profiling approach that is able to quantify multiple cellular responses of drug-treated cells.40 Specifically, an unbiased image-based screening technique entitled “cytological profiling” (CP) has been developed in recent years.41-43

CP is a technique which integrates automated fluorescence microscopy and image analysis to evaluate small molecules and their effects on several cellular phenotypes.18

CP screens are governed by two main steps. The first one is the use of diverse fluorescent probes to visualize various important cellular components (e.g. organelles, cytoskeleton).41 The second step employs fluorescence microscopy and sophisticated

image processing systems to effectively quantify any physiological perturbations induced in every cell.

Compared with target-based screening, CP approach captures a global view by determining the overall effect rather than examining specific molecular targets or pathways.30 In other words, if target-based strategies require that the biology underlying a disease needs to be clarified well enough to allow the assignment of the disease to a specific target, phenotypic or CP assays may address the uncertainty and complexity of the biological system by taking a broader look which in turn could help researchers discover new targets and mechanisms.35 For instance, phenotypic approaches in AD research have revealed new targets and strategies for this disease.44

One of the most beneficial attributes of CP is that it removes the bias in target- based approaches, hence generating impartial multidimensional phenotypic profiles which can be used to recognize the similarities or differences in biological space between different samples.18

The robustness of CP is also backed up by Perlman et al.42 who delivered an important finding in which the authors proposed that compounds having different potencies, but sharing similar mechanisms of action, are likely to have similar cytological profiles over a concentration-dependent manner. Based on such Perlman’s vision, many studies on MOA prediction, and MOA identification, for bioactive compounds have been carried out successfully by comparing their phenotypic influences with those of reference molecules whose MOAs have been known.18, 30, 41, 43, 45, 46

An interesting example illustrating the use of CP in drug discovery is the study of

Ochoa et al.30 (Figure 1.7) in which compounds with antimitotic and calcium channel modulating activity were isolated using CP platform. Upon screening a microbially

derived extract, they were able to identify six extracts whose cytological profiles clustered closely with previously profiled antimitotic agents. Further purification of the extracts revealed diketopiperazine XR334 possessing the observed antimitotic of the original extract and nocapyrone L whose cytological profile clustered with known calcium channel modulators. The research indicated that CP could be a powerful technique for the characterization of compound modes of action.

Figure 1.7 Discovery of the antimitotic compound XR334, regenerated from reference 30 and 60. (A) Cytological profiles of original natural products extracts and known antimitotic compounds. (B) CP analysis of diketopiperazine XR334. (C) Chemical structure of diketopiperazine XR334.

Applications of CP however are not just limited to antimitosis; prodigious progress involving CP has been made in respect of cancer30, 40, 41, 45, 47-50 and bacterial51-54 drug discovery.

Owing to the dominance of CP, especially its unbiased properties, research groups in our institute recently established a CP platform to investigate phenotypic influences of natural products on a robust PD model, human olfactory neurosphere-derived (hONS) cells with the aim to discover small molecules that can be used to explore the molecular mechanisms of PD. 55-60

1.4 Human Olfactory Neurosphere-derived Cell Model

Generally, the complex biology underlying PD has limited the availability of developing a complete model for this disease. In fact, PD studies have utilized several animal models, typically generated from xenobiotic subjections (e.g. 6-OHDA- or MPTP- treated mice),61, 62 genetic manipulation (e.g. mice with mutations of genes located in

PARK loci),63 or a combination of both to induce PD-like phenotypes. These models, although having offered insights into PD research, are accompanied by certain limitations. Particularly, in the intoxication models, while the toxins can impact the dopaminergic system, the models fail to replicate the classic pathological hallmarks and the progressive nature of human disease.64 For example, baboons subjected to treatment of MPTP were found to develop fine granular α-synuclein accumulations and some larger deposits, but these inclusions did not reproduce the architecture of Lewy bodies found in human.65, 66 Similarly, continuous infusion of MPTP with minipumps into mice was reported to induce PD-like syndrome with the formation of intracellular aggregated bodies,67 which also failed to resemble the human unique Lewy bodies in term of morphology.66

The genetic models also do not succeed in phenocopying PD in a way that they generally lack a parkinsonian behavioural phenotype and/or the characteristic features of the human disease.64 The fruit fly Drosophila melanogaster model can be taken as an example. Drosophila genes are highly homologous to those in human, and entire genetic pathways have been conserved during evolution,66, 68 making it a valuable model system for the research of many biological processes. Overexpression of human wild-type and mutant (A53T and A30P) α-synuclein in the above transgenic fruit fly has shown selective depletion of dopaminergic neurons.69 The degenerating dopaminergic regions also contained α-synuclein-positive inclusions, which were composed of granules and

filaments whose size and location was reminiscent of Lewy bodies of PD patients.66, 70

However, only a few neurons were found to carry the inclusions compared to the widespread distribution occurring in human synucleinopathies,71 and they did not contain insoluble α-synuclein.72 Moreover, the nervous system of the fly is relatively simple, which limits the model in a way that only a narrow range of locomotor behaviours can be tested.66 Another example for the limitation of genetic model is the Nurr1 mice. Nurr1 belongs to the nuclear receptor superfamily and is involved in the differentiation and development of nigrostriatal dopaminergic neurons.63 It was demonstrated that mutations in Nurr1 affected the transcription of dopamine transporter73 and may cause chronic dopamine alternations that could increase vulnerability to PD.63 Indeed, Le et al.74 reported that Nurr1 mutations may be associated with familial PD. The importance of

Nurr1 and its potent relationship with PD progression have led to the development of

Nurr1-manipulated mouse models from which much has been learnt. Interestingly, it was found that Nurr1 knockout mice exhibited a decrease in dopamine levels in the striatum, decreased dopaminergic nigrostriatal neurons, and decreased locomotor activity and rotarod performance, as they age.75 This model is possible for PD studies because it possesses the progressive dopaminergic phenotype which may bear a resemblance to that seen human disease, yet it can only show pathology in the nigrostriatal pathway but cannot reproduce the broader pathology of PD.63

Overall, although the models in PD research have helped uncover various key elements in the pathogenesis of this disease, no animal model to date is able to capture the whole spectrum of PD, especially in modelling sporadic, late-onset condition, which accounts for over 90% of human case.64 This leads research groups in our institute to find an alternative, but complementary approach, being the development of a novel PD model

based on cells derived from the human olfactory epithelium,64 entitled human olfactory neuroshere-derived (hONS) cells.

Figure 1.8 The olfactory epithelium by Encyclopædia Britannica. Adapted from https://www.britannica.com/science/olfactory-epithelium/media/1/427517/48235. Access Date: June 25, 2019.

The olfactory epithelium (Figure 1.8) is the epithelial layer located inside the nasal cavity and consists of several cell types, one of which is the olfactory receptor neurons.76 These neurons are considered as the most important cell type as it maintains the sense of smell. Olfactory receptor neurons are generated from stem cells throughout life to replace the loss of old or injured neurons.77 The regeneration of the olfactory epithelium, together with the availability of attaining the cells from the human olfactory mucosa, typically through biopsy in human adults,78 has made olfactory epithelium a source of biological materials for the neurological studies such as PD.76, 77, 79, 80

The non-transformed and non-immortalized hONS cells (Figure 1.9) are thought to be able to model functional aspects of PD,56, 64, 80 proposedly due to the disease-specific

differences in term of metabolic features and phenotypes between PD patent-derived cells and those of healthy controls.64, 80, 81

Figure 1.9 hONS cells; image adapted from reference 59 with permission.

In the above CP platform, a total of 38 cytological parameters were assessed by staining PD patient-derived hONS cells, belonging to the cell line C1200080013, with fluorescent probes targeting several cellular pathways and organelles implicated in PD.

These included mitochondria, early endosomes, lysosomes, cytoskeleton, apoptosis and autophagy. The phenotypic profiles were then assessed using fold change analysis.

Inspired by the work, this current study has also applied cytological profiling as part of investigating chemical probes for future studying of PD.

1.5 Chemical Probes

Chemical probes are defined as small molecule modulators that can be utilized to study and manipulate their molecular targets in physiology and pathology.82 In other words, they are compounds with the capability to induce perturbations of one or multiple components in a protein system, resulting in a unique biological response.83 Chemical probes have demonstrated as powerful tools in recent biological research, owing to their unique advantages.84 Probes can work with almost any cell lines, and when applied to cells or organisms, they can rapidly modulate a protein or a protein domain, reversely switching it on or off via a binding interaction.18, 82 The flexibility of chemical probes in

producing perturbations that can be tunable in a dose-dependent manner has made them more favorable than genetic strategies, which involves introducing mutations, mostly with delayed and long lasting effects.18 For example, chemical probes are highly harmonious with the application of RNA interference (RNAi), particularly in being able to disrupt a specific function of the targeted protein rather than completely removing the protein, hence warding off unwanted function or scaffold issues.85 Moreover, they can also distinguish between effects induced by scaffolding and effects due to inhibition of catalytic when coupled with RNAi.82 Importantly, the ability of chemical probes to mimic the effects or mode of actions of known therapeutic drugs is extraordinarily useful for translational research,82 paving the way for the discovery of new therapies for devastating diseases.

Diazonamide A (Figure 1.10) is an interesting example of a natural product as chemical probe that was used to discover a new role of the enzyme ornithine δ-amino transferase (OAT) in mitosis, leading to a potential strategy in fighting cancer.28 It is well known that using tubulin-targeting agents (e.g., paclitaxel, vinblastine) to inhibit mitosis, hence effectively blocking cell division, is an important therapy in cancer. This treatment, however, is usually accompanied by serious side effects such as significant low blood counts or weight loss, due to the global interference of these drugs in mitosis of all cells that are going through proliferation.28, 86 Therefore, investigation of compounds that are more selective or that target components other than the tubulins may offer a more

Figure 1.10 Structure of Diazonamide A.

favorable drug class with fewer unwanted effects than the current mitotic inhibitory approaches. Diazonamide A, a macrocyclic natural product isolated from the colonial marine ascidian Diazona chinensis,87 may fall within the above criteria by showing significant inhibition towards several cancer cell lines, such as HCT116 human colon carcinoma and B16 murine melanoma.87, 88

Diazonamide A was initially supposed to be an antimitotic agent as cells treated with this compound exhibited failure to form mitotic spindles, a characteristic phenotype related to tubulin-interacting molecules.89 However, there was no evidence in vitro that confirmed the direct interactions between Diazonamide A and tubulin or microtubules.88

In addition, Williams et al.90 demonstrated that a structural and functional analog of this compound was well tolerated by mice when administered. Moreover, no significant side effect was observed in these mice compared to the tubulin-binding treatments.90 This information implied that there was an unknown mechanism of action by which

Diazonamide A may work. It was later revealed by Wang et al.91 that instead of tubulin, the cellular receptor of Diazonamide A was the ornithine δ-amino transferase enzyme, a mitochondrial protein involved in ornithine metabolism but previously unknown to have any function in mitosis.28, 89 These data proposed that there may be involvement of OAT in mitosis and that Diazonamide A could be a valuable probe of the oncology research related to OAT-dependent mitosis,89 potentially offering a new target for chemotherapy drug development.

Chemical probes are doubtlessly useful molecular tools to interrogate biological questions. Nonetheless, to be considered as a high-quality chemical probe, a small molecule needs to fall within some certain criteria. Several strict requirements have been proposed to govern its definition, but generally, chemical probes must exhibit sufficient potency and selectivity data to confidently link their in vitro profiles to their in vivo or

cellular profiles.92 Possessing a well-defined mechanism of action to interrogate biological questions is another critical principle of chemical probes.82, 85 In addition, they also need to have sufficient aqueous solubility and membrane penetrability.18, 82, 85

Chemical probe application is gradually spreading to neurology research with more and more studies utilizing this molecular tool to probe several biological pathways.

Figure 1.11 gives a list of small molecules used to probe PD biology that have progressed to clinical trial as disease-modifying or neuroprotective agents.93 For example, creatine

(1.7) is a natural compound that plays an important function in cellular energy. Creatine can be converted to phosphocreatine, an intermediate that can then donate a phosphoryl group to ADP to synthesize mitochondrial ATP. As PD is believed to be linked to mitochondrial dysfunction, utilizing creatine as a probe to modulate mitochondria features is an emerging approach in PD research.94, 95

Figure 1.11 Chemical structures of some probes in PD study. 1.7: Creatine, which is used to modulate mitochondrial function. 1.8: Deferiprone, an iron chelator. 1.9: Inosine, a urate precursor. 1.10: Isradipine, a calcium antagonist. 1.11: Sarsasapogenin, an oral neurotrophic modulator.

Obviously, natural products have long been recognized as privileged scaffolds because they have evolved specifically to interact with biological macromolecules, especially proteins.96, 97 As evidence of this fact, natural product-based screening libraries routinely yield higher hit rates than synthetic small molecule libraries.98 In respect to this belief, this current study utilized TCM as a source of chemical probes for the future research on PD.

1.6 NMR fingerprinting

The well-known nuclear magnetic resonance spectroscopy has long been the core for chemists to determine the chemical structures of compounds ranging from very simple molecules to extremely complex systems. The use of NMR, however, is not only limited to structural elucidations; it is constantly extended and improved, with applications spanning many different areas including analytical chemistry, food industries99 or interestingly, metabolomics.100, 101 Knowing that NMR is a robust tool in chemistry, this research has employed NMR fingerprinting or NMR-guided isolation, one of several aspects of NMR, to assist the isolation of chemical compounds from the selected TCM.

Undoubtedly, the main goal in natural product chemistry is to identify and isolate constituents with desired bioactivities from complex natural product mixtures.102 As a result, quite a few techniques have been continuously developed to achieve the above objective. One important development, which should not be undervalued, is the blooming of bioassay-guided isolation, also known as bioassay-guided fractionation.102 Bioassay- guided isolation, generally, takes the function of both chemical fractionations and bioassays. Upon each fractionation, the subfractions are assayed to determine which one would be chosen for further purification based on the obtained results of the bioactivity tests; and each level of fractionation also reduces the complexity of the mixture. The

process is repeated until the constituents responsible for the biological effects can be purified.102

Since the dawn of its invention, bioassay-guided isolation has been the gold standard in natural product research, delivering many molecules for biological studies and drug developments such as artemisinin14 and vinblastine.103

The study of He et al.104 on the identification of antibacterial metabolites from the endophytic fungus Emericella sp. TJ29 can be viewed as an example of bioassay-guided isolation (Figure 1.12). The EtOAc extract of the fungus was screen against five drug- resistant microbial pathogens (MRSA ATCC 43300, E. faecalis ATCC 29212, ESBL- producing E. coli ATCC 35218, P. aeruginosa ATCC 15442, and K. pneumoniae ATCC

700603). The sample exhibiting potential antibacterial activities was fractionated by column chromatography to obtain several sub-fractions which were later tested to determine the most active one. Further purification on that fraction revealed eight compounds, five of which were new. Compound 1.12 (emervaridone A) and 1.14

A fungal extract Emericella sp TJ29

Figure 1.12 Bioassay-guided isolation of 5 new compounds, emervaridone A-C (1.12-1.14) and varioxiranediol A-B (1.15-1.16) from Emericella sp. TJ129. Adapted with permission from reference 104. Copyright 2017 American Chemical Society.

(varioxiranediol A) were reported to have low toxicity to mammalian cells and show strong inhibitory effects against ESBL-producing E. coli and P. aeruginosa.

Although taking the leading role in the drug discovery area owing to its effectiveness, bioassay-guided isolation has certain drawbacks.102, 105 One disadvantage is that this technique is a time- and cost- consuming process as it puts together two major areas, bioassay and fractionation; these two processes need to be repeatedly carried out one after another on a crude material before a pure bioactive compound is identified, resulting in a considerably large amount of workflow. The rediscovery of previously known compounds could be another issue of bioassay-guided isolation106 since the chemical information regarding the structures of the constituents in active fractions remain unclear during the process and are only revealed when they have been characterized. Furthermore, this process tends to biasedly target dominant constituents in each extract or fraction; this sometimes fails to detect minor products which also possess bioactivities.107

NMR fingerprinting, in contrast, is fast, reliable and less expensive compared to bioactivity-escorted approach. The NMR spectrum of a metabolic matrix is a fingerprint of its entire chemical components.56 The distribution of the signals, along with their splitting patterns and intensities give more information about the structures of the constituents present in the fractions in comparison with the traditional bioassay direction, enabling detailed chemical analyses though out the fractionations to target constituents of interest,56, 108 avoiding the known compounds being redetected. NMR fingerprinting also effectively reveals the presence of minor constituents, hence overcoming the critical disadvantage of bioassay guidance being that those constituents may be overlooked.

Shang et al.109 demonstrated their research in which NMR-guided isolation was used to isolate a set of formyl‐phloroglucinolmeroterpenoids (FPMs) from the leaves of

Eucalystus robusta. The characteristic signal of the compounds, which was the hydroxyl proton, was tracked during the process, resulting in the isolation of nine FPMs, one of which, eucalrobusone C, exhibited significant cytotoxicity against several cancer cell lines with IC50 less than 10 µM.

Another interesting example comes from Grkovic et al. work108 in which minor compounds from the active fraction inhibiting leucine transport in prostate cancer cells were successfully isolated by utilizing NMR fingerprinting (Figure 1.13). Two new

Figure 1.13 1H NMR fingerprint analysis, taken from reference 108. (A) Complete NMR fingerprint of the HTS active fraction. (B) Comparison of the active fraction NMR fingerprint with those of the spectra acquired for the pure compounds 1–4. The spectra are expanded in the region δH 6.35 to 4.35, and the resonances used to exemplify the presence of venuloside A (1) are highlighted with a red rectangle and that of venuloside B (2) with a blue rectangle. (C) Comparison of the active fraction NMR fingerprint with that of the spectrum acquired for venuloside C (3). The spectra are expanded in the region δH 5.25 to 4.45, and the resonance used to exemplify the presence of 3 in the active fraction is highlighted with a green rectangle. (D) Comparison of the active fraction NMR fingerprint with that of the spectrum acquired for venuloside D (4). The spectra are expanded in the region δH 5.15 to 4.45, and the resonance used to exemplify the presence of 4 in the active fraction is highlighted with a purple rectangle. All depicted spectra were acquired in DMSO-d6 at 600 MHz.

monoterpene glycosides, venulosides C and D were identified following the NMR analysis on the proton signals of minor metabolites as illustrated in Figure 1.13.

NMR fingerprinting is emerging as an innovative strategy in natural product discovery to rapidly identify bioactive constituent.56 Although it may not entirely replace the traditional bioassay-guided methods, this technique can be applied cooperatively with traditional bioassay guidance to target metabolites that could possess interesting biological activities in complex natural product mixtures.

1.7 Research objectives

1.7.1 Aims

This research project will principally investigate the chemistry of a herbal plant and will also evaluate the bioactivities of the natural products obtained. Consequently, the aims are:

 To extract, isolate and purify major and minor secondary metabolites including

known and new compounds to potentially be used chemical probes from

Macleaya cordata by NMR fingerprinting.

 To use structural elucidation techniques (1D and 2D NMR, UV, IR, LRESIMS,

HRESIMS) to determine the structures of all isolated compounds.

 To evaluate whether the purified compounds can cause certain perturbations to

hONS cellular components by utilizing cytological profiling.

1.7.2 Expected outcomes and significance

It is expected that based on the traditional knowledge of Macleaya cordata, the chemistry of this species will be intensively investigated, which results in the identification of both known and new compounds to be utilized as chemical probes to

study the biological pathways underlying complex diseases. This will also provide more information about this plant and will be a contribution to its phytochemistry research.

Moreover, as the molecular mechanisms of Parkinson’s Disease still exists as a mystery, the bioactivity outcomes of the compounds from this plant against PD cells are therefore important. There is an expectation that these compounds will cause certain perturbations in the PD cell phenotypes. These changes will not only contribute to the identification of chemical probes against this disease but also provide a better understanding about PD at the cell level, giving the opportunities to explore the biological pathways and processes, which, hopefully, will be studied further to identify the biological targets, eventually developing effective neuroprotective therapies for PD.

1.8 Project outline

This project focused on the isolation of natural products from the TCM Macleaya cordata, which could potentially be used as chemical probes to study the biology underlying PD. This was done by carrying out the NMR fingerprinting to target the compounds of interest. The isolated compounds were profiled by employing CP on PD hONS cells. Each compound was tested at a single concentration in triplicate. The results were clustered to generate a closer look on the CP profiles.

The outline of the following chapters is as follow:

Chapter 2 will provide details of all experimental procedures utilized in this project and a synopsis of the isolation process, as well as a brief summary of spectroscopic data of the isolated compounds.

Chapter 3 will give rationale for why Macleaya cordata was selected for chemical investigation in respect of PD, followed by a brief review of the chosen biota. An intensive assessment on the 1H NMR-guided prioritization of the fractions will also be

discussed later, together with the purification and structural elucidation of the two new metabolites and the fourteen known compounds.

Chapter 4 will present the CP screening results and identification of compounds potentially as chemical probes for PD research. Future directions on further clarification of the probes will also be discussed.

1.9 References

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CHAPTER 2. EXPERIMENTAL METHODS

2.1 General Experimental Procedures

NMR spectra were recorded in DMSO-d6, methanol-d4 or chloroform-d at 298 K on a Bruker Avance III HD 500 MHz spectrometer equipped with a BBO probe or a

Bruker Ascend 800 MHz spectrometer coupled to a TCI CryoProbe. Tetramethylsilane was used as internal reference. Chemical shifts were referenced to the solvent peak (δH

2.50 and δC 39.52 for DMSO-d6, δH 3.31 and δC 49.00 for mehtanol-d4, and δH 7.26 and

1 δC 77.16 for chloroform-d). Deuterated solvents used for NMR spectroscopy were purchased from Cambridge Isotope Laboratories Inc. All NMR spectra were processed with Mestrelab Research MNova 11 software. Gaussian 03 and HyperChem 7.5 software was employed to acquire calculated ECD spectra using DFT calculations. All UV spectra were conducted on a CAMSPEC M501 UV/vis spectrometer. IR spectra were recorded on a Bruker Tensor 27 spectrometer, and CD spectra on a JASCO J-715 spectrometer.

Optical rotations were measured on a JASCOP-1020 polarimeter with a quartz cell of 10 cm path length. LRESIMS was conducted on a Thermo Scientific MSQ Plus single quadrupole ESI mass spectrometer which was coupled to a Thermal Scientific Dionex

Ultimate 3000 RS UHPLC system equipped with an Accucore C18 LC column (2.6 μm,

150 × 2.1 mm). HRESIMS data were acquired on a Bruker MaXis Q-TOF mass spectrometer. Machery Nagel Polyamide CC6 (0.05-0.16 mm) or Sephadex LH-20 was used as stationary phase for open flash column chromatography. Thin-layer chromatography (TLC) was carried out on Merck Aluminium oxide 60 F254 neutral or

Silica gel 60 F254 Aluminium sheets. Preparative TLC was performed on Merck

Aluminium oxide 60 F254 basic glass plates. TLC was stained with Iodine and

Dragendorff’s reagent or visualized with ultraviolet wavelengths 254 and 365 nm. An

Alltech Hyperprep PEP C18 (300 Å, 8 μm, 250 × 22 mm) column and a Phenomenex

Luna C18 (100 Å, 5 μm, 250 × 10 mm) were used for semipreparative reverse-phase

HPLC. The HPLC system used for purification was Thermal Scientific Dionex Ultimate

3000 RS UHPLC, which consisted of a high-pressure pump with a degasser, an autosampler, a diode array detector and an automated fraction collector. All solvents used for chromatography, UV, IR, [α]D, CD and MS were Honeywell or RCI Labscan HPLC grade; the water used was filtered through a Millipore Milli-Q PF system.

2.2 Extraction and Isolation of Macleaya cordata

2.2.1 Plant Material

The whole plant of M. cordata was collected in Wufeng County, Yichang City,

Hubei Province, China in August 2013 and identified by Prof. Jingquan Yuan (Guangxi

Botanical Garden of Medical Plants, Nanning, China).

2.2.2 Extraction and Isolation of Compounds

The crude extract of M. cordata (40 g) was obtained through maceration of the air-dried and ground whole plant (750 g) with 95% ethanol in water (5L x 3). The compound isolation process in this project is summarized in Figure 2.1.

A portion of this extract (20 g) was initially chromatographed with a Polyamide

CC6 open flash column using 1L of 0%, 30%, 40%, 70%, 90% and 100% MeOH in water to yield six fractions A-F, respectively. Fraction B, C, D and E were selected for further purification after 1H NMR fingerprinting evaluation; this will be discussed more detailed in Chapter 3.

Fraction B (1.0 g) and C (0.3 g) were combined based on 1H NMR and subsequently subjected to Saphadex LH-20 chromatography with 100% MeOH elution, yielding 85 subfractions. TLC was then performed on each subfraction to combine those

M. cordata crude extract

Polyamide CC6 Flash

Fraction A Fraction B Fraction C Fraction D Fraction E Fraction F

Sephadex LH-20 + TLC

Fraction BC1 Fraction BC2 Fraction BC3 Fraction BC4 ……………. Fraction BC8

Repeated RP-HPLC Repeated RP-HPLC RP-HPLC

Compound 2.5 Compound 2.3 Compound 2.7 Sephadex LH-20 + TLC Compound 2.6 Compound 2.5 Compound 2.8 Compound 2.6 Compound 2.9 Compound 2.11 Compound 2.10 Compound 2.12 Compound 2.14 Compound 2.15 Compound 2.16

Fraction DE1 Fraction DE2 Fraction DE3 ……………. Fraction DE6

RP-HPLC RP-HPLC + Preparative TLC

Compound 2.13 Compound 2.1 Compound 2.2 Compound 2.4

Figure 2.1 Overview of the isolation process. sharing similar TLC profile. Eight subfractions (BC1 – 8) were obtained in total after

TLC analysis. 1H NMR guidance indicated that fractions BC2, BC3 and BC 4 contained characteristic signals of compounds of chemical interest; further purification was therefore carried out on these three subfractions. Compound 2.5 and 2.6 were initially obtained in small quantities from subfraction BC2 (70 mg) using a Phenomenex Luna

C18 column (gradient elution, MeOH:H2O, 0.1% TFA; 0 to 50 min, 25:75 to 65:35, 50 to 60 min, 65:35 to 100:0; flow rate, 4 mL/min; fraction collection, 1 fraction/min).

Subfraction BC3 (220 mg) was first chromatographed by reverse-phase HPLC utilizing an Alltech Hyperprep PEP C18 column (gradient elution, MeOH:H2O, 0.1% TFA; 0 to

50 min, 15:85 to 80:20, 50 to 60 min, 80:20 to 100:0; flow rate, 9 mL/min; fraction collection, 1 fraction/min) to afford 60 HPLC-subfractions. HPLC-subfractions 10 to 18 were combined and later purified with the Luna C18 column to afford three polyphenols, namely 3-O-Feruloylquinic acid (2.14, 4.0 mg, 0.00053% dry wt), methyl 3-O-

Feruloylquinate (2.15, 3.7 mg, 0.00049% dry wt) and Ferulic acid 4-O-glucoside (2.16,

2.7 mg, 0.00036% dry wt). 1H NMR analysis suggested the combination of HPLC- subfractions 25 to 29, from which three alkaloids, specifically columbamine (2.11, 1.2 mg, 0.00016% dry wt), berberine (2.12, 1.3 mg, 0.00017% dry wt) and chelerythrine (2.5,

4.1 mg, 0.00055% dry wt), were isolated using the Luna C18 column (gradient elution,

MeOH:H2O, 0.1% TFA; 0 to 50 min, 35:65 to 50:50, 50 to 60 min, 50:50 to 100:0; flow rate, 4 mL/min; fraction collection, 1 fraction/min). Bocconoline (2.3, 0.8 mg, 0.00011% dry wt) and chelilutine (2.6, 2.0 mg, 0.00027% dry wt) were purified from the combined fraction of HPLC-subfraction 35 to 40 by utilizing the same Luna C18 column (gradient elution, MeOH:H2O, 0.1% TFA; 0 to 50 min, 50:50 to 65:35, 50 to 60 min, 65:35 to

100:0; flow rate, 4 mL/min; fraction collection, 1 fraction/min). During the purification carried out on this fraction, trace of compound 10-methoxybocconoline (2.1) was identified. Literature database indicated that 2.1 was a new compound; however, given the low purity and little amount of 2.1, necessary spectroscopic data for this compound were not completely acquired. Efforts were made to detect the existence of 2.1 in other fractions by employing various methods including TLC, LRESIMS and 1H NMR.

Fraction DE3 was eventually found to contain 2.1; the isolation of this compound will be discussed later.

Further purification of fraction BC4 (140 mg) was also achieved by reverse-phase

HPLC with the Alltech Hyperprep PEP C18 column (gradient elution, MeOH:H2O, 0.1%

TFA; 0 to 50 min, 20:80 to 65:35, 50 to 55 min, 65:35 to 100:0, 55 to 60 min, 100:0; flow rate, 9 mL/min; fraction collection, 1 fraction/min), also yielding 60 HPLC-subfractions, each of which was later subjected to 1H NMR. HPLC-subfractions 24 to 25, 30 to 33 and

38 to 39 contained tetradehydroscoulerine (2.10, 2.7 mg, 0.00036% dry wt), sanguinarine

(2.8, 1.7 mg, 0.00023% dry wt) and chelirubine (2.7, 1.6 mg, 0.00021% dry wt), respectively. The Luna C18 column (gradient elution, MeOH:H2O, 0.1% TFA; 0 to 50 min, 35:65 to 50:50; flow rate, 4 mL/min; fraction collection, 1 fraction/min) was used to obtain coptisine (2.9, 2.0 mg, 0.00027% dry wt) from the combination of HPLC- subfraction 26 to 28.

Subfractions BC5 and BC6 were also subjected to HPLC purification, yet no other pure compound was identified due to the complexity of the mixtures.

Fraction D (2.4 g) and E (1.4 g) were also combined following 1H NMR analysis.

As the Sephadex LH-20 chromatography was found to give good separation for the previous fractions, it was later employed to characterize the combined fraction D and E.

By using the same conditions and protocols as previously carried out, 90 subfractions were obtained after the Sephadex LH-20 chromatography, and 6 fractional combinations

(DE1 ‒ 6) were achieved after TLC analysis. Fraction DE2 (32 mg) was first chromatographed with the Alltech Hyperprep PEP C18 column (gradient elution,

MeOH:H2O, 0.1% TFA; 0 to 50 min, 45:55 to 80:20, 50 to 55 min, 80:20 to 100:0, 55 to

60 min, 100:0; flow rate, 9 mL/min; fraction collection, 1 fraction/min) and later with the

Luna C18 column (gradient elution, MeOH:H2O, 0.1% TFA; 0 to 50 min, 40:60 to 60:40,

50 to 55 min, 60:40 to 100:0, 55 to 60 min, 100:0; flow rate, 4 mL/min; fraction collection,

1 fraction/min) to afford p-coumaroyltyramine (2.13, 0.7 mg, 0.00009% dry wt).

As previously discussed, the new compound 2.1 was present in subfraction DE3.

In order to decomplexify this mixture, subfraction DE3 (20 mg) was subjected to further purification with reverse-phase HPLC, affording 60 HPLC-subfractions by utilizing the

Alltech Hyperprep PEP C18 column (gradient elution, MeOH:H2O, 0.1% TFA; 0 to 50 min, 50:50 to 65:35, 50 to 55 min, 65:35 to 100:0, 55 to 60 min, 100:0; flow rate, 9 mL/min; fraction collection, 1 fraction/min). HPLC-subfraction 53 to 56 were combined and subsequently purified by the Luna C18 column (gradient elution, MeOH:H2O, 0.1%

TFA; 0 to 50 min, 60:40 to 65:35, 50 to 55 min, 65:35 to 100:0, 55 to 60 min, 100:0; flow rate, 4 mL/min; fraction collection, 1 fraction/min) to give 6-(1-Hydroxyethyl)-5,6- dihydrochelerythrine (2.4, 0.5 mg, 0.00007% dry wt).

Given the relatively low quantity of the fractions as well as the compound 2.1, mass spectrometry was employed as a more sensitive approach compared to 1H NMR to trace this compound. As a result, LRESIMS indicated that HPLC-subfractions 30 to 34 contained 2.1 based on similarities in mass spectra. These subfractions were later combined. Attempt to enrich 2.1 was made by performing a further purification on the obtained mixture with a Luna C18 column (gradient elution, MeOH:H2O, 0.1% TFA; 0 to 50 min, 55:45 to 65:35, 50 to 55 min, 65:35 to 100:0, 55 to 60 min, 100:0; flow rate, 4 mL/min; fraction collection, 1 fraction/min), followed by the same Luna C18 column

(gradient elution, MeOH:H2O, 0.1% TFA; 0 to 50 min, isocratic 55:45, 50 to 55 min,

55:45 to 100:0, 55 to 60 min, 100:0; flow rate, 4 mL/min; fraction collection, 1 fraction/min). However, this failed to achieve the compound of interest with acceptable purity because of the complexity and the poor separation of the HPLC run. TLC of the mixture containing 2.1 with basic aluminium oxide plates, using DCM:Methanol of 99:1

as the solvent system, showed that this compound was well-separated from the mixture.

The effort of purification of 2.1 was therefore shifted to employing preparative TLC. 10-

Methoxybocconoline (2.1, 0.1 mg, 0.00001% dry wt) was eventually isolated by applying the mixture onto a Merck Aluminium oxide 60 F254 basic glass plate, with DCM:Methanol of 99:1 as the mobile phase.

Assessment of the LRESIMS data of the HPLC-subfractions generated from the purification process also revealed the existence of 6-(1-Hydroxyethyl)-10-methoxy-5,6- dihydrochelerythrine (2.2) at HPLC-subfraction 47 in the above isocratic separation. The mass of this compound, together with the structure predicted from the 1H NMR and literature database, suggested that 2.2 could be another new compound. The subfraction was later subjected to preparative TLC with a Merck Aluminium oxide 60 F254 basic glass plate, developed with 100% DCM to afford 2.2 (0.1 mg, 0.00001% dry wt).

2.2.3 Spectroscopic Data of Isolated Compounds

10-Methoxybocconoline (2.1): White powder; 12 1 12a 2 O 11 O + 10 10b HRESIMS m/z 410.1600 [M+H] (calcd. for C23H24NO6, 9 4a O 4b 3 10a 4 6a 6 N 8 410.1598). UV (CH2Cl2) λmax (logε) 233 (4.72), 279 O 7 O 1' OH (4.70), 329 (4.41). IR νmax 2929.7, 1466, 1335.7, 1239.5, 2.1 -1 1 1041 cm . H NMR (800 MHz, CDCl3): 7.11 (s, 1H, H-1), 7.66 (s, 1H, H-4), 4.64 (dd,

2.4, 10.4, 1H, H-6), 6.59 (s, 1H, H-9), 8.32 (d, 8.8, 1H, H-11), 7.47 (d, 8.8, 1H, H-12),

6.06 (d, 1.6, 1H, -OCH2O-), 6.04 (d, 1.6, 1H, -OCH2O-), 3.89 (s, 3H, 7-OCH3), 3.95 (s,

3H, 8-OCH3), 3.93 (s, 3H, 10-OCH3), 2.71 (s, 3H, N-CH3), 3.48 (m, 1H, H1'a), 3.02 (m,

13 1H, H1'b), 2.75 (d, 10.4, 1H, 1'-OH). C NMR (200 MHz, CDCl3): 104.5 (C-1), 148.3

(C-2), 147.7 (C-3), 99.8 (C-4), 127.0 (C-4a), 138.2 (C-4b), 60.0 (C-6), 128.6 (C-6a),

140.5 (C-7), 152.5 (C-8), 97.5 (C-9), 153.7 (C-10), 113.4 (C-10a), 123.1 (C-10b), 124.6

(C-11), 123.5 (C-12), 130.7 (C-12a), 101.2 (2,3-OCH2O-), 61.5 (7-OCH3), 56.3 (8-

OCH3), 56.0 (10-OCH3), 42.4 (N-CH3), 61.6 (C-1').

6-(1-Hydroxyethyl)-10-methoxy-5,6- dihydrochelerythrine (2.2): White powder; HRESIMS

+ m/z 424.1752 [M+H] (calcd. for C24H26NO6, 424.1755).

UV (MeOH) λmax (logε) 201 (4.16), 225 (4.11), 279 (4.10),

-1 1 327 (3.77). IR νmax 3395, 2985.6, 1593.2, 1466, 1382.2, 1248.8, 1081.3 cm . H NMR

(800 MHz, CDCl3): 7.11 (s, 1H, H-1), 7.65 (s, 1H, H-4), 4.18 (d, 9.6, 1H, H-6), 6.60 (s,

1H, H-9), 8.31 (d, 8.8, 1H, H-11), 7.46 (d, 8.8, 1H, H-12), 6.06 (d, 1.6, 1H, -OCH2O-),

6.05 (d, 1.6, 1H, -OCH2O-), 3.87 (s, 3H, 7-OCH3), 3.96 (s, 3H, 8-OCH3), 3.93 (s, 3H, 10-

13 OCH3), 2.68 (s, 3H, N-CH3), 3.14 (dq, 9.6, 5.6, 1H, H-1’), 1.09 (d, 5.6, 3H, 1’-CH3). C

NMR (200 MHz, CDCl3): 104.6 (C-1), 148.4 (C-2), 147.7 (C-3), 99.5 (C-4), 126.7 (C-

4a), 138.4 (C-4b), 64.9 (C-6), 128.2 (C-6a), 141.1 (C-7), 152.9 (C-8), 97.4 (C-9), 153.5

(C-10), 113.0 (C-10a), 123.4 (C-10b), 124.6 (C-11), 123.5 (C-12), 130.7 (C-12a), 101.2

(2,3-OCH2O-), 61.0 (7-OCH3), 56.0 (8-OCH3), 56.2 (10-OCH3), 41.8 (N-CH3), 66.6 (C-

1’), 18.8 (1’-CH3).

Bocconoline (2.3): White powder; LRESIMS m/z 380

+ 1 [M+H] . H NMR (800 MHz, DMSO-d6): 7.29 (s, 1H, H-

1), 7.72 (s, 1H, H-4), 4.46 (dd, 2.4, 8.8 1H, H-6), 7.08 (d,

8.0, 1H, H-9), 7.62 (d, 8.0, 1H, H-10), 7.78 (d, 8.0, 1H, H-

11), 7.52 (d, 8.8, 1H, H-12), 6.13 (d, 1.6, 1H, -OCH2O-), 6.12 (d, 0.8, 1H, -OCH2O-),

3.84 (s, 3H, 7-OCH3), 3.86 (s, 3H, 8-OCH3), 2.60 (s, 3H, N-CH3), 3.15 (dd, 2.4, 11.2, 1H,

13 Ha, 6-CH2OH), 3.01 (dd, 9.6, 11.2, 1H, Hb, 6-CH2OH). C NMR (200 MHz, DMSO-d6):

104.1 (C-1), 147.7 (C-2), 147.2 (C-3), 100.6 (C-4), 126.6 (C-4a), 139.2 (C-4b), 60.0 (C-

6), 126.0 (C-6a), 145.8 (C-7), 151.9 (C-8), 112.1 (C-9), 118.7 (C-10), 124.4 (C-10a),

122.9 (C-10b), 119.7 (C-11), 123.5 (C-12), 130.6 (C-12a), 101.1 (-OCH2O-), 60.5 (7-

13 OCH3), 55.7 (8-OCH3), 42.8 (N-CH3), 61.8 (6-CH2OH). The C NMR data resembled those previously reported.2

6-(1-Hydroxyethyl)-5,6-dihydrochelerythrine (2.4):

White powder; LRESIMS m/z 394 [M+H]+. 1H NMR (800

MHz, CDCl3): 7.12 (s, 1H, H-1), 7.63 (s, 1H, H-4), 4.21

(d, 9.6, 1H, H-6), 6.99 (d, 8.8, 1H, H-9), 7.53 (d, 8.8, 1H,

H-10), 7.70 (d, 8.8, 1H, H-11), 7.50 (d, 8.8, 1H, H-12), 6.07 (d, 1.6, 1H, -OCH2O-), 6.06

(d, 1.6, 1H, -OCH2O-), 3.93 (s, 3H, 7-OCH3), 3.94 (s, 3H, 8-OCH3), 2.70 (s, 3H, N-CH3),

13 3.21 (dq, 8.8, 6.4, 1H, H-1’), 1.10 (d, 5.6, 3H, 1’-CH3). C NMR (200 MHz, CDCl3):

104.9 (C-1), 148.7 (C-2), 147.7 (C-3), 99.7 (C-4), 126.8 (C-4a), 138.1 (C-4b), 64.8 (C-

6), 125.6 (C-6a), 147.2 (C-7), 152.3 (C-8), 112.0 (C-9), 119.2 (C-10), 125.1 (C-10a),

124.1 (C-10b), 119.9 (C-11), 124.5 (C-12), 131.2 (C-12a), 101.3 (-OCH2O-), 61.0 (7-

OCH3), 56.0 (8-OCH3), 42.4 (N-CH3), 67.0 (C-1’), 18.7 (1’-CH3). The NMR data were in accordance to those reported in the literature.3

Chelerythrine (2.5): Yellow needles; LRESIMS m/z 348

+ 1 [M] . H NMR (800 MHz, DMSO-d6): 7.78 (s, 1H, H-1),

8.31 (s, 1H, H-4), 10.10 (s, 1H, H-6), 8.30 (d, 8.8, 1H, H-

9), 8.84 (d, 8.8, 1H, H-10), 8.83 (d, 8.8, 1H, H-11), 8.32

(d, 8.8, 1H, H-12), 6.35 (s, 2H, -OCH2O-), 4.18 (s, 3H, 7-OCH3), 4.12 (s, 3H, 8-OCH3),

13 4.99 (s, 3H, N-CH3). C NMR (200 MHz, DMSO-d6): 105.8 (C-1), 148.8 (C-2), 148.7

(C-3), 104.3 (C-4), 120.2 (C-4a), 131.7 (C-4b), 150.8 (C-6), 119.4 (C-6a), 145.4 (C-7),

150.6 (C-8), 126.1 (C-9), 119.3 (C-10), 128.0 (C-10a), 125.3 (C-10b), 118.8 (C-11),

131.1 (C-12), 132.3 (C-12a), 102.8 (2,3-OCH2O-), 62.2 (7-OCH3), 57.0 (8-OCH3), 52.3

4 (N-CH3). The NMR data were in accordance to those reported in the literature.

Chelilutine (2.6): Yellow powder; LRESIMS m/z 378

+ 1 [M] . H NMR (800 MHz, DMSO-d6): 7.75 (s, 1H, H-1),

8.21 (s, 1H, H-4), 10.02 (s, 1H, H-6), 7.77 (s, 1H, H-9),

9.42 (d, 8.8, 1H, H-11), 8.24 (d, 8.8, 1H, H-12), 6.34 (s,

2H, -OCH2O-), 4.08 (s, 3H, 7-OCH3), 4.16 (s, 3H, 8-OCH3), 4.26 (s, 3H, 10-OCH3), 4.93

13 (s, 3H, N-CH3). C NMR (200 MHz, DMSO-d6): 105.1 (C-1), 148.8 (C-2), 148.3 (C-3),

104.3 (C-4), 119.9 (C-4a), 131.7 (C-4b), 150.7 (C-6), 119.9 (C-6a), 139.0 (C-7), 151.2

(C-8), 108.3 (C-9), 153.6 (C-10), 116.5 (C-10a), 125.5 (C-10b), 121.9 (C-11), 130.2 (C-

12), 131.2 (C-12a) , 102.7 (2,3-OCH2O-), 62.3 (7-OCH3), 57.1 (8-OCH3), 57.4 (10-

OCH3), 52.2 (N-CH3). The NMR data were in accordance to those reported in the literature.5

Chelirubine (2.7): Red gum; LRESIMS m/z 362 [M]+. 1H O O

NMR (800 MHz, CD3OD): 7.51 (s, 1H, H-1), 8.03 (s, 1H, O N H-4), 9.82 (s, 1H, H-6), 7.78 (s, 1H, H-9), 9.44 (d, 8.8, 1H, O O 2.7 H-11), 8.12 (d, 8.8, 1H, H-12), 6.47 (s, 2H, 7,8-OCH2O-),

13 6.26 (s, 2H, 2,3-OCH2O-), 4.21 (s, 3H, 10-OCH3), 4.87 (s, 3H, N-CH3). C NMR (200

MHz, CD3OD): 106.3 (C-1), 150.3 (C-2), 150.8 (C-3), 104.9 (C-4), 121.5 (C-4a), 133.0

(C-4b), 150.3 (C-6), 111.2 (C-6a), 141.4 (C-7), 149.7 (C-8), 105.5 (C-9), 154.9 (C-10),

116.6 (C-10a), 128.2 (C-10b), 123.5 (C-11), 131.7 (C-12), 133.3 (C-12a), 104.2 (2,3-

OCH2O-), 106.2 (7,8-OCH2O-), 57.9 (10-OCH3), 52.6 (N-CH3). The NMR data were in accordance to those reported in the literature.5

Sanguinarine (2.8): Yellow powder; LRESIMS m/z 332 O

+ 1 [M] . H NMR (800 MHz, CD3OD): 7.56 (s, 1H, H-1), 8.15 O N (s, 1H, H-4), 9.92 (s, 1H, H-6), 7.95 (d, 8.8, 1H, H-9), 8.52 O O (d, 8.8, 1H, H-10), 8.62 (d, 8.8, 1H, H-11), 8.21 (d, 8.8, 1H, 2.8

13 H-12), 6.27 (s, 2H, 2,3-OCH2O-), 6.53 (s, 2H, 7,8-OCH2O), 4.95 (s, 3H, N-CH3). C

NMR (200 MHz, CD3OD): 107.0 (C-1), 150.9 (C-2), 150.8 (C-3), 105.0 (C-4), 122.0 (C-

4a), 133.2 (C-4b), 150.7 (C-6), 111.3 (C-6a), 148.2 (C-7), 149.5 (C-8), 121.2 (C-9), 118.3

(C-10), 129.1 (C-10a), 127.6 (C-10b), 119.6 (C-11), 132.9 (C-12), 134.2 (C-12a), 104.3

(2,3-OCH2O-), 106.5 (7,8-OCH2O-), 52.8 (N-CH3). The NMR data were in accordance to those reported in the literature.4

Coptisine (2.9): Yellow gum; LRESIMS m/z 320 [M]+. 1H

NMR (800 MHz, CD3OD): 7.68 (s, 1H, H-1), 6.96 (s, 1H, H-

4), 3.26 (m, 2H, H-5), 4.89 (m, 2H, H-6), 9.72 (t, 0.8, 1H, H-

8), 7.89 (d, 8.8, 1H, H-11), 7.86 (d, 8.8, 1H, H-12), 8.74 (s,

13 1H, H-13), 6.11 (s, 2H, 2,3-OCH2O-), 6.47 (s, 2H, 9,10-OCH2O-). C NMR (200 MHz,

CD3OD): 106.4 (C-1), 150.0 (C-2), 152.2 (C-3), 109.4 (C-4), 131.8 (C-4a), 28.1 (C-5),

57.2 (C-6), 145.3 (C-8), 113.7 (C-8a), 149.3 (C-9), 145.8 (C-10), 122.3 (C-11), 123.1 (C-

12), 134.4 (C-12a), 122.5 (C-13), 139.0 (C-13a), 121.9 (C-13b), 103.7 (2,3-OCH2O-),

106.2 (9,10-OCH2O-). The NMR data were in accordance to those reported in the literature.6

Tetradehydroscoulerine (2.10): Yellow gum; LRESIMS

+ 1 m/z 322 [M+H] . H NMR (800 MHz, CD3OD): 7.53 (s, 1H,

H-1), 7.00 (s, 1H, H-4), 3.25 (m, 2H, H-5), 4.88 (m, 2H, H-

6), 9.67 (s, 1H, H-8), 7.85 (d, 8.8, 1H, H-11), 7.83 (d, 8.8,

13 1H, H-12), 8.64 (s, 1H, H-13), 6.45 (s, 2H, 9,10-OCH2O-), 3.97 (s, 3H, 3-OCH3). C

NMR (200 MHz, CD3OD): 113.0 (C-1), 148.2 (C-2), 152.4 (C-3), 112.0 (C-4), 128.4 (C-

4a), 27.7 (C-5), 57.5 (C-6), 145.1 (C-8), 113.6 (C-8a), 145.6 (C-9), 149.1 (C-10), 122.2

(C-11), 123.0 (C-12), 134.5 (C-12a), 122.0 (C-13), 139.2 (C-13a), 120.7 (C-13b), 106.1

(9,10-OCH2O-), 56.6 (3-OCH3).

Columbamine (2.11): Yellow gum; LRESIMS m/z 338

+ 1 [M] . H NMR (800 MHz, DMSO-d6): 7.55 (s, 1H, H-1),

7.06 (s, 1H, H-4), 3.20 (m, 2H, H-5), 4.92 (m, 2H, H-6), 9.85

(s, 1H, H-8), 8.18 (d, 8.8, 1H, H-11), 8.05 (d, 8.8, 1H, H-12),

8.82 (s, 1H, H-13), 3.89 (s, 3H, 3-OCH3), 4.09 (s, 3H, 9-OCH3), 4.06 (s, 3H, 10-OCH3).

13 C NMR (200 MHz, DMSO-d6): 112.3 (C-1), 146.4 (C-2), 150.7 (C-3), 111.5 (C-4),

127.1 (C-4a), 26.0 (C-5), 55.6 (C-6), 145.4 (C-8), 121.4 (C-8a), 143.6 (C-9), 150.3 (C-

10), 126.7 (C-11), 123.6 (C-12), 133.2 (C-12a), 119.6 (C-13), 137.8 (C-13a), 119.2 (C-

13b), 60.0 (3-OCH3), 61.9 (9-OCH3), 57.1 (10-OCH3). The NMR data were in accordance to those reported in the literature.7

+ 1 Berberine (2.12): Yellow gum; LRESIMS m/z 336 [M] . H O N NMR (800 MHz, DMSO-d6): 7.80 (s, 1H, H-1), 7.09 (s, 1H, O O H-4), 3.20 (m, 2H, H-5), 4.93 (m, 2H, H-6), 9.90 (s, 1H, H- O 8), 8.21 (d, 8.8, 1H, H-11), 8.00 (d, 8.8, 1H, H-12), 8.93 (s, 2.12

13 1H, H-13), 6.17 (s, 2H, 2,3-OCH2O-), 4.10 (s, 3H, 9-OCH3), 4.07 (s, 3H, 10-OCH3). C

NMR (200 MHz, DMSO-d6): 105.4 (C-1), 147.7 (C-2), 149.8 (C-3), 108.4 (C-4), 130.7

(C-4a), 26.3 (C-5), 55.2 (C-6), 145.5 (C-8), 121.4 (C-8a), 143.7 (C-9), 150.4 (C-10),

126.8 (C-11), 123.5 (C-12), 133.0 (C-12a), 120.2 (C-13), 137.5 (C-13a), 120.4 (C-13b),

102.1 (2,3-OCH2O-), 61.9 (9-OCH3), 57.1 (10-OCH3). The NMR data were in accordance to those reported in the literature.6 p-Coumaroyltyramine (2.13): Yellow gum;

LRESIMS m/z 284 [M+H]+. 1H NMR (800 MHz,

CD3OD): 6.38 (d, 15.2, 1H, H-2), 7.44 (d, 15.2, 1H,

H-3), 7.40 (d, 8.8, 2H, H-5, H-9), 6.79 (d, 8.8, 2H, H-6, H-8), 3.46 (t, 7.2, 2H, H-1'), 2.75

(t, 7.2, 2H, H-2'), 7.05 (d, 8.0, 2H, H-4', H-8'), 6.72 (d, 8.0, 2H, H-5', H-7'). 13C NMR

(200 MHz, CD3OD): 169.2 (C-1), 118.4 (C-2), 141.8 (C-3), 127.7 (C-4), 130.5 (C-5, C-

9), 116.7 (C-6, C-8), 160.5 (C-7), 42.5 (C-1'), 35.8 (C-2'), 131.3 (C-3'), 130.7 (C-4', C-

8'), 116.3 (C-5', C-7'), 156.9 (C-6'). The NMR data were in accordance to those reported in the literature.8

3-O-Feruloylquinic acid (2.14): Yellow gum;

LRESIMS m/z 369 [M+H]+. 1H NMR (800 MHz,

DMSO-d6): 2.03 (dd, 4.0, 12.8, 1H, H-2a), 1.84 –

1.92 (m, 1H, H-2b), 5.18 (m, 1H, H-3), 3.56 (dd,

3.2, 6.4, 1H, H-4), 3.87 (m, 1H, H-5), 1.84 – 1.92 (m, 2H, H-6a, H-6b), 6.41 (d, 16.0, 1H,

H-8), 7.53 (d, 16.0, 1H, H-9), 7.28 (d, 1.6, 1H, H-2'), 6.79 (d, 8.0, 1H, H-5'), 7.08 (dd,

13 8.0, 1.6, 1H, H-6'), 3.82 (s, 3H, 3'-OCH3). C NMR (200 MHz, DMSO-d6): 72.9 (C-1),

35.1 (C-2), 71.0 (C-3), 71.0 (C-4), 67.4 (C-5), 39.2 (C-6), 166.2 (C-7), 115.5 (C-8), 144.4

(C-9), 125.8 (C-1'), 111.0 (C-2'), 148.0 (C-3'), 149.1 (C-4'), 115.5 (C-5'), 122.9 (C-6'),

55.7 (3'-OCH3), 176.0 (1-COOH). The NMR data were in accordance to those reported in the literature.9

Methyl 3-O-Feruloylquinate (2.15): Yellow OH HO + 1 5 6 gum; LRESIMS m/z 383 [M+H] . H NMR (800 O 4 2' 9 3 1 O 1' O 7 3' O MHz, DMSO-d6): 2.07 (dd, 4.0, 12.8, 1H, H-2a), 8 2 OH 4' O HO 6' 5' 1.89 (dd, 9.6, 12.8, 1H, H-2b) , 5.17 (ddd, 3.2, 4.0, 2.15

9.6, 1H, H-3), 3.62 (dd, 3.2, 5.6, 1H, H-4), 3.83 (m, 1H, H-5), 1.99 (dd, 5.6, 13.6, 1H, H-

6a), 1.84 (dd, 3.2, 13.6, 1H, H-6b), 6.43 (d, 16.0, 1H, H-8), 7.54 (d, 16.0, 1H, H-9), 7.29

(d, 2.4, 1H, H-2'), 6.79 (d, 8.0, 1H, H-5'), 7.09 (dd, 2.4, 8.0, 1H, H-6'), 3.82 (s, 3H, 3'-

13 OCH3), 3.59 (s, 3H, 1-COOCH3). C NMR (200 MHz, DMSO-d6): 72.7 (C-1), 34.9 (C-

2), 70.4 (C-3), 69.8 (C-4), 67.9 (C-5), 38.3 (C-6), 166.1 (C-7), 115.4 (C-8), 144.5 (C-9),

125.8 (C-1'), 111.0 (C-2'), 148.0 (C-3'), 149.2 (C-4'), 115.5 (C-5'), 123.0 (C-6'), 174.2

(1-COO-), 55.7 (3'-OCH3), 51.6 (1-COOCH3).

Ferulic acid 4-O-glucoside (2.16): Yellow gum; HO 6'' O 2' 3 + 1 HO O 1' LRESIMS m/z 379 [M+Na] . H NMR (800 MHz, 5'' O 1 OH 4'' 3' 2 3'' 1'' 4' 2'' 6' DMSO-d6): 6.46 (d, 16.0, 1H, H-2), 7.53 (d, 16.0, 1H, HO O 5' OH H-3), 7.34 (d, 1.6, 1H, H-2'), 7.09 (d, 8.8, 1H, H-5'), 2.16

7.18 (dd, 8.8, 1.6, 1H, H-6'), 4.97 (d, 8.0, 1H, H-1''), 3.24 ‒ 3.28 (m, 1H, H-2''), 3.33 (m,

1H, H-3''), 3.24 ‒ 3.28 (m, 1H, H-4''), 3.16 (m, 1H, H-5''), 3.66 (dd, 2.4, 12.0, 1H, H-

13 6''a), 3.44 (dd, 6.4, 12.0, 1H, H-6''b), 3.81 (s, 3H, 3'-OCH3). C NMR (200 MHz,

DMSO-d6): 167.8 (C-1), 117.2 (C-2), 144.0 (C-3), 128.1 (C-1'), 111.1 (C-2'), 149.1 (C-

3'), 148.3 (C-4'), 114.9 (C-5'), 122.2 (C-6'), 55.7 (3'-OCH3), 99.6 (C-1''), 73.1 (C-2''),

77.1 (C-3''), 76.8 (C-4''), 69.6 (C-5''), 60.6 (C-6''). The 13C NMR data resembled those previously reported.10

2.3 Biological Experimental Procedures

2.3.1 Cell Culture

Human olfactory neurosphere-derived (hONS) cell line C1200080013 obtained from a PD patient was used for cytological profiling assays throughout this study. The procedure of cell culture in this project closely followed that of previous research.11, 12

Generally, cells were grown in a two-dimensional culture configuration. Initially, a frozen aliquot of cells at passage five was thawed and grown in DMEM/F12 (Invitrogen) with

10% fetal bovine serum FBS (Invitrogen) in 75 mm2 cell culture flasks (ThermoFisher) at 37 °C and 5% CO2. The medium was refreshed every other day until the cells reached

90% confluence. At reaching the desired confluence, the cells were then expanded from passage five to passage seven. Cells at passage eight were used in this experiment.

2.3.2 Cytological Profiling

The cytological profiling protocol used in this project was also based on that previously reported.12, 13 There were two staining sets in this procedure. Set 1 comprised

Fractions and Compounds

Plating

Cell seeding Cell seeding

hONS cells Plate 1 Plate 2

Stain set 1: Stain set 2:  MitoTracker  LysoTracker  Anti α-tubulin antibody  Anti EEA1 antibody  Anti LC3b antibody  CellMask  DAPI  DAPI

High-Content Imaging System

Quantification

38 Cellular Parameters (Complete list in Table 2.1):  Cell Area  Nucleus Morphology Length  Lysosomal Marker Intensity * * *  Mitochondrial Marker Intensity

Figure 2.2 Overview of the cytological profiling process.

mitochondrial marker, α-tubulin and LC3b markers. Set 2 included lysosome marker and early endosome marker. Each set was performed independently of each other. The process is illustrated in Figure 2.2.

Table 2.1 Complete list of 38 cytological parameters generated after quantification process.

No Cytological Feature No Cytological Feature

1 α-Tubulin marker intensity in the cytoplasm 20 EEA1 marker intensity in the cytoplasm α-Tubulin marker intensity in outer region EEA1 marker intensity in outer region of 2 21 of cytoplasm cytoplasm α-Tubulin marker intensity in inner region EEA1 marker intensity in inner region of 3 22 of cytoplasm cytoplasm 4 α-Tubulin marker texture index 23 EEA1 marker texture index Mitochondria marker intensity in the 5 24 Number of EEA1 marker spots in cytoplasm cytoplasm Mitochondria marker intensity in outer Number of EEA1 marker spots in outer region 6 25 region of cytoplasm of cytoplasm Mitochondria marker intensity in inner Number of EEA1 marker spots in inner region 7 26 region of the cytoplasm of cytoplasm Number of EEA1 marker spots per area of 8 Mitochondria marker texture index 27 cytoplasm Number of EEA1 marker spots per area of 9 LC3b marker intensity in the cytoplasm 28 outer region of cytoplasm LC3b marker intensity in the outer region of Number of EEA1 marker spots per area of 10 29 cytoplasm inner region of cytoplasm LC3b marker intensity in inner region of 11 30 Lysosome marker intensity mean cytoplasm 12 LC3b marker texture index 31 Lysosome marker intensity outer region mean 13 Nucleus morphology area (μm2) 32 Lysosome marker intensity inner region mean 14 Nucleus morphology width (μm) 33 Lysosome marker texture index 15 Nucleus morphology length (μm) 34 Cell morphology area (μm2) 16 Nucleus morphology ratio width to length 35 Cell morphology width (μm) 17 Nucleus morphology roundness 36 Cell morphology length (μm) 18 Nucleus marker intensity 37 Cell morphology ratio width to length 19 Nucleus marker texture index 38 Cell morphology roundness

Initially, stock DMSO solutions of extracts (300 nL, 5 mg/mL) and compounds

(300 nL, 5 mM) were robotically transferred into two optically clear bottom CellCarrier

384-well plates (PerkinElmer) by Compounds Australia.14 hONS cells from the culture were counted and later added manually to the wells of the plates at a density of 1350 cells per well, leading to a final concentration of 0.6% for DMSO, 30 µg/mL for fractions and

30 µM for compounds in 50 µL of growth medium (DMEM/F12, 10% FBS) for each well. DMSO 0.6% was utilized as negative control; Rotenone (20 µM, 0.6% DMSO) and

Chloroquine (10 µM, 0.6% DMSO) worked as positive controls. The cells were then incubated for 24 hours at 37 °C under 5% CO2.

Following 24 hours of incubation, one 384-well plate was treated with

MitoTracker Orange CMTMRos (Life Technologies) (400 nM) for 30 minutes at 37 °C under 5% CO2. The other 384-well plate was treated with LysoTracker Red DND-99 (Life

Technologies) (100 nM) for 1 hour at 37 °C under 5% CO2. 4% paraformaldehyde (pH

7.4) was then used to fixed the cells in both plates with for 5 minutes at room temperature

(rt), and cells were subsequently washed twice with phosphate-buffered saline (PBS,

Sigma-Aldrich), followed by treatment with 3% goat serum (Sigma-Aldrich) and 0.2%

Triton X-100 (Sigma Aldrich) in PBS for 45 minutes at rt.

Antibodies, including primary and secondary, were then used to stain cellular components. For primary antibodies, mouse anti-EEA1 1/200 (Sigma-Aldrich) was added to the plate previously treated with LysoTracker. Mouse anti-α-tubulin 1/4000 (Sigma-

Aldrich) and rabbit anti-LC3b 1/335 (Sigma-Aldrich) were added to the plate treated with

MitoTracker. Plates were incubated in the dark at room temperature for 1 h, then washed twice with PBS.

Secondary antibodies goat anti-mouse Alexa-647 1/500 (Life Technologies) and goat anti-rabbit Alexa-488 1/500 (Life Technologies) were added to the MitoTracker- treated plate, and goat anti-mouse Alexa-488 1/500 (Life Technologies) was added to the plate previously treated with Lysotracker for 30 minutes at rt. Cells in both plates were washed twice with PBS and stained with 4′,6′-diamidino-2-phenylindole 1/5000 (Dapi,

Life Technologies) and with CellMask Deep Red 1/5000 (Life Technologies) for the

LysoTracker-treated plate and incubated for 10 minutes at rt. Cells were finally washed

twice with PBS, and plates were covered with aluminium foil and stored in the dark at 4

°C with 25 μL of PBS per well.

2.3.3 Cell Imaging

Cell images were taken automatically by PerkinElmer Operetta High-Content

Imaging system, which were later evaluated and extracted to raw numerical data using

PerkinElmer Harmony High-Content Imaging and Analysis and/or Columbus Image Data

Storage and Analysis software through a set of analysis sequences that effectively quantified biological features and cellular components based on the sizes and shapes of nuclei and cells, textures and intensities of mitochondria, nuclei, lysosome, tubulin, autophagosome and early endosome. These features are displayed in Table 2.1.

2.3.4 Data Analysis

Statistical software (R Studio and SPSS) were utilized throughout the analysis stage to process the raw data. Hierarchical clustering was performed on Cluster 3.0 software, and heatmaps were generated on Java TreeView software.

The comprehensive analysis of the results will be discussed in Chapter 4. A summary of the data analysis is presented in Figure 2.3. Generally, the preliminary analysis involved the removal of invalid and zero values from the raw data. Normalization of intensity features (e.g. LC3b marker intensity, nucleus marker intensity) will then be performed against cellular or nucleus areas. Logarithmic transformation was subsequently executed on the dataset.

The next stage of the analysis was the validation of positive and negative control using principal component analysis (PCA), followed by Pearson correlation coefficient to evaluate the relationship among parameters. Downstream processing included phenotypic clustering integrated with fold change analysis by calculation of binary

logarithm of compounds against negative control. Wilcoxon rank statistic was then employed to identify hits in the experiment.

Raw Data

Normalization

Wilcoxon Test Correlation Analysis Log2 Ratio

Heatmap

Insignificance Significance Hit Identification p > 0.05 p ≤ 0.05

Figure 2.3 Overview of the data analysis process.

2.4 References

1. Fulmer, G. R.; Miller, A. J.; Sherden, N. H.; Gottlieb, H. E.; Nudelman, A.; Stoltz, B. M.; Bercaw, J. E.; Goldberg, K. I., NMR chemical shifts of trace impurities: common laboratory solvents, organics, and gases in deuterated solvents relevant to the organometallic chemist. Organometallics 2010, 29 (9), 2176-2179. 2. Tarus, P.; Coombes, P.; Crouch, N.; Mulholland, D., Benzo[c]phenanthridine alkaloids from stem bark of the Forest Knobwood, Zanthoxylum davyi (Rutaceae). South African Journal of Botany 2006, 72 (4), 555-558. 3. Hu, J.; Zhang, W. D.; Liu, R. H.; Zhang, C.; Shen, Y. H.; Li, H. L.; Liang, M. J.; Xu, X. K., Benzophenanthridine alkaloids from Zanthoxylum nitidum (Roxb.) DC, and their analgesic and anti‐inflammatory activities. Chemistry & Biodiversity 2006, 3 (9), 990-995. 4. Liu, Q.; Sun, C.; Meng, F.; Zhao, W.; Li, D.; Wang, X., Preparative Separation of Chelerythrine and Sanguinarine from Macleaya cordata by pH-Zone-Refining Counter-current Chromatography. Journal of Liquid Chromatography & Related Technologies 2015, 38 (20), 1789-1793.

5. Marek, R.; Toušek, J.; Dostál, J.; Slavík, J.; Dommisse, R.; Sklenář, V., 1H and 13C NMR study of quaternary benzo[c]phenanthridine alkaloids. Magnetic Resonance in Chemistry 1999, 37 (11), 781-787. 6. Grycová, L.; Hulová, D.; Maier, L.; Standara, S.; Nečas, M.; Lemière, F.; Kareš, R.; Dostál, J.; Marek, R., Covalent bonding of azoles to quaternary protoberberine alkaloids. Magnetic Resonance in Chemistry 2008, 46 (12), 1127-1134. 7. Tong, S.; Yan, J.; Lou, J., Preparative Isolation and Purification of Alkaloids from Corydalis yanhusuo WT Wang by High Speed Counter‐Current Chromatography. Journal of Liquid Chromatography & Related Technologies 2005, 28 (18), 2979-2989. 8. Zhang, L.; Bai, B.; Liu, X.; Wang, Y.; Li, M.; Zhao, D., α-Glucosidase inhibitors from Chinese Yam (Dioscoreaopposita Thunb.). Food Chemistry 2011, 126 (1), 203-206. 9. Erel, S. B.; Karaalp, C.; Bedir, E.; Kaehlig, H.; Glasl, S.; Khan, S.; Krenn, L., Secondary metabolites of Centaurea calolepis and evaluation of cnicin for anti- inflammatory, antioxidant, and cytotoxic activities. Pharmaceutical Biology 2011, 49 (8), 840-849. 10. Materska, M.; Perucka, I.; Stochmal, A.; Piacente, S.; Oleszek, W., Quantitative and qualitative determination of flavonoids and phenolic acid derivatives from pericarp of hot pepper fruit cv. Bronowicka Ostra. Polish Journal of Food and Nutrition Cciences 2003, 12 (Suppl. 2), 72-76. 11. Murtaza, M.; Shan, J.; Matigian, N.; Todorovic, M.; Cook, A.; Ravishankar, S.; Dong, L.; Neuzil, J.; Silburn, P.; Mackay-Sim, A., Rotenone Susceptibility Phenotype in Olfactory Derived Patient Cells as a Model of Idiopathic Parkinson’s Disease. PloS One 2016, 11 (4), e0154544. 12. Tran thi, M. T. Cytological Profiling of Natural Product Scaffolds Libraries for Parkinson’s Disease. Master's Dissertation, Griffith University, Brisbane, Queensland, Australia, 2015. 13. Wang, C. Using Cytological Profiling to Discover Chemical Probes from Traditional Chinese Medicines against Parkinson's Disease. Unpublished Doctoral Dissertation, Griffith University, Brisbane, Queensland, Australia, 2018. 14. Griffith University Compounds Australia. https://www.griffith.edu.au/griffith- sciences/compounds-australia (accessed April 22, 2019).

CHAPTER 3 NMR-FINGERPRINTING ISOLATION OF

Macleaya cordata (Willd) R. Br.

3.1 Introduction

With regards to our ongoing research utilizing an unbiased cytological profiling to discover chemical probes to study the biology underlying Parkinson’s Disease, a traditional Chinese medicinal biota was chosen for chemical and biological investigation.

As part of a doctorate project in our research group focusing on the promise of traditional knowledge against PD, a library of various TCM herbs was selected for CP screens based on their roles in relation to this disease.1 Basically, 152 biotas were collected in several locations in China and later divided into three groups: Group 1 were herbs that were used singly or in combination with other TCMs to treat neurodegenerative diseases based on traditional literature. Group 2 consisted of herbs exhibiting anti- inflammatory and/or antioxidant activities, which are important indications in neuroprotection. Herbs that were randomly chosen without prior knowledge of their relation to PD were listed in Group 3. Table 3.1 gives the number of biotas present in each group.

Table 3.1 Number of herbal plants in each group, extracted from the previous PhD study.

Group 1 Group 2 Group 3

Number of Herbs 24 61 67

Each herb was then sequentially macerated in MeOH and EtOAc to give two extracts per sample. CP screens were then performed on a total of 299 testable extracts obtained from the above 152 biotas, against hONS cells harvested from PD patients. The assays utilized the same CP protocol previously discussed in Chapter 2, and the final

concentration of the extracts was set to 30 µg/mL. In order to narrow the selection range of the herbs for subsequent chemical investigation, prioritization of the extracts was then carried out based on several factors, such as CP profiles, 1H NMR fingerprinting, literature search as well as the availability of the biotas. The process of prioritizing is illustrated in Figure 3.1. Specifically, the samples were initially prioritized based on the strength of the effects they caused to the hONS cellular organelles in the CP screens.

Extracts inducing distinct hONS cellular phenotypes with statistical significance were retained for further investigation, whereas those showing weak alternations to the cytological parameters of hONS cells were removed. 1H NMR fingerprinting was employed as a second stage of the prioritization. Extracts exhibiting a widespread distribution of proton resonance signals in the 1H NMR spectrum were of greater interest compared to those displaying limited or uninteresting signals. An example of the NMR

152 TCMs

CP Profiles

51 TCMs

1H NMR fingerprinting

29 TCMs

Literature Search

12 TCMs

Availability

Phlegmariurus carinatus Asarum sieboldii var. Macleaya cordata seoulense

Figure 3.1 Flow chart demonstrating the prioritization process, reproduced from the previous PhD project. The number of TCMs was reduced following each stage of the process. Three biotas were chosen for chemical and biological investigation, two of which, in the red boxes, were already studied.

fingerprinting strategy is demonstrated in Figure 3.2 where two 1H NMR spectra were stacked to differentiate one from the other. It is obvious that sample B showed large distribution of proton signals ranging from high field (δH ⁓ 1.0) to aromatic region (δH

7.0 ‒ 8.0). In contrast, sample A only displays two dominant aliphatic resonance (δH ⁓

0.9 and 1.2). Sample A is therefore less interesting, in term of chemistry, than sample B.

1 Figure 3.2 H NMR (600 MHz, DMSO-d6) comparison between Sample A and Sample B, taken from the previous PhD project. The signals in Sample A are much simpler than those in Sample B, indicating that Sample B is a potent candidate for chemical investigation.

Further prioritization was achieved by conducting literature searches to select biotas of interest based on the number of known compounds isolated from the plants. The

Dictionary of Natural Products (DNP) website was utilized as a database throughout the project with a standard cut-off of 60 compounds, meaning that herbs having more than

60 previously reported metabolites recorded in the database were excluded from the selection pool, while those showing less than the limit were of more favour. The availability of the plants in large quantities was the final factor to prioritize the samples.

Following the process, one herbal plant belonging to Group 2, Macleaya cordata,

together with other two biotas, Phlegmariurus carinatus and Asarum sieboldii var. seoulense, which were already characterized in the previous PhD research, felt within the prioritization criteria and was therefore chosen for further investigation.

Macleaya cordata was collected as the whole plant in Wufeng County, Yichang

City, Hubei Province, China. The cytological profiling of the MeOH and EtOAc extract of the plant are present in Figure 3.3. Both extracts were generally similar in inducing a

-1.5 -1 -0.5 0 0.5 1 1.5 -1.5 -1 -0.5 0 0.5 1 1.5

1 1

3 3

5 5

7 7 9 9 11 11 13 13 15 15 17 17 19 19 21 21 23 23 25 25 27 27 29 29 31 31 33 33 35 35 37 37

MeOH EtOAc

Figure 3.3 Cytological profiling of the MeOH and EtOAc extract of M. cordata against hONS cells, data obtained from the previous PhD study. X axis: log2 ratio of extract (30 µg/mL) versus DMSO. Y axis: 38 cytological parameters: 1. Nucleus area (μm2). 2. Nucleus width (μm). 3. Nucleus length (μm). 4. Nucleus ratio width to length. 5. Nucleus roundness. 6. Nucleus marker texture index. 7. Nucleus marker intensity. 8. Cell area (μm2). 9. Cell width (μm). 10. Cell length (μm). 11. Cell ratio width to length. 12. Cell roundness. 13. α-Tubulin marker intensity in cytoplasm. 14. α-Tubulin marker intensity in outer region of cytoplasm. 15. α-Tubulin marker intensity in inner region of cytoplasm. 16. α-Tubulin marker texture index. 17. Mitochondria marker intensity in cytoplasm. 18. Mitochondria marker intensity in outer region of cytoplasm. 19. Mitochondria marker intensity in inner region of cytoplasm. 20. Mitochondria marker texture index. 21. LC3b marker intensity in cytoplasm. 22. LC3b marker intensity in outer region of cytoplasm. 23. LC3b marker intensity in inner region of cytoplasm. 24. LC3b marker texture index. 25. Lysosome marker intensity in cytoplasm. 26. Lysosome marker intensity in outer region of cytoplasm. 27. Lysosome marker intensity in inner region of cytoplasm. 28. Lysosome marker texture index. 29. Number of EEA1 marker spots in cytoplasm. 30. Number of EEA1 marker spots in inner region of cytoplasm. 31. Number of EEA1 marker spots in outer region of cytoplasm. 32. Number of EEA1 marker spots per area of cytoplasm. 33. EEA1 marker intensity in outer region of cytoplasm. 34. EEA1 marker intensity in inner region of cytoplasm. 35. EEA1 marker intensity in cytoplasm. 36. Number of EEA1 marker spots per area of outer region of cytoplasm. 37. Number of EEA1 marker spots per area of inner region of cytoplasm. 38. EEA1 marker texture index.

diversity of phenotypes of hONS cells. As illustrated in Figure 3.3, there were strong negative deviations of the mitochondria features (parameter 17, 18 and 19) compared to moderate positive deviations of the cell morphology (parameter 8,10 and 11) and EEA1 features (parameter 29, 30 and 31). The fold changes can be calculated based on the binary logarithms (log2) of the ratio of extract to DMSO on the x axis. For example, in the MeOH extract, parameter 8 represented the cell area feature; the log2 ratio on the x axis corresponding with that parameter showed a value of roughly 0.57, which in turn gives the ratio of the extract versus DMSO of 20.57 ≈ 1.48. This meant that the MeOH extract caused the mean of hONS cellular area to increase by 1.48 folds compared to that influenced by the negative control DMSO. Similar effects were also seen in parameters

29, 30 and 31 which were associated with the EEA1 features. In contrast, the mitochondrial marker intensity in cytoplasm (parameter 17) induced by the extract was nearly a half of that caused by DMSO (as ≈ 2-1 = 0.5). These data indicated that the extracts of M. cordata has significantly altered several cytological components of hONS cells in certain ways, resulting in various distinct phenotypes which are believed to be implicated in Parkinson’s Disease. Constituently, in term of biology, Macleaya cordata is a potent biological source of secondary metabolites that possess interesting bioactivities associated with PD. These metabolites could be utilized as chemical probes to explore the biology behind this disorder.

In the chemistry point of view, this biota is also a strong candidate for compound mining, having exhibited rich proton resonance signals in the 1H NMR spectrum of the

EtOH extract as illustrated in Figure 3.4. A global distribution of proton signals spreading from around δH 0.9 to δH 8.0 was seen in the spectrum. The signals at the high field region

(δH 0.9 ‒ 3.0) may indicate the presence of fatty acids and aliphatic compounds, while those appearing complex between δH 3.0 and δH 4.0 may represent carbohydrates and

methoxy-containing molecules. A DNP search for M. cordata revealed only 13 compounds recorded in the database, most of which bore the benzo[c]phenanthridine scaffold, a type of characteristic alkaloids principally present in this species. Figure 3.5 demonstrates the benzo[c]phenanthridine skeleton and some compounds having this scaffold.

8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f1 (ppm)

1 Figure 3.4 H NMR data (800 MHz, CD3OD) of the EtOH extract of Macleaya cordata. The spectrum displays a widespread distribution of proton signals.

Several compounds of this type possess one or two dioxolane rings attached to either ring A or ring D of the benzo[c]phenanthridine scaffold, as shown in the examples above. This information greatly matches the proton NMR spectrum of the crude extract

(Figure 3.4) which displayed signals at around δH 6.0 and some aromatic resonance between δH 6.5 and δH 7.5. Those appearing at around δH 6.0 may represent the acetal protons of the dioxolane ring, while those in the aromatic domain could belong to protons in ring A, B and D of the benzo[c]phenanthridine skeleton.

12 1 11 12a 2 10 10b B A 10a 9 4a 3 4b D C 4 N 8 6a 7 6

Figure 3.5 The benzo[c]phenanthridine scaffold with numbering system and some examples of this type of alkaloid. It is obvious that a large number of these types of alkaloids have been isolated from several plants restricted to the Papaveraceae, Fumariaceae and Rutaceae family.2

These compounds have caught a lot of attention from both synthetic chemistry and biochemistry owing to their wide spectrum of biological activities, such as antibacterial,3,

4 anti-inflammatory,5, 6 antifungal,7, 8 anticancer9-12 property, etc. The proton NMR data of the crude extract and literature searches, in general, have signified that M. cordata could contain various compounds of diverse scaffolds, which is crucial for molecule discovery.

Another reason M. cordata has been selected in the first place is that this biota has been documented in traditional literature to have possessed anti-inflammatory property, which is an important indication in neuroprotection. It has been well-known that neuronal inflammation is associated with the pathogenesis of several neurodegenerative disorders including PD.13 A tremendous number of studies have been conducted in relation to neuroprotective and anti-inflammatory effect, and it appears that there is a strong connection between these two activities. For example, Lee et al.14 demonstrated that

eicosanoyl-5-hydroxytryptamide (EHT), a component in coffee, exhibited neuroprotective effect in a MPTP model of PD, via various mechanisms including anti- inflammation. In addition, Esposito et al.15 proposed that nonsteroidal anti-inflammatory

(NSAIDs) drugs could be related to neuroprotection, possibly due to their involvement in several anti-inflammatory pathways in the dopaminergic neurons.

Overall, possessing several characteristic features, both in chemistry and in biology, especially the ability to induce perturbations in the cytological components of

PD patient-derived hONS cells, Macleaya cordata has proven itself to be a potent candidate for bioactive natural products discovery, giving opportunities to find small molecules that could be utilized as chemical probes to investigate the complex biology underlying Parkinson’s Disease.

3.2 Macleaya cordata (Willd) R.Br.

The genus Macleaya belongs to the family Papaveraceae and includes only two related species, Macleaya cordata (Willd) R. Br. and Macleaya microcarpa (Maxim)

Fedde.16, 17 M. cordata (Figure 3.6) was first named as Bocconia cordata by Carl Ludwig

Willdenow in 1797 and was later renamed to Macleaya cordata by Robert Brown in

Figure 3.6 Image of the leaves of Macleaya cordata (Willd.) R.Br. by Megan Hansen, https://www.flickr.com/photos/24495410@N03/4710774814, licensed under CC BY-SA 2.0 https://creativecommons.org/licenses/by-sa/2.0/?ref=ccsearch&atype=rich. Image was generated from cropping of the original.

1826.16, 18 The plant is also known as plume poppy (English), bo luo hui (Chinese), takenigusa (Japanese), or Federmohn (German).16, 18, 19

M. cordata is a perennial herb primarily distributed in China, North America and

The Europe.20 The plant is very abundant in China and can be found in riversides, foothills, forests, among shrubs or tussocks on low mountains in several suburbs.17, 19

Owing to its medicinal properties, it is also cultivated across the country and in Russia as a primary source of the benzo[c]phenanthridine alkaloids.17, 21

3.2.1 Taxonomy

The taxonomy of Macleaya cordata was adapted from reference 22.

Kingdom: Viridiplantae

Phylum: Tracheophyta

Class: Magnoliopsida

Order: Ranunculales

Family: Papaveraceae

Genus: Macleaya

Species: Macleaya cordata (Willd.) R.Br.

3.2.2 Medicinal uses of Macleaya cordata

Like many other herbal plants, M. cordata exhibits several interesting bioactivities such as pesticidal, antiedemic, carminative, depurative, diuretic, anti-tumour, anti- inflammatory, antifungal and antimicrobial effect.17, 23, 24

Historically, M. cordata has long been utilized in traditional Chinese medicine for the treatment of incised wound, arthritis, rheumatism arthralgia, and trichomonas vaginalis.17, 25 The roots are used as an alternative therapy for cancer.24 The leaves can be

used to treat insect bites, and a decoction of the leaves and stems can also be used for ringworm infection. The sap is very poisonous and can be used to kill larvae, insects, and snail.23 The powered mixture of the leaves, capsules, and seeds is the main ingredient of the feed additive for rearing pigs, broilers and dairy cattle.17

Given the abundant number of alkaloids presence in M. cordata, it is believed that these compounds are mainly responsible to the biological activities of this plant.26 Lin et al.17 published an in-depth review about the phytochemistry and biology of the genus

Macleaya which included M. codara. It was revealed that this plant has been intensively studied, which resulted in the identification of more than 100 metabolites, several of which were the alkaloids belonging to the protopine or benzo[c]phenanthridine. This somewhat contradicts the Dictionary of Natural Products which holds record of only 13, proposedly due to large amount of information being recorded in the Chinese language which has yet to be available to the DNP. Chelerythrine (3.1) and sanguinarine (3.2) are the two major compounds in this biota, and their biological activities were also comprehensively studied.17

Chelerythrine exhibited various bioactivities such as antimicrobial effect against

E. coli, P. aeruginosa or S. agalactiae,21, 27 anti-inflammation in mice through COX-2 regulation,6 and anti-carcinogenic properties against several cancer cell lines.28-30

Sanguinarine is well known for its cytotoxicity, such as suppressing prostate cancer via stat3 inhibition,31 induced apoptosis of HT-29 human colon and A549 human lung cancer cells32, 33, inhibited cervical cancer cell growth.34

3.3 NMR-Fingerprinting-guided Isolation of Compounds

Since M. cordata has demonstrated to be a potential candidate for chemical probes discovery against PD, chemical investigation of this biota was conducted aiming to identify small molecules which would subsequently be profiled against hONS cells utilizing cytological profiling.

Purification of bioactive compounds from an extract is undoubtedly critical in natural product discovery, yet this process may become strenuous without supports of proper techniques. It is undeniable that bioassay guidance has been a golden standard in compound isolation; however, given the complexity and the cost of phenotypic assays, bioassay-guided isolation in the form of phenotypic techniques was not considered in this project. Instead, a less expensive, spectroscopy-based approach, entitled NMR fingerprinting or NMR guidance, was employed to assist the isolation process in the viewpoint of chemistry rather than that of biology.

3.3.1 Polyamide CC6 Chromatography

The chemistry of crude extracts is generally complex; as a result, open column chromatography is usually employed to simplify the crude prior to HPLC purification.

This project utilized Polyamide CC6 and Sephadex LH-20 as stationary phases for column chromatography. As previously mentioned in Chapter 2, a portion of the crude

M. cordata crude (20 g)

Polyamide CC6 Flash Chromatography (MeOH:H2O)

Fraction A Fraction B Fraction C Fraction D Fraction E Fraction F (100% H2O) (30% MeOH) (40% MeOH) (70% MeOH) (90% MeOH) (100% MeOH) (12.0 g) (1.0 g) (0.3 g) (2.4 g) (1.4 g) (0.2 g)

Figure 3.7 Six fractions obtained from the Polyamide CC6 chromatography of the M. cordata crude ethanol extract.

Ethanol extract of Macleaya cordata (20 g) was initially subjected to a Polyamide CC6 flash column, eluted with MeOH:H2O at different ratios to obtain 6 fractions which are displayed in Figure 3.7. 1H NMR spectroscopy was then performed on each fraction, and the data are shown in Figure 3.8.

10.5 10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f1 (ppm)

1 Figure 3.8 H NMR spectra (800 MHz, DMSO-d6) of the six Polyamide CC6 chromatography fractions. A. 100% H2O. B. 30% MeOH. C. 40% MeOH. D. 70% MeOH. E. 90% MeOH. F. 100% MeOH

As seen in Figure 3.8, the proton NMR spectrum of fraction F (100% MeOH) contained little or no signals at all, while that of fraction A (100% H2O) was dominant with complex resonance between δH 3.00 and 4.00 diagnostic for the presence of a large number of carbohydrates in this fraction. This was justified by the signals at δH 4.25, 4.80 or 6.00 which may belong to the anomeric protons of sugar compounds. There were also aromatic resonances in the spectrum of fraction A. Nonetheless, they appeared relatively simple, proposedly indicating the existence of small water-soluble aromatic acids. In the point of view of natural product isolation, both fraction A and fraction F were trivial as they showed uninteresting signals in the NMR data and were therefore not taken into

10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 f1 (ppm)

1 Figure 3.9 A close-up view of the H NMR data (800 MHz, DMSO-d6) of fraction B and fraction C whose signals were of nearly identical chemical shifts. The two fractions were later combined. further consideration. In contrast, having exhibited plenty of signals in the low field region (δH 6.00 ‒ 9.00), the spectral data of fraction B (30% MeOH) and C (40% MeOH) may give indications for the presence of numerous aromatic compounds, especially the principal alkaloids of M. cordata, owing to the characteristic proton resonance at around

1 δH 6.00. Comparisons of the H NMR data of fraction B and C (Figure 3.9) revealed that the signals appeared at very similar chemical shifts, suggesting that both fractions contain several identical compounds. Consequently, fraction B and fraction C were combined in order to simplify future workload and enrich the constituents.

Assessments of the proton NMR data were also conducted for fraction D (70%

MeOH) and E (90% MeOH) (Figure 3.10). Aliphatic and non-polar molecules were dominant in these two fractions due to the existence of high field signals (δH 0.50 ‒ 3.00).

The signals resonated at around δH 5.4 could be associated with the olefinic protons in fatty acids. Compared to fraction B and C, fraction D and E were of lesser interest in term

of chemical isolation because the overall intensity of the signals was relatively lower than that of fraction B and C. Nonetheless, minor aromatic compounds may be present in these two fractions, with regards to the small intensities of the aromatic signals (δH 6.00 ‒ 8.00).

Fraction D and E were later combined based on the similarity of the chemical shifts.

In general, compared to the 1H NMR spectrum of the crude extract, those of the derived fractions appeared more detailed, indicating that the flash chromatography utilizing Polyamide CC6 was successfully performed on the crude extract of M. cordata as the initial step of the compound isolation.

10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f1 (ppm)

1 Figure 3.10 H NMR data (800 MHz, DMSO-d6) of fraction D and fraction E showing signals at very similar chemical shifts. These two fractions were also combined.

3.3.2 Sephadex LH-20 Chromatography

The next stage of the purification process was the involvement of Sephadex LH20 chromatography to decomplexify the fractions previously obtained from the flash

Polyamide column. The mixture of fraction B and C was loaded onto an open column

packed with Sephadex LH20, eluted with 100% MeOH to yield 85 subfractions. As the number of the afforded subfractions was large, NMR spectroscopy was not applied under this circumstance. TLC was instead carried out to combine subfractions having similar profiles. As a result, 8 fractional combinations (BC1 ‒ 8) were generated based on TLC analysis (Figure 3.11), their proton spectral data shown in Figure 3.12.

Fraction B Fraction C (30% MeOH) (40% MeOH) (1.0 g) (0.3 g)

Sephadex LH-20 Chromatography (100% MeOH) TLC

BC1 BC2 BC3 BC4 BC5 BC6 BC7 BC8 (1 to 13) (14 to 19) (20 to 29) (30 to 44) (45 to 50) (51 to 65) (66 to 72) (73 to 85) (160 mg) (70 mg) (220 mg) (140 mg) (50 mg) (100 mg) (9 mg) (7 mg)

Figure 3.11 Sephadex LH-20 chromatography result of fraction B + C with 8 fractions obtained following TLC analysis.

BC8

BC7

BC6

BC5

BC4

BC3

BC2

BC1

10.5 10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f1 (ppm)

1 Figure 3.12 H NMR spectra (800 MHz, CD3OD) of the 8 fractions (BC1 ‒ 8) from the Sephadex LH-20 chromatography.

It can be seen that the more downstream the purification went, the more information regarding the chemistry of the fractions were revealed. Following 1H NMR analysis, the first and the last two fractions (BC1, BC7 and BC8) were discarded because their proton NMR data showed limited signals. On the contrary, fractions BC4 to BC6 displayed abundant resonance signals in the aromatic zone (δH 6.3 ‒ 7.5), as well as in the methoxyl proton region (δH 3.5 ‒ 4.0). This information signified that polyphenol compounds may exist in large quantities in these three fractions. It was also noted that there were copious signals at around δH 6.0 ‒ 6.5 diagnostic for benzo[c]phenanthridine alkaloids. These sets of signals again appeared in fraction BC2 and BC3, indicating that these two fractions were also of great interest for isolation. In summary, subfractions BC2 to BC6 were selected for further HPLC purification.

Since the Sephadex LH-20 chromatography provided good results for the previous separation, it was later utilized to fractionate the mixture of fraction D and fraction E. In total, by eluting the column with 100% MeOH, 90 subfractions were afforded, and 6 combinations of subfractions were made after TLC analysis (Figure 3.13). 1H NMR data were acquired for each combination.

Fraction D Fraction E (30% MeOH) (40% MeOH) (2.4 g) (1.4 g)

Sephadex LH-20 Chromatography (100% MeOH) (TLC)

DE1 DE2 DE3 DE4 DE5 DE6 (1 to 22) (23 to 27) (28 to 37) (38 to 52) (53 to 63) (64 to 90)

Figure 3.13 Six smaller fractions were obtained following Sephadex LH-20 chromatography of mixture of D and E. According to the proton NMR spectra in Figure 3.14, fraction DE1 was of no interest because of the lack of proton signals. Compared to the NMR data of previous

Sephadex LH-20 chromatography, those obtained for this separation appeared much more complex, meaning that it was difficult to differentiate one fraction from the others. In other words, there was a certain degree of similarities among them. Characteristic signals of the benzo[c]phenanthridine alkaloids (δH 6.0 ‒ 6.5) were present in almost all fractions, with significant intensities in fraction DE5. However, fatty compounds were also distributed across the fractions owing to the appearance of resonance at δH ⁓5.5, along with aliphatic signals. Based on such information, it can be concluded that the Sephadex

LH-20 chromatography did not give a good fractionation for the mixture of D and E, proposedly due to the column being overloaded by the large sample size. Given the above complexities and the time constraints of this project, prioritization was made for fraction

DE3 because new compounds were detected in this fraction, even though fraction DE5 appeared to be the most potent candidate owing to the rich proton signals.

DE6

DE5

DE4

DE3

DE2

DE1

10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f1 (ppm)

1 Figure 3.14 H NMR spectroscopy (800MHz, CD3OD) of fractions from Sephadex LH-20 chromatography of mixture of D and E. The spectra appeared complex, with no distinct fractions being identified.

3.3.3 Purification of Compounds

The chosen fractions after proton NMR analysis were subjected to purification to afford pure compounds. RP-HPLC was employed as the main technique in this stage. The comprehensive process of HPLC isolation was discussed in Chapter 2. In total, sixteen metabolites (Figure 3.15) were isolated from the fractions, fourteen of which were acquired by HPLC, including bocconoline (2.3), 6-(1-Hydroxyethyl)-5,6- dihydrochelerythrine (2.4), chelerythrine (2.5), chelilutine (2.6), chelirubine (2.7),

Figure 3.15 Compounds isolated from Macleaya cordata in this project.

sanguinarine (2.8), coptisine (2.9), tetradehydroscoulerine (2.10), columbamine (2.11), berberine (2.12), p-coumaroyltyramine (2.13), 3-O-feruloylquinic acid (2.14), methyl 3-

O-feruloylquinate (2.15) and ferulic acid 4-O-glucoside (2.16). The other two compounds, 10-methoxybocconoline (2.1) and 6-(1-hydroxyethyl)-10-methoxy-5,6- dihydrochelerythrine (2.2), were new; discussions of their isolation and structural elucidation are present below. The structures of all isolates were confirmed by means of

NMR and MS, as well as comparisons with reported data. All of the obtained compounds were profiled against hONS cells harvested from a PD patient using the above-mentioned cytological profiling assay to discover compounds potentially being chemical probes to study about PD.

3.3.3.1 Isolation and Structural Elucidation of 10-Methoxybocconoline (2.1)

Further HPLC purification on fraction BC3 (Figure 3.16) led to the isolation of bocconoline (2.3) (retention time at 48 min), chelilutine (2.6) (retention time at 14 min) and trace of compound 2.1 (retention time at 39 min). The 1H NMR spectroscopy of compound 2.1, regrettably, did not give strong evidence that this compound was of acceptable purity since there were lots of noise signals present in the obtained 1H NMR

Cmpd. 2.3 Cmpd. 2.6 Fatty cmpd. Cmpd. 2.1

Figure 3.16 HPLC chromatogram of the purification of bocconoline (2.3). Condition: Phenomenex Luna C18 (100 Å, 5 μm, 250 × 10 mm) column, gradient elution, MeOH:H2O, 0.1% TFA; 0 to 50 min, 50:50 to 65:35, 50 to 60 min, 65:35 to 100:0; flow rate, 4 mL/min; fraction collection, 1 fraction/min. 2.3 was identified at the peak with retention time 48 min. The subfraction at retention time 39 min contained trace of compound 2.1.

and MS spectra (Figure 3.17). However, compared to the 1H NMR data of 2.3, those of the above fraction exhibited some similarities, except for an additional methoxy moiety at δH 3.8 and a conversion of two doublets to a singlet in the aromatic region (Figure

3.18). Therefore, this fraction was predicted to have contained a methoxy substituent of bocconoline, proposedly 10-methoxybocconoline (2.1) based on literature review. An exact match search for this compound on SciFinder database returned no result, indicating

Impurities

B Cmpd. 2.1 C

Impurities

Figure 3.17 Spectroscopic data for the initial fraction containing compound 2.1. A. 1H NMR (800 MHz, DMSO-d6) spectrum demonstrating several noise signals along with real ones. B. LC chromatogram also showing impurity traces coexisting with the compound. C. Positive-mode mass spectrum at retention time 8.1 min in the LC chromatogram displaying the pseudo-molecular ion of 2.1 as [M+H]+ = 410. The data indicated that the obtained compound was not pure enough to confirm the structure.

4.00 3.95 3.90 3.85 3.80 3.75 f1 (ppm)

8.6 8.4 8.2 8.0 7.8 7.6 7.4 7.2 7.0 6.8 6.6 6.4 6.2 6.0 5.8 5.6 5.4 5.2 5.0 4.8 4.6 4.4 4.2 4.0 3.8 3.6 3.4 3.2 3.0 2.8 2.6 2.4 2.2 f1 (ppm)

1 Figure 3.18 H NMR (800 MHz, DMSO-d6) spectrum comparisons between bocconoline (2.3) (top) and the selected HPLC fraction at retention time of 39 min (bottom). Black rectangles represent similarities, while yellow rectangles indicate differences. The main dissimilarities for the bottom spectrum are an addition of a methoxy group (as seen in the expansion between δH 3.75 ‒ 4.00) and a replacement of two doublets with a singlet in the aromatic region. The fraction was therefore suggested to have contained 10- Methoxybocconoline (2.1).

that 2.1 could be a new compound. Due to the low purity of 2.1, other spectroscopic data

were not acquired to confirm its structure. Since the concentration of 2.1 was low,

attempts were made to detect it in other fractions by applying mass spectrometry, a more

sensitive approach than 1H NMR, with the hope to enrich this compound. Several

fractions were injected into the LC-MS system to trace the existence of 2.1 based on the

mass of this compound. Fortunately, an ion with m/z 410, which resembled the molecular

ion of 2.1, was found in the MS data of fraction DE3 and at identical retention time in the

LC trace (Figure 3.19). This information strongly suggested that 2.1 exist in fraction

DE3. Notwithstanding, the amount of this compound would be again very limited, with

reference to its low relative intensity compared to others.

As the compound of interest was detected in DE3, further purification of this

Cmpd. 2.1

Figure 3.19 MS-guided detection of compound 2.1. The compound was found at retention time 8.1 min and had the pseudo-molecular ion m/z 410 in the LC and MS spectrum of fraction DE3 (top), compared with the spectral data of the impure 2.1 acting as reference (bottom). Condition of LC-MS: Thermo Scientific Accucore C18 LC (2.6 μm, 150 × 2.1 mm) column, gradient elution, MeOH:H2O, 0.1% FA; 0 to 1 min, isocratic 50:50, 1 to 9 min, 50:50 to 100:0, 9 to 10.5 min, isocratic 100:0, 10.5 to 10.8 min, 100:0 to 50:50, 10.8 to 12 min, isocratic 50:50; flow rate, 0.3 mL/min. fraction was taken into account. Given the low quantity of 2.1, considerations were made to minimize the number of fractionations for DE3 in order to preserve the compound.

Three HPLC isolations were performed on this fraction under the guidance of MS (Figure

3.20). Unfortunately, although the targeted compound was enriched, none of the above purifications was able to isolate the compound due to the poor separations in the HPLC.

Other chromatographic techniques were then considered with the hope to successfully obtain 2.1.

It was later revealed that TLC on the basic aluminium oxide plates of the 2.1- containing fraction derived from previous HPLC purifications gave a promising result for the isolation of this compound (Figure 3.21). 2.1 was apparently separated from the mixture with a Rf value of 0.45 when using DCM:MeOH of 99:1 as the eluent.

Constituently, efforts were made to shift the purification of 2.1 from HPLC to preparative

TLC. The mixture was loaded onto a basic aluminium oxide glass plate (20 x 20 cm). The

C Cmpd. 2.2

Impurities Impurities Cmpd. 2.1

Figure 3.20 Downstream HPLC purification of 2.1, guided by MS. A. HPLC chromatogram of fraction DE3; Condition: Alltech Hyperprep PEP C18 column (gradient elution, MeOH:H2O, 0.1% TFA; 0 to 50 min, 50:50 to 65:35, 50 to 55 min, 65:35 to 100:0, 55 to 60 min, 100:0; flow rate, 9 mL/min; fraction collection, 1 fraction/min); compound 2.1 was detected from retention time 35 to 41 min. B. HPLC chromatogram of the second purification (from previous fractions 35 to 41); Condition: Luna C18 column (gradient elution, MeOH:H2O, 0.1% TFA; 0 to 50 min, 55:45 to 65:35, 50 to 55 min, 65:35 to 100:0, 55 to 60 min, 100:0; flow rate, 4 mL/min; fraction collection, 1 fraction/min). C. HPLC chromatogram of the third purification; Condition: Luna C18 column (gradient elution, MeOH:H2O, 0.1% TFA; 0 to 50 min, isocratic 55:45, 50 to 55 min, 55:45 to 100:0, 55 to 60 min, 100:0; flow rate, 4 mL/min; fraction collection, 1 fraction/min); 2.1 was enriched but still not pure as its peak was overlapped with others.

plate was then developed two times with 100 mL of DCM:MeOH 99:1 and checked under

UV light to ensure good separation was achieved. The band associated with the compound of interest was collected and subsequently eluted with 10 mL of DCM:MeOH 90:10 to yield 2.1 (0.1 mg, 0.00001% dry wt).

Solvent Front

Cmpd. 2.1

Rf 0.45

1 2 A Origin Line B

Figure 3.21 TLC separation of compound 2.1, performed on Merck Aluminium oxide 60 F254 basic glass plates; mobile phase: DCM:MeOH 99:1; visualized with UV 254 nm. A. Analytical TLC of the mixture enriched with 2.1 (1) and the initially obtained 2.1 (2); following comparisons between (1) and (2), the spot with Rf 0.45 was believed to be the compound of interest. B. Preparative TLC of the fraction containing 2.1 (double eluted); the bottom UV-active band (blue color in red rectangle) was determined as the targeted compound and was subsequently collected to give compound 2.1.

Compound 2.1 was obtained as white powder and had a molecular formula of

13 C23H23NO6 inferred from HRESIMS. The C NMR data displayed 24 signals, one of which was thought to be derived from impurities according to the HSQC spectrum. The

1 H NMR and HSQC data showed the presence of three aromatic singlets (δH 7.66, 7.11, and 6.59), two aromatic doublets (δH 8.32, and 7.47), one pair of methylene doublets (δH

6.06 and 6.04), one methine doublet of doublets (δH 4.64), one set of two methylene multiplets (δH 3.48 and 3.02), three methoxy singlets (δH 3.95, 3.93 and 3.89), one methyl singlet (δH 2.71) and one exchangeable doublet (δH 2.75). It was noted that there were also a few resonances from δH 0.5 to δH 1.3 which did not correlate with the main proton

or carbon signals of the molecule in the 2D NMR data. They therefore were believed to come from the substances in the aluminium oxide plate used in the preparative TLC. To confirm this, a blank proton NMR run was done for the plain aluminium oxide powder of the same plate type. Specifically, a small amount of plain aluminium oxide was collected from a Merck Aluminium oxide 60 F254 basic glass plate and filtered through a cartridge with DCM:MeOH 90:10. 1H NMR data were acquired for the filtrate and were later compared with those of compound 2.1 (Figure 3.22). These impurities could be easily removed by performing a single HPLC purification; however, it was not executed due to the low yield of 2.1.

Water peak

Water peak B

9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f1 (ppm)

1 Figure 3.22 H NMR spectrum (800 MHz, CDCl3) comparison of compound 2.1 (A) and the plain aluminium oxide from Merck Aluminium oxide 60 F254 basic glass plate (B). Identical signals (from δH 0.5 to δH 1.3) were found in both spectra (black rectangle), signifying that the impurities in compound 2.1 were from the stationary phase used in pTLC.

A presence of an acetal group was confirmed by the methylene doublet pair at δH

6.06 and 6.04 and its attachment to the carbon at δC 101.2 in the HSQC spectrum. The

HMBC data revealed that the above two protons also correlated with the two quaternary

carbons at δC 148.3 and 147.7. These carbon chemical shifts were highly in accordance with those of oxygenated aromatic carbons. The above data therefore indicated the existence of a dioxolane ring fused with a benzene to form a 1,3-benzodioxole moiety

(Figure 3.23).

Figure 3.23 The 1,3-benzodioxole fragment

The HMBC spectrum also showed correlations from the two oxygenated carbon of the above moiety (δC 148.3 and 147.7) to the two aromatic singlets at δH 7.11 and 7.66, verifying that those two protons belonged to the benzene ring of the 1,3-benzodioxole fragment. In addition, since they appeared as singlets, they had to be para-related to each other, and this can only be seen at position R1 and R4 in Figure 3.23, leading to the assignment of the two aromatic protons to these positions (Figure 3.24).

Figure 3.24 Further assignment of the 1,3-benzodioxole fragment

1 A pair of aromatic doublets at δH 8.32 and 7.47 was also detected in the H NMR data. These two protons were coupled to each other according to the COSY spectrum and would have ortho relationship with regards to their equal coupling constant of 8.8 Hz.

This pair of protons cannot be assigned to position R2 and R3 because it would contradict the singlet pattern of the two protons in Figure 3.24. In other words, this suggested the presence of another benzene ring which contained the above two doublets at ortho position. This new benzene ring was determined to be attached to the 1,3-benzodioxole

fragment at R2 and R3 to form the 2,3-methylenedioxynaphthalene moiety (Figure 3.25), owing to the ROESY correlation from the singlet at δH 7.11 to the doublet at δH 7.47.

Figure 3.25 The 2,3-methylenedioxynaphthalene fragment with key ROESY correlation

The HRESIMS data verified the presence of the heteroatom nitrogen. This was further confirmed by the IR spectrum which showed absorptions between 1000 and 1350 cm-1 characteristic for the stretching vibration of C-N bonds in amines. In agreement with

1 these data, the H NMR spectrum also displayed an N-methyl singlet at δH 2.71. Analysis of the HMBC and HSQC spectra revealed that this N-methyl singlet correlated with the methine carbon at δC 60.0 which bound to the doublet of doublets at δH 4.64 (Figure

3.26A). The doublet of doublets splitting of this signal can be explained by its coupling to the two methylene multiplets at δH 3.48 and 3.02 (Figure 3.26B), as seen in the COSY spectrum. The chemical shifts of these two methylene protons were relatively higher than those in aliphatic methylene groups, signifying that this methylene moiety was attached to an electron withdrawing species, which in this case was a hydroxyl group, pursuant to the characteristic chemical shift of the methylene carbon at δC 61.6. In summary, the above data contributed to the formation of the N-methylethanolamine moiety as shown in

Figure 3.26C. Further analysis of the ROESY spectrum demonstrated that there was a

A B C

Figure 3.26 Structural elucidation revealing the N-methylethanolamine fragment

cross-peak between the aromatic singlet at δH 7.66 in the 2,3-methylenedioxynaphthalene moiety (Figure 3.25) and the N-methyl singlet at δH 2.71 in the N-methylethanolamine fragment (Figure 3.26C); this proved that these two fragments were linked to each other via position R1 rather than R2 in the 2,3-methylenedioxynaphthalene moiety, in favor of the ROESY correlation (Figure 3.27).

Figure 3.27 Linkage of the 2,3-methylenedioxynaphthalene and the N-methylethanolamine fragment by ROESY correlation The molecular formula of this compound from HRESIMS gave a total of 13 degrees of saturation, 8 of which were already found in the fragment in Figure 3.27.

Therefore, the remaining part of the molecule would account for the other 5 degrees. It was noted that there were no proton signals of alkene groups found in the 1H NMR spectrum except for the unassigned aromatic singlet at δH 6.59. Consequently, the remaining 5 degrees of saturation was suggested to result from the presence of another benzene and an additional ring. With respect to the HMBC correlations from the methine doublet of doublets at δH 4.64 in the N-methylethanolamine moiety and from the doublet at δH 8.32 in the 2,3-methylenedioxynaphthalene fragment to the two aromatic quaternary carbon at δC 128.6 and δC 113.4, the newly identified benzene was believed to connect to

Figure 3.28 Further structural elucidation of compound 2.1 by HMBC data revealing the benzo[c]phenanthridine alkaloid scaffold

the fragment in Figure 3.27 via both position R2 and R3, establishing the benzo[c]phenanthridine scaffold (Figure 3.28) which is characteristic for the Macleaya cordata species; this also satisfied the number of degrees of saturation.

The remaining unassigned proton signals were the aromatic singlet at δH 6.59 and the three methoxy singlets at δH 3.95, 3.93 and 3.89. These would be distributed to the last four positions in the benzo[c]phenanthridine fragment in Figure 3.28. To do this, assessment of the ROESY data was taken into account. A cross-peak between the methoxy singlet at δH 3.89 and the methine doublet of doublets at δH 4.64 was seen in the

ROESY spectrum, indicating that position R4 was a methoxy group, with regards to the smallest distance to the δH 4.64 proton compared to others (Figure 3.29A). Similarly, position R1 was also determined to be a methoxy, pursuant to the correlation from the signal at δH 3.93 to the doublet at δH 8.32 (Figure 3.29B). In the HMBC spectrum, the aromatic singlet at δH 6.59 correlated with the carbon at δC 113.4; this information was in favor of this proton being at position R2 (3 bonds from the carbon) as this correlation would be too weak to be seen if the proton was to connect to position R3 (4 bonds from the carbon) (Figure 3.29C). Lastly, the remaining methoxy singlet at δH 3.95 was assigned to position R3, fully characterizing the molecule.

A B C

Figure 3.29 Assignments of the methoxy groups to the benzo[c[phenanthridine scaffold by ROESY (—) and HMBC (—) correlations.

In general, the above findings verified that 2.1 was a methoxy substituent of bocconoline(2.3) as previously predicted. Constituently, compound 2.1 was confirmed to be 10-Methoxybocconoline. Table 3.2 summarizes the NMR spectral data of 2.1, and

Figure 3.30 displays key correlations for this compound.

1 13 Table 3.2 NMR spectroscopic data (800 MHz for H and 200 MHz for C, CDCl3) of 10- Methoxybocconoline (2.1).

Position H (ppm), J (Hz) C (ppm) COSY HMBC 1 7.11 (s, 1H) 104.5 - 2 or 3, 4a, 12 2 - 148.3 - - 3 - 147.7 - - 4 7.66 (s, 1H) 99.8 - 2 or 3, 4b, 12a 4a - 127.0 - - 4b - 138.2 - - 6 4.64 (dd, 2.4, 10.4, 1H) 60.0 1' 1', 4b, 6a 6a - 128.6 - - 7 - 140.5 - - 8 - 152.5 - - 9 6.59 (s, 1H) 97.5 - 7, 8, 10, 10a 10 - 153.7 - - 10a - 113.4 - - 10b - 123.1 - - 11 8.32 (d, 8.8, 1H) 124.6 12 4b, 10a, 12a 12 7.47 (d, 8.8, 1H) 123.5 11 1, 4a, 10b, 12a 12a - 130.7 - - 6.06 (d, 1.6, 1H, Ha) -OCH2O-, Hb 2, 3 -OCH2O- 101.2 6.04 (d, 1.6, 1H, Hb) -OCH2O-, Ha 2, 3 3.48 (m, 1H, H1'a) 6, 1'b, 1'-OH - 1' 61.6 3.02 (m, 1H, H1'b) 6, 1'a 6 N-CH3 2.71 (s, 3H) 42.4 - 4b, 6 7-OCH3 3.89 (s, 3H) 61.5 - 7 8-OCH3 3.95 (s, 3H) 56.3 - 8 10-OCH3 3.93 (s, 3H) 56.0 - 10 1'-OH 2.75 (d, 10.4, 1H) - 1'b 1', 6

: COSY : HMBC : ROESY

Figure 3.30 Key 1H-1H COSY (—), HMBC (→) and ROESY (↔) correlations of 10-Methoxybocconoline (2.1)

Compound 2.1 possesses one chiral center at position 6, resulting in two possible absolute configurations for this compound, (6R)-10-Methoxybocconoline and (6S)-10-

Methoxybocconoline (Figure 3.31).

(6R)-10-Methoxybocconoline (6S)-10-Methoxybocconoline

Figure 3.31 Two possible absolute configurations of 10-Methoxybocconoline

To determine the absolute configuration of the isolated 2.1 in this project, calculated ECD spectra for 10-Methoxybocconoline were acquired using TD-DFT (time- dependent density functional theory) and were subsequently compared with the experimental spectra obtained for 2.1 (Figure 3.32). Specifically, DFT calculations using

B3LYP method with 6-31G(d) basis set were performed on Gaussian 03. HyperChem 7.5 software was used to carry out the preliminary conformational search. The theoretical

ECD spectra were generated according to Boltzmann weighting of the conformers.

Δε : Calculated ECD spectrum for the R configuration 40 : Calculated ECD spectrum for the S configuration 30 : Experimental ECD spectrum for compound 2.1

0 200 220 240 260 280 300 320 340 360 380 400 -10

-20

-30

-40 Wavelength (nm)

Figure 3.32 Calculated ECD data of 10-Methoxybocconoline (blue and red line) and experimental ECD spectrum obtained for compound 2.1 (black line).

As seen in Figure 3.32, the experimental ECD spectrum of compound 2.1 (black graph) exhibited certain similarities to the calculated data for the R configuration of 10-

Methoxybocconoline (red graph). Consequently, based on these findings, the isolated 2.1 was proposed to be (6R)-10-Methoxybocconoline.

3.3.3.2 Isolation and Structural elucidation of 6-(1-Hydroxyethyl)-10-methoxy-5,6-

dihydrochelerythrine (2.2)

Compound 2.2 was detected during the third HPLC seperation attempt of compound 2.1 (Figure 3.33 or Figure 3.20C). The UV absorptions at retention time 47 min appeared as a single sharp peak. A quick purity check by LC-MS for this fraction revealed a parent ion with m/z 406 and a potential molecular ion with m/z 424. There was

A Cmpd. 2.2

Impurities Impurities Cmpd. 2.1

B C Cmpd. 2.2 Impurities

Figure 3.33 MS-guided detection of compound 2.2. A. Isocratic purification of compound 2.1, taken from Figure 3.C; compound 2.2 was found in the fraction at retention time 47 min. B. LC trace of the targeted fraction showing impurities coexisting with the main compound. C. Positive-mode mass spectrum of the main compound at retention time 8.6 min in the LC chromatogram showing the pseudo-molecular ion at + + m/z 424 ([M+H] ) and an adduct ion at m/z 406 ([M+H-H2O] ).

no record for these MS data in the literature review of M. codata, suggesting that 2.2 could be another new compound. Because impurities were found in the fraction, (Figure

3.33B), further purification of 2.2 was established. In respect to the very low amount of

2.2, as well as the previous success in applying pTLC to achieve compound 2.1, pTLC technique was again considered to isolate 2.2. Indeed, there was also a good seperation in the analytical TLC for this compound using the basic aluminium oxide plates, with a clear spot having the Rf value of 0.6 when using DCM:MeOH 99:1 as the solvent system.

Large scale purification was therefore performed on a Merck Aluminium oxide 60 F254 basic glass plate (20 x 20 cm), developed with DCM:MeOH 99:1. The procedure was quite similar to the one applied for compound 2.1; the targeted compound was identified based on visualization of the plate with UV light. The UV-active band was collected and eluted with DCM:MeOH 90:10 to give compound 2.2 (0.1 mg, 0.00001% dry wt).

Compound 2.2 was isolated as white powder. As it was obtained via pTLC on basic aluminium oxide plates, characteristic signals of the impurities from the stationary phase were also seen in the 1H NMR spectrum. A comparison of the 1H NMR data of 2.2 with those of the plain aluminium oxide harvested from the same plate type was also made to validate the above observation (Figure 3.34). The HRESIMS analysis for this compound returned a protonated molecular ion ([M+H]+) consistent with a molecular formula of C24H25NO6. This MS result, together with the IR data of 2.2 displaying disgnostic absorbtions of amines (1081.3, 1248.8, 1382.2 cm-1), gave indications that 2.2 would also be an alkaloid, proposedly having the same benzo[c]phenanthridine scaffold as compound 2.1. Constituently, in order to facilitate the structural elucidation of 2.2, its

1H NMR spectrum was compared with that of 2.1 to find the key differences in the signals

(Figure 3.35). It was obvious that the aromatic signals in the spectrum of 2.2 had the same splitting patterns and appeared at nearly identical chemical shifts to those in the

Water peak

9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f1 (ppm)

1 Figure 3.34 Comparisons of the H NMR data (800 MHz, CDCl3) of compound 2.2 (top) and the plain aluminium oxide stationary phase in the TLC plate (bottom); characteristic impurity signals were also found in the spectrum of 2.2. proton spectrum of 2.1, as seen in the black rectangle in Figure 3.35. Diagnostic features of the benzo[c]phenanthridine scaffold were also present in the spectral data of 2.2, such as the methylenedioxy proton signals of the dioxolane ring at δH 6.05 and 6.06, the

13 aromatic resonances from δH 6.50 to δH 9.00, and the N-methyl singlet at δH 2.68. The C

NMR spectrum also showed the existence of several aromatic carbons that were similar to those in compound 2.1. The above information strongly suggested that 2.2 possess the benzo[c]phenanthridine skeleton.

This compound was also found to bear three methoxy groups, with reference to the presence of three methoxy singlets at δH 3.96, 3.93 and 3.87. These proton signals correlated with the aromatic carbons of the benzo[c]phenanthridine scaffold in the HMBC spectrum. Since there was no significant change in chemical shifts of both proton and carbon signals in the aromatic region of 2.2 compared to 2.1, the three methoxy groups

8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f1 (ppm)

1 Figure 3.35 Comparisons between the H NMR data (800 MHz, CDCl3) of 10-Methoxybocconoline (2.1) (top) and those of 2.2 (bottom). The similarities are grouped in the black rectangles, while red arrows indicate the differences. were believed to be at the same positions in the benzo[c]phenanthridine skeleton as seen in 2.1. This was further confirmed by the ROESY spectrum which displayed correlations consistent with the structure. In summary, these findings signified that the structure of compound 2.2 was highly similar to that of compound 2.1, having a 7,9,10-methoxy- subsituted benzo[c]phenanthridine scaffold; the only difference was the aliphatic chain attached to position 6 (Figure 3.36).

Figure 3.36 The benzo[c]phenanthridine fragment of compound 2.2, identified by comparisons of the NMR data with 2.1.

Indeed, analysis of the 1H NMR spectrum of 2.2 in the high field region demonstrated some noticeable dissimilarities compared to the data of 2.1. Figure 3.37

shows an expansion from δH 2.50 to δH 4.80 of the stacked proton NMR spectrum for the two compounds. The peak at δH 2.61 (far bottom right) in 2.2 was thought to be from impurities because there was no HMBC correlation from it to any carbon in the molecule.

It appeared that the methine doublet of doublets at δH 4.64 in 2.1 has been replaced by a methine doublet at δH 4.18 in 2.2, while there was a conversion of the two methylene multiplets at δH 3.02 and 3.48 in 2.1 to a methine multiplet at δH 3.14. Further assessment

N-Methyl

OH-Exchangeable

N-Methyl Impurities Impurities

Methoxy

4.8 4.7 4.6 4.5 4.4 4.3 4.2 4.1 4.0 3.9 3.8 3.7 3.6 3.5 3.4 3.3 3.2 3.1 3.0 2.9 2.8 2.7 2.6 2.5 f1 (ppm)

J2 = 9.6 Hz

J1 = 5.6 Hz

3.170 3.165 3.160 3.155 3.150 3.145 3.140 3.135 3.130 3.125 3.120 3.115 3.110 3.105 f1 (ppm)

1 Figure 3.37 Key differences in the H NMR data (800 MHz, CDCl3) of 2.1 and 2.2 from δH 2.50 to δH 4.80, illustrated by red arrows. A. 1H NMR spectrum of 2.1. B. 1H NMR spectrum of 2.2. C. Splitting pattern of the multiplet at δH 3.14 in 2.2.

of this multiplet revealed that it was actually a doublet of quartets with the two J coupling constants of 9.6 ad 5.6 Hz. The COSY spectrum indicated that this signal correlated with the doublet at δH 4.18 whose coupling constant was 9.6 Hz; this explained its doublet splitting. To satisfy the other splitting, which was a quartet, it also had to be coupled with a methyl doublet having a J value of 5.6 Hz. This methyl doublet was later detected at δH

1.09, in the region of the aluminium oxide impurities, based on its correlation with the

1 multiplet at δH 3.14 in the COSY data and its integration of 3.00 in the H NMR spectrum.

The chemical shifts of the methine doublet of quartets (δH 3.14) and the carbon to which it was bound (δC 66.6) were diagnostic for this methine group being attached to a hydroxyl moiety. Overall, with regards to the above spectroscopic data, a 2-hydroxypropane-1,1- diyl fragment was identified as a part of the molecule (Figure 3.38).

Figure 3.38 The 2-hydroxypropane-1,1-diyl fragment.

2D NMR spectra were utilized to link the above fragment to the benzo[c]phenanthridine scaffold. There were cross-peaks in the HMBC spectrum between the δH 4.18 doublet in the 2-hydroxypropane-1,1-diyl and the N-methyl carbon at δC 41.8, as well as the aromatic carbons C6a at δC 128.2 and C10a at δC 113.0 in the benzo[c]phenanthridine skeleton. This permitted the 2-hydroxypropane-1,1-diyl moiety to be connected to the benzo[c]phenanthridine fragment via position 6, establishing the

Figure 3.39 Linkage of the two fragments by HMBC (—) correlations, forming the full structure of 2.2.

complete structure for compound 2.2 (Figure 3.39). This assignment was also allowed by the ROESY data which exhibited compatible correlations. Figure 3.40 shows main

2D NMR (COSY, HMBC and ROESY) correlations for 2.2, and Table 3.3 gives a summary of the spectroscopic data for this compound.

1 13 Table 3.3 NMR spectroscopic data (800 MHz for H and 200 MHz for C, CDCl3) of 6-(1-Hydroxyethyl)- 10-methoxy-5,6-dihydrochelerythrine (2.2).

Position H (ppm), J (Hz) C (ppm) COSY HMBC 1 7.11 (s, 1H) 104.6 - 2 or 3, 4a, 12 2 - 148.4 - - 3 - 147.7 - - 4 7.65 (s, 1H) 99.5 - 2 or 3, 4b, 12 4a - 126.7 - - 4b - 138.4 - - 6 4.18 (d, 9.6, 1H) 64.9 1' 1', 2', 4b, 6a, 10a 6a - 128.2 - - 7 - 141.1 - - 8 - 152.9 - - 9 6.60 (s, 1H) 97.4 - 7, 8, 10a 10 - 153.5 - - 10a - 113.0 - - 10b - 123.4 - - 11 8.31 (d, 8.8, 1H) 124.6 12 4b, 10a, 12a 12 7.46 (d, 8.8, 1H) 123.5 11 1, 4a, 10b, 12a 12a - 130.7 - - 6.06 (d, 1.6, 1H, Ha) -OCH2O-, Hb 2, 3 -OCH2O- 101.2 6.05 (d, 1.6, 1H, Hb) -OCH2O-, Ha 2, 3 1' 3.14 (dq, 9.6, 5.6, 1H) 66.6 2', 6 - 2' 1.09 (d, 5.6, 3H) 18.8 1' 1', 6 N-CH3 2.68 (s, 3H) 41.8 - 4b, 6 7-OCH3 3.87 (s, 3H) 61.0 - 7 8-OCH3 3.96 (s, 3H) 56.0 - 8 10-OCH3 3.93 (s, 3H) 56.2 - 10

: COSY : HMBC : ROESY

Figure 3.40 Key 1H-1H COSY (—), HMBC (→) and ROESY (↔) correlations of 6-(1-Hydroxyethyl)-10- methoxy-5,6-dihydrochelerythrine (2.2).

Compound 2.2 has four possible absolute configurations, pursuant to its two chiral centers at positions 6 and 1'. Although the experimental and calculated ECD data of this compound were acquired (Figure S14, Supporting Information), it was difficult to confirm the correct configuration because the spectra appeared relatively complex. Other techniques may be considered, including but not limited to 1H & 13C NMR DFT calculation optimized with DP4 probability, enrichment of the compound for chemical reactions or X-ray crystallography.

3.3.3.3 Isolation of The Known Compounds

The 14 known compounds were afforded by repeated RP-HPLC. The conditions for obtaining the compounds were already present in Chapter 2. This section will give an illustrative discussion on the isolation of these metabolites.

Chelerythrine (2.5) and chelilutine (2.6) were initially obtained from fraction BC2 with a Phenomenex Luna C18 column (Figure 3.41). As these two are major compounds of this plant species, they were also found in other fractions.

Non-polar Cmpd. 2.5 Cmpd. 2.6 cmpds.

Not pure

Figure 3.41 HPLC purification of chelerythrine (2.5) and chelilutine (2.6) from fraction BC2. Conditions: Phenomenex Luna C18 (100 Å, 5 μm, 250 × 10 mm) column, gradient elution, MeOH:H2O, 0.1% TFA; 0 to 50 min, 25:75 to 65:35, 50 to 60 min, 65:35 to 100:0; flow rate, 4 mL/min; fraction collection, 1 fraction/min.

Fraction BC3 was first characterized with an Alltech Hyperprep PEP C18 column to yield 40 fractions (Figure 3.42). Fraction 10 to 18 were combined and repeatedly

10-18 19-24 25-29 30-34 35-40

Figure 3.42 HPLC characterization of BC3. Fractions from retention time 10 to 18, 19 to 24, 25 to 29, 30 to 34 and 35 to 41 were combined, as illustrated by colored braces, for further purification. Conditions: Alltech Hyperprep PEP C18 column (gradient elution, MeOH:H2O, 0.1% TFA; 0 to 50 min, 15:85 to 80:20, 50 to 60 min, 80:20 to 100:0; flow rate, 9 mL/min; fraction collection, 1 fraction/min). purified with a Luna C18 column to afford three phenolic compounds,

3-O-feruloylquinic acid (2.14), methyl 3-O-feruloylquinate (2.15) and ferulic acid 4-O- glucoside (2.16). Similarly, repeated purification with the same Luna C18 column of fraction 25 to 29 gave columbamine (2.11), berberine (2.12) and the above chelerythrine

(2.5) (Figure 3.43). Compound 2.5 was also dominant in fractions 30 to 34. Bocconoline

(2.3) and chelilutine (2.6) were afforded from fractions 35 to 41, also by purification with the Luna C18 column (Figure 3.16). The 1H NMR spectrum of the combined fraction from 19 to 24 in Figure 3.42 suggested that this could be a mixture of two compounds.

These two compounds were inseparable by RP-HPLC and even by TLC, proposedly due

Cmpd. 2.11 Cmpd. 2.12 Cmpd. 2.5 Non-polar cmpds.

Figure 3.43 HPLC purification on subfraction 25 to 29. Columbamine (2.11) was identified at retention time 25 min, and berberine (2.12) at 30 min; chelerythrine (2.5) was present from 35 to 41 min.

1 Figure 3.44 H NMR spectra (800 MHz, CD3OD) of fractions 19 to 24. A mixture of two compounds, potentially enantiomers, were detected based on the integrations of the signals at around δH 6.00. to them being a pair of enantiomers. In this case, chiral chromatography can be considered for this purification. There were two classes of compound isolated from fraction BC3, the coumaroyl derivatives (2.14, 2.15 and 2.16) and the principle alkaloids of M. cordata

(2.3, 2.5, 2.6, 2.11 and 2.12). These two types of compound shared similar 1H NMR features in the aromatic region because they both possessed aromatic rings.

Notwithstanding, unique NMR traits for the coumaroyl type which differentiated it from the alkaloids can be spotted in the spectra. As shown in Figure 3.45, there were an AB system with a coupling constant of 16.0 Hz consistent with a pair of trans olefinic protons, and an ABX system diagnostic of a 1,2,4-trisubstituted benzene. These data were highly compatible with the coumaroyl moiety. Coumaroyl derivatives are generally popular in the plant kingdom. Therefore, in respect of this study, they were not of greater interest than the characteristic alkaloids of M. cordata. This spectral information hence can be

used as fingerprints for the coumaroyl-type molecules, which facilitated the isolation process by removing them from later purifications.

8.0 7.9 7.8 7.7 7.6 7.5 7.4 7.3 7.2 7.1 7.0 6.9 6.8 6.7 6.6 6.5 6.4 6.3 6.2 6.1 6.0 f1 (ppm)

Figure 3.45 Aromatic region of the 1H NMR spectra of the fraction containing principle alkaloids of M. cordata (top) and the fraction containing coumaroyl-type compounds (bottom). Characteristic signals of the coumaroyl moiety, including olefinic protons of an AB system (red arrows) and an aromatic ring of an ABX system (black arrows), are fingerprints for these compounds.

Further characterization of fraction BC4 was accomplished using the Alltech

Hyperprep PEP C18 column (Figure 3.46). 1H NMR fingerprints of the coumaroyl compounds were found from subfractions 13 to 23 (Figure 3.47). These subfractions were later combined, and purification would be considered in the future.

Tetradehydroscoulerine (2.10) was found in subfractions 24 to 25. The Luna C18 column was used to afford coptisine (2.9) from subfractions 26 to 28. Subfractions 30 to 33 contained sanguinarine (2.8). Chelerythrine (2.5) was again detected in subfractions 34 and 35. Chelirubine (2.7) was dominant in subfractions 38 and 39. Compared to the 1H

NMR data of chelirubine (2.7), those of subfractions 44 to 47 showed a replacement of two aromatic doublets to an aromatic singlet, as well as an addition of

Cmpd. 2.10 Cmpd. 2.9 Cmpd. 2.8 Cmpd. 2.5 Cmpd. 2.7

Macarpine (proposedly)

Coumaroyl-type Cmpds.

Figure 3.46 HPLC purification of BC4. Conditions: Alltech Hyperprep PEP C18 column (gradient elution, MeOH:H2O, 0.1% TFA; 0 to 50 min, 20:80 to 65:35, 50 to 55 min, 65:35 to 100:0, 55 to 60 min, 100:0; flow rate, 9 mL/min; fraction collection, 1 fraction/min).

8.2 8.0 7.8 7.6 7.4 7.2 7.0 6.8 6.6 6.4 6.2 6.0 5.8 5.6 5.4 5.2 5.0 4.8 4.6 4.4 4.2 4.0 3.8 3.6 3.4 3.2 3.0 2.8 2.6 2.4 2.2 2.0 1.8 f1 (ppm)

1 Figure 3.47 H NMR spectra (800 MHz, CD3OD) of subfractions 13 to 23 from BC4 separation. NMR fingerprints of the coumaroyl-type molecules were detected from δH 6.20 to 7.80 (red rectangle), with a pair of trans olefinic protons and a benzene system. These subfractions were combined but were not favorable for further purification. a methoxy group (Figure 3.48). This information suggested the presence of Macarpine, an analogue of chelirubine with a methoxy substituent at position 12. This compound, however, had decomposed before other spectroscopic data for it were acquired.

4.3 4.2 4.1 f1 (ppm)

10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 f1 (ppm)

1 Figure 3.48 H NMR comparisons (800 MHz, CD3OD) between chelirubine (2.7) (top) and subfractions 44 (bottom). The differences in the spectra (illustrated by black arrows and rectangle) indicated the presence of Macarpine in subfraction 44.

Coumaroyl compounds were proposed to exist in large quantities in fractions BC5 and BC6, according to their 1H NMR data showing strong characteristic signals of this type of molecules (Figure 3.11). Indeed, these two fractions were subjected to RP-HPLC, but no other type of compounds was obtained, except for the above coumaroyl derivatives as predicted.

The structures of all isolates were elucidated based on extensive 1D and 2D analysis as well as comparisons with data in the literature.

3.4 Conclusions and Future Directions

The biota Macleaya cordata, with reference to its various effects on hONS cellular parameters, has demonstrated to be a powerful candidate for the discovery of natural compounds that can potentially be utilized to assist the research on Parkinson’s Disease.

Chemical investigation of this plant in this project under 1H NMR and MS guidance

returned the isolation of sixteen compounds having unique scaffolds. Among them, (6R)-

10-methoxybocconoline (2.1) and 6-(1-hydroxyethyl)-10-methoxy-5,6- dihydrochelerythrine (2.2) were a pair of new benzo[c]phenanthridine alkaloids.

Although compound 2.2 has been structurally elucidated, its absolute configuration (AC) has not yet been uncovered. It was previously noted that comparison of experimental and calculated ECD spectra for 2.2 was not conclusive in determining the AC. Literature-based AC determination for 2.2 was also not definitive because although a structurally related analogue of 2.2, compound 2.4, was first reported by Hu et al., the authors were only able to assign AC for position C-1' which is attached to a secondary alcohol group using Mosher method.35 Constituently, other techniques will be taken into consideration in the future to solve this problem. The strategy may involve chemical reactions; several methods regarding chemical derivatization to explore AC have been established. For example, García and coworkers proposed a novel methodology that allowed the assignment of the AC of secondary alcohols by esterification with α-methoxyphenylacetic acid followed by complexation with barium(II) salt. AC was determined based on the differences in NMR spectra between the starting material and the product.36 Given that compound 2.2 also has a chiral secondary alcohol group, the above method would be valuable in identifying AC for 2.2. X-ray crystallography, a robust approach for determining atomic and molecular structure, can be another approach; however, crystals of the compound must be obtained in order to carry out this technique. The current low amount of 2.2 could hinder the formation of precise crystals. Both of the above techniques certainly require sufficient quantity of 2.2; consequently, enrichment of this compound will definitely be considered in the future.

Recently, computational calculation of NMR spectra using DFT is emerging as an effective strategy in AC assignments. Many studies have succeeded in determining AC

of compounds or rearranging incorrect structures by utilizing DFT calculations of NMR and DP4 probability.37-39 This approach may also be suitable in this case as it generally does not require large sample size.

In conclusion, the results from the chemistry cohort of this study have contributed to the phytochemical research of M. cordata and implied that 1H NMR fingerprinting is a robust assistant for compound isolation. By looking at specific traits in the spectrum, unwanted compounds can be easily excluded after each fractionation, which greatly facilitates the purification process. Moreover, this technique can help reveal the structural information of the molecules even before they are isolated, which is nearly impossible in bioassay guidance. Nonetheless, 1H NMR fingerprinting mainly takes the view of chemistry to fulfil the goal of achieving metabolites bearing interesting structures, without having regards to their bioactivities. This may become the critical drawback of this technique as compounds which are potentially bioactive can be overlooked if they do not show characteristic proton signals. Consequently, implementation of 1H NMR guidance should be in parallel with taking careful analysis of the spectra and intensive assessment of structure-activity relationships into account. Overall, although 1H NMR fingerprinting cannot entirely replace bioassay-guided isolation, the two techniques can be utilized cooperatively to accomplish compounds possessing both interesting bioactivities and unique structures, one of the main aims in natural product discovery.

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3. Cheng, P.; Wang, B.; Liu, X.; Liu, W.; Kang, W.; Zhou, J.; Zeng, J., Facile synthesis of tetrahydroprotoberberine and protoberberine alkaloids from protopines and study on their antibacterial activities. Natural Product Research 2014, 28 (7), 413-419. 4. Parhi, A.; Kelley, C.; Kaul, M.; Pilch, D. S.; LaVoie, E. J., Antibacterial activity of substituted 5-methylbenzo[c]phenanthridinium derivatives. Bioorganic & Medicinal Chemistry Letters 2012, 22 (23), 7080-7083. 5. Chaturvedi, M. M.; Kumar, A.; Darnay, B. G.; Chainy, G. B.; Agarwal, S.; Aggarwal, B. B., Sanguinarine (pseudochelerythrine) is a potent inhibitor of NF-κB activation, IκBα phosphorylation, and degradation. Journal of Biological Chemistry 1997, 272 (48), 30129-30134. 6. Niu, X.-F.; Zhou, P.; Li, W.-F.; Xu, H.-B., Effects of chelerythrine, a specific inhibitor of cyclooxygenase-2, on acute inflammation in mice. Fitoterapia 2011, 82 (4), 620-625. 7. Meng, F.; Zuo, G.; Hao, X.; Wang, G.; Xiao, H.; Zhang, J.; Xu, G., Antifungal activity of the benzo[c]phenanthridine alkaloids from Chelidonium majus Linn against resistant clinical yeast isolates. Journal of Ethnopharmacology 2009, 125 (3), 494-496. 8. Yang, X.-J.; Miao, F.; Yao, Y.; Cao, F.-J.; Yang, R.; Ma, Y.-N.; Qin, B.-F.; Zhou, L., In vitro antifungal activity of sanguinarine and chelerythrine derivatives against phytopathogenic fungi. Molecules 2012, 17 (11), 13026-13035. 9. Tsukamoto, H.; Kondo, S.; Mukudai, Y.; Nagumo, T.; Yasuda, A.; Kurihara, Y.; Kamatani, T.; Shintani, S., Evaluation of anticancer activities of benzo[c]phenanthridine alkaloid sanguinarine in oral squamous cell carcinoma cell line. Anticancer Research 2011, 31 (9), 2841-2846. 10. Lasák, P.; Motyka, K.; Kryštof, V.; Stýskala, J., Synthesis, Bacteriostatic and Anticancer Activity of Novel Phenanthridines Structurally Similar to Benzo[c]phenanthridine Alkaloids. Molecules 2018, 23 (9), 2155. 11. Cho, W.-J.; Yoo, S.-J.; Chung, B.-H.; Choi, B.-G.; Cheon, S. H.; Whang, S.- H.; Kim, S.-K.; Kang, B.-H.; Lee, C.-O., Synthesis of benzo[c]phenanthridine derivatives and theirin vitro antitumor activities. Archives of Pharmacal Research 1996, 19 (4), 321. 12. Steinhauer, T. N.; Girreser, U.; Meier, C.; Cushman, M.; Clement, B., One‐Step Synthetic Access to Isosteric and Potent Anticancer Nitrogen Heterocycles with the Benzo[c]phenanthridine Scaffold. Chemistry: A European Journal 2016, 22 (24), 8301- 8308. 13. Wang, Q.; Liu, Y.; Zhou, J., Neuroinflammation in Parkinson’s disease and its potential as therapeutic target. Translational Neurodegeneration 2015, 4 (1), 19. 14. Lee, K.-W.; Im, J.-Y.; Woo, J.-M.; Grosso, H.; Kim, Y.-S.; Cristovao, A. C.; Sonsalla, P. K.; Schuster, D. S.; Jalbut, M. M.; Fernandez, J. R., Neuroprotective and anti-inflammatory properties of a coffee component in the MPTP model of Parkinson’s disease. Neurotherapeutics 2013, 10 (1), 143-153. 15. Esposito, E.; Di Matteo, V.; Benigno, A.; Pierucci, M.; Crescimanno, G.; Di Giovanni, G., Non-steroidal anti-inflammatory drugs in Parkinson's disease. Experimental Neurology 2007, 205 (2), 295-312.

16. Koblitz, H., Macleaya spp.: Morphogenesis and the Production of Secondary Metabolites. In Medicinal and Aromatic Plants II, Springer: 1989; pp 332-348. 17. Lin, L.; Liu, Y. C.; Huang, J. L.; Liu, X. B.; Qing, Z. X.; Zeng, J. G.; Liu, Z. Y., Medicinal plants of the genus Macleaya (Macleaya cordata, Macleaya microcarpa): A review of their phytochemistry, pharmacology, and toxicology. Phytotherapy Research 2018, 32 (1), 19-48. 18. Wu, Z.; Raven, P.; Hong, D., Flora of China, Volume 7: Menispermaceae through Capparaceae. Flora of China, Volume 7: Menispermaceae through Capparaceae. 2008. 19. Grey-Wilson, C., Poppies: a guide to the poppy family in the wild and in cultivation. BT Batsford Ltd.: 1993. 20. Psotová, J.; Klejdus, B.; Večeřa, R.; Kosina, P.; Kubáň, V.; Vičar, J.; Šimánek, V.; Ulrichová, J., A liquid chromatographic–mass spectrometric evidence of dihydrosanguinarine as a first metabolite of sanguinarine transformation in rat. Journal of Chromatography B 2006, 830 (1), 165-172. 21. Kosina, P.; Gregorova, J.; Gruz, J.; Vacek, J.; Kolar, M.; Vogel, M.; Roos, W.; Naumann, K.; Simanek, V.; Ulrichova, J., Phytochemical and antimicrobial characterization of Macleaya cordata herb. Fitoterapia 2010, 81 (8), 1006-1012. 22. National Center for Biotechnology Information Macleaya cordata. https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=56857. 23. Baek, M.-Y.; Park, H.-J.; Kim, G.-M.; Lee, D.-Y.; Lee, G.-Y.; Moon, S.-J.; Ahn, E.-M.; Kim, G.-S.; Bang, M.-H.; Baek, N.-I., Insecticidal alkaloids from the seeds of Macleaya cordata on cotton aphid (Aphis gossypii). Journal of the Korean Society for Applied Biological Chemistry 2013, 56 (2), 135-140. 24. Liu, M.; Lin, Y.-l.; Chen, X.-R.; Liao, C.-C.; Poo, W.-K., In vitro assessment of Macleaya cordata crude extract bioactivity and anticancer properties in normal and cancerous human lung cells. Experimental and Toxicologic Pathology 2013, 65 (6), 775- 787. 25. Zhang, Z.; Guo, Y.; Zhang, L.; Zhang, J.; Wei, X., Chelerythrine chloride from Macleaya cordata induces growth inhibition and apoptosis in human gastric cancer BGC- 823 cells. Acta Pharmaceutica Sinica B 2012, 2 (5), 464-471. 26. Zou, H.-L.; Li, H.-Y.; Liu, B.-L.; Zhou, G.-X., A new cytotoxic benzophenanthridine isoquinoline alkaloid from the fruits of Macleaya cordata. Journal of Asian Natural Products Pesearch 2015, 17 (8), 856-860. 27. Liu, H.; Wang, J.; Zhao, J.; Lu, S.; Wang, J.; Jiang, W.; Ma, Z.; Zhou, L., Isoquinoline alkaloids from Macleaya cordata active against plant microbial pathogens. Natural Product Communications 2009, 4 (11), 1934578X0900401120. 28. Chen, X. M.; Zhang, M.; Fan, P. L.; Qin, Y. H.; Zhao, H. W., Chelerythrine chloride induces apoptosis in renal cancer HEK-293 and SW-839 cell lines. Oncology Letters 2016, 11 (6), 3917-3924. 29. Zhang, Z.-f.; Guo, Y.; Zhang, J.-b.; Wei, X.-h., Induction of apoptosis by chelerythrine chloride through mitochondrial pathway and Bcl-2 family proteins in human hepatoma SMMC-7721 cell. Archives of Pharmacal Research 2011, 34 (5), 791- 800.

30. Tang, Z.-H.; Cao, W.-X.; Wang, Z.-Y.; Lu, J.-H.; Liu, B.; Chen, X.; Lu, J.-J., Induction of reactive oxygen species-stimulated distinctive autophagy by chelerythrine in non-small cell lung cancer cells. Redox Biology 2017, 12, 367-376. 31. Sun, M.; Liu, C.; Nadiminty, N.; Lou, W.; Zhu, Y.; Yang, J.; Evans, C. P.; Zhou, Q.; Gao, A. C., Inhibition of Stat3 activation by sanguinarine suppresses prostate cancer cell growth and invasion. The Prostate 2012, 72 (1), 82-89. 32. Jang, B.-C.; Park, J.-G.; Song, D.-K.; Baek, W.-K.; Yoo, S. K.; Jung, K.-H.; Park, G.-Y.; Lee, T.-Y.; Suh, S.-I., Sanguinarine induces apoptosis in A549 human lung cancer cells primarily via cellular glutathione depletion. Toxicology in Vitro 2009, 23 (2), 281-287. 33. Lee, J. S.; Jung, W.-K.; Jeong, M. H.; Yoon, T. R.; Kim, H. K., Sanguinarine induces apoptosis of HT-29 human colon cancer cells via the regulation of Bax/Bcl-2 ratio and caspase-9-dependent pathway. International Journal of Toxicology 2012, 31 (1), 70-77. 34. Xu, J.-Y.; Meng, Q.-H.; Chong, Y.; Jiao, Y.; Zhao, L.; Rosen, E. M.; Fan, S., Sanguinarine inhibits growth of human cervical cancer cells through the induction of apoptosis. Oncology Reports 2012, 28 (6), 2264-2270. 35. Hu, J.; Zhang, W. D.; Liu, R. H.; Zhang, C.; Shen, Y. H.; Li, H. L.; Liang, M. J.; Xu, X. K., Benzophenanthridine alkaloids from Zanthoxylum nitidum (Roxb.) DC, and their analgesic and anti‐inflammatory activities. Chemistry & Biodiversity 2006, 3 (9), 990-995. 36. García, R.; Seco, J. M.; Vázquez, S. A.; Quinoá, E.; Riguera, R., Absolute configuration of secondary alcohols by 1H NMR: in situ complexation of α- methoxyphenylacetic acid esters with barium (II). The Journal of Organic Chemistry 2002, 67 (13), 4579-4589. 37. Garson, M.; Hehre, W.; Pierens, G., Revision of the Structure of Acremine P from a Marine-Derived Strain of Acremonium persicinum. Molecules 2017, 22 (4), 521. 38. Smith, S. G.; Goodman, J. M., Assigning stereochemistry to single diastereoisomers by GIAO NMR calculation: The DP4 probability. Journal of the American Chemical Society 2010, 132 (37), 12946-12959. 39. White, A. M.; Pierens, G. K.; Forster, L. C.; Winters, A. E.; Cheney, K. L.; Garson, M. J., Rearranged diterpenes and norditerpenes from three Australian Goniobranchus mollusks. Journal of Natural Products 2015, 79 (3), 477-483.

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CHAPTER 4. CYTOLOGICAL PROFILING ASSAY

As described in previous chapters, phenotypic assay, particularly cytological profiling, was carried out to profile all pure metabolites isolated from M. cordata, along with the crude extract and fractions (illustrated in Figure 4.1), against hONS cellular parameters.

Crude Extract

Fraction A Fraction B + C Fraction D + E

Fraction BC 3 Fraction BC 4 Fraction DE 2 Fraction DE 4

Compound 2.3 Compound 2.7 Compound 2.13 Compound 2.2 Compound 2.5 Compound 2.8 Compound 2.4 Compound 2.6 Compound 2.9 Compound 2.11 Compound 2.10 Compound 2.12 Compound 2.1 Compound 2.14 Compound 2.15 Compound 2.16 Fraction DE 5 Fraction DE 6

Figure 4.1 Fractions and pure compounds subjected to cytological profiling

PD hONS cells were treated with 30 µg/mL of crude extract and fractions, and 30

μM of each compound for 24 h and subsequently stained with fluorescence probes targeting various cellular components believed to be implicated in PD. These components included cytoskeleton, mitochondria, autophagosomes, lysosomes and early endosomes.

An automated microscopy imaging system was employed to capture images of every

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single cell, which in turn generated numerical data of 38 cellular parameters that describe nuclear morphology, intensity and texture of various cellular components as well as overall cell morphology. These cytological features are displayed in Table 4.1.

Table 4.1 Cytological features generated and their associated stains and antibodies.

No. Cytological Parameter Stain/Antibody 1 Lysosome marker intensity mean 2 Lysosome marker intensity outer region mean LysoTracker Red 3 Lysosome marker intensity inner region mean DND-99 4 Lysosome marker texture index 5 Mitochondria marker intensity in the cytoplasm 6 Mitochondria marker intensity in outer region of cytoplasm MitoTracker Orange 7 Mitochondria marker intensity in inner region of the cytoplasm CMTMRos 8 Mitochondria marker texture index 9 α-Tubulin marker intensity in the cytoplasm 10 α-Tubulin marker intensity in outer region of cytoplasm 11 α-Tubulin marker intensity in inner region of cytoplasm 12 α-Tubulin marker texture index 13 Cell morphology area (μm2) Anti-αtubulin Antibody 14 Cell morphology width (μm) 15 Cell morphology length (μm) 16 Cell morphology ratio width to length 17 Cell morphology roundness 18 Nucleus morphology area (μm2) 19 Nucleus morphology width (μm) 20 Nucleus morphology length (μm) 21 Nucleus morphology ratio width to length DAPI 22 Nucleus morphology roundness 23 Nucleus marker intensity 24 Nucleus marker texture index 25 LC3b marker intensity in the cytoplasm 26 LC3b marker intensity in the outer region of cytoplasm Anti-LC3b Antibody 27 LC3b marker intensity in inner region of cytoplasm 28 LC3b marker texture index 29 EEA1 marker intensity in the cytoplasm 30 EEA1 marker intensity in outer region of cytoplasm 31 EEA1 marker intensity in inner region of cytoplasm 32 EEA1 marker texture index 33 Number of EEA1 marker spots in cytoplasm Anti-EEA1 Antibody 34 Number of EEA1 marker spots in outer region of cytoplasm 35 Number of EEA1 marker spots in inner region of cytoplasm 36 Number of EEA1 marker spots per area of cytoplasm 37 Number of EEA1 marker spots per area of outer region of cytoplasm 38 Number of EEA1 marker spots per area of inner region of cytoplasm

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CP is a high-content screening platform and therefore normally generates gigabytes of images from which several biological descriptors can be extracted. Because of the vast amount of data obtained, prober data processing must be carried out in order to answer the biological questions. Several statistical approaches with high degree of confidence have been developed to assist the identification of hits. The downstream data analysis in this project adhered to that previously reported.1

4.1 Removal of Cytotoxic Fractions and Compounds

Cytotoxicity may affect the integrity of a cell-based assay. Therefore, filtering out cytotoxic compounds was one of the initial steps in the assessment of the raw data for this experiment. The cell number in each well was used as a factor to detect the compounds possessing cytotoxic activity. Wells having less than 100 cells were deemed to have contained cytotoxic compounds. Under this assumption, fraction BC3 and DE6, together with compound 2.5 (Chelerythrine), 2.6 (10-Methoxychelerythrine), 2.7 (Chelirubine) and 2.8 (Sanguinarine), exhibited cytotoxicity against hONS cells and were excluded from further analysis.

4.2 Control Validation

In this assay, rotenone (20 µM, 0.6% DMSO) and chloroquine (10 µM, 0.6%

DMSO) were used as positive control, while DMSO (0.6%) worked as negative control.

The positive control profiles are used to determine if the experiment has worked properly, in respect to previous research. Chloroquine is an autophagy inhibitor that prevent the fusion of autophagosome and lysosome,2 thus disrupting cellular trafficking. This can be reflected in the change of the Lysotracker texture, as seen in Panel A of Figure 4.2 where chloroquine affected the Lysotracker staining, making it more punctate compared to the

DMSO. Instead, Rotenone, a broad-spectrum insecticide, has been found to inhibit

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mitochondrial respiratory chain complex I,3, 4 leading to cellular apoptosis. Panel B in

Figure 4.2 showed that mitochondrial staining of cells treated with Rotenone was altered compared that of DMSO-treated cells.

Chloroquine DMSO

Rotenone DMSO

Figure 4.2 Images of cells treated with positive and negative control. Panel A: Lysotracker staining of cells treated with chloroquine and DMSO. Panel B: Mitotracker staining of cells treated with Rotenone and DMSO.

Principle component analysis (PCA) on all variables was used to see the change in the overall cytological profiles of positive and negative compounds. Ellipses are drawn at 95 % confidence interval. PCA clustering shows that the controls have worked as can be seen by three distinct clusters.

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Figure 4.3 PCA clusters of Chloroquine, Rotenone and DMSO.

The above data showed that the positive controls caused certain distinct phenotypes of hONS cells compared to the negative control DMSO. It can therefore be concluded that the experiment has worked and the results from the assays would be reliable.

4.3 Correlation Analysis

In order to optimize the analysis, Spearman’s correlation was performed to investigate the relationship among the cellular parameters. In statistic, Spearman’s correlation measures the monotonic relationship between two variables and has a coefficient rs ranging from -1.0 to 1.0 which is associated with the strength and the direction of the relationship. Previous research utilizing CP against hONS cells in our institute noticed that data for some biological features were likely to follow the same trend,1 meaning that they together displayed either positive or negative deviation from the negative control. This implied that the number of parameters could be reduced for assessment in order to facilitate the analysis. With this view, a Spearman’s correlation

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was performed on the data of this project, and the results are shown in Figure 4.4 in which the colors (blue and red) represent the strength of correlations. It was revealed that some cytological features are highly correlated with others, pursuant to the deep colors. For example, mitochondria marker intensity in the inner region showed strong correlations with mitochondria marker intensity in the outer region and with α-tubulin marker intensity in the outer region, as indicated by the dark blue color in Figure 4.4. This means that these three biological descriptors would exhibit similar results, compared to the negative control. Constituently, assessment of these three features can be reduced to evaluation of

Figure 4.4 Spearman’s correlation of the 38 parameters. Blue color (positive) represents monotonic direct relationship, and red color (negative) accounts for monotonic inverse relationship.

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only one parameter. In this study, only monotonic increasing with the threshold of 0.9 was used to remove correlated variables, meaning that parameters having correlations with rs ≥ 0.9 were excluded, and those with rs < 0.9 were retained.

In respect of this study, Spearman’s correlation is a useful approach to simplify the data processing in such a way that the number of biological descriptors can be greatly reduced based on the strong correlations among them. As a result, there were 16 variables showing monotonically increasing relationship with others with the coefficients greater than 0.9 (Table 4.2). Therefore, those 16 parameters were removed from the 38 variables initially generated. The 22 remaining cytological features will be subjected to assessment and are displayed in Table 4.3.

Table 4.2 List of 16 parameters highly correlated with others with coefficients rs greater than 0.9.

No. Parameter (Removed) Correlated Parameter rs 1 Lysosome marker intensity outer region mean Lysosome marker intensity mean 0.998 2 Lysosome marker intensity inner region mean Lysosome marker intensity mean 0.994 LC3b marker intensity in the outer region of LC3b marker intensity in the inner 3 0.989 cytoplasm region of cytoplasm LC3b marker intensity in the inner 4 LC3b marker intensity in the cytoplasm 0.973 region of cytoplasm Number of EEA1 marker spots in inner region Number of EEA1 marker spots in 5 0.922 of cytoplasm outer region of cytoplasm Number of EEA1 marker spots in outer region Number of EEA1 marker spots in 6 0.968 of cytoplasm cytoplasm EEA1 marker intensity in outer region of EEA1 marker intensity in the 7 0.989 cytoplasm cytoplasm EEA1 marker intensity in inner region of EEA1 marker intensity in the 8 0.981 cytoplasm cytoplasm α-Tubulin marker intensity in outer region of α-Tubulin marker intensity in the 9 0.997 cytoplasm cytoplasm α-Tubulin marker intensity in inner region of α-Tubulin marker intensity in the 10 1 cytoplasm cytoplasm LC3b marker intensity in the 11 α-Tubulin marker intensity in the cytoplasm 0.918 cytoplasm Mitochondria marker intensity in outer region Mitochondria marker intensity in 12 0.999 of cytoplasm inner region of the cytoplasm Mitochondria marker intensity in the Mitochondria marker intensity in 13 1 cytoplasm inner region of the cytoplasm 14 Cell morphology width (μm) Cell morphology area (μm2) 0.967 15 Cell morphology length (μm) Cell morphology area (μm2) 0.943 16 Nucleus morphology width (μm) Nucleus morphology area (μm2) 0.911

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Table 4.3 Cellular parameters retained after Spearman’s correlation analysis.

No of Parameter Name of Parameter 1 EEA1 marker texture index 2 Number of EEA1 marker spots per area of inner region of cytoplasm 3 Number of EEA1 marker spots per area of outer region of cytoplasm 4 Number of EEA1 marker spots per area of cytoplasm 5 Number of EEA1 marker spots in cytoplasm 6 EEA1 marker intensity in the cytoplasm 7 Lysosome marker texture index 8 Lysosomal Marker Intensity Mean 9 Mitochondria marker intensity in inner region of the cytoplasm 10 LC3b marker intensity in inner region of cytoplasm 11 Cell Morphology Ratio Width to Length 12 Cell Morphology Roundness 13 Cell Morphology Area (μm2) 14 Nucleus marker texture index 15 Nucleus Morphology Ratio Width to Length 16 Nucleus Morphology Length (μm) 17 Nucleus Morphology Roundness 18 Nucleus Morphology Area (μm2) 19 Nucleus Marker Intensity 20 LC3b marker texture index 21 Mitochondrial marker texture index 22 Tubulin marker texture index

4.4 Hierarchical Clustering and Heatmap

Binary logarithms (log2) of the ratio of the compounds to DMSO were calculated for each retained parameter in Table 4.3. The results were hierarchically clustered using

Cluster 3.0 with centroid linkage and uncentered correlation. Heatmaps were generated with Java TreeView to depict the biological profiles of the compounds.

Clusters were defined based on the similarities of the biological profiles with the correlation coefficients greater than 0.7 in the dendrogram. This was visualized by the red line in Figure 4.5. Figure 4.5 showed a general heat map of all extracts and pure

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0.7

Figure 4.5 Heat map depicting the cytological profiles of fractions (30 µg/mL) and compounds (30 µM) with hierarchical clustering. Red line indicates Pearson’s correlation coefficient of 0.7. y-axis: fractions and compounds subjected to CP. x-axis: 22 cytological parameters. 1. EEA1 marker texture index. 2. Number of EEA1 marker spots per area of inner region of cytoplasm. 3. Number of EEA1 marker spots per area of outer region of cytoplasm. 4. Number of EEA1 marker spots per area of cytoplasm. 5. Number of EEA1 marker spots in cytoplasm. 6. EEA1 marker intensity in the cytoplasm. 7. Lysosome marker texture index. 8. Lysosomal Marker Intensity Mean. 9. Mitochondria marker intensity in inner region of the cytoplasm. 10. LC3b marker intensity in inner region of cytoplasm. 11. Cell Morphology Ratio Width to Length. 12. Cell Morphology Roundness. 13. Cell Morphology Area (μm2). 14. Nucleus marker texture index. 15. Nucleus Morphology Ratio Width to Length. 16. Nucleus Morphology Length (μm). 17. Nucleus Morphology Roundness. 18. Nucleus Morphology Area (μm2). 19. Nucleus Marker Intensity. 20. LC3b marker texture index. 21. Mitochondrial marker texture index. 22. Tubulin marker texture index. compounds subjected to cytological profiling, except for compounds 2.5, 2.6, 2.7, 2.8 and fractions BC3, BC6 which were previously determined to be cytotoxic. The effects were plotted as the log2 of the ratios. Black color indicated no perturbation (log2 ratio = 0), while yellow and blue color represented positive deviation (log2 ratio > 0) and negative deviation (log2 ratio < 0), respectively. Under a Pearson’s correlation of 0.7, the heatmap can be roughly divided into six bio-clusters. One of the most noticeable cluster was the cluster 1 (Figure 4.6), which mostly contained fractions exhibiting certain biological profiles. For example, fraction D and E both showed strong negative deviations to DMSO for mitochondria marker intensity in inner region of the cytoplasm and LC3b marker

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Figure 4.6 Bio-cluster 1, extracted from Figure 4.. x-axis: 22 parameters as defined in Figure 4.5. y-axis: fractions and compounds. intensity in inner region of cytoplasm (parameter 9 and 10, respectively), as well as a moderate positive deviation in cell morphology area (parameter 13); these effects can be seen more clearly in Figure 4.7. The profiles of these two fractions were generally very similar for most parameters, and they indeed had the Pearson’s correlation coefficient of

0.97 in the dendrogram; this indicated that D and E could contain the same compounds bearing responsibilities for the above effects. This information in fact was highly in accordance with the chemistry perspective as the 1H NMR data for fraction D and E were nearly identical to each other (Figure 3.10). Notably, compound 2.3 was clustered with

Frtn. D Frtn. E 2.3 -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -1 -0.5 0 0.5 1 1 1 1 3 3 3 5 5 5 7 7 7 9 9 9 11 11 11 13 13 13 15 15 15 17 17 17 19 19 19 21 21 21

Figure 4.7 Bar plots showing biological profiles of fractions D and E, and compound 2.3. Rectangles represent clustering. x-axis: log2 ratio against DMSO. y-axis: 22 cytological parameters as defined in Figure 4.5.

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D and E despite being obtained from fraction B+C. This was also understandable because

2.3 was a minor metabolite in fraction B+C while a large quantity of this compound was presence in D+E as seen by LC-MS. It can therefore be suggested that this type of compounds may be responsible for the biological profiles of D and E, particularly at parameters associated with mitochondria (9), LC3b (10), cell morphology (13) and nucleus (19).

Also notable in this bio-cluster were fraction DE2, DE4 and DE5. These three fractions were derived from D and E by Sephadex chromatography; however, in comparison with their parent fraction, they showed decreased effects for parameter 9, 10,

13 and 19 whilst having stronger positive deviations in number of EEA1 marker spots in cytoplasm (parameter 5) (Figure 4.6 and Figure 4.8). This proposed that the compounds which accounted for this profile were enriched in DE2, DE4 and DE5. Thus, further purification of these three fractions may have a higher chance in achieving metabolites that could alter the EEA1 features.

Frtn. DE2 Frtn. DE4 Frtn. DE5 -1.5 -1 -0.5 0 0.5 1 -1 -0.5 0 0.5 1 -1 -0.5 0 0.5 1 1 1 1 3 3 3 5 5 5 7 7 7 9 9 9 11 11 11 13 13 13 15 15 15 17 17 17 19 19 19 21 21 21

Figure 4.8 Bar plots showing biological profiles of fractions DE2, DE4 and DE5. Rectangles represent clustering. x-axis: log2 ratio against DMSO. y-axis: 22 cytological parameters as defined in Figure 4.5.

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Figure 4.9 Bio-cluster 2, extracted from Figure 4.5. x-axis: 22 parameters as defined in Figure 4.. y-axis: fractions and compounds.

Bio-cluster 2 comprised pure compounds (2.4 and 2.12 - 2.16) (Figure 4.9).

Compared to bio-cluster 1 in which there was a wide spectrum of effects, this cluster displayed more specific features, primarily on LC3b marker intensity in inner region of cytoplasm (parameter 10) (Figure 4.10). Compounds 2.4, 2.14 and 2.16 appeared to

2.4 2.14 2.12 2.13 -1.2 -0.7 -0.2 0.3 -1.2 -0.7 -0.2 0.3 -0.5 -0.3 -0.1 0.1 -0.7 -0.3 0.1 1 1 1 1 3 3 3 3 5 5 5 5 7 7 7 7 9 9 9 9 11 11 11 11 13 13 13 13 15 15 15 15 17 17 17 17 19 19 19 19 21 21 21 21

2.16 2.15 -1 -0.6 -0.2 0.2 -0.7 -0.3 0.1 1 1 3 3 5 5 7 7 9 9 11 11 13 13 15 15 17 17 19 19 21 21

Figure 4.10 Bar plots showing biological profiles of compounds in bio-cluster 2. Rectangles represent clustering. x-axis: log2 ratio against DMSO. y-axis: 22 cytological parameters as defined in Figure 4.5.

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exhibit highest levels of negative deviations from the control. It was interesting that several structurally different metabolites were grouped in this cluster. For example, compound 2.4, a benzo[c]phenanthridine alkaloid, and compound 2.14, a coumaroylquinic acid, had the coefficient of 0.94. Likewise, clusters of 2.12, a protopine alkaloid, 2.13, a coumaroyl amide, and 2.16, a coumaroyl glucoside was also seen with a high uncentered coefficient. In contrast, compound 2.2, a structural analogue of 2.4, did not fall in this cluster but belonged to a totally different group which did not show interesting effects on the 22 parameters (Figure 4.11). This reflected that chemical similarity may not influence biological responses. In fact, this was previously observed in an earlier study in our institute where cytological profiling was performed on a library

Frtn. A

2.9

2.2

Frtn. A 2.1 2.11 2.10 2.9 2.2 -0.7 -0.3 0.1 -0.7 -0.3 0.1 -0.7 -0.3 0.1 -0.7 -0.3 0.1 -0.7 -0.3 0.1 -0.7 -0.3 0.1 1 1 1 1 1 1

3 3 3 3 3 3

5 5 5 5 5 5

7 7 7 7 7 7

9 9 9 9 9 9

11 11 11 11 11 11

13 13 13 13 13 13

15 15 15 15 15 15

17 17 17 17 17 17

19 19 19 19 19 19

21 21 21 21 21 21

Figure 4.11 Heat maps and bar plots for other bio-clusters, including fraction A, compound 2.1, 2.2, 2.9- 2.11. Heat maps were extracted from Figure 4.5. For bar plots, rectangles represent clustering. x-axis: log2 ratio against DMSO. y-axis: 22 cytological parameters as defined in Figure 4.5.

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of structurally diverse natural products.5 It was later proposed that compounds with different structures might function on different targets in a similar pathway or on different mechanisms leading to the same phenotype. In addition, substitutions may also play an important role in driving biological effects or pathways, with evidence being that compound 2.3 demonstrated strong perturbations on multiple parameters (Figure 4.7), while its methoxy-substituent, compound 2.1, only impacted weakly on LC3b-related marker (Figure 4.11). The remaining bio-clusters (also in Figure 4.11) did not express remarkable responses (compound 2.2 and 2.9), except for fraction A which displayed some influences on EEA1-associated features (parameter 1-5).

4.5 Hit Identification

Although the heat maps revealed that some metabolites indeed impacted the cytological profiles of hONS cells, it was difficult to tell which compounds would really have strong influences on the tested parameters. Generally, compound is considered active if it induces distinct phenotypes with statistical significances (p < 0.05). In this study, and with respect to previous research on CP against hONS cells in our group, the minimum threshold of 1.5 in fold change in comparison with DMSO with a p value less than 0.05 for any parameter was used to define hit for that parameter. Wilcoxon rank-sum test, a nonparametric equivalent of Student’s t-test, were utilized to measure the significances. The null hypothesis in Wilcoxon test was that there was no difference in biological responses between cells treated with compounds and those treated with control

(Medcompound = Medcontrol), and the alternative hypothesis claimed that cells with compound treatments exhibited significantly different phenotypes compared with cells treated with control (Medcompound ≠ Medcontrol). The null hypothesis is rejected if the p value is less than or equal to 0.05.

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The 1.5 limit can be converted to binary logarithm value as log2(1.5) ≈ 0.58. This meant that the window from -0.58 to 0.58 for the log2 ratios was used as selection range outside which the compounds or fractions were determined as active or hits. In order to have a more obvious look, a bar plot for all fractions, and for all pure compounds, with the red vertical lines representing the 0.58 limit, was generated (Figure 4.12). Under the above criteria, fractions A, D, E BC4, DE2, DE4 and DE5, along with compounds 2.3 and 2.4, exhibited significant differences (p values < 0.05) from DMSO with |log2 ratios|

> 0.58 for some certain parameters. It was noted that compounds 2.14 and 2.16 showed

0.8 0.58 0.6 0.4 0.2 0 -0.2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 -0.4 -0.6 -0.58 -0.8 -1 -1.2 -1.4 Frtn. B+C Frtn. D Frtn. E Frtn. A Frtn. BC4 Frtn. DE2 Frtn. DE4 Frtn. DE5 Crude

0.6 0.58

0.4

0.2

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 -0.2

-0.4

-0.6 -0.58 -0.8

-1

-1.2 2.4 2.13 2.14 2.15 2.16 2.2 2.3 2.1 2.11 2.12 2.10 2.9

Figure 4.12 Bar charts displaying the perturbations of biological profiles for fractions (top) and pure compounds (bottom). x-axis: 22 cytological parameters as defined in Figure 4.5. y-axis: log2 ratio to DMSO. Red lines representing the 0.58 limit beyond which hits were identified.

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strong negative deviations at parameter 10, yet this was not significant in term of statistic as the p values were greater than 0.05. The results are summarized in Table 4.4.

Table 4.4 List of fractions and compounds having significant effects to tested parameters, generated from Figure 4.12.

Name Parameter affected log2 ratio to DMSO p value (FDR corrected) Frtn. A 5 -0.65 0.022 Frtn. BC4 2 -0.76 0.024 9 -1.13 0.022 10 -1.33 0.022 Frtn. D 13 0.71 0.022 19 -0.92 0.022 9 -1.26 0.022 10 -1.16 0.024 Frtn. E 13 0.60 0.022 19 -0.79 0.022 5 0.65 0.022 Frtn. DE2 8 -0.67 0.022 9 -0.93 0.022 5 0.72 0.022 Frtn. DE4 8 -0.61 0.022 9 -0.73 0.022 Frtn. DE5 9 -0.64 0.026 6 -0.73 0.022 Cmpn. 2.3 9 -0.85 0.022 Cmpn. 2.4 10 -0.99 0.044 Cmpn. 2.14 10 -0.75 >0.05 Cmpn. 2.16 10 -0.89 >0.05

Obviously, the statistical analysis of data not only justified the observations from the heat maps, but also further revealed some information. For example, fraction BC4 was found to induce strong phenotypic change in number of EEA1 marker spots per area of inner region of cytoplasm (parameter 2). This effect, nonetheless, was not seen for compounds obtained from this fraction (2.9 and 2.10), suggesting that other metabolites in BC4 or synergistic effect may account for this phenotype. Besides, fraction DE5 was different to DE2 and DE4 as it mainly affected parameter 9; these three fractions were relatively difficult to differentiate in the heat maps. In addition, fraction A primarily impacted number of EEA1 marker spots in cytoplasm (parameter 5). Compared to fractions DE2 and DE4 on this parameter, fraction A induced this profile in an opposite direction, which were negative deviations to DMSO instead of positive perturbations; this

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implied that compounds in fraction A were different to those in DE2 and DE4, and might work in different modes of action leading to an opposite phenotype. 1H NMR indicated that fraction A was dominant by carbohydrates. The role of sugar compounds in regulating EEA1 protein is not well documented. However, this result demonstrated that carbohydrates may, in some ways, alter the EEA1 staining of hONS cells used in this assay.

Importantly, data assessment also identified pure compounds that greatly influenced distinct phenotypes. Compounds 2.3 perturbed multiple parameters, most significantly on EEA1- and mitochondria-associated features (Figure 4.13), while compound 2.4 mainly impacted LC3b-related profiles (Figure 4.14). Although compounds 2.14 and 2.16 did not show statistical significance, they greatly affected the

LC3b staining (Figure 4.15 and Figure 4.16). The involvement of 2.14 in the autophagosome LC3b could be consistent with previous research. Domitrović et al.6 found that chlorogenic acid, a demethoxy analogue of 2.14, suppressed the expression of

LC3b, suggesting the inhibition of autophagy in mice. The statistical insignificances of

2.14 and 2.16 were proposedly due to large variation in the technical repeats and will be further investigated. In general, these compounds can be considered as hits and may be potentially used as molecular tools for PD research.

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0.5

0 1 3 5 7 9 11 13 15 17 19 21

-0.5

-1

DMSO treatment Compound treatment

Figure 4.13 Structure and effects of 2.3 on hONS cell staining (Mitotracker, LC3b, CellMask, Dapi). For bar chart, x-axis: 22 cytological parameters as defined in Figure 4.5. y-axis: log2 ratio to DMSO.

0.5

0 1 3 5 7 9 11 13 15 17 19 21

-0.5

-1

DMSO treatment Compound treatment

Figure 4.14 Structure and effects of 2.4 on hONS cell staining (LC3b). For bar chart, x-axis: 22 cytological parameters as defined in Figure 4.5. y-axis: log2 ratio to DMSO.

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0.5

0 1 3 5 7 9 11 13 15 17 19 21

-0.5

-1

DMSO treatment Compound treatment

Figure 4.15 Structure and effects of 2.14 on hONS cell staining (LC3b). For bar chart, x-axis: 22 cytological parameters as defined in Figure 4.5. y-axis: log2 ratio to DMSO.

0.5

0 1 3 5 7 9 11 13 15 17 19 21

-0.5

-1

DMSO treatment Compound treatment

Figure 4.16 Structure and effects of 2.16 on hONS cell staining (LC3b). For bar chart, x-axis: 22 cytological parameters as defined in Figure 4.5. y-axis: log2 ratio to DMSO.

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4.6 Conclusions and Future Directions

Cytological profiling is indeed a sensitive and robust technique to unbiasedly profile small molecules to study their effects on several organelles within the cells, which in turn generates much valuable biological information. In this project, CP platform was utilized to investigate the phenotypic responses of hONS cells following treatments with mixtures and pure isolates derived from the TCM herbal plant Macleaya cordata. Data analysis revealed that several fractions and compounds strongly influenced certain cellular phenotypes. Four out of sixteen compounds (2.3, 2.4, 2.14 and 2.16) were among the most potential candidates in inducing various features of the cell line tested. Previous studies in our institute showed that hONS cells harvested from Parkinson’s Disease patients recapitulate some functional and molecular aspects of PD and may potentially be used as a cell model for this disease.7, 8 Given that the cytological parameters involved in the assay were believed to be implicated in the pathogenesis of PD, the identification of the above four metabolites was an important contribution to the compound library which can later be used as chemical probes to interrogate the biological pathways of this disorder.

It was also revealed that several fractions from M. cordata can drive some biological responses of the cells but were yet to be chemically investigated. Consequently, further purification on these fractions will be considered with the hope of obtaining more potential probes to serve the research of PD.

Notwithstanding, more justifications must be made to make sure the identified compounds can be entitled as useful probes. As previously stated, high-quality chemical probes need to function in a dose-dependent manner. In this project, only a single concentration for the compounds was used; therefore, multiple doses will need to be tested in order to clarify the effects of the candidate probes. Mechanism of action also

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plays an important role in molecular tools. Although the compounds were determined to have influences on certain features of the cells, how they functioned was not precisely known. In other words, the MOAs leading to the observed phenotypes were not well understood. For future research, the MOAs of the promising probes will be defined in order to have a better understanding of biological pathways implicated in PD. This may involve metabolomics and proteomics studies to explore MOAs, and FT-MS to assess binding affinity. The chemical structure of probes should also be fully characterized; this includes absolute configurations (ACs). The ACs of 2.3 and 2.4 were not completely assigned even though they are known compounds; this issue will also be investigated, proposedly using the methods as mentioned in Chapter 3. Another requirement for chemical probes is that they have to exhibit sufficient membrane permeability, or even better, drug-like properties. The drug-likeness is usually evaluated by Lipinski’s rule of five which are related to certain chemical and physical features of a molecule such as number of hydrogen bond donors, number of hydrogen bond acceptors, molecular weight and octanol-water partition coefficient. These properties will be taken into account for the potential probes for a more effective interrogation of MOAs underlying PD.

Finding specific biological features of diseases, especially complicated neurodegeneration disorders like PD, is undoubtedly critical in researching for their therapeutic treatment. α-synuclein aggregation has been found to be another hallmark of

PD, in addition to neuronal depletion and is potentially becoming a target for PD. With respect to α-synucleinopathy studies, the ability of the above compounds in inhibiting accumulation of α-synuclein protein is under investigation in our institute. Other members in our group are also currently addressing the profiling of these small molecules against multiple hONS cell lines from different PD patients, aiming to discover other disease-specific phenotypes which would assist the molecular mechanism studies of PD.

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4.7 References

1. Tran thi, M. T. Cytological Profiling of Natural Product Scaffolds Libraries for Parkinson’s Disease. Master's Dissertation, Griffith University, Brisbane, Queensland, Australia, 2015. 2. Mauthe, M.; Orhon, I.; Rocchi, C.; Zhou, X.; Luhr, M.; Hijlkema, K.-J.; Coppes, R. P.; Engedal, N.; Mari, M.; Reggiori, F., Chloroquine inhibits autophagic flux by decreasing autophagosome-lysosome fusion. Autophagy 2018, 14 (8), 1435-1455. 3. Tanner, C. M.; Kamel, F.; Ross, G. W.; Hoppin, J. A.; Goldman, S. M.; Korell, M.; Marras, C.; Bhudhikanok, G. S.; Kasten, M.; Chade, A. R., Rotenone, paraquat, and Parkinson’s disease. Environmental Health Perspectives 2011, 119 (6), 866-872. 4. Li, N.; Ragheb, K.; Lawler, G.; Sturgis, J.; Rajwa, B.; Melendez, J. A.; Robinson, J. P., Mitochondrial complex I inhibitor rotenone induces apoptosis through enhancing mitochondrial reactive oxygen species production. Journal of Biological Chemistry 2003, 278 (10), 8516-8525. 5. Vial, M.-L.; Zencak, D.; Grkovic, T.; Gorse, A.-D.; Mackay-Sim, A.; Mellick, G. D.; Wood, S. A.; Quinn, R. J., A grand challenge. 2. Phenotypic profiling of a natural product library on Parkinson’s patient-derived cells. Journal of Natural Products 2016, 79 (8), 1982-1989. 6. Domitrović, R.; Cvijanović, O.; Šušnić, V.; Katalinić, N., Renoprotective mechanisms of chlorogenic acid in cisplatin-induced kidney injury. Toxicology 2014, 324, 98-107. 7. Cook, A. L.; Vitale, A. M.; Ravishankar, S.; Matigian, N.; Sutherland, G. T.; Shan, J.; Sutharsan, R.; Perry, C.; Silburn, P. A.; Mellick, G. D., NRF2 activation restores disease related metabolic deficiencies in olfactory neurosphere-derived cells from patients with sporadic Parkinson's disease. PLoS One 2011, 6 (7), e21907. 8. Matigian, N.; Abrahamsen, G.; Sutharsan, R.; Cook, A. L.; Vitale, A. M.; Nouwens, A.; Bellette, B.; An, J.; Anderson, M.; Beckhouse, A. G., Disease-specific, neurosphere-derived cells as models for brain disorders. Disease Models & Mechanisms 2010, 3 (11-12), 785-798.

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Supporting Information for Chapter 3

List of Supporting Information:

Figure S1 HRESIMS spectrum of 10-Methoxybocconoline (2.1) ...... 124

Figure S2 IR spectrum of 10-Methoxybocconoline (2.1) ...... 125

Figure S3 UV spectrum of 10-Methoxybocconoline (2.1) ...... 126

Figure S4 ECD spectrum of 10-Methoxybocconoline (2.1) ...... 126

1 Figure S5 H NMR (800 MHz, CDCl3) spectrum of 10-Methoxybocconoline (2.1) ...... 127

13 Figure S6 C NMR (200 MHz, CDCl3) spectrum of 10-Methoxybocconoline (2.1) ...... 128

1 1 Figure S7 H- H COSY spectrum of 10-Methoxybocconoline (2.1) in CDCl3 ...... 129

1 1 Figure S8 H- H ROESY spectrum of 10-Methoxybocconoline (2.1) in CDCl3 ...... 130

Figure S9 HSQC spectrum of 10-Methoxybocconoline (2.1) in CDCl3 ...... 131

Figure S10 HMBC spectrum of 10-Methoxybocconoline (2.1) in CDCl3 ...... 132

Figure S11 HRESIMS spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2) ... 133

Figure S12 IR spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2) ...... 134

Figure S13 UV spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2) ...... 135

Figure S14 ECD spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2) ...... 135

1 Figure S15 H NMR (800 MHz, CDCl3) spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6- dihydrochelerythrine (2.2) ...... 136

13 Figure S16 C NMR (200 MHz, CDCl3) spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6- dihydrochelerythrine (2.2) ...... 137

Figure S17 1H-1H COSY spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2) in

CDCl3 ...... 138

Figure S18 1H-1H ROESY spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2) in

CDCl3 ...... 139

Figure S19 HSQC spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2) in CDCl3

...... 140

Figure S20 HMBC spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2) in CDCl3

...... 141

123

Figure S1 HRESIMS spectrum of 10-Methoxybocconoline (2.1)

124

Figure S2 IR spectrum of 10-Methoxybocconoline (2.1)

125

5 Δε

0 200 250 300 350 400 -5

-10

-15

-20 Wavelength (nm) Wavelength (nm)

Figure S3 UV spectrum of 10-Methoxybocconoline (2.1) Figure S4 ECD spectrum of 10-Methoxybocconoline (2.1)

126

1 Figure S5 H NMR (800 MHz, CDCl3) spectrum of 10-Methoxybocconoline (2.1)

127

13 Figure S6 C NMR (200 MHz, CDCl3) spectrum of 10-Methoxybocconoline (2.1)

128

4 ) m p p (

5 1 f

9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 f2 (ppm)

1 1 Figure S7 H- H COSY spectrum of 10-Methoxybocconoline (2.1) in CDCl3

129

4 ) m p p (

5 1 f

9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 f2 (ppm)

1 1 Figure S8 H- H ROESY spectrum of 10-Methoxybocconoline (2.1) in CDCl3

130

10 20 30 40 50 60 70 )

80 m p p ( 90 1 f 100 110 120 130 140 150 160

9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 f2 (ppm)

Figure S9 HSQC spectrum of 10-Methoxybocconoline (2.1) in CDCl3

131

10 20 30 40 50 60 70

80 ) m p p (

90 1 f 100 110 120 130 140 150 160

9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 f2 (ppm)

Figure S10 HMBC spectrum of 10-Methoxybocconoline (2.1) in CDCl3

132

Figure S11 HRESIMS spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2)

133

Figure S12 IR spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2)

134

0.5

0 200 250 300 350 400 -0.5

Δε -1

-1.5

-2

-2.5

-3 Wavelength (nm) Wavelength (nm)

Figure S13 UV spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6- Figure S14 ECD spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6- dihydrochelerythrine (2.2) dihydrochelerythrine (2.2)

135

3 l O C D D C H

0 5 9 8 0 8 8 6 6 4 4 2 0 9 1 4 6 2 8 4 0 6 3 8 7 0 1 0 9 1 0 4 6 5 6 9 5 5 4 4 8 7 6 2 7 5 4 4 3 3 3 2 8 7 9 9 1 1 3 3 6 4 4 2 1 5 0 0 0 0 1 1 9 9 8 1 1 1 1 1 1 1 1 6 5 0 0 ...... 8 8 7 7 7 7 7 6 6 6 6 6 4 4 3 3 3 3 3 3 3 3 3 3 3 2 1 1 1 0 2 1 2 1 1 6 2 7 9 5 4 1 1 0 0 0 1 0 0 0 0 3 3 1 0 2 7 ...... 1 1 1 1 1 1 1 1 3 3 3 1 3 3

9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f1 (ppm)

1 Figure S15 H NMR (800 MHz, CDCl3) spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2)

136

13 Figure S16 C NMR (200 MHz, CDCl3) spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2)

137

4 ) m p p (

5 f

9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f2 (ppm)

1 1 Figure S17 H- H COSY spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2) in CDCl3

138

4 ) m p p (

5 1 f

9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f2 (ppm)

1 1 Figure S18 H- H ROESY spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2) in CDCl3

139

0 10 20 30 40 50 60

70 ) m p

80 p (

1 90 f 100 110 120 130 140 150 160

9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f2 (ppm)

Figure S19 HSQC spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2) in CDCl3

140

0 10 20 30 40 50 60 70 ) m

80 p p (

90 f 100 110 120 130 140 150 160

9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f2 (ppm)

Figure S20 HMBC spectrum of 6-(1-Hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2.2) in CDCl3

141