alterations of microrna expression patterns in...
Post on 05-Aug-2019
214 Views
Preview:
TRANSCRIPT
ALTERATIONS OF MICRORNA EXPRESSION PATTERNS IN
HUMAN CERVICAL CARCINOMA CELLS (CA SKI) TOWARDS
1’S-1’-ACETOXYCHAVICOL ACETATE (ACA) AND
CISPLATIN (CDDP)
PHUAH NEOH HUN
FACULTY OF SCIENCE
UNIVERSITY OF MALAYA
KUALA LUMPUR
2012
ALTERATIONS OF MICRORNA EXPRESSION
PATTERNS IN HUMAN CERVICAL CARCINOMA
CELLS (CA SKI) TOWARDS
1’S-1’-ACETOXYCHAVICOL ACETATE (ACA)
AND CISPLATIN (CDDP)
PHUAH NEOH HUN
DISSERTATION SUBMITTED IN FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
INSTITUTE OF BIOLOGICAL SCIENCES
FACULTY OF SCIENCE
UNIVERSITY OF MALAYA
KUALA LUMPUR
2012
ii
ABSTRACT
The main aims of this study were to investigate the combined effects of a natural
compound 1’S-1’-acetoxychavicol acetate (ACA) with cisplatin (CDDP) on human
cervical carcinoma cells Ca Ski, and to identify microRNAs (miRNAs) asscociated with
response towards ACA and/or CDDP. Data obtained from MTT cell viability assays
indicated that both ACA and CDDP induced dose- and time-dependent cytotoxicity on
Ca Ski cells when used as a standalone agent. The in vitro studies also demonstrated
that ACA potentiates the cytotoxic effects of CDDP when used in combination through
synergistic interactions. The miRNA microarray was used to identify global miRNA
expression profiles on Ca Ski cells following administration of ACA and/or CDDP, and
it was found that 25 miRNAs were differentially expressed in response towards ACA
and/or CDDP with markedly different pattern of miRNA expressions between different
treatment regimens. Three promising miRNA candidates (hsa-miR-138, hsa-miR-210
and hsa-miR-744) which exhibited the highest fold-change in combination
chemotherapy and whose expressions were among those validated by qRT-PCR, were
selected for bioinformatic analyses. The hypothetical pathway model comprising
interaction between candidate miRNAs with their putative target genes indicated that
the cytotoxic effects induced by ACA in combination with CDDP may be regulated by
miRNA expression. Therefore, our study provides a platform for potential therapeutic
approaches in chemotherapy, whereby miRNA expression can be exploited to further
improve efficacy in combination chemotherapy.
iii
ABSTRAK
Tujuan utama penyelidikan ini adalah untuk menyiasat kesan gabungan
kompaun semulajadi 1’S-1’-acetoxychavicol acetate (ACA) dengan cisplatin (CDDP)
ke atas sel-sel karsinoma serviks manusia Ca Ski, serta mengenal pasti miRNA yang
bertindak balas terhadap rawatan ACA dan/atau CDDP. Data yang diperolehi daripada
eksperimen MTT menunjukkan bahawa ACA dan CDDP menyebabkan sitotosik yang
bergantung pada dos dan tempoh rawatan apabila digunakan sebagai ejen ‘standalone’.
Selain itu, kajian in vitro juga menunjukkan bahawa ACA mempotensikan kesan
sitotoksik CDDP melalui hubungan sinergistik apabila digabungkan. MiRNA
microarray digunakan untuk mengenal pasti profil ekspresi global miRNA setelah
dirawat dengan ACA dan/atau CDDP, dan didapati bahawa terdapat sebanyak 25
miRNA yang diekspreskan secara berlainan apabila dirawat dengan ACA dan/atau
CDDP dengan corak ekspresi miRNA yang berbeza antara regimen rawatan yang
berlainan. Tiga calon miRNA yang menjanjikan (hsa-miR-138, hsa-miR-210 and hsa-
miR-744), yang merupakan antara beberapa miRNA yang disahkan ekspresinya dengan
qRT-PCR, dipilih untuk analisis bioinformatik kerana mereka mempamerkan ‘fold-
change’ yang paling tinggi dalam kemoterapi kombinasi. ‘Hypothetical pathway model’
yang melibatkan interaksi miRNA dengan target gen putatif miRNA menunjukkan
bahawa kesan-kesan sitotoksik yang ditunjukkan oleh ACA dan/atau CDDP
berkemungkinan besar disebabkan oleh ekspresi miRNA. Maka, kajian kami
menyediakan satu platform bagi pendekatan terapeutik dalam kemoterapi, dimana
ekspresi miRNA boleh dimanipulasikan untuk meningkatkan efikasi dalam kemoterapi
kombinasi.
iv
ACKNOWLEDGEMENT
First of all, I would like to express my deepest appreciation to my project
supervisor, Associate Professor Dr. Noor Hasima Nagoor, for her support and guidance
in helping me to complete my project successfully.
I am indebted to Dr. Lionel In Lian Aun for his willingness to share his vast
expertise and knowledge with me. I am also thankful to my fellow lab mates from
Cancer Research Laboratory in BGM2 for lending me a helping hand whenever needed,
and also in making this an invaluable experience for me.
I am grateful to Professor Dr. Khalijah Awang and Mr. Mohamad Nurul Azmi
from Centre for Natural Product Research and Drug Discovery (CENAR) for providing
1’S-1’-acetoxychavicol acetate (ACA), without which this research project would not
possible. Special thanks to Oral Cancer Research and Coordinating Centre (OCRCC)
for allowing me to use their software to analyze my microRNA microarray results.
I would like to acknowledge and thank University of Malaya for their generosity
in financing this research project through University of Malaya Postgraduate Research
Grant (PPP) (PS240-2009C and PV044-2011B) and University of Malaya Research
Grant (UMRG) (RG037-10BIO). I am also grateful to University of Malaya for
sponsoring my studies through the University of Malaya Fellowship Scheme.
Last but not least, I would also like to thank my family and friends for their
constant love, care and support throughout this duration as well as everyone else who
has played a part in completion of this research project.
v
TABLE OF CONTENTS
Page No.
Abstract ii
Abstrak iii
Acknowledgement iv
Table of contents v
List of Figures ix
List of Tables ix
List of Abbreviations xii
Chapter 1: Introduction 1
1.1 Study objectives 4
Chapter 2: Literature review 5
2.1 Cancer
2.1.1 Overview 5
2.1.2 Multistep carcinogenesis 6
2.2 Cervical cancer
2.2.1 Overview 8
2.2.2 Cervical cancer subtypes 8
2.2.3 Etiology of cervical cancers 9
2.2.4 Cervical carcinogenesis 10
2.2.5 Grading in cervical cancer 11
2.2.6 Staging of cervical cancer 11
2.2.7 Epidemiology in cervical cancer 12
2.2.8 Human epidermoid cervical carcinoma Ca Ski 13
vi
2.3 Cell cycle
2.3.1 Overview 14
2.3.2 Cell cycle regulation 14
2.3.3 Cell cycle and cancer 17
2.4 Cell death
2.4.1 Overview 19
2.4.2 Apoptosis 19
2.4.3 Apoptosis and cancer 21
2.5 Chemotherapy
2.5.1 Overview 23
2.5.2 Drug resistance 24
2.5.3 Combination therapy 25
2.5.4 Combination analysis 26
2.5.5 Cisplatin 28
2.6 Natural compounds as anti-cancer agents
2.6.1 Overview 30
2.6.2 Plant derived anti-cancer agents 30
2.6.3 1’S-1’-acetoxychavicol acetate 31
2.7 MicroRNAs
2.7.1 Overview 33
2.7.2 Naming conventions for miRNAs 34
2.7.3 Biogenesis of miRNAs 35
2.7.4 MiRNAs and chemotherapy 37
Chapter 3: Materials and methods 38
3.1 1’S-1’-acetoxychavicol acetate (ACA) and cisplatin (CDDP)
3.1.1 1’S-1’-acetoxychavicol acetate (ACA) 38
3.1.2 Cisplatin (CDDP) 38
vii
3.2 Cell cultures
3.2.1 Cell lines and culture conditions 39
3.2.2 Subculturing monolayer cell lines 38
3.2.3 Cryopreservtion of cell lines 40
3.2.4 Thawing of cryopreserved cell lines 41
3.2.5 Cell count 41
3.3 MTT cell viability assays 42
3.4 Total RNA extraction and quality control
3.4.1 Total RNA extraction 43
3.4.2 RNA quality control
3.4.2.1 RNA analysis using UV spectrophotometry 44
3.4.2.2 RNA analysis using Agilent RNA 6000 Nano Kit 45
3.5 MiRNA microarray
3.5.1 FlashTag™
RNA labeling 46
3.5.2 ELOSA quality control assays 47
3.5.3 Hybridization of Affymetrix arrays 48
3.5.4 Washing and staining of Affymetrix arrays 49
3.5.5 Statistical and miRNA expression analysis 49
3.6 Validation of miRNA microarray data
3.6.1 Reverse transcription PCR 50
3.6.2 Real-time PCR amplification and quantitation 50
3.7 Bioinformatic analyses of miRNA gene targets 51
3.8 Statistical analysis 51
Chapter 4: Results 52
4.1 Cytotoxicity assays
4.1.1 Cytotoxic effects of ACA and CDDP on Ca Ski cells 52
4.1.2 Cytotoxic effects of ACA in combination with CDDP on Ca Ski cells 53
viii
4.2 Combination analysis 56
4.3 Normal cells and solvent control
4.3.1 Cytotoxic effects of ACA and CDDP on normal cells 58
4.3.2 Cytotoxic effects of DMSO on various cell lines 60
4.4 RNA quality control 61
4.5 MiRNA expression on Ca Ski cells
4.5.1 MiRNA microarray 62
4.5.2 Validation by qRT-PCR 63
4.5.3 Correlation between miRNA microarray data and qRT-PCR data 65
4.6 Bioinformatic analyses of miRNA gene targets 65
Chapter 5: Discussion 67
5.1 Cytotoxic effects of ACA and/or CDDP 67
5.2 Alterations in miRNAs expression patterns by ACA and/or CDDP 69
5.3 Interaction between candidate miRNAs with their putative target genes 71
5.4 Future research 76
Chapter 6: Conclusion 77
References 78
Appendices 99
Appendix 1: Solutions, formulations and commercial kits 99
Appendix 2: MTT assays on Ca Ski cells for standalone ACA 102
Appendix 3: MTT assays on Ca Ski cells for standalone CDDP 103
Appendix 4: MTT assays on Ca Ski cells for ACA in combination with CDDP 104
Appendix 5: MTT assays on NP69 cells for standalone ACA 105
Appendix 6: MTT assays on NP69 cells for standalone CDDP 105
Appendix 7: MTT assays on NP69 cells for ACA in combination with CDDP 105
Appendix 8: Predicted gene targets using TargetScanHuman v5.2 106
ix
LIST OF FIGURES
Page No.
Figure 2.1 Comparison between cell division in normal cells and cancer
cells
6
Figure 2.2 Multistep carcinogenesis 7
Figure 2.3 Cervical carcinogenesis 10
Figure 2.4 Cell cycle regulation 15
Figure 2.5 Apoptotic pathways 21
Figure 2.6 Mechanisms of drug resistance in cancer cells 25
Figure 2.7 Chemical structure of cisplatin (CDDP) 28
Figure 2.8 Chemical structure of 1’S-1’-acetoxychavicol acetate (ACA) 31
Figure 2.9 Biogenesis of microRNAs 36
Figure 3.1 Chemical structure of 1’S-1’-acetoxychavicol acetate (ACA) 38
Figure 3.2 Chemical structure of cisplatin (CDDP) 38
Figure 4.1 Comparative dose-response curve for standalone ACA on Ca
Ski cells at various concentrations and incubation hours
53
Figure 4.2 Comparative dose-response curve for standalone CDDP on Ca
Ski cells at various concentrations and incubation hours
53
Figure 4.3 Comparative dose-response curve for ACA and CDDP on Ca
Ski cells for different combinations following 24 h exposure
55
Figure 4.4 Isobologram analysis of ACA in combination with CDDP 57
Figure 4.5 Comparative dose-response curve at various concentrations of
standalone ACA (0 - 12.0 µM) and CDDP (0 - 200.0 µM) on
NP69 cells following 24 h exposure.
59
Figure 4.6 Comparative dose-response curve for ACA in combination
with CDDP on NP69 cells following 24 h exposure
59
x
Figure 4.7 Cytotoxic effects of DMSO at 1.2% (v/v) on Ca Ski and NP69
cells following 24 h exposure
60
Figure 4.8 Normalized fold expression for hsa-miR-138, hsa-miR-210,
hsa-miR-224 and hsa-miR-744
64
Figure 4.9 Correlation between miRNA microarray data and qRT-PCR
data
65
Figure 4.10 Pathway model comprising of interaction between candidate
miRNAs with their putative target genes
66
xi
LIST OF TABLES
Page No.
Table 2.1 FIGO staging of cervical carcinomas 12
Table 2.2 Cyclin-dependent kinase inhibitor (CKI) proteins 16
Table 3.1 Summary of experimental setup used in this study indicating
treatment groups with biological replicates
44
Table 3.2 Setup for FlashTag™
RNA labeling 46
Table 3.3 Setup for hybridization solution for ELOSA quality control
assays
48
Table 3.4 Setup for array hybridization cocktail 48
Table 3.5 Summary of washing and staining protocol for each array 49
Table 3.6 PCR setup for reverse transcription PCR 50
Table 3.7 PCR setup for real-time PCR amplification and quantitation 51
Table 4.1 Summary of combination regimens employed for MTT cell
viability assay to evaluate the combined effects of ACA and
CDDP on Ca Ski cells.
55
Table 4.2 Summary of IC50 values for standalone ACA, standalone
CDDP and ACA in combination with CDDP at various
concentrations following 24 h exposure
56
Table 4.3 Combination index analysis of ACA in combination with
CDDP
58
Table 4.4 RNA analysis using UV spectrophotometry 61
Table 4.5 RNA analysis using Agilent RNA 6000 Nano Kit 62
Table 4.6 List of miRNA expression fold-change alterations following the
administration of ACA and/or CDDP on Ca Ski cells over 2 h
63
xii
LIST OF ABBREVIATIONS
3’ UTR Three prime untranslated region
5’ UTR Five prime untranslated region
% Percentage
× Times
°C Degree Celsius
µ Micro
µg Microgram
µg/ml Microgram per militre
µl Microlitre
µM Micromolar
® Registered
(v/v) Volume per volume
(w/v) Weight per volume
A Absorbance
ASR Age-standardized incidence rate
ATP Adenosine triphosphate
Bfl-1/A1 Bcl-2-related protein A1
ACA 1’S-1’-acetoxychavicol acetate
AIF Apoptosis inducing factor
Apaf-1 Apoptotic protease-activating factor-1
ATM Ataxia-telangiectasia-mutated
Bad Bcl-2-agonist death promoter
Bax Bcl-2 associated X protein
Bcl-2 B-cell lymphoma 2
xiii
Bcl-xL B-cell lymphoma-extra large
bp Base pairs
BSA Bovine serum albumin
cAMP Cyclic adenosine monophosphate
CARIF Cancer Research Initiative Foundation
Caspase Cysteine aspartate protease
CaV Voltage-gated calcium channels
CBP CREB-binding protein
CDC Cell-division cycle
CDDP Cisplatin
CDK Cyclin-dependent kinase
cDNA Complimentary deoxyribonucleic acid
CI Combination index
CIN Cervical intraepithelial neoplasia
CKI Cyclin-dependent kinase inhibitor
cm Centimeter
cm2 Centimeter square
CO2 Carbon dioxide
CREB cAMP response element-binding
DAVID Database for Annotation, Visualization and Integrated Discovery
DEPC Diethylpyrocarbonate
dH20 Distilled water
DISC Death-inducing signaling complex
DMEM Dulbecco’s Modified Eagle Medium
DMSO Dimetyl sulfoxide
DNA Deoxyribonucleic acid
xiv
DNase Deoxyribonuclease
dNTP Deoxy-nucleoside-triphosphate
DR Death receptor
Dsh Dishevelled
EDTA Ethylenediaminetetraacetic acid
ELOSA Enzyme Linked Oligosorbent Assay
ERK Extracellular signal-regulated protein kinase
et al And others
FADD Fas-associated death domain
FAK Focal adhesion kinase
FDA Food and Drug Administration
FBS Fetal bovine serum
FIGO Fédération of Internationale de Gynécologic et d’Obstétrique
FLIP FLICE-like inhibitory protein
Fzd Frizzled
g Gravity
g Gram
GSH Glutathione
h Hour
hCG Human chorionic gonadotropin
HCl Hydrochloric acid
HIF Hypoxia-inducible factor
HPV Human papillomavirus
HSV-2 Herpes simplex virus-2
IACR International Agency for Research on Cancer
IAP Inhibitor of apoptotic protein
xv
IC50 50% inhibitory concentration
ICO Institut Català d'Oncologia
IGF Insulin growth factor
IKK IκB kinase
IL-3 Interleukin-3
Inc. Incorporated
JNK c-Jun N-terminal kinases
kDA Kilo Dalton
KEGG Kyoto Encyclopedia of Genes and Genomes
kg Kilogram
KSFM Keratinocyte serum-free medium
LEF Lymphoid enhancer factor
M Molar
MAPK Mitogen-activated protein kinase
MEK MAPK kinase
mg Milligram
mg/ml Milligram per millitre
MgCl2 Magnesium chloride
min Minute
miRNA Micro ribonucleic acid
ml Milliliter
mm Millimetre
mM Millimolar
MnCl2 Manganese chloride
mRNA Messenger ribonucleic acid
MPT Mitochondrial permeability transition
xvi
MTT 3-(4,5-dimethylthiazol-2-γl)-2,5-diphenyl-tetrazoliumbromide
NCI National Cancer Institute
NCR National Cancer Registry
NCX Sodium-calcium exchanger
NF-κB Nuclear factor kappa B
ng Nanogram
nm Nanometer
nM Nanomolar
p53 Protein 53
Pap Papanicolaou
PBS Phosphate buffered saline
PDGF Platelet-derived growth factor
PDGFR Platelet-derived growth factor receptor
pH Potential of hydrogen
PI3K Phosphoinositide 3-kinase
PMCA Plasma membrane Ca2+ ATPase
pRb Retinoblastoma protein
psi Pound force per square inch
r Pearson correlation coefficient
RIN RNA Integrity Number
RIP Receptor interacting protein
RISC RNA-induced silencing complex
RMA Robust Multichip Average
RNA Ribonucleic acid
RNase Ribonuclease
ROC Receptor-operated calcium channels
xvii
rpm Revolutions per minute
rRNA Ribosomal ribonucleic acid
S Svedberg
s Second
SA-HRP Streptavidin-horseradish peroxidase
SD Standard deviation
SDS Sodium dodecyl sulfate
SOS Son of sevenless
SSC Saline sodium citrate
TCF T-cell factor
TGF-β Transforming growth factor-β
™ Trademark
TMB 3,3′,5,5′-Tetramethylbenzidine
TNF Tumor necrosis factor
TNFR Tumor necrosis factor receptor
TRADD Tumor necrosis factor receptor associated death domain
TRAF2 Tumor necrosis factor receptor-associated factor 2
U Unit
USA United States of America
UV Ultraviolet
WHO World Health Organization
WNT Wingless-type MMTV integration site family
top related