miRNA - mRNA INTERACTION MAP IN BREAST CANCER

miRNA - mRNA INTERACTION MAP IN BREAST CANCER
miRNA - mRNA INTERACTION MAP IN
BREAST CANCER
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
IN
LIFE SCIENCE
SUBMITTED TO
NATIONAL INSTITUTE OF TECHNOLOGY, ROURKELA
BY
MITALI RANA
ROLL NO. 411LS2062
UNDER THE SUPERVISION OF
DR. BIBEKANAND MALLICK
DEPARTMENT OF LIFE SCIENCE
NATIONAL INSTITUTE OF TECHNOLOGY
ROURKELA-769 008, ODISHA, INDIA
Dr. Bibekanand Mallick, M.Tech., Ph.D.
Assistant Professor
RNA Biology & Functional Genomics Lab.
Department of Life Science
National Institute of Technology
(Ministry of H.R.D, Govt. Of India)
Rourkela - 769 008, Odisha, India
Telephone: +91-661-246 2685 (O)
E-mails: [email protected], [email protected]
Homepage: http://vvekslab.in
Date: 09. 05. 2013 CERTIFICATE
This is to certify that the thesis entitled “miRNA - mRNA INTERACTION MAP IN
BREAST CANCER” submitted to National Institute of Technology; Rourkela for the
partial fulfillment of the Master degree in Life science is a faithful record of bonafide
and original research work carried out by MITALI RANA under my supervision and
guidance.
(Dr. Bibekanand Mallick) Fax: +91‐661‐2472926 Email: [email protected] Home page: http://vvekslab.in ACKNOWLEDGEMENTS
The satisfaction and euphorbia that accompanies the successful completion of any task
should be incomplete without the mention of the people who made it possible with constant
guidance, support and encouragement that crowns all the efforts with success. It gives me
immense pleasure to express my utmost respect and deepest sense of gratitude to my supervisor
Dr. Bibekanand Mallick, Assistant Professor, Department of Life Science, National Institute of
Technology, Rourkela for his esteemed guidance, assistance, time to time inspiration and
encouragement throughout my project.
I also convey my deep gratitude to Prof. S.K. Sarangi, Director, National Institute of
Technology, Rourkela for providing excellent facilities in the Institute for carrying out research.
I would like to take the opportunity to acknowledge quite explicitly with gratitude my
debt to all the Professors and Staff, Department of Life Science, National Institute of
Technology, Rourkela for his encouragement and valuable suggestions during my project work.
My heartfelt thanks to Ms. Devyani Samantray and Ms. Debashree Das for giving me
experimental ideas throughout my project, without whose guidance it would not have been a
success. My special thanks to all the other PhD scholars and Bini, Bibhudutta and Subhrata for
their overwhelming inspiration and support throughout my project work.
Finally my deepest gratitude to my parents for their moral support and best wishes.
And to the almighty, who made all things possible………..
(MITALI RANA)
Contents
Page no.
1. Introduction……………………………………………………….1
2. Review of literature……………………………………………….3
3. Objective…………………………………………………………..16
4. Materials and Methods…………………………………………. .17
5. Results and Discussions…………………………………………..34
6. Conclusion………………………………………………………...50
7. Future prospectives…………………………………………….... 51
8. References…………………………………………………………52
LIST OF TABLES:
Table 1: Tumor Nomenclature
Table 2: miRNAs and their targets in breast cancer
Table 3: Primer name and sequence with length and its amplicon size
Table 4: Cycle temperature and time for qRT-PCR
Table 5: List of microRNAs with fold change and its regulation
Table 6: Diseases reported to be involved with MARCKS & SIK1
LIST OF FIGURES:
Figure 1: Biogenesis of MicroRNA
Figure 2: Role of microRNAs in hallmarks of cancer
Figure 3: MicroRNAs and the significant confirmed targets (PCGs)
Figure 4: Gene interaction network analysis of miRNA targets in breast cancer
Figure 5: Cycle temperature and time for qRT-PCR
Figure 6: Analysis of all entities in the sample
Figure 7: Statistical analysis by taking the p-value cut-off ≤ 0.05
Figure 8: 225 entities (genes) are identified when Fold change cut-off was taken ≥2.0
Figure 9: Interaction Map of mRNAs and miRNAs involved in breast cancer
Figure 10: The interaction pairs where it is seen that hsa-miR-21 target BTG2 and
hsa-miR-27a target both SIK1 and MARCKS
Figure 11: Involvement of MARCKS in keratinocyte differentiation pathway
Figure 12: Gene network of MARCKS
Figure 13: LKB1 signalling events in SIK1
Figure 14: Gene network of SIK1
Figure 15: BTG family proteins and cell cycle regulation
Figure 16: Gene network of BTG2
Figure 17: Melting temperature curve of MARCKS & SIK1 with respect to control
Figure 18: Melting temperature curve of SIK1 with respect to control
Figure 19: Melting temperature curve of MARCKS with respect to control
Figure 20: Relative expression of MARCKS and SIK1 with respect to control
ABSTRACT
MicroRNAs are a class of small endogenous RNA molecules that is involved in the posttranscriptional inhibition of gene expression. They directly interact with target gene transcripts
and influence cellular physiology. MicroRNAs have been reported to be involved in breast
cancer tumorigenesis and metastasis thus playing a vital role in cancer progression. Our study
aims at identification of novel miRNA-mRNA target pairs that are hypothesized to play a role in
breast cancer through a miRNA- mRNA interaction map analysis of microarray data and
experimental validation of selected set of mRNAs. The target interaction map analysis revealed
three novel target pairs, hsa-miR-27a–MARCKS, hsa-miR-27a–SIK1 and hsa-miR-21–BTG2
which can be potential therapeutic targets in breast cancer. Therefore, with the better
understanding of the regulation of miRNAs, the gene networks and cellular pathways regulated
by miRNAs, it will be of immense significance to further comprehend breast cancer pathogenesis
and target interaction as a therapeutic for breast cancer.
Keywords: microRNAs, transcriptional inhibition, gene expression, tumorigenesis, metastasis,
microarray, target interaction map
INTRODUCTION
INTRODUCTION
Breast cancer is the most commonly diagnosed cancers in women which is the second
leading cause of cancer deaths that accounts for approximately 22% of all new cancer cases
worldwide. It is seen that more than one million new cases arise every year. Globally, 0.45
million patients die from breast cancer annually, which constitutes 13.7% of female cancer
deaths (Parkin, 2001 and Jemal et al. 2011). Breast cancer is prone to metastasis involving
secondary sites such as the lung, liver, bone, and brain. Metastasis is seen to occur even after
many years of the removal of the primary tumor, minimizing the survival rate from 85% for
early detection to 23% for patients with lung or bone metastasis, therefore being the main cause
of death for breast cancer patients (Lorusso, 2012). Genetic mutations are said to be the
contributing causes of tumorigenesis and metastasis in breast cancer. Presently, the mechanisms
for controlling metastasis are poorly understood and thus the treatments for metastatic late-stage
breast cancer are still inefficient (Lee, 2012). Hence, it is of a great clinical importance to
understand the molecular mechanisms involved in primary tumor cell invasion and its spread to
distant sites, and thus to identify new molecular targets for cancer therapies.
In the recent past, microRNAs (miRNAs) got revealed as the focal point in the molecular
dissection of human cancer (Calin et al., 2002). An emerging list of information recommends
that microRNAs critically participate in cancer initiation and progression. Evidences have shown
that microRNAs play a vital role in breast cancer. Such MicroRNAs have been described as a
family of small, noncoding, double-stranded RNA molecules of short 18 to 25 nucleotides
noncoding genes excised from 60-110 nucleotide hairpin RNA precursors that is involved in the
regulation of expression of protein-coding genes (PCGs). Consequently it is seen that the events
that activate or inactivate miRNAs were said to cooperate with PCG abnormalities in human
tumorigenesis (Porter et al., 2008).
Hundreds of miRNAs have been identified till date in mammals, some of which are
expressed in a tissue-specific and developmental stage-specific way. Since the discovery of this
regulatory RNA phenomenon, much progress has been made in recent times towards
understanding the mechanisms by which this process occurs and in the identification of cellular
machinery involved in RNA-mediated silencing (Novina et al., 2004 and Meister et al., 2004).
The miRNAs generally interact with target mRNAs with only partial or imperfect
complementarity by causing either mRNA degradation or translation inhibition and can
Page | 1 negatively regulate the expression of target genes with their complementary sequence in cells
(Novina et al., 2004 and Meister et al., 2004). More recently, it is cited that miRNA down
regulation was recommended to play a role in cancer progression (Johnson et al., 2005 and
Cimmino, et al., 2005).
MicroRNAs play essential roles in normal cellular development but may functionally act
as either oncogenes or tumor suppressors by targeting analogous oncogenes or tumor suppressor
genes (Chen, 2005). MicroRNAs possess the capacity to directly target gene transcripts and
influence cellular physiology that is involved in cancer etiology. As miRNAs can elucidate their
function through regulation of specific mRNAs, there has been an immense interest in
identifying their targets. Among the differentially expressed miRNAs in breast cancer, miR-10b,
miR-125b, miR-145, miR-21, and miR-155 were revealed to be the most consistently
deregulated. The down regulation of miR-10b, miR-125b, and miR-145 and up regulation of
miR-21 and miR-155 suggested that these miRNAs could play a role as tumor suppressor genes
or oncogenes (Enders et al, 2009).
The efficacy of miRNA-based breast cancer therapy has been explored by emerging
studies emphasizing their importance in breast cancer. A better understanding of the gene
networks and cellular pathways regulated by miRNAs will facilitate further elucidation of breast
cancer pathogenesis and therapy. This can be accomplished by identifying the genome-wide
targets of miRNAs that is vital.
In our present study, we sought to generate an miRNA- mRNA interaction map for
identification of novel mRNA-miRNA target pairs that are hypothesized to play a role in breast
cancer through an mRNA- miRNA interaction map analysis of microarray data and experimental
validation of selected set of mRNAs which has not been reported yet and may be helpful in
treatment of breast cancer through miRNA therapeutics.
Page | 2 REVIEW OF LITERATURE
REVIEW OF LITERATURE
Cancer is defined by the uncontrollable growth and proliferation of abnormal cells inside the
body. Characteristic immortality of cancer cells lead to their growth and invasion into other
adjacent tissues and form a tumor. Cancer cells have the potential to pass into the bloodstream or
lymph vessels and travel to other parts of the body, where they can grow and form new tumors
that replace normal tissue through a process called metastasis.
Based on such characteristics tumors are categorized into two types. They are:
•
Benign tumors: Generally considered as non-cancerous, benign tumors form mass of
cells that lacks the ability to invade adjacent tissue or spread to distant sites by
metastasizing and generally have a slower growth rate. These tumors are typically
surrounded by an outer surface or fibrous sheath of connective tissue or remain with the
epithelium. They can usually be removed from the body and in most case they never
come back.
•
Malignant tumors: These are characterized by a mass of cells which divide and grow
uncontrollably, are capable of invading into adjacent tissues and spreading to distant
tissues to become progressively worse and potentially resulting into death. Malignancy
in cancers is characterized by invasiveness, anaplasia and metastasis.
Characterized by a myriad of aspects, Cancer is considered not just as a disease rather as a group
of diseases that causes uncontrolled growth of abnormal cells in the body. Cancers can be
classified on basis of the tissue from which they originate or on basis of the location in the body
where they first develop.
Therefore, the different types of cancer classification are illustrated below:
a) On the basis of Tissue and Blood classifications of cancer:
•
Carcinoma- It is a cancer found in body epithelial tissue that covers or lines
surfaces of organs, glands, or body structures. It accounts for almost 80-90% of
all cancer cases. For example: Stomach cancer and Breast cancer.
•
Sarcoma- It is a malignant tumor growing from connective tissues, such as
cartilage, fat, muscles, tendons and bones. It usually occurs in young adults. For
example: Osteosarcoma (bone) and Chondrosarcoma (cartilage).
Page | 3 •
Lymphoma- It is a cancer that originates in the nodes or glands of the lymphatic
system, in the function in production of white blood cells and cleaning body
fluids, or in organs such as the brain and breast. It is of two types: Hodgkin’s
lymphoma and Non-Hodgkin’s lymphoma.
•
Leukemia- It is a cancer of the bone marrow that restricts the marrow from
producing normal red and white blood cells and platelets. It is also known as
Blood cancer. WBCs are needed to resist infection; RBCs are needed to prevent
anemia and Platelets keep the body from easy bruising and bleeding. For
example:
Acute
lymphocytic
leukemia,
chronic
lymphocytic
leukemia
myelogenous leukemia and chronic myelogenous leukemia.
•
Myeloma- It grows in the plasma cells of the bone marrow. It is of two types:
Plasmacytoma where the myeloma cells accumulate in one bone and form a single
tumor and Multiple myeloma where the myeloma cells form many bone tumors
by accumulating in many bones.
•
Blastoma- It is a cancers derived from immature "precursor" cells or embryonic
tissue. It is more common in children than in older adults.
b) On the basis of tissue origin: (Table 1)
•
Epithelial tissue tumor- Epithelial tissue consists of skin tissue that covers and
lines the body as well as covering all the body organs such as the digestive system
organs and lining the body cavity such as the abdominal cavity and chest cavity.
The epithelial cells cancers are called carcinomas. There are different types of
epithelial cells and these can develop into different types of cancer. For example:
Squamous cell carcinoma (squamous cells of the skin lining of oesophagus),
Adenocarcinoma (glandular cells of kidney cells or breast cells), Transitional cell
carcinoma (transitional cells of lining of bladder).
•
Mesenchymal or Connective tissue tumor- Cancers of connective tissues are
called sarcomas. Mesenchymal or Connective tissues are the supporting tissues of
the body such as the bones, cartilage, tendons and fibrous tissue that support the
body organs. Sarcomas are much less common than carcinomas and can develop
Page | 4 from bone, cartilage and muscle. They are usually grouped into two main types bone sarcomas (osteosarcoma) and soft tissue sarcomas. For example:
Chondrosarcoma (cancer of cartilage) and Rhabdomyosarcoma (cancer of a
muscle).
•
Blood and Lymph tissue tumor- There are many different types of blood
and lymph tissue cells. Haematopoetic tissue is the tissue present in the bone
marrow that is responsible for the formation of blood cells. Blood tissue can
develop into Leukemias (cancer of the blood cells) and lymph tissue can develop
into Lymphomas (cancer of the lymphatic system).These are the most common
type of cancer affecting children. Brain tumors are the biggest group of rare
cancers which develop from special connective tissue cells called glial cells that
support the nerve cells in the brain. The cancers of the glial cells are called
Gliomas.
c) On basis of location in the body:
Cancers are also named by their origin of initiation apart from its spread to other
areas. For example, breast cancer that has spread to the liver is still called breast cancer,
not liver cancer. Similarly, prostate cancer that has spread to the bone is called metastatic
prostate cancer, not bone cancer. Other examples are cervical cancer, oral cancer, etc.
Page | 5 Table 1. Tumor Nomenclature
ORIGIN
CELL TYPE
BENIGN TUMOR
MALIGNANT TUMOR
Adeno
Gland
Adenoma
Adenocarcinoma
Basal cell
Basal cell
Basal cell adenoma
Basal cell carcinoma
Squamous cell
Squamous cell
Karatoacanthoma
Squamous cell carcinoma
Melano
Pigmented cell
Mole
Melanoma
Terato
Multipotential cell
Teratoma
Teratocarcinoma
Chondroma
Chondrosarcoma
Epithelial
Mesenchymal
Chondro
Cartilage
Fibro
Fibroblast
Fibroma
Fibrosarcoma
Hamangio
Blood vessels
Hemangioma
Hemangiosarcoma
Leiomyo
Smooth muscle
Leiomyoma
Leiomyosarcoma
Lipo
Fat
Lipoma
Liposarcoma
Meningio
Meninges
Meningioma
Meningiosarcoma
Myo
Muscle
Myoma
Myosarcoma
Osteo
Bone
Osteoma
Osteosarcoma
Rhabdomyo
Striated muscle
Rhabdomyoma
Rhabdomyosarcoma
Blood and Lymph
Lympho
Lymphocyte
Erythro
Erythrocyte
Myelo
Bone marrow
Lymphoma
Erythrocytic leukemia
Myeloma
Page | 6 Breast cancer is a malignant tumor originating from breast epithelial tissue, most
commonly from the inner lining of milk ducts or the lobules that supply the ducts with milk. It is
found mostly in women, but men can get breast cancer rarely. It is estimated that approximately
greater than 1,300,000 cases of breast cancer are reported each year worldwide out of which
around 450,000 resulting in deaths.
Types of Breast cancer:
•
Pre-Invasive Breast Cancer
It is an early stage of cancer, when it is still confined to the layer of cells where it began and is
named for cancer cells that stay inside the milk ducts or milk sacs (lobules) of the breast. They
do not spread into deeper tissues in the breast or to other organs in the body.
1. Ductal carcinoma in situ (DCIS)
When cancer cells are confined to the ducts it is called Ductal carcinoma in situ. It increases the
risk of developing invasive breast cancer. It’s important to treat DCIS, to lower the risk of
developing invasive breast cancer. DCIS can be found in women at any age mostly between 50
and 59 years old.
2. Lobular carcinoma in situ (LCIS)
When cancer cells are confined to the lobules it is called lobular carcinoma in situ. It is not a true
cancer or pre-cancer. The cells on the inside of the lobules become abnormal in shape and size,
proliferate and stay inside the lobules in the breast this is called Atypical lobular hyperplasia
(ALH). If the abnormal cells stay inside the ducts in the breast this is called Atypical ductal
hyperplasia (ADH). LCIS, ALH and ADH cannot be felt as a breast lump or other breast change
and these situations are usually found by chance when a woman has undergone a breast biopsy.
•
Invasive Breast Cancer
It is a type of cancer which already grown beyond the layer of cells spreading to the lymph nodes
in the breast or armpit area from where it started. These can be either invasive ductal carcinoma
or invasive lobular carcinoma.
•
Locally Advanced Breast Cancer
It is larger than 5cm and may have spread from the breast into the lymph nodes or other tissues
adjacent to the breast.
Page | 7 •
Metastatic Breast Cancer
It is the most malignant stage of breast cancer where the disease has spread to distant metastases.
It primarily metastasizes to the bone, lungs, lymph nodes, liver and brain. Metastatic breast
cancer cells frequently differ from the preceding primary breast cancer as it has a developed
resistance to previous treatment. It has a poor prognosis and causes about 90% of deaths.
The accurate cause of breast cancer is unidentified and there are no permanent causes for
breast cancer. Some of the causes that are associated with breast cancer are:
•
Age: Older woman is at the higher risk of developing breast cancer including over 80%
of all female breast cancers occurring among women aged 50+ years after menopause.
•
Inheritance: Family history of close relative like sister, daughter and mother who has
been diagnosed with breast cancer increases the risk factor.
•
Genetics: It plays a more significant role by causing a hereditary syndrome for breast and
ovarian cancer carrying the BRCA1 and BRCA2 gene mutation. Other mutated genes
involve p53, PTEN and STK11, CHEK2, BRIP1, ATM and PALB2.
•
Early menopause: Early start onset of menses and early menopause are also associated
with breast cancer.
•
Radioactivity: Radioactive rays’ exposure is carcinogenic and increases the chances of
breast cancer.
•
Hormone Replacement Therapy: Prolonged exposure to the hormones estrogen and
progesterone for uninterrupted periods can affect breast cancer risks.
•
Exposure to harmful chemicals: Chemical factory workers use harmful chemicals like
organochlorines.
•
Nullyparity or Late childbearing: Late childbearing or nullyparity also appear to be a
minor risk factor in the development of breast cancer.
•
Alcohol consumption - More alcohol for a woman regularly creates higher risk of
developing breast cancer. (DeSantis et al., 2012 and Breast cancer overview, American
Cancer Society, 2012) Page | 8 MicroRNAs MicroRNAs have been recently reported to be actively participating in initiation
and progression of breast cancer. Such microRNAs (miRNAs) are described as a class of
genes that encodes for small non-coding double-stranded RNA molecules of short 18 to 25
nucleotides noncoding genes excised from 60-110 nucleotide hairpin RNA precursors that is
involved in the regulation of expression of protein-coding genes (PCGs). It mainly binds the
3’UTR of the target mRNA depending on the respective miRNA directed sequence, thus
promoting mRNA degradation at a post transcriptional level or inhibiting the initiation of
translation by translational repression (Quesne and Caldas, 2010) (Figure 1).
Figure 1: Biogenesis of MicroRNA (Samantarrai et al. 2013)
Page | 9 Rules of microRNA targeting
MicroRNA targeting follows certain rules. According to such rules pairing of
mRNA and miRNA requires conserved Watson-Crick paring to the 5’ region of the miRNA,
centered on nucleotide 2-7 called as the miRNA “seed”. Conserved paring to the seed region can
also be sufficient on its own for predicting conserved targets above the noise of false positive
prediction. The highly conserved target has many conserved targets. Nonconserved targeting is
even more widespread than conserved targeting. The 3’- supplementary pairing are the numerous
potential pairing possibilities involving the 3’ portion of the miRNA and the UTR. The 3’ pairing
optimally centers on miRNA nucleotides 13-16 and the UTR region directly opposite this
miRNA segment. Pairing to the 3’ portion of the miRNA can not only supplement a 7-8mer
match and can also compensate for a single nucleotide bulge or mismatch in the seed region.
Presentation of 2-8 nucleotides prearranged in a geometry resembling an A-form helix enhances
both the affinity and specificity for matched mRNA segment, enabling 7-8 nucleotide sites to
suffice for most target functions.
Affinity to the seed is stronger than that to the other regions of the RNA. The
positioning within the 3’UTR at least 15 nucleotide from the stop codon increases site efficacy
whereas away from the center of long UTR also increases site efficacy. Site efficacy is also
boosted by AU rich nucleotide composition near the site or other measures of site accessibility
and by proximity to sites for co-expressed miRNA. Although most miRNA function has been for
sites in 3’ UTR, targeting can also occur in 5’UTR and open reading frames. The 3’UTRs with
non-conserved sites are most often found in genes primarily expressed in tissues where the
cognate miRNA is absent. Target recognition that relies heavily on 7mer nucleotide matches to
the seed region creates possibility for a lot of non conserved targeting. ORF targeting is expected
to be much more effective in messages that are already inefficiently translated. The hierarchy of
site efficacy follows: 8mer>>7mer-m8>7mer-A1>>6mer>no site, with the 6mer differing only
slightly from no site at all. For genes that should not be expressed in a particular cell type, the
cell can come to depend on its miRNAs to act as binary off-switches to help to repress target
protein output to inconsequential levels. Conserved targets have the tendency to be present at
low levels in the same tissues as the miRNAs. It is seen that more than 90% of the conserved
miRNA: target interactions involve only a single site to the miRNA, and therefore most of these
Page | 10 targets would be expected to be down regulated by less than 50%. miRNA targeting interaction
can be disrupted by perturbing an endogenous site through homologous recombination.
MicroRNA target sites tend to reside in an unstable region, and tend to lack stabilizing elements
called as long stems (Bartel, 2009).
Role of microRNAs in cancer
Figure 2. Role of microRNAs in hallmarks of cancer
Role of microRNAs in Breast cancer metastasis
MicroRNAs as balancers between suppression and activation:
The involvement of microRNAs in the development of metastases was initially
discovered by Ma and coworkers from Robert Weinberg’s group who revealed that up regulation
of miR-10b promote breast cancer invasion and metastasis and are over-expressed in about 50%
of metastatic breast cancers. They also proved that HOXD10, a homeobox transcription factor is
the target of miR-10b. Later, Tavazzoie and colleagues of the Joan Massague group revealed
that miR-335 suppresses metastasis and migration by targeting the transcription factor SOX4 and
tenascin C which is an extracellular matrix component. At the same time, Huang and coworkers
including the Reuven Agami group reported that miR 373 and miR-520c induce cancer cell
migration and invasion by suppression of CD44. These landmark studies reveal a fine balance of
microRNAs as activation and suppression of metastasis and identify several targets.
Page | 11 Balance between repression of miRNA targets and regulation of miRNA expression:
MicroRNA profiling studies have led to the identification of miRNAs that are
aberrantly expressed in human breast cancer with miR-145, miR-10b and miR-125b being down
regulated and miR-21 and miR-155 being up regulated. More recent studies have identified
microRNA downstream targets and associated particular miRNA expression with prognostic
information. Also microRNA has been shown either to be consistently down regulated or up
regulated. Tumor formation may arise due to down regulation of a tumor suppressor miRNA
and/or over expression of an oncogenic miRNA. Therefore, microRNA has been regarded as the
key factors in breast cancer that possess both oncogenic and tumor-suppressing roles (Sidney et
al. 2011; Negrini and Calin. 2008) (Figure 2).
Figure 3: MicroRNAs and the significant confirmed targets (PCGs) are shown to be
involved in initiation (early), progression (intermediate) and metastasis (late) stages in
tumorigenesis. Initiation and progression stages involves the over expression of oncogenic
miRNAs i.e. miR-21 and miR-221 results into the down regulation of PTEN & KIT whereas
down regulation of tumor suppressor miRNAs i.e. miR-16 family and Let-7 family results into
hyper expression of RAS, CDK6, CDC25 & BCL2. Simultaneously, over expression of
oncogenic miRNAs i.e. miR-10b, miR-373 and miR-520c results into the down regulation of
HOXD10 & CD44 whereas down regulation of tumor suppressor gene i.e. miR-335 results into
hyper expression of SOX4, TNC, PTPRN2 & MERTK respectively in metastasis (Negrini and
Calin. 2008).
Page | 12 The expression and function of various miRNAs in breast cancer is summarized (Table 2).
Table 2. miRNAs and their targets in breast cancer
MicroRNAs
TARGET
FUNCTIONAL PATHWAY
miR-206
ESR1
ER signaling
miR-17-5p
AIB1, CCND1, E2F1
Proliferation
miR-125a, b
HER2, HER3
Anchorage-dependent growth
miR-200c
BMI1, ZEB1, ZEB2
TGF-β signaling
let-7
H-RAS, HMGA2, LIN28, PEBP1
Proliferation, differentiation
miR-34a
CCND1, CDK6, E2F3, MYC
DNA damage, proliferation
miR-31
FZD3, ITGA5, M-RIP, MMP16, RDX,
Metastasis
miR-355
RHOA
Metastasis
miR-27b
SOX4, PTPRN2, MERTK, TNC, CYP1B1
Modulation of the response of tumor to anti-cancer
Tumor suppressor miRNAs
drugs
miR-126
IRS-1
Cell cycle progression from G1/G0 to S
miR-101
EZH2
oncogenic and metastatic activity
miR-145
p53-mediated repression of c-Myc
Suppresses Cell Invasion and Metastasis
miR-146a/b
NF-Κb
Negatively regulate factor-κB, and impaired
invasion and migration capacity
miR-205
ErbB3 and VEGF-A expression
Inhibits tumor cell growth and cell invasion
miR-21
BCL-2, TPM1, PDCD4, PTEN, MASPIN
Apoptosis
miR-155
RHOA
TGF-β signaling
miR-10b
HOXD10
Metastasis
miR-373/520c
CD44
Metastasis
miR-27a
Zinc finger ZBTB10, Myt-1
Cell cycle progression G2- M checkpoint
Oncogenic miRNAs
regulation
miR221/222
p27Kip1
Tamoxifen resistance
Source: O’Day and Lal (2010) Sidney W. Fu et al. (2011)
Page | 13 Gene interaction network for breast cancer miRNA targets
The effectiveness of miRNA-based breast cancer therapy has been explained by a
better understanding of the gene networks and cellular pathways regulated by miRNAs, by
identifying the genome-wide targets of miRNAs. It is reported that miRNAs inhibit the
expression of many genes which signifies that inclusive regulation can be achieved by over
expressing a single miRNA. Simultaneously the deregulation of miRNAs would consequently
change the expression of many genes resulting into induced tumorigenesis. The functions of the
experimentally validated breast cancer miRNAs and their gene targets that might be incorporated
within the pathogenesis of breast cancer can be better understood by a gene interaction network
analysis.
In a study, a list of 34 genes was generated that is recognized to be altered by the 11
miRNAs. 19 genes formed a well-connected gene interaction network in which MYC act as the
central node that is a target of miR-34a. Similarly, more highly interacting genes were BCL-2,
E2F1, CCND1 and ESR1, respectively. The results suggested that breast cancer is associated
with the alteration in the expression of multiple miRNAs that disturb a network of genes which
can either activate or inhibit each other’s expression. The down regulation of miR-206 enhanced
ESR1 and MYC expression. CCND1, E2F1 and E2F3 expressions are activated by MYC
elevation. Additionally, miR-17-5p (regulates CCND1 and E2F1) and miR-34a (regulates E2F3,
CCND1 and CDK6) also elevate the levels of these proteins. Thus suggested that down
regulation of several tumor suppressor miRNAs may lead to up regulation of oncogenes in breast
cancer via direct or indirect mechanisms. Increased levels of miR-21 regulate TPM1, miR-31
target MMP16 and miR-373/520c target CD44 that is seen to be repressed in breast cancer cells.
The tumor suppressor or oncogenic miRNAs regulate the transcription of some of these genes by
targeting transcription factors. The gene interaction network essential for breast cancer
progression undergoes a change due to loss of miRNA regulation leading to a cascade of events
at different stages of progression and metastasis possibly in most gene regulatory events. Other
several miRNAs involved in breast cancer are miR-7, miR-128a, miR-210, miR-27b, miR-335,
miR-126, miR-145 and miR-27a (Blenkiron et al., 2007) (Figure 3).
Page | 14 Figure 4. Gene interaction network analysis of miRNA targets in breast cancer. The direct
interaction network of 34 published targets of 11 miRNAs implicated in breast cancer
pathogenesis is determined using Ingenuity software and is shown: miR-206, miR-17-5p, miR125a/b, miR-200, let-7, miR-34, miR-31, miR-21, miR-155, and miR-373/520c. Central highly
connected network of targets are formed on MYC. Protein-protein or protein-DNA interactions
are indicated by arrows suggesting these targets coordinate the expression and/or function of one
another (Blenkiron et al., 2007).
MicroRNA profiling is a promising and powerful diagnostic tool that can be useful to
differentiate the characteristics of different tumor types in breast cancer, such as the miRNA
signatures that can clearly distinguish normal and malignant breast tissue and also categorize
between breast cancer subtypes. Thus, the detection of the genes regulated by miRNAs and the
clarification about their integrating mechanisms in breast cancer gene interaction network can
better support in understanding this malignant disease.
Page | 15 OBJECTIVES
OBJECTIVES
OBJECTIVE 1
Microarray expression analysis for identification of differentially expressed mRNA in breast
cancer
OBJECTIVE 2
Microarray expression analysis for identification of differentially expressed miRNA in breast
cancer
OBJECTIVE 3
Target prediction and generation of miRNA–mRNA interaction map for differentially expressed
sets of miRNA and mRNA
OBJECTIVE 4
Identification of novel miRNA–mRNA target pairs involved in breast cancer & experimental
validation of mRNA expression by RT-PCR in MDA-MB-231 cell lines
Page | 16 MATERIALS AND METHODS
MATERIALS & METHODS
1) Gene Expression data
The gene expression data is taken in order to conduct a genome wide analysis of
mRNA and miRNA expression of normal and diseased sample in breast cancer, and also to study
and distinguish between the various expressions patterns for diagnosis and therapeutic
mechanisms.
The gene expression data were retrieved from Gene Expression Omnibus (GEO)
database. GEO a public functional genomics data repository supporting MIAME-compliant data
submissions that archives and freely distributes next-generation sequencing, microarray and
other forms of high-throughput functional genomic data submitted by the scientific community.
A collection of web-based interfaces and applications are available to provide help users query
and download experiments and curated gene expression profiles stored in GEO. The GEO data
contains raw microarray data that involves images that are to be converted into gene expression
matrices, where rows represent genes, columns represent various samples such as tissues or
experimental conditions, and numbers in each cell illustrate the expression level of the particular
gene in the particular sample. Analysis of the matrices can be further done in order extract any
biological process and its understanding. Platform describes the list of features on the array (e.g.,
cDNAs, oligonucleotides, etc.). There is an importance of using different platforms as because of
the diversity of technical and analytical sources that can affect the results of an experiment.
Therefore, a comparison among experiments, its standardization within a single platform may be
inefficient. So, large-scale comparison studies involving microarrays can be done for
optimum reproducibility measurements using various platforms.
The gene expression data of mRNA and miRNA were taken. The mRNA breast
cancer data taken were on breast epithelium from reduction mammoplasty of reduction
mammoplasty patient and breast epithelium adjacent to tumor of breast cancer patient. The
miRNA breast cancer data taken were on mastectomy samples of Normal breast tissue and
Invasive breast cancer. Respective mRNA data were chosen in order to find out the mRNA
differential expression in mammoplasty patient and breast cancer patients. The miRNA data
Page | 17 were selected in order to investigate the miRNA differential expression in normal breast tissue
and invasive breast cancer tissues.
•
The PLATFORM taken were:
mRNA
[HG-U133A] Affymetrix Human Genome U133A Array (GPL96)
miRNA
Agilent-031181 Unrestricted_Human_miRNA_V16.0_Microarray
030840 (GPL15018)
•
The SAMPLE taken were:
mRNA
GSE9574 (Gene expression abnormalities in histologically
normal breast epithelium of breast cancer patients)
miRNA
GSE38867 (MicroRNAs expression profiling during breast
cancer progression)
2) Microarray analysis of gene expression dataa) Retrieval of gene expression data –
•
The SERIES taken for mRNA were: GSM242005
GSM242006
GSM242007
GSM242020
GSM242021
GSM242022
•
The data were taken in triplicates. Samples in an experiment have associated
experiment parameters and corresponding parameter values, so triplicates were
taken.
•
The raw files were downloaded in .CEL format.
Page | 18 •
Then the files were unzipped, extracted and renamed as control and test.
mRNA
•
Control
Test
GSM242005
GSM242020
GSM242006
GSM242021
GSM242007
GSM242022
The SERIES taken for miRNA were: GSM951044
GSM951048
GSM951052
GSM951046
GSM951050
GSM951054
•
The data were taken in triplicates. Samples in an experiment have associated
experiment parameters and corresponding parameter values, so triplicates were
taken.
•
The raw files were downloaded in .CEL format.
•
Then the files were unzipped, extracted and renamed as control and test.
miRNA
Control
Test
GSM951044
GSM951046
GSM951048
GSM951050
GSM951052
GSM951054
Page | 19 b) Analysis of gene expression dataThe software used for gene expression analysis was Genespring GX software. It is a
powerful microarray expression data analysis tool sold by Agilent, and something of a standard
for such task which consists of a wide range of analytical algorithms for gene expression
microarray experiments. It is used for identifying classes of transcripts that show expression
patterns that are correlated with the experiment variables, displaying these transcripts against a
backdrop of biochemical pathways and querying for transcripts using e.g., gene symbol, gene
name or GO ontology terms.
Working in GeneSpring GX is organized into projects. A project comprises one or
more related experiments. An experiment comprises samples (i.e. data sources), interpretations
(i.e. groupings of samples based on experimental parameters), and analyses (i.e. statistical steps
and associated results, typically entity lists). Statistical steps and methods of analysis are driven
by a workflow which finds prominent mention on the right side of GeneSpring GX.
A project is the key organizational element in GeneSpring GX. It is a container for a
collection of experiments. For example, one experiment measures gene expression profiles of
individuals with and without breast cancer. A new project can be created from Project −New
Project by just specifying a name for the project and optionally any user notes.
An experiment in GeneSpring GX represents a collection of samples for which arrays
have been run in order to answer a specific scientific question. A new experiment is created from
Project −New Experiment by loading samples of a particular technology and performing a set of
customary pre-processing steps like, normalization, etc., that will convert the raw data from the
samples to a state where it is ready for analysis. An experiment consists of multiple samples,
with which it was created and multiple interpretations by grouping these samples by user-defined
experimental parameters, and all other objects created as a result of various analysis steps in the
experiment.
Page | 20 An experiment comprises a collection of samples. These samples are the actual
hybridization results. Each sample is associated with a chip type or its technology and will be
imported and used along with a technology.
GeneSpring GX technology contains information on the array design as well as biological
information about all the entities on a specific array type. A technology initially must be installed
for each new array type to be analyzed. For standard arrays from Affymetrix, Agilent and
Illumina, technologies have been created. An experiment comprises samples which all belong to
the same technology.
Steps followed for gene expression analysis in GeneSpring:•
For mRNA expression analysis:
Normalization of the data was done to minimize systematic non-biological
differences to reveal true biological differences, which may include systematic variations from
sources like unequal quantities of starting RNA, differences in hybridization between chips and
differences between manufactured chips in microarray experiments. Profile plot of Normalized
intensity map values is obtained after Normalization of data. Data is normalized to 75th
percentile of signal intensity to standardize each chip for cross-array comparison. The main
objective is for eliminating redundancy and ensuring that the data make sense with minimum
number of entities.
An option of Create new experiment was chosen that allows creating a new
experiment. The Experiment type should then be specified as Affymetrix Gene Chip-HGU113A. Once the experiment type is selected, the workflow type needs to be selected as Guided
workflow.
An experiment can be created using choose sample option. Experimental setup was
done by adding average parameter to help define the experimental grouping as test and control
and replicate structure of the experiment.
Page | 21 Quality control of samples was done by Filter Probesets by Errors. This operation is
performed on the raw signal values. The cutoff for filtering is set at 20 percentile of all the
intensity values and generates a profile plot of filtered entities. The plot is generated using the
normalized (not raw) signal values and samples grouped by the active interpretation.
Depending upon the experimental grouping, the Significance Analysis was done by
performing T-test unpaired analysis as there are 2 groups, i.e. the Control and the Test, with
replicates.
Statistical analysis was done by T-test unpaired as a test choice. The test has been used
for computing p-values, type of correction used and P-value computation type by Asymptotic
method. It assumes expression values for a gene within each population which is normally
distributed and variances are equal between populations. The p-value cut-off taken was ≤ 0.05.
Multiple testing correction was done by using Benjamini-Hochberg FDR algorithm.
This algorithm is used to reduce the number of false positives or the false discovery rate. This
correction is the least stringent and tolerates more false positives. There are chances of less false
negative genes. If the p-value is ≤ 0.05, it is significant.
Fold change analysis is used to identify genes with expression ratios or differences
between a test and a control that are outside of a given cutoff or threshold. Fold change gives the
absolute ratio of normalized intensities between the average intensities of the samples grouped.
The entities satisfying the significance analysis are passed on for the fold change analysis. The
fold change cut-off taken is ≥2.0.
The analyzed data was exported by export entity list with normalized signal values
consisting of Normalization values, Gene symbol, Entrez gene IDs etc. with interpretation of all
samples. The entity list was then saved as .txt file.
The software used for clustering and generation of Heat map was done by using
CLUSTER 3.0. It is a program that provides a computational and graphical environment for
analyzing data from DNA microarray experiments by organizing and analyzing the data in a
Page | 22 number of different ways. The Cluster program provides several clustering algorithms.
Hierarchical clustering methods organize genes in a tree structure, based on their similarity and
assemble a set of items (genes or arrays) into a tree. Items are joined by very short branches if
they are very similar to each other and longer branches if their similarity decreases.
The software used for visualization of Heat map was done by Java TreeView which
allows the organized data to be visualized and browsed by a .cdt file generated through
CLUSTER 3.0 and was exported as image.
•
For miRNA expression analysis:
Normalization of the data was done to minimize systematic non-biological
differences to reveal true biological differences, which may include systematic variations from
sources like unequal quantities of starting RNA, differences in hybridization between chips and
differences between manufactured chips in microarray experiments. Profile plot of Normalized
intensity map values is obtained after Normalization of data. Data is normalized to 75th
percentile of signal intensity to standardize each chip for cross-array comparison. The main
objective is for eliminating redundancy and ensuring that the data make sense with minimum
number of entities.
An option of Create new experiment was chosen that allows creating a new
experiment. The Experiment type should then be specified as Agilent_031181. Once the
experiment type is selected, the workflow type needs to be selected as Guided workflow.
An experiment can be created using choose sample option. Experimental setup was
done by adding average parameter to help define the experimental grouping as test and control
and replicate structure of the experiment.
Quality control of samples was done by Filter Probesets by Flags. This operation is
performed on the raw signal values. The cutoff for filtering is set at 20 percentile of all the
intensity values and generates a profile plot of filtered entities. The plot is generated using the
normalized (not raw) signal values and samples grouped by the active interpretation.
Page | 23 Depending upon the experimental grouping, the Significance Analysis was done by
performing T-test unpaired analysis as there are 2 groups, i.e. the Control and the Test, with
replicates.
Statistical analysis was done by T-test unpaired as a test choice. The test has been used
for computing p-values, type of correction used and P-value computation type by Asymptotic
method. It assumes expression values for a gene within each population which is normally
distributed and variances are equal between populations. The p-value cut-off taken was ≤ 0.05.
Multiple testing correction was done by using Benjamini-Hochberg FDR algorithm.
This algorithm is used to reduce the number of false positives or the false discovery rate. This
correction is the least stringent and tolerates more false positives. There are chances of less false
negative genes. If the p-value is ≤ 0.05, it is significant.
Fold change analysis is used to identify genes with expression ratios or differences
between a treatment and a control that are outside of a given cutoff or threshold. Fold change
gives the absolute ratio of normalized intensities between the average intensities of the samples
grouped. The entities satisfying the significance analysis are passed on for the fold change
analysis. The fold change cut-off taken is ≥2.0.
The analyzed data was exported by export entity list with normalized signal values
consisting of Normalization values, Gene symbol, etc. with interpretation of all samples. The
entity list was then saved as .txt file.
Page | 24 c) Analysis of the gene listThe common set of genes was analyzed using GO database (Gene Ontology
database), various technologies like Genomatrix and web based tools like Web based gene set
analysis tool kit and their involvement in various pathways was studied. From the common set of
genes two genes were selected based upon their regulation and association with cancer for
further validation by qRT-PCR.
A set of genes involved in breast cancer with its regulation and fold change value was
created through export list in an excel file. Similarly, a list of miRNAs involved in breast cancer
with its regulation and fold change value with cut off ≥2.0 was created.
•
For mRNA gene list analysis:
The analysis was done by comparing the fold change value and regulation of the control
and test samples. The list of down regulated genes in breast cancer was sorted out.
•
For miRNA gene list analysis:
The analysis was done by comparing the fold change value and regulation of the control
and test samples. The list of up regulated miRNAs in breast cancer was sorted out too.
The genes and miRNAs are chosen on basis of their regulation i.e. highly down
regulated and highly up regulated.
3) Target Interaction Map analysis through Magia2 software:
The integrated analysis of in silico target prediction, miRNA and gene expression data
for the reconstruction of post-transcriptional regulatory networks is performed by using software
called Magia2. The gene expression profile can be resulted because of different levels of
regulation and a highly connected network of regulatory elements and their interactors. Magia2
software is a web based tool designed to cope with low sensitivity of target prediction algorithms
by exploiting the integration of target predictions with miRNA and gene expression profiles to
Page | 25 improve the detection of functional miRNA–mRNA, for in silico target prediction through
miRNA-target expression where regulatory elements and their integrators generate a highly
interconnected network of mRNA, miRNA and Transcription Factor (TF). Functional
enrichment of the gene network component can be performed directly using DAVID
platform. The miRNA-target interactions experimentally validated (as reported in miRecords
and TarBase databases) are specifically marked.
The respective data i.e. the gene expression data of mRNA and miRNA expression data
of miRNA were uploaded and submitted. The analysis was done using Pearson correlation
method that aims to display the target interaction map for matched miRNAs and gene expression
data.
An interaction map was generated where the interconnected network of mRNA, miRNA
and Transcription Factor were seen. From the interaction map, two genes and two miRNAs were
selected based upon their regulation and association with breast cancer for further validation by
qRT-PCR.
These selected genes (mRNAs) and miRNAs or the target pairs are not yet been reported
to be involved in breast cancer. Therefore, web based tool and technology like Genomatrix is
also used to study their involvement in breast cancer and various pathways.
4) Experimental ValidationCell cultureHuman breast carcinoma cell line, MDA MB 231 was obtained from National Centre For
Cell Science (NCCS), Pune, India. The medium used for culturing the cell is MEM (Invitrogen;
MEM with NEAA (non essential amino acids) and L-Glutamine) with 10% FBS (Fetal bovine
serum from HIMEDIA) and 1% antibiotic solution (Penstrep solution from HIMEDIA). The
culture flask containing the cell line is kept in the CO2 incubator with the level of CO2
maintained at 5%. With the utilization of medium the color of the medium changes from red to
orange and then pale yellow because of change in pH of the medium.
Page | 26 The steps for cell culture was as followed:
1. The cells were harvested first.
•
Cells were grown in suspension i.e. 1 x 107 cells. The number of cells was determined.
The appropriate number of cells was pelleted by centrifuging for 5 min at 300 x g in a
centrifuge tube. Carefully removed all supernatant by aspiration completely from the cell
culture medium.
•
To trypsinize and collect cells: The number of cells was determined. The medium was
aspirated, and the cells were washed with PBS. Then the PBS was aspirated, and 0.1–
0.25% trypsin in PBS was added. After the cells detach from the flask, medium
(containing serum to inactivate the trypsin)was added , the cells were transferred to an
RNase-free glass or polypropylene centrifuge tube and centrifuged at 300 x g for 5 min.
The supernatant was aspirated completely, and proceeded to step 2. 2. The cells was disrupted by adding Buffer RLT:
•
For pelleted cells, loosen the cell pellet thoroughly by flicking the tube. 350 μl Buffer
RLT was added. Vortexed or pipetted to mix, and ensured that no cell clumps were
visible and proceeded to step 3.
3. The lysate was homogenize for 30 s using a rotor–stator homogenizer and proceeded to
step 4.
4. 1 volume of 70% ethanol was added to the homogenized lysate, and mixed well by
pipetting. Did not centrifuge.
5. 700 μl of each sample was transferred from step 4, including any precipitate to each
RNeasy spin column on the vacuum manifold.
6. The vacuum was switched on and was applied until transfer was complete. Then switched
off the vacuum and ventilated the vacuum manifold.
7. 700 μl Buffer RW1was added to each RNeasy spin column.
8. The vacuum was switched on and was applied until transfer was complete. Then switched
off the vacuum and ventilated the vacuum manifold.
9. 500 μl Buffer RPE was added to each RNeasy spin column.
10. The vacuum was switched on and was applied until transfer was complete. Then switched
off the vacuum and ventilated the vacuum manifold.
11. 500 μl Buffer RPE was added to each RNeasy spin column.
Page | 27 12. The vacuum was switched on and was applied until transfer was complete. Then switched
off the vacuum and ventilated the vacuum manifold.
13. The RNeasy spin columns was removed from the vacuum manifold, and was placed each
in a 2 ml collection tube. The lids were closed gently, and centrifuged at full speed for 1
min.
14. Each RNeasy spin column was placed in a new 1.5 ml collection tube. 30–50 μl RNase
free water was added directly to each spin column membrane. The lids were closed
gently, and centrifuged for 1 min at 8000 x g (10,000 rpm) to elute the RNA.
15. If the expected RNA yield is >30 μg, then step 15 was repeated using another 30–50 μl
RNase free water or using the eluate from step 14 (if high RNA concentration is
required). The collection tubes were reused from step 14.
Note: If using the eluate from step 14, the RNA yield will be 15–30% less than that obtained
using a second volume of RNase-free water, but the final RNA concentration will be higher.
RNA IsolationThe kit used for RNA isolation was from QIAGEN.
1. A maximum of 1×107 cells was harvested, as a cell pellet or by direct lysis of/in vessel.
The appropriate volume of Buffer RLT was added.
2. 1 volume of 70% ethanol was added to the lysates and mixed well by pipetting. Did not
centrifuge. Proceeded immediately to step 3.
3. Up to 700 µl of the sample was transferred, including any precipitation, to an RNeasy
Mini spin column placed in a 2ml collection tube (supplied). The lid was closed and
centrifuged for 15s at ≥8000×g. The flow –through was discarded.
4. 700 µl Buffer RW1 was added to the RNeasy spin column. The lid was closed and
centrifuged for 15s at 8000×g. The flow –through was discarded.
5. 500 µl Buffer RPE was added to the RNeasy spin column. The lid was closed and
centrifuged for 15s at ≥8000×g. The flow –through was discarded.
6. 500 µl Buffer RPE was added to the RNeasy spin column. The lid was closed and
centrifuge for 2 min at ≥8000×g.
Page | 28 7. The RNeasy spin column was placed in the new 1.5 ml collection tube. 30-50 µl RNasefree water was added directly to the spin column membrane. The lid was closed and
centrifuged for 1min at ≥8000×g to elute the RNA.
8. If the expected RNA yield is >30 µg, then step 7 was repeated using another 30-50µl of
RNase- free water, or using the eluate from step-7. The collection tubes were reused
from step-7.
9. The purity and yield of RNA yield was measured by Eppendorf NanoDrop. It is a
cuvette free spectrophotometer which eliminates the need for other sample containment
devices and allows for clean up in seconds. It measures 1 µl samples with high accuracy
and reproducibility. The full spectrum (220nm-750nm) spectrophotometer utilizes a
patented sample retention technology that employs surface tension alone to hold the
sample in place. A 1 µl sample is pipetted onto the end of a fiber optic cable (the
receiving fiber). A second fiber optic cable (the source fiber) is then brought into contact
with the liquid sample causing the liquid to bridge the gap between the fiber optic ends.
The gap is controlled to both 1mm and 0.2 mm paths. A pulsed xenon flash lamp
provides the light source and a spectrometer utilizing a linear CCD array is used to
analyze the light after passing through the sample. The instrument is controlled by PC
based software, and the data is logged in an archive file on the PC.
cDNA synthesiscDNA synthesis was carried out using SuperScript First-Strand Synthesis System for RT-PCR by
Invitrogen using oligo dT primers.
The steps in cDNA synthesis:
1. Each of the components were mixed and briefly centrifuged before use.
2. For each reaction, the following in a sterile 0.2 or 0.5ml tube was combined.
Page | 29 Components
Amount
RNA
4 µl
10 mM dNTP mix
1 µl
Primer (0.5µg/µl oligo (dT)12-18 or 2µM gene
specific primer)
DEPC treated water
1µl
4 µl
3. The RNA/primer mixture at 65·c for 5 minutes was incubated, and then placed on
ice for at least 1 minute.
4. In a separation tube, the following 2X reaction was prepared by adding each
component in the indicated order.
Components
1RXn
10 RXns
10X RT buffer
2 µl
20 µl
25mM Mgcl2
4 µl
40 µl
0.1M DTT
2 µl
20 µl
RNase out TM (400/ µl)
1 µl
10µl
5. 9µl of the 2X reaction mixture was added to each RNA/primer mixture from
step3, mixed gently and collected by briefly centrifuge.
6. It was incubate at 42°c for 2 minutes.
7. 1µl of super script TM II RT was added to each tube.
8. It was incubate at 42°c for 50 minutes.
9. The reaction was terminated at 70·c for 15 minutes. Chilled on ice.
10. The reaction was collected by brief centrifugation. 1µl of RNase H was added to
each tube and incubated for 20minutes at 37·c. The reaction was used for PCR
immediately.
Page | 30 Quantitative Real Time RT-PCR AnalysisReal-time PCR is the continuous collection of fluorescent signal from one or more
polymerase chain reactions over a range of cycles. Quantitative RT-PCR involves the conversion
of the fluorescent signals from each reaction into a numerical value for each sample. Fluorescent
marker is used which binds to the DNA. Therefore, as the number of gene copies increases
during the reaction so the fluorescence intensity increases. This is advantageous because the rate
of the reaction and efficiency can be seen. Intercalating fluorescent dyes (e.g. SYBR green) are
the simplest and cheapest way to monitor a PCR in real-time. The SYBR green dye fluoresces
only when bound to double-stranded DNA. The major disadvantage of using a dye such as this is
the lack of specificity.
The Gene specific primer sequence were obtained from Primer Bank database (Harvard)
and ordered from the SIGMA GENOSYS. All the primers were desalted and UV absorbance was
used to assess the quality of primer synthesis.
Procedure:
To perform PCR using RNA as a starting template which must first be reverse
transcribed into cDNA in a reverse transcription (RT) reaction, where the cDNA is used as
template for real-time PCR with gene specific primers.
Table 3: Primer name and sequence with length and its amplicon size
Primer name
5’< ----sequence---- >3’
Length
Amplicon Size (nts)
beta actin F
CATGTACGTTGCTATCCAGGC
21
250
beta actin R
CTCCTTAATGTCACGCACGAT
21
MARCKS F
AGCCCGGTAGAGAAGGAGG
19
MARCKS R
TTGGGCGAAGAAGTCGAGGA
20
SIK1 F
CTCCGGGTGGGTTTTTACGAC
21
SIK1 R
CTGCGTTTTGGTGACTCGATG
21
110
93
Page | 31 Real-time PCR was carried out in Eppendorf Masterplex Real Time PCR.
1. The primer concentrations were normalized and gene-specific forward and reverse primer
pair was mixed. Each primer (forward or reverse) concentration in the mixture was 3.5 μl.
2. The experiment was set up and the following PCR program was made on. A copy of the
setup file was saved and all PCR cycles were deleted. The threshold frequency taken was
33%. The cycle temperatures taken were as follows:
Table 4: Cycle temperature and time for qRT-PCR
STAGE
TEMPERATURE (°C)
TIME
CYCLE
Stage 1
95
20 sec
1
95
15 sec
55
15 sec
68
20 sec
95
15 sec
60
15 sec
95
15 sec
Stage 2
Stage 3
40
1
Figure 5. Cycle temperature and time for qRT-PCR
Page | 32 3. cDNA was diluted 1:20 ratio concentration and the primer was added.
4. 10 μl of a real-time PCR reaction volume was made.
5. The following mixture was made in each optical tube as follows:
SYBR Green Mix (2x)
35 μl
cDNA stock (cDNA: dH2O [1:20])
40 μl
primer pair mix (3.5 μl each primer)
7 μl
6. The dissociation curve analysis was performed with the saved copy of the setup file.
7. The real-time PCR result was analyzed with the in-built software. It was also checked
to see if there was any bimodal dissociation curve or abnormal amplification plot.
8. After PCR is finished, the tubes were removed from the machine.
Page | 33 RESULTS AND DISCUSSIONS
RESULTS & DISCUSSIONS
Microarray Analysis
mRNA Expression analysis result:
Figure 6. Analysis of all entities in the sample
Figure 7. Statistical analysis by taking the p-value cut-off ≤ 0.05
Page | 34 Figure 8. 225 entities (genes) are identified when Fold change cut-off was taken
≥2.0
The total entities of differentially expressed genes in breast cancer are 225, out of which 73 are
down regulated and 152 are up-regulated.
Page | 35 miRNA expression analysis result:
From the miRNA expression analysis we found out that the total number of
differentially expressed miRNAs in breast cancer is 94 by taking the p-value cut-off ≤ 0.05 and
fold change cut off ≥2.0, out of which 82 are down-regulated and 12 are up-regulated.
Table 5: List of microRNAs with fold change and its regulation
microRNA
Fold change (cut off ≥2.0)
Regulation
hsa-let-7f
2.114769
Upregulated
hsa-let-7i
2.9767618
Upregulated
hsa-miR-1274a
5.035936
Upregulated
hsa-miR-1274b
2.7655098
Upregulated
hsa-miR-16
2.2772193
Upregulated
hsa-miR-193b
4.4158297
Upregulated
hsa-miR-200b
2.5386198
Upregulated
hsa-miR-21
8.594158
Upregulated
hsa-miR-331-3p
3.0384824
Upregulated
hsa-miR-24
2.3400607
Upregulated
hsa-miR-27a
2.127638
Upregulated
hsa-miR-22
2.2989354
Upregulated
Page | 36 Target interaction map analysis result:
The interconnected network of mRNA, miRNA and Transcription Factor was
generated as an interaction map (Figure 4).
Figure 9. Interaction Map of mRNAs and miRNAs involved in breast cancer
Page | 37 Figure 10. The interaction pairs where it is seen that hsa-miR-21 target BTG2 and
hsa-miR-27a target both SIK1 and MARCKS
From the interaction analysis it was seen that the two miRNAs i.e. hsa-miR-21 target
BTG2 whereas hsa-miR-27a target SIK1 and MARCKS. The genes MARCKS, SIK1and BTG2
are reported to be highly down regulated in breast cancer whereas the miRNAs hsa-miR-21 and
hsa-miR-27a, are reported to be highly up regulated in breast cancer.
•
Using GENOMATRIX the respective pathway, regulation and function of the genes are
known.
MARCKS
It is abbreviated as Myristoylated Alanine-rich C kinase Substrate gene which is the
most prominent cellular substrate for protein kinase C (80 kDa protein, light chain). It is also
known as MACS, PRKCSL (Homo sapiens). MARCKS gene is the most prominent cellular
substrate for protein kinase C (Spizz and Blackshear, 1996, 1993). It is localised in the plasma
membrane and is an actin filament cross-linking protein. Subcellular location of MARCKS is
more prominent in cytoplasm than the plasma membrane (Seykora et al., 1996). MARCKS binds
to plasma membranes via the dual actions of a hydrophobic myristoylated N-terminus and a
polybasic stretch within the so called effector domain (ED) that mediates electrostatic
interactions with acidic membrane Phospholipids (Brooks et al., 1996). Phosphorylation of
Page | 38 protein kinase C or binding to Calcium calmodulin displaces MARCKS from the plasma
membrane inhibiting its association with filamentous actin linking activity with the plasma
membrane and also its presence in the cytoplasm (Allen and Aderem, 1995). Myristic acid
targets MARCKS at the N-terminal of glycine to bind the plasma membrane and phosphorylate
by PKC. This leads to the down-regulation of MARCKS (Jonsdottir et al., 2005 & Arbuzova et
al, 2002).
The protein is involved in cell motility, membrane trafficking, phagocytosis and
mitogenesis. These are acidic proteins with high proportions of Alanine, Glycine, Proline and
Glutamic acid. (Ramsden, 2000). It plays an important role in cell shape, secretion,
transmembrane transport and cell-cycle regulation. (Finlayson et al.,2009). Recently, MARCKS
has been implicated in the exocytosis of a number of vessicles and granules such as mucin and
chromaffin. MARCKS are also seen to interact with TOB1. Also, MARCKS is seen to be
reported in breast cancer (Finlayson and Freeman, 2009).
Function:
•
Actin filament binding (in unphosphorylated form)
•
Calmodulin binding
•
Protein kinase C binding
Pathway:
•
Fc gamma R-mediated phagocytosis
•
BDNF signaling pathway
•
Integration of energy metabolism
•
Regulation of insulin secretion by Acetylcholine
•
Regulation of insulin secretion
•
Effects of Calcineurin in keratinocyte differentiation (Figure 6)
Page | 39 Figure 11. Involvement of MARCKS in keratinocyte differentiation pathway
Figure 12. Gene network of MARCKS
Page | 40 SIK1
It is abbreviated as Salt Inducible Kinase 1 which is a tumor suppressor gene that plays
a key role in p53 dependent anoikis. It belongs to protein kinase superfamily i.e. CAMK Ser/Thr
protein kinase family and AMPK subfamily. It contains one protein kinase domain and one LBA
domain (Katoh et al., 2004). The arginine-lysine RK region determines the subcellular location
and its catalytic activity by phosphorylation on Thr-182 taking magnesium as a co-factor (Doi et
al., 2002).
SIK1 is a part of sodium sensing signaling network, mediating phosphorylation of
PPME1, increase in intracellular sodium. SIK1 is activated by CaMK1 phosphorylating PPME1
subunit of PP2A leading to dephosphorylation of NA+/K+ ATPase by increasing its activity
(Kowanetz et al., 2008 and Stewart et al, 2013).
Defects in SIK1is reported to be involved in ovarian cancer and breast cancer (Cheng et
al., 2009). SIK1 expression is found to be significantly lower or down regulated in primary
breast cancer than the normal breast tissues (Sjöström et al., 2007).
Function:
•
SIK1 acts as the regulators of muscle cells by phosphorylating and inhibiting HDAC4
and HDAC5 , leading to expression of MEF2 target genes in myocytes.
•
It also regulate cardiomyogenesis by removing cardiomyoblast from cell cycle via down
regulation of CDNK1C.
•
It is also known as the regulator of hepatic gluconeogenesis by phosphorylating and
repressing TORC1/CRTC1 & TORC2/CRTC2 inhibiting CREB activity.
•
It regulates hepatic lipogenesis by phosphorylating and inhibiting SREBF1.
Pathway:
•
Liver kinase B1 (LKB) signaling events (Figure 8)
Page | 41 Figure 13. LKB1 signalling events in SIK1
Figure 14. Gene network of SIK1
Page | 42 Table 6. Diseases reported to be involved with MARCKS & SIK1 MARCKS
SIK1
malignantsyringoma
medullary sponge kidney
syringoma
nephrocalcinosis
Bipolar disorder
nephrolithiasis
lymphocytic leukemia
breast cancer
neuroblastoma
gastric cancer
alzheimer's disease
hepatitis
Asthma prostatitis
malaria
melanoma
neuronitis
leukemia
manic-depressive illness anoxia
Fibromyalgia
chronic lymphocytic leukemia
Arthritis
neuronitis
liver cirrhosis hepatitis
Colorectal cancer
choroiditis cerebritis
hepatocellular carcinoma
retinoblastoma
prostate cancer
Breast cancer
adenocarcinoma
Page | 43 BTG2
It is abbreviated as B-Cell Translocation Gene. Also known as
pheochromacytoma cell-3, NGF-inducible anti-proliferative protein PC3, B-cell
translocation gene 2 and nerve growth factor-inducible anti-proliferative. The protein
encoded by this gene is a member of the BTG/Tob family (Putnik et al., 2012). This
family has structurally related proteins that appear to have anti-proliferative properties.
This encoded protein is involved in the regulation of the G1/S transition of the cell cycle
(Möllerström et al., 2010).
The post translational modification involves phosphorylation at Ser-149 by
MAPK14 and at Ser-147 by MAPK1/ERK2 and MAPK3/ERK1, leading to PIN1-binding
and mitochondrial depolarization (Takahashi et al., 2011). BTG2 is preferentially
expressed in quiescent cells, anti-proliferative p53 dependent component of the DNA
damage cellular response pathway, homolog to murine Pc3/Tis21 and human BTG1 (see
symbol),at the onset of neurogenesis in single neuroepithelial cells that switch from
proliferative to neuron-generating division, Tob/BTG1 family. BTG2 is a vital gene
reported to be involved in breast cancer (Zhang et al., 2013).
Function:
•
Involved in cell cycle regulation
•
Involved in the growth arrest and differentiation of the neuronal precursors
•
Modulates transcription regulation mediated by ESR1
•
Anti-proliferative protein
•
Involved in mitochondrial depolarization and neurite outgrowth
Pathway:
•
Direct p53 effectors
•
Deadenylation-dependent mRNA decay
•
Cyclins and Cell Cycle Regulation
Page | 44 Figure 15. BTG family proteins and cell cycle regulation
Figure 16. Gene network of BTG2
Page | 45 Experimental Validation
RNA isolation
•
260/280 Ratio: This ratio indicates the absorbance of DNA and RNA at 260 nm and 280
nm, which is used to assess the purity of DNA and RNA. A ratio is expected
approximately 1.8 and generally accepted as “pure” for DNA; a ratio of approximately
2.0 is generally accepted as “pure” for RNA. In either case, if the ratio is significantly
lower it may indicate the presence of phenol, protein or other contaminants that absorb
strongly at or near 280 nm.
•
260/230 Ratio: This ratio is used as a secondary measure of nucleic acid purity. The
260/230 values for “pure” nucleic acid are often higher than the respective 260/280
values. Expected 260/230 values are normally in the range of 2.0-2.2. If the ratio is
significantly lower than expected, the presence of contaminants which absorb at 230 nm
is indicated.
•
Here, we got following results of two samples; Sample1 has perfect ratio absorbance in
260nm and 280 nm wavelengths where Sample2 has less value.
•
Sample1 = 500.6 µg/ml
At (260/280) ratio = 1.99
At (260/230) ratio = 2.01
•
Sample 2 = 125.2 µg/ml
At (260/280) ratio = 1.54
At (260/230) ratio = 1.10
Page | 46 qRT-PCR
• qRT- PCR melting curve analysis used to quantify nucleic acid, mutation detection and
for genotype analysis.
•
The melting temperature curve for the two gene MARCKS and SIK1 with respect to
control , β-actin was observed.
Figure 17. Melting temperature curve of MARCKS and SIK1 with respect to control
Page | 47 • From the qRT PCR analysis of SIK1, a fine melting temperature curve of SIK1 is
observed in comparison to the control gene, β-actin. The melting temperature of SIK1 is
82°c. As the samples of gene were taken in triplicates. Three peaks of SIK1 positioned at
one place were observed. The relative quantification of SIK1 with respect to β-actin
shows high expression of SIK1 (Figure 13).
Figure 18. Melting temperature curve of SIK1 with respect to control
• But MARCKS has no result in qRT- PCR (Figure 14).
Figure 19. Melting temperature curve of MARCKS with respect to control
Page | 48 1
MARCKS
SIK1
0.1
0.01
Figure 20. Relative expression of MARCKS and SIK1 with respect to control
The graph shows down-regulation of MARCKS and SIK1 with respect to control. According to
our microarray data analysis these two genes were down regulated in BC. It was further validated
by qRT PCR. This is in accordance to studies reported by Li et al., 2009 and Lönn P et al., 2012.
From the microarray data analysis we found out that MARCKS is down regulated with a fold
change of 0.34107733 and SIK1 is down regulated with a fold change of 1.9210892.
As the genes are down regulated, therefore from the miRNA-mRNA interaction map we have
seen that MARCKS and SIK1 are targeted by miR-27a. Also, BTG2 was found to be targeted by
miR-21. Therefore, 3 target pairs, hsa-miR-27a–MARCKS, hsa-miR-27a–SIK1 and hsa-miR-21–
BTG2 have been hypothesized to be good target pairs to be validated further by luminometer or
Luciferase Reporter Assay.
Page | 49 CONCLUSION
CONCLUSION
Our study aimed at identification of novel mRNA-miRNA target pairs that are hypothesized to
play a role in breast cancer through an mRNA- miRNA interaction map analysis of microarray
data and experimental validation of selected set of mRNAs. From mRNA microarray expression
analysis, we found that the total number of differentially expressed genes in breast cancer is 225,
out of which 73 are down regulated and 152 are up-regulated. Further, from miRNA expression
analysis we found out that the total number of differentially expressed miRNAs in breast cancer
is 94, out of which 82 are down-regulated and 12 are up-regulated. From the target interaction
map analysis, we found that 7 miRNAs showed specific target binding for 25 genes, out of which
these 3 pairs, hsa-miR-27a–MARCKS, hsa-miR-27a–SIK1 and hsa-miR-21–BTG2 are seen to be
novel target pairs. MARCKS, SIK1 and BTG2 being tumor suppressor genes are seen to be
significantly down regulated in breast cancer. hsa-miR-21 and hsa-miR-27a being oncomiRs are
seen to be highly up regulated in breast cancer because of their property to induce metastasis.
The over expression of MARCKS and SIK1 can strongly repress the proliferation of cancer cells.
The lower expression of hsa-miR-21 and hsa-miR-27a can even contribute in suppressing
metastasis in breast cancer. Therefore, hsa-miR-27a–MARCKS, hsa-miR-27a–SIK1 and hsamiR-21–BTG2 have been hypothesized to be novel target pairs which can be further
experimentally validated. Therefore, a better perceptive about the miRNA-gene networks and
cellular pathways can be a promising concept in understanding and further elucidating the role of
miRNAs involved in breast cancer pathogenesis and thereby may revolutionize the future of
breast cancer therapeutics.
Page | 50 FUTURE PROSPECTIVES
FUTURE PROSPECTIVES
The effectiveness of miRNA-based breast cancer therapy can be a landmark in breast cancer
studies. Identification of the genome-wide targets of miRNAs is a promising approach which can
be experimentally validated to have a vital role in breast cancer. Henceforth, with a better
perceptive about the gene networks and their cellular pathways regulated by miRNAs, the
elucidation of breast cancer pathogenesis and therapeutics can be facilitated. Furthermore,
experimental validation of hsa-miR-27a–MARCKS, hsa-miR-27a–SIK1 & hsa-miR-21–BTG2
through Luciferase Reporter Assay or any other molecular techniques will strengthen the
foundation of miRNA-mediated regulation in breast cancer. Subsequent analysis of these novel
miRNA-target pairs will enhance our understanding to manipulate pathways/networks for
treatment of breast cancer through miRNA therapeutics.
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Page | 56 
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