Download Does fat provide energy for breast tumour cell invasion and

Does fat provide energy
for breast tumour cell
invasion and
metastasis?
Morgan Jones
Supervisors
Dr. Margaret Currie
Dr. Elisabeth Phillips
Prof. Mark Hampton
A thesis submitted for the degree of
Bachelor of Biomedical Science with Honours
University of Otago, Christchurch
2015
i
Abstract
Breast cancer is the most commonly diagnosed cancer in New Zealand women. Obese breast
cancer patients are more likely to have tumours with advanced clinical stage and high vascular
and lymph node involvement. The tumour microenvironment provides vital support for tumours
during development and progression of cancer, yet the local effects of stromal adipocytes on
breast cancer cells have been largely overlooked. Recent studies by Dirat et al. (2011) and Bochet
et al. (2011) have shown that breast cancer cells co-cultured with adipocytes become more
resistant to radio- and chemotherapy, and more invasive. However, little is known about the
metabolic changes that occur in breast cancer cells when they are cultured with adipocytes.
Nieman et al. (2011) determined that lipids were transferred from omental adipocytes to ovarian
cancer cells and were consequently used in β-oxidation. It was hypothesised that β-oxidation is
increased in breast cancer to exploit the glycerol and fatty acids released by lipolysis from
adipocytes in order to support the migration and invasion of breast cancer cells.
In this study, breast cancer cell lines MCF7 (ER+; oestrogen receptor positive) and MDA-MB231 (ER-/PR-/HER2-; oestrogen, progesterone and human epidermal growth factor receptor
negative) were co-cultured with adipocytes isolated from breast adipose tissue. Adipose tissue
samples were collected via the Cancer Society Tissue Bank from patients at Christchurch
Hospital undergoing surgery for therapeutic mastectomy, prophylactic mastectomy and breast
reductions. A Seahorse XF24 Analyser was used to measure oxygen consumption and
extracellular acidification, as indicators of oxidative phosphorylation and glycolysis,
respectively, in breast cancer cells grown alone or in co-culture with human breast adipocytes.
MCF7 cells were found to have upregulated glycolysis after co-culture with adipocytes. Western
blotting was used to assess differences in the expression of proteins involved in β-oxidation
between breast cancer cells grown alone or in co-culture with adipocytes. Levels of carnitine
palmitoyltransferase 1 (CPT1A), a protein involved in translocation of fatty acids into the
mitochondrial matrix for β-oxidation, showed no change. However, phosphorylated acetyl-CoA
carboxylase (ACC), a key metabolic enzyme that when inhibited relieves inhibition of CPT1A
to allow fatty acid translocation into mitochondria, showed increased levels in both MCF7 and
MDA-MB-231 cells after co-culture with adipocytes. These results support the concept that
breast cancer cell metabolism, specifically glycolysis and β-oxidation, is being altered in the
presence of adipocytes to utilise fatty acids and glycerol released by adipocytes during lipolysis.
ii
Acknowledgements
I would like to begin by thanking my primary supervisor, Dr. Margaret Currie, for all her
efforts in supporting me this year. Thank you so much for accepting me for this project
and for guiding me through it. You patiently coped with my Ultimate Frisbee absences,
whilst putting a little bit of “fear” in me. Time stamped emails could only hint at how
many hours you put in behind the scenes to help me, despite it being a busy year for you
too.
A huge thank you is also due to my supervisor, Dr. Elisabeth Phillips. Heeeey
Elisabeth…? Did you know you answered my endless questions, even when you hadn’t
had a morning coffee yet? You’ve taught me so many valuable lab skills and I’m so
happy to have had a supervisor who showed me how to celebrate with an enthusiastic
data dance.
Thanks as well to my last supervisor, Dr. Mark Hampton, for juggling me with his other
students. Thank you for providing expertise and access to the Seahorse and for
questioning me about experimental design.
A special thank you to Dr. Karina O’Connor and Dr. Andree Pearson for helping me with
my Seahorse troubles whenever I knocked on their doors. Thanks also to Karina for
helping me with conducting experiments and interpreting the Seahorse data. I gratefully
acknowledge Helen and the Cancer Society Tissue Bank for providing the adipose
samples needed for this project. All the other people in the McKenzie Cancer Research
Group who helped me through this, whether with lab advice or just some food and a
laugh, thank you so much. To the other Honours students, we made it!
Lastly, the people who didn’t have to deal with me in the lab, but had to put up with me
the rest of the time. Thank you to my flatmates for putting up with my general complaints
and irritability, to the Ultimate Frisbee community for allowing me to let off steam
playing and partying with them, to my parents for their support in financing me and
organising various parts of my life, and lastly to Ben for always being there for me and
surviving all my misguided frustrations.
iii
List of Abbreviations
Abbreviation
Description
ACC
acetyl-CoA carboxylase
AMP
adenosine monophosphate
AMPk
AMP-activated protein kinase
ATP
adenosine triphosphate
BMI
body mass index
CAA
cancer associated adipocyte
CAF
cancer associated fibroblast
CoA
coenzyme A
CPT1
carnitine palmitoyl transferase 1
DMEM
Dulbecco’s modified eagle medium
DTT
dithiothreitol
ER
oestrogen receptor
FADH2
flavin adenine dinucleotide
FAO
fatty acid oxidation
FASN
fatty acid synthase
FBS
foetal bovine serum
FCCP
carbonyl cyanide-p-trifluoromethoxyphenylhydrazone
GPD2
glycerol-3-phosphate dehydrogenase
HBSS
Hank’s balanced salt solution
HEPES
4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid
HER2
human epidermal growth factor receptor 2
IBMX
3-isobutyl-1-methylxanthine
IL-6
interleukin 6
mGPDH
mitochondrial glycerol-3-phosphate dehydrogenase
MMP
matrix metalloproteinase
MTT
3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide
NADH
nicotinamide adenine dinucleotide
NADPH
nicotinamide adenine dinucleotide phosphate
OXPHOS
oxidative phosphorylation
PAI-1
plasminogen activator inhibitor-1
phospho-ACC
phosphorylated ACC
iv
PR
progesterone receptor
ROS
reactive oxygen species
RPMI
Roswell Park Memorial Institute media
SDS
sodium dodecyl sulfate
TBS
tris buffered saline
TBS-T
tris buffered saline with tween
TCA
tricarboxylic acid
TGF-β
transforming growth factor β
T3
3,3′,5-triiodo-l-thyronine
VEGF
vascular endothelial growth factor
v
Contents
Abstract.............................................................................................................................ii
Acknowledgements ........................................................................................................ iii
List of Abbreviations .......................................................................................................iv
List of Figures................................................................................................................... 5
List of Tables .................................................................................................................... 7
1 Introduction ................................................................................................................... 8
1.1 Obesity and cancer .................................................................................................. 9
1.2 Breast cancer ........................................................................................................... 9
1.2.1 Breast cancer and obesity ............................................................................... 10
1.3 Tumour microenvironment ................................................................................... 11
1.3.1 Involvement in breast cancer ......................................................................... 12
1.3.2 Cancer-Associated Adipocytes (CAAs) ......................................................... 13
1.4 Metabolism in cancer ............................................................................................ 14
1.4.1 Warburg theory .............................................................................................. 14
1.4.2 Two-compartment tumour metabolism .......................................................... 15
1.4.3 β-oxidation ..................................................................................................... 16
1.4.4 Proteins involved in β-oxidation .................................................................... 18
1.5 Summary and hypothesis ...................................................................................... 20
1.6 Aims and objectives .............................................................................................. 21
2 Materials and Methods ................................................................................................ 23
2.1 Breast adipose tissue samples and processing ...................................................... 23
2.1.1 Collection of patient samples ......................................................................... 23
2.1.2 Isolation of pre-adipocytes ............................................................................. 23
2.1.3 Culture of pre-adipocytes ............................................................................... 24
2.1.4 Differentiation of pre-adipocytes into mature adipocytes .............................. 24
2.2 Breast cancer cells ................................................................................................ 24
1
2.2.1 Passage ........................................................................................................... 25
2.3 Co-culture of mature adipocytes and breast cancer cell lines ............................... 25
2.3.1 Day 1 .............................................................................................................. 25
2.3.2 Day 4 .............................................................................................................. 26
2.3.3 Day 5 .............................................................................................................. 26
2.4 Seahorse XF24 Extracellular Flux Analyser ........................................................ 26
2.4.1 Optimisation for XF24 ................................................................................... 27
2.4.2 Mitochondrial stress test ................................................................................ 27
2.4.3 Normalisation of XF data ............................................................................... 28
2.4.4 Fatty acid oxidation test optimisation ............................................................ 29
2.5 Western Blot ......................................................................................................... 31
2.5.1 Cell lysis ......................................................................................................... 31
2.5.2 Protein quantification ..................................................................................... 31
2.5.3 SDS-PAGE ..................................................................................................... 31
2.5.4 Transfer .......................................................................................................... 32
2.5.5 Antibodies ...................................................................................................... 32
2.5.6 Detection ........................................................................................................ 33
2.5.7 Re-probing...................................................................................................... 33
2.6 Glycerol assay ....................................................................................................... 34
2.6.1 Sample collection ........................................................................................... 34
2.6.2 Detection ........................................................................................................ 34
2.7 Statistical analysis ................................................................................................. 34
2.7.1 Seahorse XF analyser ..................................................................................... 34
2.7.2 Western blotting ............................................................................................. 35
2.7.3 Glycerol Assay ............................................................................................... 35
3 Results ......................................................................................................................... 36
2
3.1 Analysis of metabolism in breast cancer cells and breast cancer cells co-cultured
with adipocytes using the Seahorse XF24 Extracellular Flux Analyser ..................... 36
3.1.1 Optimisation for MCF7 and MDA-MB-231 cell lines on the Seahorse XF24
Extracellular Flux Analyser .................................................................................... 36
3.1.2 Metabolic characterisation of MCF7 and MDA-MB-231 breast cancer cells41
3.1.5 Metabolic characterisation of MCF7 breast cancer cells grown alone and in
adipocyte co-culture ................................................................................................ 48
3.1.6 Fatty acid oxidation stress test optimisation .................................................. 52
3.2 Analysis of proteins involved in metabolism using Western blotting .................. 55
3.2.1 CPT1A............................................................................................................ 55
2.3 ACC and pACC................................................................................................. 57
3.2.4 GPD2 .............................................................................................................. 60
3.3 Analysis of glycerol in conditioned media ........................................................... 62
4 Discussion.................................................................................................................... 63
4.1 Analysis of metabolism in breast cancer cells and breast cancer cells co-cultured
with adipocytes using the Seahorse Extracellular Flux Analyser ............................... 63
4.2 Analysis of proteins involved in β-oxidation using Western blotting .................. 69
4.3 Analysis of glycerol in conditioned media ........................................................... 71
4.4 Future Work .......................................................................................................... 73
4.5 Conclusion ............................................................................................................ 75
5 Appendices .................................................................................................................. 76
5.1 Media .................................................................................................................... 76
5.1.1 Pre-adipocyte growth media .......................................................................... 76
5.1.2 Serum free media ........................................................................................... 76
5.1.3 Adipocyte differentiation media .................................................................... 76
5.1.4 Adipocyte maintenance media ....................................................................... 76
5.1.5 Breast cancer cell media................................................................................. 77
5.1.6 MTT assay media ........................................................................................... 77
3
5.1.7 Mitochondrial stress test assay media ............................................................ 77
5.1.8 Substrate limited media .................................................................................. 77
5.1.9 FAO assay media (1X) ................................................................................... 77
5.1.10 FAO assay media (5X) ................................................................................. 78
5.2 Buffers and solutions ............................................................................................ 78
5.2.1 Phosphate buffered saline (PBS) .................................................................... 78
5.2.2 Cell-Tak solution ............................................................................................ 78
5.2.3 Solubilisation solution .................................................................................... 78
5.2.4 Lysis buffer .................................................................................................... 78
5.2.5 RIPA buffer .................................................................................................... 78
5.2.6 Sample loading buffer .................................................................................... 79
5.2.7 Running buffer ............................................................................................... 79
5.2.8 Transfer buffer ............................................................................................... 79
5.2.9 TBS-T ............................................................................................................. 79
5.2.10 Mild stripping buffer .................................................................................... 79
5.3 Supplementary Figures ......................................................................................... 80
References ...................................................................................................................... 86
4
List of Figures
Figure 1.1: Interactions between tumour cells and mature adipocytes as described in
text……………………………………………………………………………………..14
Figure 1.2: Key proteins and reactions in the fatty acid oxidation pathway as described
in the text……………………………………………………………………………….17
Figure 1.3: Summary of metabolic changes that occur in interacting ovarian cancer cells
and adipocytes as described in the text…………………………………………………18
Figure 1.4: Hypothesis of breast cancer-adipocyte interactions………………………..21
Figure 3.1: Comparison of MCF7 and MDA-MB-231 cell seeding using representative
10X photographs……………………………………………………………………….38
Figure 3.2: Pictorial representation of the changes in the electron transport chain due to
oligomycin, FCCP and antimycin-A…………………………………………………...39
Figure 3.3: FCCP optimisation of MCF7 and MDA-MB-231 breast cancer cell lines…40
Figure 3.4: Representation of a typical mitochondrial stress test, identifying the fractions
attributed to various metabolic parameters……………………………………………..41
Figure 3.5: Comparing oxidative metabolic parameters of MCF7 and MDA-MB-231
breast cancer cell lines………………………………………………………………….44
Figure 3.6: Energy graph displaying cells in their baseline and stressed phenotypes…..45
Figure 3.7: Comparing energy phenotypes of MCF7 and MDA-MB-231 breast cancer
cell lines using OCR and ECAR in baseline and stressed conditions………………….47
Figure 3.8: Comparing oxidative metabolic parameters of MCF7 and MCF7 co-cultured
with adipocytes…………………………………………………………………………49
Figure 3.9: Comparing energy phenotypes of MCF7 and adipocyte co-cultured MCF7
using OCR and ECAR in baseline and stressed conditions……………………………..51
Figure 3.10: Representation of a typical fatty acid oxidation stress test, identifying the
fractions attributed to exogenous or endogenous oxidation…………………………….52
5
Figure 3.11: FCCP optimisation of MCF7 cells in the presence of BSA……………….54
Figure 3.12: MCF7 OCR graph during FCCP optimisation with BSA or
BSA:palmitate………………………………………………………………………….54
Figure 3.13: Comparison of CPT1A protein level in MCF7 and MDA-MB-231 cells
compared to their adipocyte co-cultured counterparts……………………………….....56
Figure 3.14: Comparison of ACC1 protein level in MCF7 and MDA-MB-231 cells
compared to their adipocyte co-cultured counterparts…………………………………58
Figure 3.15: Comparison of phospho-ACC protein level in MCF7 and MDA-MB-231
cells compared to their adipocyte co-cultured counterparts……………………………59
Figure 3.16: Comparison of GPD2 protein level between MCF7 and MDA-MB-231…60
Figure 3.17: Comparison of GPD2 protein level in MCF7 and MDA-MB-231 cells
compared to their adipocyte co-cultured counterparts….……………………….……...61
Figure 3.18: Comparison of glycerol levels in various conditioned media samples from
MDA-MB-231 and MCF7 adipocyte co-cultures………………………………………62
Figure 5.1: Representative photographs of adipocyte samples before and after co-culture
with breast cancer cells…………………………………………………………………80
Figure 5.2: Representative photographs of cell seeding for MCF7 and MDA-MB-231
from 5x105-1x104 in an XF 24-well plate………………………………………………81
Figure 5.3: Representative photographs of cell seeding for MCF7 and MDA-MB-231
from 5x105-1x104 in an XF 24-well plate………………………………………………82
Figure 5.4: MTT assay showing viability of MCF7 and MDA-MB-231 cells at varying
densities………………………………………………………………………………..83
Figure 5.5: Glycerol levels in varying stages of individual adipocyte co-cultures with
MCF7 and MDA-MB-231 cells………………………………………………………..84
Figure 5.6: MTT assays of MCF7 and MDA-MB-231 cells grown on inserts or not on
inserts as controls to adipocyte co-cultured cells……………………………………….85
6
List of Tables
Table 2.1: Mitochondrial stress test protocol…………………………………………...28
Table 2.2: Fatty acid oxidation stress test protocol……………………………………..30
Table 2.3: Primary and secondary antibody concentrations used in Western blotting….33
Table 3.1: Calculations to determine metabolic parameters of the mitochondrial stress
test……………………………………………………………………………………...42
Table 3.2: Calculations to determine metabolic parameters of the fatty acid oxidation
stress test……………………………………………………………………………….53
7
1 Introduction
Obesity is a significant problem worldwide that is only now being fully defined. Over
the last 35 years worldwide obesity has more than doubled (1). Obesity is not only a
concern for those directly affected; it also impacts greatly on countries’ health systems
and is therefore of economic concern (2, 3). New Zealand is one of many countries
struggling with the problem of rising obesity rates; 35% of adults are overweight and an
additional 31% of adults are obese (4, 5), which is a significant increase from the 19%
obesity rate in 1997 (4). Obesity rates are also much greater in Pacific (68% obese) and
Maori (48% obese) ethnicities, which is of specific concern to New Zealand and our
health system (5).
Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer
death in women worldwide (6). New Zealand statistics reflect this with breast cancer as
the most commonly diagnosed cancer in women, accounting for 28.7% of female cancers
(7). Breast cancer is also the most expensive cancer to treat using 15% of the $511 million
spent every year on cancer in New Zealand (8).
Obesity is known to increase the risk of post-menopausal breast cancer and contributes
to poorer prognosis for all breast cancers (9, 10). The tumour microenvironment has an
important role in cancer development and progression (11, 12) and is one of the ways in
which obesity can have a local effect on breast cancer (13, 14). This is through breast
cancer cell interactions with adipocytes, causing formation of cancer-associated
adipocytes (CAA) (15). CAA can make breast cancer more aggressive, invasive, and
resistant to radiotherapy and chemotherapy (15-17). One of the mechanisms CAAs may
work through is by altering breast cancer cell metabolism. Stromal cells from the tumour
microenvironment have been observed to provide high energy nutrients to breast cancer
cells (18). It is proposed that these nutrients fuel rapid growth, proliferation, invasion and
metastasis of breast cancer (19). In ovarian cancer, adipocytes have been observed to
transfer lipids to neighbouring ovarian cancer cells where they are metabolised by βoxidation (17). Available fatty acids and glycerol from CAAs may be utilised by
glycolysis and β-oxidation in breast cancer cells to fuel tumour growth, invasion, and
metastasis.
8
1.1 Obesity and cancer
Although obesity is very heterogeneous it can be broadly defined as having an excess of
body fat (20). It is most commonly measured by body mass index (BMI), a calculation
based on kg/m2 (21). Those with a BMI of 25-29.9 are considered overweight and those
with a BMI >30 are considered obese (21).
Obesity is a dangerous condition made all the more serious by its preventability. Obesity
is associated with many diseases such as type 2 diabetes, cardiovascular disease, stroke,
and cancer (2, 22, 23). It has been found that severely obese people die 8-10 years sooner
than their normal weight counterparts (2, 23, 24). Obese people are more likely to have
morbidity from chronic disorders, a poorer quality of life, and die prematurely (2, 24).
Due to this acknowledged danger, there are many public health efforts focused on obesity
(23, 25).
Previous studies show obese and overweight people are at increased risk and have a
poorer prognosis for many cancers including kidney, postmenopausal breast,
oesophageal adenocarcinoma, endometrium, pancreas, gallbladder, colon, thyroid, and
renal cancer (26, 27). It is estimated that being either overweight or obese contributes to
20% of all cancer diagnoses (28). Obesity is a modifiable cause of cancer and thus a
useful area for research as it can be improved. Obesity has a clear link to cancer risk and
progression but the mechanisms are not widely understood. It is likely that different
cancers are caused through different mechanistic pathways as cancers are so variable in
their aetiology (28). There is a very complex interaction between different systemic and
local effects of obesity that leads to the development and progression of cancer (29, 30).
The main mechanisms proposed are insulin resistance, changes in adipokine levels,
inflammatory processes, and changes in obesity-related hormone levels (29, 31-33). This
study will focus on the local effects of obesity on cancer; more specifically the local
effects of stromal adipocytes on cancer cell metabolism.
1.2 Breast cancer
Breast cancer is the abnormal development of breast cells into a malignant tumour which
can result in metastasis to other locations in the body (34). This is due to mutations in
genes and as such, breast cancer has been classified into at least 5 molecular subtypes:
luminal A, luminal B, HER-2-enriched, claudin-low, triple negative/basal-like (35).
9
These have varying characteristics at the molecular level and consequently at a clinical
level (35). The variety of breast cancer is echoed by the range of risk factors including
age, age at menarche, age at pregnancy, parity, breast-feeding, abortion, exogenous
hormones, as well as genetic factors (36).
1.2.1 Breast cancer and obesity
As discussed above, obesity can alter the risk and prognosis of various cancers. Breast
cancer is no exception.
The first study looking at the relationship between breast cancer and obesity was
conducted by Abe in 1976 (37, 38). They found that obese patients have poorer 5-year
survival rates and are more likely to have tumours with vascular involvement, lymph
node metastasis and advanced clinical stage when compared to non-obese counterparts
(37). This is in line with recent findings that obese women have more distant metastases
at diagnosis and a higher mortality rate (39). This poor prognosis associated with obesity
has been shown to be independent of menopausal status, tumour stage and breast tumour
hormone receptor status (10, 38, 40).
Obesity also leads to increased risk of developing breast cancer (39, 41-43). Studies
regarding obesity effects on post-menopausal and pre-menopausal breast cancer have
been more unclear with most studies showing an increase in risk for post-menopausal
women but a less clear link between obesity and risk in pre-menopausal women (9, 38).
The reasons for higher risk and poorer prognosis of breast cancer in obese patients are
unclear. The two main arguments for poor prognosis debate whether it is the biology of
the breast cancer itself or the treatment of obese patients (39, 42). Obese patients show
more advanced tumours at diagnosis which may be due to delayed detection (10, 42).
They also have a greater likelihood of treatment failure which is probably linked to
reduced chemotherapy doses in obese patients (42). However, there is also a strong
argument that the tumours are not just aggressive due to detection and treatment errors
but rather due to the impacts of obesity on tumour biology (10, 39, 42).
1.2.1.1 Mechanisms of obesity and breast cancer association
Proposed mechanisms of obesity on breast cancer range from systemic effects, including
elevated levels of circulating oestrogens, hyperinsulinemia and insulin resistance, and
10
chronic inflammation; to local effects of adipocytes in the tumour stroma, such as
adipokine secretions (9, 10, 38, 43, 44).
It has been found that post-menopausal obese patients have higher levels of circulating
oestrogens than their normal weight counterparts (9). Oestrogen is a key hormone in
normal breast development and growth and as such is involved in cancer development
and progression (44). This obesity related increase in oestrogen has been associated with
increased risk of breast cancer (43, 44). Obese patients also have high levels of insulin
known as hyperinsulinemia which can result from insulin resistance (43). There is an
increased risk of breast cancer and poorer prognosis for patients with hyperinsulinemia
(43). Chronic inflammation is seen in obese patients and data suggests that this state of
inflammation may influence breast cancer risk and prognosis (9, 40). These are some of
the systemic mechanisms that obesity is thought to act through.
One of the local mechanisms that obesity works through to alter breast cancer is
adipocyte secretions, such as adipokines. Two common adipokines linked to obesity and
breast cancer are leptin and adiponectin; these hormones regulate insulin sensitivity (38,
44). Leptin levels are increased in obesity (38) and adiponectin levels are decreased (44).
These trends are also seen in breast cancer (38, 44, 45). Their effects may be mediated
by the up regulation of aromatase which leads to higher local levels of oestrogen (44).
This shows the local effect adipocytes can have on breast cancer.
1.3 Tumour microenvironment
Tumours
primarily consist
of
cancer
cells
with
the
surrounding
tumour
microenvironment consisting of stromal cells, immune cells, vasculature and
extracellular matrix (46, 47). The microenvironment provides support for tumours and as
such has a key role in the development and progression of cancer (11, 12, 48).
The importance of the tumour microenvironment was noted in very early research by
Paget’s “seed and soil” hypothesis (49). Paget states that for the seed to grow the soil
must be hospitable; which in context means the cancer needs a nourishing environment
(49, 50). This was originally hypothesised to explain why cancer is more likely to
metastasise in certain organs; some organs provide a better environment or “soil” for
cancer progression (49). The “seed and soil” hypothesis has recently been extended to
cancer cells and the importance of their microenvironment in their development (50).
11
It is thought that the tumour and its microenvironment undergo “dynamic reciprocity”, a
term developed by Mina Bissell to describe the interaction of the extracellular matrix
with breast cancer cells (51, 52). This has been expanded to include the various
microenvironment and tumour components and now describes how they continuously
respond to each other and shape the shared environment (53). It is often debated whether
the microenvironment undergoes changes which allow for initiation of cancerous growth
or whether the cancer begins independently of the microenvironment and then influences
it to become a support system (53, 54). It is likely that both occur and once initiated the
reciprocal communication leads to the involvement of both the microenvironment and
the tumour in cancer progression (53).
It is thought that obesity can alter the microenvironment to make it more supportive of
tumour growth (13, 14, 31). Adipose tissue in the microenvironment functions as an
endocrine organ and can have systemic and local effects (55). This may be through
hormonal, insulin-related, or inflammatory mechanisms (14, 55). A key area emerging
in the role of obesity is the local effects of adipocytes on metabolism both in the tumour
itself and the tumour microenvironment.
1.3.1 Involvement in breast cancer
The specific microenvironment of breast tissue is necessary for normal mammary gland
development and undergoes many changes that make it suitable for tumour development
(54). Thus an altered microenvironment can contribute to breast cancer proliferation,
survival and invasion (47, 56). It has been shown that breast cancer cells can lose their
malignant phenotype when they are grown in a normal mammary microenvironment
(57). This is one of the most convincing arguments that breast cancer requires the altered
tumour microenvironment for growth and progression (47).
Various cells in the microenvironment have a role in breast cancer. Genetic and
epigenetic alterations occur in the cells that compose the tumour microenvironment and
this is one of the many ways they can influence the tumour (58, 59). Tumour associated
macrophages have a role in angiogenesis and tumour progression (53, 60) and as such
are correlated with poorer prognosis (61). Mesenchymal stem cells can enhance invasion
and metastasis of breast cancer (62).
12
One of the well-studied stromal players in breast cancer is the fibroblast. In the presence
of breast cancer, fibroblasts undergo changes to become cancer-associated fibroblasts
(CAFs) (63). CAFs are the most prominent non-cancerous cells in the tumour stroma and
secrete various factors, such as VEGF (vascular endothelial growth factor), IL-6
(interleukin-6), TGF-β (transforming growth factor β), and matrix metalloproteinases
(MMP) that affect nearby cancer cells (61). In doing so CAFs promote breast cancer
development, angiogenesis, invasion and metastasis (61, 64-66).
1.3.2 Cancer-Associated Adipocytes (CAAs)
Adipocytes (fat cells) are often overlooked as tumour stromal cells and disregarded as
passive energy storage (67). However, recent studies have revealed they may have an
important role in the tumour microenvironment and in breast cancer progression (15-17,
68, 69). An experimental co-culture system has been developed to grow breast tumour
cells with adipocytes (15). This co-culture alters both adipocyte and breast tumour cell
phenotypes. Adipocytes become dedifferentiated, less lipid rich, and secrete factors
which enhance survival and migration of breast tumour cells (15, 17, 68). These cells are
called cancer-associated adipocytes (CAA). Breast cancer cells co-cultured with CAA
become more resistant to radiation therapy (16) and chemotherapy (our labs own
unpublished data), show enhanced growth (70), and are more migratory and invasive
(Figure 1.1) (15, 17, 68, 71).
Various adipocyte secreted factors have been implicated in promoting this aggressive
phenotype in breast cancer cells. For example, CAA-secreted proteases, MMP-11 and
PAI-1 (plasminogen activator inhibitor-1) (68, 69, 72), and inflammatory cytokine IL-6
have been suggested as key factors in the acquired resistance to radiation therapy
observed in breast cancer cells (15). However, little is known about tumour cell
metabolism in breast cancer cells that have been exposed to CAAs.
13
Figure 1.1: Interactions between tumour cells and mature adipocytes as described in text. Adapted from
Wang et al. (73). Reprinted with permission from Elsevier.
1.4 Metabolism in cancer
Metabolism has been detailed as an emerging hallmark of cancer and is thought to be
important for cancer development (74, 75). Tumour growth and survival are also
supported by changes in fundamental oncogenic pathways which alter tumour
metabolism (76). Metabolism has an important role in cancer cells as it is the basis of
ATP generation, biosynthesis, and maintenance of redox status (76). Alterations in these
pathways can create the support necessary for the rapid proliferation and growth of a
tumour.
Early metabolic research in cancer was pioneered by Warburg and primarily focused on
glycolysis. Since then research has expanded and many pathways in metabolism have
been revealed to have important oncogenic roles (75, 76). The tumour microenvironment
has been implicated as having an important role in these metabolic changes (14, 77).
1.4.1 Warburg theory
Otto Warburg, a German biochemist, in the 1920s proposed that even when oxygen is
present cancer cells use glycolysis for ATP synthesis (78, 79). This was called aerobic
glycolysis and the phenomenon became known as the “Warburg Effect” (80). Warburg
posited that this was due to the oxidative phosphorylation (OXPHOS) pathway being
disrupted by mitochondrial dysfunction, which led cancer cells to switch from OXPHOS
14
to glycolysis to produce sufficient ATP (80, 81). His theory has been challenged in more
recent years as it has been found that in many cancer cells, although there is upregulated
aerobic glycolysis, OXPHOS is not disrupted (76, 80, 81). This upregulated glycolysis
may be helping to compensate for high biosynthetic demands to keep up with the
proliferation rate of cancer cells whilst reducing oxidative stress (80). It is likely that
these changes seen in cancer cells are due to mutations in oncogenes and tumour
suppressors that have an effect on metabolism (76).
1.4.2 Two-compartment tumour metabolism
Not all cancer cells show the metabolic phenotype associated with the Warburg effect
(17, 82). Pavlides and colleagues (2009) discovered that the stromal fibroblasts in breast
tumours can be induced, via oxidative stress from the cancer cells, to undergo aerobic
glycolysis (63). This results in the secretion of metabolites creating an energy-rich
microenvironment for tumour growth; they termed this the “Reverse Warburg Effect”
(63). This holds true to Warburg’s original observations that tumours undergo aerobic
glycolysis, the glycolysis is just occurring in the tumour microenvironment compartment;
specifically the CAFs (18).
Through further in vitro studies they expanded this with a parallel model called the
autophagic tumour stroma model of cancer, in which cancer cells are fuelled by stromal
cells undergoing autophagy (18). Autophagy is a pathway involved in self-degradation
of organelles and proteins and therefore can have a role in cancer through provision of
nutrients and prevention of cell death (83, 84). This metabolic coupling is termed “twocompartment tumour metabolism” where the catabolic stromal compartment undergoes
aerobic glycolysis and autophagy which drives the anabolic growth of tumours in the
cancer cell compartment via oxidative metabolism (19). This study was conducted in
CAFs and breast cancer cells. The glycolytic CAFs produce L-lactate and ketone bodies,
which are high energy nutrients that can be utilised by the neighbouring breast cancer
cells for ATP production (46). They also produce reactive oxygen species (ROS) that
promote mutations in the cancer cells to drive tumour-stroma co-evolution (46). Previous
studies, and our own, have reported reduced size and loss of lipids in CAAs (15, 17). It
is possible that this two-compartment tumour metabolism model is analogous to
interactions between CAAs and breast cancer cells. This would be observed as adipocytes
15
undergoing lipolysis and the produced glycerol and fatty acids being metabolised by the
breast cancer cells.
1.4.3 β-oxidation
β-oxidation is often an overlooked metabolic pathway in studies of cancer metabolism.
Fatty acids are a good source of fuel for ATP production and they also provide substrates
to counteract oxidative stress (85). Fatty acids may be present in the tumour
microenvironment and therefore β-oxidation should be considered as a potential pathway
supporting the rapid proliferation and growth of cancer (17).
β-oxidation is a catabolic bioenergetic process that occurs in peroxisomes and
mitochondria (85). It involves the breakdown of long chain fatty acids into acetyl-CoA,
NADH and FADH2 in a cyclical 4-step sequence (85, 86). The NADH and FADH2
subsequently enter the electron transport chain where they produce ATP (Figure 1.2)
(85). Acetyl-CoA enters the Kreb’s Cycle and one of the products is citrate; citrate in the
cytoplasm can generate NADPH (85). NADPH is capable of counteracting oxidative
stress (85); this is important in cancer cells to balance the ROS for cancer cell survival
(87). ROS are highly reactive radicals that are capable of damaging various cellular
components by oxidation reactions (88). This promotes genetic instability so cancer cells
can mutate and ‘evolve’ survival mechanisms more readily (87). ROS are produced by
cancer cells under oxidative stress. Almost all types of cancer are found to have increased
ROS levels which promote tumour development and progression (88).
16
Figure 1.2: Key proteins and reactions in the fatty acid oxidation pathway as described in the text.
Adapted from (85). ACC, acetyl-CoA carboxylase; AMPK, AMP kinase; CPT1, carnitine palmitoyl
transferase 1; TCA, the citric acid cycle.
Fatty acid synthesis is a well-studied topic in cancer research but fatty acid catabolism is
largely ignored. Fatty acids need to be synthesised in proliferating cells to build cell
membranes and various cellular components such as lipid signalling molecules (89).
Tumour cells synthesise a large proportion of their fatty acids de novo regardless of
available dietary lipids (89). Fatty acid synthase (FASN), the key enzyme in fatty acid
synthesis, is expressed at increased levels in cancer cells, including breast cancer, and is
associated with poorer prognosis (90, 91). Fatty acids can therefore be endogenous or
exogenous but both can be used in β-oxidation or synthesis (85, 89, 91-93). This shows
that fatty acids are already an important factor in cancer growth and may have a role not
only in synthesis but also metabolism.
As outlined above research has mostly focused on glycolysis due to the “Warburg Effect”
and its prevalence in tumours. However recent studies have begun to investigate βoxidation in cancer cells. Fatty acid oxidation was observed by Liu in prostate cancer
(82). Low levels of glycolysis are common in prostate cancer and upon further analysis
it was found that prostate cancer cells have increased β-oxidation to provide the ATP for
rapid growth and proliferation (82). Although two-compartment tumour metabolism
17
originally looked at fibroblasts and breast cancer cells it has been extended by Nieman
and colleagues (2011) to adipocytes and ovarian cancer cells (17). They showed that
lipids were transferred to ovarian cancer cells from omental adipocytes via induced
adipocyte lipolysis. This was followed by β-oxidation of the transferred fatty acids in the
mitochondria of the cancer cells (Figure 1.3) (17). They concluded that the fatty acids
provided by adipocytes were used to fuel rapid tumour growth and the associated
migration and invasion that results in metastasis.
Figure 1.3: Summary of metabolic changes that occur in interacting ovarian cancer cells and adipocytes
as described in the text (94). Reprinted with permission from Nature Publishing Group.
This expands our understanding of how adipocytes in the tumour microenvironment may
affect tumour growth and spread. It is possible that β-oxidation is altered in breast cancer
to take advantage of fatty acids released by lipolysis from surrounding adipocytes. Breast
cancer cells may also be signalling the breast adipocytes to release the fatty acids by
lipolysis so they can be used as fuel or building blocks for tumour growth, invasion and
metastasis.
1.4.4 Proteins involved in β-oxidation
1.4.4.1 Acetyl-CoA carboxylase (ACC)
Fatty acid oxidation has various rate-limiting enzymes one of which is acetyl-CoA
carboxylase (ACC); a key regulator of fatty acid oxidation (95, 96). ACC catalyses the
reaction that converts acetyl-CoA to malonyl-CoA which determines levels of fatty acid
synthesis and metabolism (Figure 1.2) (95). Malonyl-CoA is an inhibitor of carnitine
18
palmitoyl transferase 1 (CPT1); an enzyme that moves fatty acids across the
mitochondrial membrane for β-oxidation (86, 97). As a regulatory enzyme in fatty acid
metabolism, ACC is thought to have a role in obesity and may also have a role in cancer
(98).
AMP-activated protein kinase (AMPK) functions in regulating energy balance in cells
(99). This is achieved by sensing cellular ATP levels and altering the rate of ATPsynthesis and ATP-consuming pathways to balance cellular needs (99, 100). This is
important in cell survival and as such AMPK is a critical enzyme that may be altered in
cancer (101). Stress signals from the tumour microenvironment can activate AMPK and
play a role in activating lipid metabolism pathways (101). When AMPK is activated it
phosphorylates and inhibits ACC which reduces malonyl-CoA levels and therefore
increases CPT1 activity; increasing β-oxidation (Figure 1.2) (96, 102). Due to its effects
on ACC, AMPK is a key regulator of fatty acid metabolism and synthesis (101). Both
AMPK and ACC are thought to be linked to obesity, and phosphorylation by leptin and
adiponectin stimulates fatty acid oxidation (96, 103).
1.4.4.2 Carnitine palmitoyl transferase 1 (CPT1)
CPT1 is an important transmembrane protein in fatty acid oxidation (104, 105). It is
located in the outer mitochondrial membrane (106) and creates acylcarnitine by
catalysing the transfer of acyl-CoA from a long-chain acyl-CoA ester to carnitine (86).
Acylcarnitine can be shuttled across the mitochondrial membrane making it available for
β-oxidation in the mitochondrial matrix (86).
There is potential for CPT1 to have an oncogenic role in cells due to its rate limiting role
(104). When CPT1 activity is increased this allows increased movement of fatty acid
products into the mitochondria for β-oxidation (85). CPT1 has already been associated
with an oncogenic effect in breast cancer cells (104). Linher-Melville and colleagues
(2011) showed that breast cancer cells, but not normal breast epithelial cells, stimulated
with prolactin had increased CPT1 expression and activity (104). They suggested that the
cancer cells may use increased CPT1 activity to provide fuel for their high energy
demands. If the surrounding adipocytes are indeed undergoing lipolysis and releasing
free fatty acids, an increase in CPT1 activity could allow the utilisation of this alternate
energy source as a fuel for the cancer cells.
19
1.4.4.3 Mitochondrial glycerol-3-phosphate dehydrogenase (mGPDH)
mGPDH, also known as GPD2, is the simplest component in the glycerophosphate
shuttle (107). It has two alternate isoforms produced by alternative splicing; 81kDa and
68kDa (108). The glycerophosphate shuttle connects the cytoplasmic and mitochondrial
metabolic pathways and allows glycolysis produced NADH to contribute to oxidative
phosphorylation (109). Thus, mGPDH functions at the intersection of oxidative
phosphorylation, glycolysis, and fatty acid oxidation (110). Altering the function of
mGPDH has been shown to affect cell metabolism and energy levels (110). mGPDH can
be regulated by free fatty acids and may be involved in fatty acid oxidation pathways
(110). mGPDH produces ROS which explains why it is suppressed in most tissues but
detected at an increased level in cancer cells (111-113). Prostate cancer cell lines are
found to have elevated mGPDH levels and activity compared to normal prostate
epithelial cell lines (111). Due to the increased glycolysis also observed in these prostate
cancer cells it is hypothesised that mGPDH plays a role in sustaining the high rate of
metabolism required for prostate cancer growth and proliferation (111). It has also been
observed that some breast carcinomas have increased mGPDH activity (114), and thus,
it is a protein of interest in metabolic pathways.
1.5 Summary and hypothesis
Previous research in the Mackenzie Cancer Research Group (MCRG) has shown that
adipocyte breast cancer co-culture leads to increased resistance to chemotherapy agents
(Doxorubicin and Paclitaxel) in both oestrogen receptor positive (MCF7) and hormone
receptor negative (MDA-MB-231) breast cancer cell lines. Furthermore, both MCF7 and
MDA-MB-231 breast cancer cells become more highly motile after co-culture with
human breast adipocytes. However, as yet, nothing is known about breast cancer cell
metabolism after co-culture with human breast adipocytes.
Previous studies in breast cancer have already noted the reverse Warburg effect and
autophagy in the tumour stroma leading to the two-compartment tumour metabolism
model (18, 46, 63). These models have mostly overlooked the presence of adipocytes in
the breast tumour stroma. Only a few studies have investigated adipocyte effects on
cancer cell metabolism, and breast cancer was not one of the examined cancers (17, 82).
No previous study has looked at how the interactions between human breast adipocytes
and breast cancer cells may be affecting cancer cell metabolism. Adipocytes may be
20
providing the fuel for glycolysis and β-oxidation in the breast cancer cells allowing
growth, invasion, and metastasis.
In this project, the aim is to investigate whether adipocytes alter the metabolism of
tumour cells when they are cultured together. This will give greater insight into the
biology of adipocytes and the interactions between breast tumour cells and this largely
neglected stromal cell type. It is hypothesised that breast tumour cells are capable of
activating lipid release by nearby adipocytes, and may metabolise the resulting glycerol
and fatty acids to fuel migration and invasion (Figure 1.4).
Breast cancer cell
Cancer-Associated Adipocyte
Induce
Lipolysis
β-oxidation
lipolysis
Migration
Glycerol,
Invasion
Fatty acids
Figure 1.4: Hypothesis of breast cancer-adipocyte interactions. The induction of lipolysis in cancerassociated adipocytes by breast cancer cells results in release of nutrients. Glycerol and fatty acids can then
be up taken by the breast cancer cells and used to fuel migration and invasion.
1.6 Aims and objectives
To observe metabolism in breast tumour cells and look at potential changes when the
breast tumour cells are co-cultured with adipocytes. Specific objectives are outlined
below:
1. Tumour cell metabolism will be characterised in breast cancer cell lines MCF7
(ER+) and MDA-MB-231 (ER-/PR-/HER2-). Furthermore tumour cell
metabolism will be investigated in MCF7 and MDA-MB-231 cells grown in
adipocyte co-culture. This will investigate if breast cancer cells are using glycerol
and fatty acids for tumour cell glycolysis, β-oxidation and mitochondrial
metabolism.
2. β-oxidation of fatty acids may be an important source of energy for tumour cell
migration and invasion. This will be analysed by determining the levels of tumour
cell cytosolic and mitochondrial proteins that are involved in metabolism.
21
3. Adipocytes and breast tumour cells will be co-cultured, and adipocyte-derived
glycerol and free fatty acids will be measured in the resulting cell culture media.
This will also allow analysis of whether adipocytes are induced to undergo
lipolysis by breast tumour cells.
Due to time restrictions in this project, these objectives were reduced. Alterations are
listed below:
1. Investigation of tumour cell metabolism in cells grown in adipocyte co-culture
will only be undertaken in MCF7 cells.
2. Only adipocyte-derived glycerol will be measured in the adipocyte-breast cancer
cell co-culture media.
22
2 Materials and Methods
2.1 Breast adipose tissue samples and processing
2.1.1 Collection of patient samples
Breast adipose tissue samples were collected from female patients at Christchurch
Hospital undergoing surgery for therapeutic mastectomy, prophylactic mastectomy and
breast reductions. Ethical approvals were obtained for banking adipose tissue via the
Cancer Society Tissue Bank Christchurch (Upper South Ethics Committees, 05/11/09),
and collection of human adipose tissue for short-term culture experiments (UO Human
Ethics Approval 12/319). All patients gave their informed written consent. Patients were
from 25-85 years of age. A total of 15 adipose tissue samples were collected over the
duration of this study (April – August, 2015).
2.1.2 Isolation of pre-adipocytes
Protocol adapted from Lee et al. (2005) (115). Adipose tissue samples were processed by
removal of fibrous and vascular areas. The adipose tissue was minced, before digestion
by collagenase (1 mg/ml in HBSS). Collagenase was used at a ratio of 1ml collagenase
to 1 g adipose tissue. Collagenase mix was incubated at 37°C for 2 hours with shaking
every 15 minutes. An equal amount of pre-adipocyte media (Appendix 5.1.1) was added
to collagenase-adipose mix. Sample had fibrous tissue removed using a metal sieve and
was then transferred to a 50 ml tube (BD FalconTM) and centrifuged for 10 minutes at
2500 x g (Multifuge® 1 S-R, Heraeus) at room temperature. Top fraction was discarded;
this left the remaining pre-adipocyte fraction as a pellet. The pellet was resuspended in
10 ml pre-adipocyte media and sieved using a cell sieve (Cell strainer 70µm nylon, BD
Falcon™) into a new 50 ml tube. Sample was spun again for 10 minutes at 2500 x g at
room temperature. Supernatant was discarded and cell pellet resuspended in 1 ml of preadipocyte media per 0.6 g adipose tissue. Suspension was plated 1 ml/well into 12-well
CellBIND® plates (Costar®, Corning) and incubated at 37°C with 5% CO2 (Function
Line, Heraeus).
2.1.2.1 Removal of red blood cells
The day after plating, 1 ml of 1X Phosphate Buffered Saline (PBS) (Appendix 5.2.1) was
added to each pre-adipocyte well. PBS was pipetted across the bottom of the well to
23
remove red blood cells. Media was then removed and replaced with 1X PBS and the wash
was repeated. PBS was removed and replaced with 1 ml/well pre-adipocyte media.
2.1.3 Culture of pre-adipocytes
Pre-adipocytes were grown in pre-adipocyte media which was changed every 2-3 days.
They were observed using light microscopy (CK40, Olympus) until they reached 100%
confluence. Differentiation treatment began two days after confluence was observed.
Pre-adipocyte media was replaced with 1ml/well serum free media (Appendix 5.1.2) 24
hours prior to co-culture.
2.1.4 Differentiation of pre-adipocytes into mature adipocytes
Differentiation of pre-adipocytes to mature adipocytes took 14-21 days. Serum free
media was removed and collected (section 2.6.1). Pre-adipocytes were differentiated for
5 days in 1 ml/well differentiation media (Appendix 5.1.3), which was then replaced with
1 ml/well differentiation media minus IBMX (Appendix 5.1.3) for a further 5 days. This
media was subsequently replaced with 1 ml/well maintenance media (Appendix 5.1.4)
which was renewed every 3-4 days. Cells were monitored for maturation by light
microscopy. Maturation was observed as lipid droplets accumulating and filling the
adipocytes (Supplementary Figure 5.1). Typically, cells reached maturation at
approximately 7 days after maintenance media was added, and were considered ready for
experiments when mature adipocyte confluence was >30% of the plate area.
2.2 Breast cancer cells
The human breast cancer cell lines MCF7 and MDA-MB-231 were obtained from
American Tissue Culture Collection (Cryosite Distribution Pty. Ltd., Australia). The
MCF7 cell line (ATCC® HTB-22™) is an oestrogen receptor positive (ER+) human
breast epithelial cell adenocarcinoma that was acquired from a 69 year old Caucasian
female. The MCF7 cell lines is classified as molecular subtype luminal A and is more
receptive to hormonal treatment due to its ER+ status (116). The MDA-MB-231 cell line
(ATCC® CRM-HTB-26™) is commonly referred to as a triple negative breast cancer
cell line. It is an oestrogen, progesterone, and human epidermal growth factor receptor
negative (ER-/PR-/HER2-) human breast epithelial cell adenocarcinoma acquired from
a 51 year old Caucasian female, and is classified as claudin-low (116). Claudin-low cell
lines have mesenchymal and stem cell-like features that cause attraction of stromal cells
into the microenvironment (35). Hormonal treatment is less effective in MDA-MB-231
24
due to its triple negative hormone receptor status (116). These cell lines were chosen
because of these different hormone receptor profiles. In summary, MDA-MB-231 is a
very invasive and aggressive cell line in comparison to MCF7 (117-119).
Cell lines were grown in BD FalconTM tissue culture flasks (75 cm2 and 25 cm2) in breast
cancer cell media (Appendix 5.1.5) and incubated at 37°C with 5% CO2. Cells were
stored in liquid nitrogen until used.
2.2.1 Passage
Cells were passaged every 2-3 days up to passage 30 to minimise the effects of
phenotypic drift on the cell lines. Media was removed from the flask and cells washed
with 1X PBS before addition of TrypLE (TrypLE™ Express, Gibco®, Thermo Fisher
Scientific). Cells were incubated with TrypLE at 37°C for 3-5 minutes and gently agitated
at which stage they were observed to be detached from the flask. Cells were then
transferred to a 15 ml tube and spun for 3 minutes at 500 x g (Centrifuge 5702,
Eppendorf). Supernatant was discarded and cells resuspended in breast cancer cell media.
Appropriate quantities of cells were added to a new 25 cm2/75 cm2 tissue culture flask
with 6 ml/12 ml of media respectively. MCF7s were split 1/6 and MDA-MB-231s 1/8.
2.3 Co-culture of mature adipocytes and breast cancer cell lines
2.3.1 Day 1
Protocol adapted from Dirat et al. (2011) (15). Maintenance media on mature adipocytes
was replaced with 1ml/well serum free media (Appendix 5.1.2) 24 hours prior to coculture. Transwell inserts (Transwell® permeable supports 0.4 µm polyester membrane
12 mm insert, Costar®, Corning) were soaked in serum free media at 37°C for ≥1 hour.
Mature adipocytes to be used were photographed for the record; examples can be seen in
Appendix 5.3.1. Serum free media on mature adipocytes was removed and collected
(section 2.6.1) and replaced with 1 ml/well serum free media. Breast cancer cells were
trypsinised and counted using a haemocytometer before being diluted to desired
concentrations in serum free media: MCF7 2.4x105 cells/ml, MDA-MB-231 2x105
cells/ml. Serum free media was removed from Transwell inserts and inserts were gently
placed into mature adipocyte wells. Breast cancer cells were added in 500 µl amounts to
inserts at a final concentration of MCF7 1.2x105 cells/insert, MDA-MB-231 1x105
cells/insert. Co-culture plates were then incubated at 37°C with 5% CO2 for 3 days.
25
2.3.2 Day 4
Serum free media was removed from Transwell inserts and mature adipocyte wells and
collected (section 2.6.1). Transwell inserts were moved to a new 12-well plate (Falcon)
and 200 µl TrypLE was added to each insert. After 3-5 minute incubation at 37°C breast
cancer cells were collected and spun at 500 x g for 3 minutes. Supernatant was discarded,
cells were resuspended in 1X PBS and the centrifuge was repeated. Cells were then
treated for use in either Seahorse experiments (section 2.4) or Western blotting (section
2.5). Mature adipocytes were washed twice with 1X PBS before addition of 1ml/well
serum free media. Mature adipocytes were photographed to record any visible changes.
Mature adipocytes were incubated at 37°C with 5% CO2 for 24 hours.
2.3.3 Day 5
Serum free media was removed from mature adipocyte wells and collected (section
2.6.1).
2.4 Seahorse XF24 Extracellular Flux Analyser
XF24 Extracellular Flux Analyser (Seahorse Biosciences) was used to assay
mitochondrial function and fatty acid utilisation of intact cells. The XF analyser uses
solid state probes embedded on a sensor plate to detect the concentration of oxygen and
protons in the media of cells cultured in a complementary microplate. Repeat
measurements (typically 2 – 5 minutes) are made in which the sensor cartridge is lowered
to create a sealed microchamber (7 µl) above the cell monolayer. The XF analyser then
measures oxygen consumption rate (OCR) as picomoles per minute (pmol/min) and
extracellular acidification rate (ECAR) as milli pH per minute (mpH/min). These
measures give indications of oxidative respiration and glycolysis, respectively. After
each measurement, the sensor cartridge is lifted allowing cells to re-equilibrate in the
excess volume of media before the next measurement cycle. The non-destructive
measurements made by the XF system allow for measuring over extended periods of time
and with the ability to use cells for post-XF analysis assays such as total protein
determination. Additionally an integrated drug port delivery system allows for the
sequential injection of up to four compounds to cell wells during assays to create
metabolic profiles (120, 121).
26
2.4.1 Optimisation for XF24
To begin work with the new cell lines MCF7 and MDA-MB-231 on the Seahorse XF24
Extracellular Flux Analyser, optimisation procedures must be undertaken. Cell seeding
density was optimised to produce an even monolayer of cells at the bottom of an XF 24well plate. MCF7 and MDA-MB-231 cells were grown to 80% confluence before being
seeded in triplicate at densities ranging from 1x104 – 5x105 cells/well in an XF 24-well
plate; the seeding density range was determined from previous literature (122-129). Cells
were grown overnight (20 hours) at 37°C with 5% CO2 and their confluence was then
evaluated using light microscopy (section 3.1.1). This followed the established protocol
for non-adherent cells published by Seahorse Biosciences (130). The concentrations of
drugs to be injected during the mitochondrial stress test (section 2.4.2) were optimised in
each cell type for maximal effect (section 3.1.1).
2.4.1.2 MTT assay
A parallel experiment was run in a standard 96-well plate (same growth surface area as
an XF 24-well plate) to confirm uncompromised viability at various densities. Cells in
the 96-well plate (section 2.4.1) underwent an MTT assay to measure cell viability at
various seeding densities to ensure cells were growing optimally Media was removed
from all wells and replaced with 100 µl MTT assay media (Appendix 5.1.6) and the plate
was incubated at 37°C for 4 hours. Following this, 100 µl of solubilisation solution
(Appendix 5.2.3) was added to each well and pipetted up and down. Absorbance was
measured at 544 nm in Victor3 (1420 multilabel counter, Perkin Elmer™).
2.4.2 Mitochondrial stress test
The mitochondrial stress test is designed to interrogate the respiratory capacity of
mitochondria enabling the investigator to create a bioenergetic profile of the cells of
interest. Electron transport chain activity is disrupted through sequential addition of
oligomycin
(ATP
synthase
inhibitor),
FCCP
(carbonyl
cyanide-p-
trifluoromethoxyphenylhydrazone) (uncoupler), and antimycin-A (complex III inhibitor)
with corresponding changes in OCR thereby, enabling the calculation of bioenergetic
parameters that can be compared between cell lines or control and treatment groups
(section 3.1) (121).
Sensor cartridges were hydrated with 1 ml/well calibrant solution (Seahorse Biosciences)
in a 37°C non-CO2 incubator overnight. MCF7 and MDA-MB-231 cells were seeded at
27
4x104 cells/well for both cell lines in 100 µl breast cancer cell media in an XF 24-well
plate. Co-cultured MCF7 cells were seeded at 4x104 – 6x104 cells/well in 100 µl serum
free media in an XF 24-well plate. This increase in seeding density was to account for
differences produced by co-culture which resulted in slower adherence and growth.
Seeded plates were left at room temperature for 30-60 minutes before incubation at 37°C
with 5% CO2 to aid even adherence. After 4 hours of incubation at 37°C, 150 µl/well of
additional media was applied before overnight incubation at 37°C with 5% CO2. After
24 hours the seeded cells underwent media transfer and 600 µl of mitochondrial stress
test assay media (Appendix 5.1.7) remained per well. The plate was then incubated at
37°C in a non-CO2 incubator for 30 minutes. Compounds oligomycin (Sigma-Aldrich),
FCCP (Sigma-Aldrich), and antimycin-A (Sigma-Aldrich) were prepared for injection.
Oligomycin was prepared to a final concentration of 1 µM after injection. FCCP was
prepared to a final concentration of 0.5 µM for MCF7 or 1 µM for MDA-MB-231 after
injection. Antimycin-A was prepared to a final concentration of 10 µM after injection
(section 2.4.1 and 3.1.1 for drug optimisation). Injection ports were loaded with the
appropriate compounds and unused ports were loaded with mitochondrial stress test
assay media. The plate was loaded into the Seahorse XF machine and the following
protocol was run (Table 2.1):
Table 2.1: Mitochondrial stress test protocol
Command
Calibrate
Mix, Wait, Measure
Inject Oligomycin
Mix, Wait, Measure
Inject FCCP
Mix, Wait, Measure
Inject Antimycin-A
Mix, Wait, Measure
Number of loops
Times (minutes)
7
3, 2, 3
3
3, 2, 3
3
3, 2, 3
3
3, 2, 3
2.4.3 Normalisation of XF data
Total protein content of cells in each well of a XF 24-well plate was quantified using a
BCA protein assay. A standard curve of bovine serum albumin (BSA) was prepared using
a dilution series in 1X PBS creating a working range of 0-2000 µg/ml. BSA curve and
controls were pipetted in 25 µl aliquots into a 96-well plate in duplicate. XF 24-well plate
had media removed and 25 µl lysis buffer (Appendix 5.2.4) was added to each well.
Working reagent was prepared at 50:1 (reagent A: reagent B); 200 µl was added to each
well on the 96-well plate and the XF 24-well plate. Plates were mixed on shaker for 30
28
seconds, protected from light, and incubated at 37°C for 30 minutes. XF 24-well plate
samples were transferred to the 96-well plate. Absorbance was measured at 544 nm using
Victor3 plate reader. The total protein content of each well was calculated and then
entered into the normalisation function of the Seahorse Wave Desktop 2.2 software.
2.4.4 Fatty acid oxidation test optimisation
The fatty acid oxidation test is designed to determine the ability of cells to oxidise
endogenous and exogenous fatty acids. It follows the same injection profile as the
mitochondrial stress test during the assay. Prior to the assay wells are treated with
palmitate and etomoxir (Sigma-Aldrich). This allows cells to be driven to oxidise
exogenous and endogenous fatty acids and the contribution to respiration can be
calculated (section 3.1.6) (131).
Sensor cartridges were hydrated with 1 ml/well calibrant solution in a 37°C non-CO2
incubator overnight. Cells were seeded at 4x104 cells/well for both MCF7 and MDAMB-231 cells in 100 µl substrate limited media (Appendix 5.1.8) in an XF 24-well plate.
After 4 hours of incubation at 37°C, 150 µl/well of substrate limited media was applied
before overnight incubation. After 24 hours the seeded cells underwent media transfer
and 495 µl of FAO assay media (1X) (Appendix 5.1.9) remained as final volume per
well. The plate was then incubated at 37°C in a non-CO2 incubator for 30 minutes.
Compounds oligomycin, FCCP, and antimycin-A were prepared for injection.
Oligomycin was prepared to a final concentration of 1 µM after injection. FCCP was
prepared to varying concentrations (section 2.4.3.1) after injection. Antimycin-A was
prepared to a final concentration of 10 µM after injection. Injection ports were loaded
with the appropriate compounds and unused ports were loaded with FAO assay media
(1X). Directly before loading, 105 µl of BSA or BSA:palmitate was applied to selected
wells. The plate was loaded into the Seahorse XF machine and the following protocol
was run (Table 2.2):
29
Table 2.2: Fatty acid oxidation stress test protocol
Command
Calibrate
Mix, Wait, Measure
Inject Oligomycin
Mix, Wait, Measure
Inject FCCP
Mix, Wait, Measure
Inject Antimycin-A
Mix, Wait, Measure
Number of loops
Times (minutes)
5
3, 2, 3
3
3, 2, 3
3
3, 2, 3
3
3, 2, 3
2.4.3.1 Optimisation of FCCP for fatty acid oxidation stress test
The fatty acid oxidation stress test was carried out with final experiment levels of BSA
or BSA:palmitate but with no etomoxir for MCF7 cells over a range of FCCP
concentrations (0.5 µM, 1 µM, 1.5 µM, 2 µM, 3 µM, 4 µM, 5 µM). The results were then
analysed to select a FCCP concentration (section 3.1.6.2).
2.4.3.2 BSA:palmitate conjugation
BSA:palmitate conjugation must be undertaken so that palmitate, the fatty acid, can be
taken up by the cells. Conjugation was completed according to Seahorse Bioscience
protocol (132). Firstly 226.7 mg fatty acid poor BSA (Gibco®, Thermo Fisher Scientific)
was added to 10 ml NaCl (150 mM) in a pre-warmed beaker with stir bar. This beaker
was covered with parafilm and placed in a water bath heated to 37°C and stirred till BSA
was completely dissolved. The BSA was then filtered and 5 ml was diluted with an
additional 5 ml NaCl (150 mM) to create a 0.17 mM stock. This stock was aliquoted into
glass vials and stored at -20°C. The remaining 5 ml was kept at 37°C for the next part of
the conjugation. Next, 30.6 mg of sodium palmitate (Sigma-Aldrich) was added to 44 ml
NaCl (150 mM) in an Erlenmeyer flask. This was covered with parafilm and placed in a
water bath and gradually heated to 70°C. For conjugation 4 ml of palmitate solution was
transferred to the BSA solution at 37°C. This was held at 37°C and stirred constantly for
1 hour. Final solution was adjusted to 10 ml with NaCl (150mM) and adjusted to pH 7.4.
This created a 0.17 mM BSA:1mM palmitate conjugation. This was aliquoted into glass
vials and stored at -20°C.
30
2.5 Western Blot
2.5.1 Cell lysis
2.5.1.1 MCF7 and MDA-MB-231 cells cultured alone
Breast cancer cells had media removed and were washed twice with 1X PBS. Cells were
moved to ice and lysed with lysis buffer. Cells were scraped off the plastic using a
modified Pasteur pipette. Lysate was transferred to pre-chilled tubes and rotated at 4°C
for 10 minutes. Samples were then sonicated on ice with the probe sonicator (OmniRuptor 4000, OMNI International Inc.) using 3 pulses at power 30. Samples were spun
at 18000 x g (Centrifuge 5417R, Eppendorf) for 10 minutes at 4°C. The supernatant was
then transferred to a pre-chilled tube and stored immediately at -20°C.
2.5.1.2 MCF7 and MDA-MB-231 cells obtained from adipocyte co-culture
Breast cancer cell pellets obtained from co-culture (section 2.3.2) were resuspended in
lysis buffer and rotated at 4°C for 10 minutes. Samples were then sonicated on ice with
the probe sonicator using 3 pulses at power 30. Samples were spun at 18000 x g for 10
minutes at 4°C. The supernatant was then transferred to a pre-chilled tube and stored
immediately at -20°C. Final co-culture samples for MCF7 and MDA-MB-231 were
cultured with three biologically separate adipocyte samples.
2.5.2 Protein quantification
Total protein content of samples was quantified using a BCA protein assay. A standard
curve of BSA was prepared using a dilution series in 1X PBS creating a working range
of 0-2000 µg/ml. BSA curve, controls and unknown samples were pipetted in 25 µl
aliquots into a 96-well plate in triplicate. Working reagent was prepared at 50:1 (reagent
A: reagent B) and 200 µl was added to each well. Plate was mixed on shaker for 30
seconds, protected from light, and incubated at 37°C for 30 minutes. Absorbance was
measured at 544 nm in Victor3.
2.5.3 SDS-PAGE
Samples were prepared so that 5-20 µg of protein was loaded per lane. From each sample
50 µl (2 mg/ml) was added to 50 µl reducing sample loading buffer (Appendix 5.2.6).
Samples were heated to 70°C for 10 minutes, cooled to room temperature, and mixed by
vortex. Gels were NuPage 4-12% 12-well (Novex). Control ladders used were SeeBlue®
Plus2 standard (Novex) and Precision Plus Protein™ standard (Bio-Rad). Precision Plus
31
Protein™ standard was used for gels that were undergoing Western blotting for large
proteins (265-280kDa) as the ladder has a larger band to compare to. Samples were
loaded into the gel and run in running buffer (Appendix 5.2.7). Gels that were blotted for
large proteins were run at 180V for 100 minutes. Gels that were blotted for small-medium
proteins were run at 160V for 70 minutes.
2.5.4 Transfer
Proteins were transferred onto PVDF membranes. The membrane was pre-soaked in
methanol for 1 minute, rinsed in H2O and soaked in transfer buffer (Appendix 5.2.8). The
Western blot was run at 25V for 60 minutes. The membrane was blocked for 1 hour with
5% w/v low fat milk in 1X TBS-T (Appendix 5.2.9) (for anti-CPT1A, anti-GPD2, antiβ-actin) or in 5% w/v BSA in 1X TBS-T (for anti-ACC1 and anti-phospho-ACC).
Membranes were then cut at 50kDa; top sections were probed for CPT1A, ACC1,
phospho-ACC or GPD2, bottom sections were probed for β-actin.
2.5.5 Antibodies
Antibodies were optimised with preliminary experiments to obtain the final
concentrations. Primary antibodies (Table 2.3) were applied to the membrane in 10 ml
quantities and incubated overnight at 4°C with gentle shaking. Primary antibodies were
kept at -20°C and reused up to 5 times. Membranes were equilibrated to room
temperature and washed in 1X TBS-T for 10 minutes; this was repeated 3 times.
Secondary antibodies (Table 2.3) were diluted in 1X TBS-T and applied to the membrane
in 10 ml quantities. Membranes were incubated with secondary antibodies at room
temperature for 1 hour.
32
Table 2.3: Primary and secondary antibody concentrations used in Western blotting
Primary
antibody
Antibody
Concentration
(catalogue
number)
Diluent (in
1X TBS-T)
Manufacturer
Secondary
Antibody
(catalogue
number)
Manufacturer
CPT1A
mouse monoclonal
antihuman/mouse/rat
1:5000 (ab128568)
5% w/v low
fat milk
Abcam
goat polyclonal
anti-mouseHRP 1:15000
(P044701)
Dako
GPD2
rabbit monoclonal
anti-human 1:1000
(ab182144)
5% w/v low
fat milk
Abcam
goat polyclonal
anti-rabbit-HRP
1:5000 (7074)
Cell Signaling
Technologies
ACC1
rabbit monoclonal
antihuman/mouse/rat
1:1000 (4190)
5% w/v BSA
Cell Signaling
Technologies
goat polyclonal
anti-rabbit-HRP
1:5000 (7074)
Cell Signaling
Technologies
PhosphoACC
rabbit monoclonal
antihuman/mouse/rat
1:1000 (11818)
5% w/v BSA
Cell Signaling
Technologies
goat polyclonal
anti-rabbit-HRP
1:5000 (7074)
Cell Signaling
Technologies
β-actin
mouse monoclonal
anti-β-actin
1:20000 (A5441)
5% w/v low
fat milk
Sigma-Aldrich
goat polyclonal
anti-mouseHRP 1:15000
(P044701)
Dako
2.5.6 Detection
Membranes were washed in 1X TBS-T for 10 minutes; this was repeated 3 times.
Detection kit was AmershamTM ECLTM Prime Western Blotting Detection Reagent (GE
Healthcare Life Sciences) following manufacturer instructions. This was followed by
visualisation in the Alliance 4.7 (UVItec, Cambridge).
2.5.7 Re-probing
After detection, membranes were washed in 1X TBS-T for 10 minutes. They were then
stripped with mild stripping buffer (Appendix 5.2.10) for 5 minutes; this was repeated 2
times. Membranes were washed in 1X TBS-T for 5 minutes; this was repeated 2 times.
Membranes were then washed in 1X TBS-T for 10 minutes; this was repeated 2 times.
Membranes were then blocked (section 2.5.4), antibodies applied (section 2.5.5), and
detection visualised (section 2.5.6). Membranes were stripped a maximum of 3 times.
Proteins of interest were always probed for first, followed by β-actin.
33
2.6 Glycerol assay
2.6.1 Sample collection
Media was collected from co-culture experiments at four time points: before
differentiation, before co-culture, after 3 day co-culture, and 24 hours after co-culture.
These conditioned media will henceforth be referred to as: pre-adipocyte, mature
adipocyte, post co-culture, and 24 hours post co-culture, respectively. Post co-culture
media was taken from both the Transwell inserts and the main well for combined
analysis. All conditioned media were centrifuged at 500 x g for 5 minutes and 1 ml was
collected and stored at -80°C.
Pre-adipocyte conditioned media was collected from five different biological adipocyte
cultures. Mature adipocyte conditioned media was collected from seven different
biological adipocyte cultures. Post co-culture and 24 hours post co-culture conditioned
media samples were collected from seven different biological adipocyte co-cultures with
MCF7 cells and three different biological adipocyte co-cultures with MDA-MB-231
cells.
2.6.2 Detection
Glycerol detection was performed using a free glycerol assay kit (Abcam). A standard
curve for glycerol was prepared creating a working range of 0-200 µM. Samples, control,
and standard curve were pipetted in 50 µl amounts into a 96-well plate in duplicate. Assay
buffer, glycerol probe and glycerol enzyme mix were combined in the ratio 23:1:1 to
create a reaction master mix. This was pipetted to create a 1:1 ratio of sample:reaction
mix in standard and sample wells. The plate was then protected from light and incubated
for 30 minutes at 37°C. Absorbance was measured at 544 nm in Victor3.
2.7 Statistical analysis
All final analyses were performed using Microsoft Excel and the add-in, Data Analysis.
2.7.1 Seahorse XF analyser
Data was gathered using Wave Controller 2.2 on the instrument. It was then analysed
using Wave Desktop 2.2 and report generators; XF Mito Stress Test Report Generator
V2 and XF Cell Energy Phenotype Test Summary Report Generator V2. Before analysis
was conducted samples were normalised (section 2.4.3). MCF7 and MDA-MB-231
sample sets had their variance compared using F-tests. Sample sets of equal variance
34
were then tested for significance using an independent two-sample t-test assuming equal
variances. Sample sets with unequal variance were tested for significance using an
independent two-sample t-test assuming unequal variance. MCF7 cells grown alone were
compared to adipocyte co-cultured MCF7 using a paired two-sample t-test for means.
Significance was assumed when p<0.05.
2.7.2 Western blotting
Western blot band density was calculated using ImageJ. Density was then normalised to
β-actin before analysis. MCF7 cells and adipocyte co-cultured MCF7 cells had their
sample set variance compared using F-tests. Sample sets of equal variance were then
tested for significance using an independent two-sample t-test assuming equal variances.
Sample sets with unequal variance were tested for significance using an independent twosample t-test assuming unequal variance. Significance was assumed when p<0.05.
2.7.3 Glycerol Assay
Data was averaged across multiple experiments before analysis by one-way ANOVA.
35
3 Results
3.1 Analysis of metabolism in breast cancer cells and breast cancer cells cocultured with adipocytes using the Seahorse XF24 Extracellular Flux Analyser
Cell lines were firstly optimised for use in the XF 24-well plates and for the
mitochondrial stress test. The mitochondrial stress test was then carried out in MCF7
cells (n=2), MDA-MB-231 cells (n=4), and co-cultured MCF7 cells (n=2) and the results
compared. Preliminary optimisation assays also began on the fatty acid oxidation stress
test for MCF7 cells.
3.1.1 Optimisation for MCF7 and MDA-MB-231 cell lines on the Seahorse
XF24 Extracellular Flux Analyser
Cell seeding density was optimised in XF24 plates to ensure an even monolayer of cells
with typical morphology (Figure 3.1: A), uncompromised viability, and OCR and ECAR
rates within instrument recommendations. Cells seeded at 5x105/well and 1x105/well for
MCF7 and MDA-MB-231 were too dense; cells were at >100% confluence and
beginning to layer upon each other. However, MCF7 and MDA-MB-231 cells seeded at
2x104/well and 1x104/well were too sparse. They displayed abnormal morphology and
some cells were not adhering (Supplementary Figure 5.2). MCF7 and MDA-MB-231
cells were also observed to be denser in the centre of the well than at the edge, for all
densities (Supplementary Figure 5.3). To improve cell adherence and evenness of
plating, Cell-Tak was trialled. Both MCF7 and MDA-MB-231 cells showed abnormal
morphology when adhered using Cell-Tak. MCF7 cells had altered morphology with a
mostly spherical appearance instead of the usual cobblestone appearance. MDA-MB-231
cells also did not have the expected elongated shape and were instead spherical (Figure
3.1: B). Cellular morphology was judged to be more important than adherence; Cell-Tak
was not used for subsequent experiments. To achieve correct morphology and even
plating, technique was modified. Cells were pipetted up and down in each well for even
dispersal and after seeding, 24-well plates were left at room temperature for 30-60
minutes before incubation at 37°C. Cells were observed to be at ~85% confluency when
plated at 4x104 cells/well for both cell lines and had more typical morphology (Figure
3.1: C). Results from an MTT assay concluded that neither MCF7 nor MDA-MB-231
cells were negatively affected when grown at this density (Supplementary Figure 5.4).
Oxygen consumption rate of both cell lines under basal conditions seeded at 4x10 4
36
cells/well, was reviewed. MCF7 cells seeded at 4x104 cells/well produced an average
basal OCR of 228 pmol/min and an average basal ECAR of 27 mpH/min. MDA-MB231 cells seeded at 4x104 cells/well produced an average basal OCR of 259 pmol/min
and an average basal ECAR of 26 mpH/min. The XF output for both cell types fell within
the basal ranges recommended by Seahorse Bioscience for OCR (50-400 pmol/min) and
ECAR (20-120 mpH/min), respectively (133).
37
MDA-MB-231
Standard seeding 80% confluence
MCF7
A)
C)
Final XF 24-well seeding 4 x 104
Cell-Tak seeding 5 x 104
B)
Figure 3.1: Comparison of MCF7 and MDA-MB-231 cell seeding using representative 10X photographs.
A) Standard culture flask seeding of MCF7 and MDA-MB-231 at 80% confluence. B) Cell-Tak seeding of
MCF7 and MDA-MB-231 at 5x104 cells/well in an XF 24-well plate. C) Final seeding of MCF7 and MDAMB-231 in an XF 24-well plate at 4x104 cells/well.
38
Next, the response of each cell type to drugs used in the mitochondrial stress test was
optimised. Oligomycin is a drug which prevents ATP synthesis by inhibiting ATP
synthase (Figure 3.2) (121). This can be visualised on the XF analyser as a drop in OCR
after the first injection (Figure 3.3: A, C). Previous literature indicated that oligomycin
concentration required for maximal ATP synthase inhibition in MCF7 and MDA-MB231 cell lines was 1 µM (123, 128, 129, 134, 135). This was therefore chosen for both
cell lines.
Figure 3.2: Pictorial representation of the changes in the electron transport chain due to oligomycin, FCCP
and antimycin-A. Adapted from (136).
The second drug injected is FCCP. FCCP collapses the mitochondrial membrane
potential by allowing uninhibited proton movement across the mitochondrial membrane
(Figure 3.2) (120). Oxygen is maximally consumed by complex IV as complexes in the
electron transport chain attempt to maintain the proton gradient across the inner
membrane (Figure 3.3: A, C) (136). Cells vary in their sensitivity toward FCCP and
therefore the concentration is optimised for maximum response in OCR before cytotoxic
levels are reached (137). Mitochondrial stress tests were carried out for both cell lines
with a range of FCCP concentrations (0.1 µM, 0.3 µM, 0.5 µM, 0.75 µM, 1 µM, 2 µM).
Optimal FCCP concentration was selected for MCF7 as 0.5 µM and for MDA-MB-231
as 1 µM (Figure 3.3: B, D).
The last drug injected is Antimycin-A which inhibits complex III, preventing
mitochondrial respiration (Figure 3.2) (121). This can be seen as a steep decrease in OCR
(Figure 3.3: A, C).Antimycin A-independent oxygen consumption will therefore be due
to non-mitochodrial sources. This was optimised based on literature; a concentration of
10 µM was chosen for MCF7 and MDA-MB-231 cells (124).
39
B)
500
400
400
300
300
OCR (%)
500
200
MCF7
OCR (%)
A)
200
100
100
0
0.00
0.1
0
50.00
100.00
Time (minutes)
0.5
0.75
150.00
1
0
0.5
1
1.5
2
FCCP concentration (µm)
2.5
0
0.5
2.5
1.5
FCCP (µm)
C)
D)
800
800
700
600
600
MDA-MB-231
OCR (%)
OCR (%)
500
400
400
300
200
200
100
0
0.00
0
50.00
100.00
150.00
Time (minutes)
0.5
0.75
1
1.5
2
FCCP concentration (µm)
1
1.5
FCCP (µm)
Figure 3.3: FCCP optimisation of MCF7 and MDA-MB-231 breast cancer cell lines.
A) MCF7 OCR graph during FCCP optimisation. B) FCCP optimisation of MCF7 (n=2). C) MDA-MB231 OCR graph during FCCP optimisation. D) FCCP optimisation of MDA-MB-231 (n=2).
Data is presented as mean ± SD with OCR measured as % baselined to data point 10 (before FCCP
injection).
40
3.1.2 Metabolic characterisation of MCF7 and MDA-MB-231 breast cancer cells
The mitochondrial stress test uses injected drugs to knock out components of the electron
transport chain, which creates measurable differences that can be attributed to various
metabolic parameters. Through sequential injection of oligomycin, FCCP, and
antimycin-A the mitochondrial stress test generates measures of basal respiration, ATPlinked respiration, proton leak, maximal respiration, spare capacity, and nonmitochondrial respiration (Figure 3.4). The calculations to determine these values are
included in Table 3.1.
Figure 3.4: Representation of a typical mitochondrial stress test, identifying the fractions attributed to
various metabolic parameters (138).
41
Table 3.1: Calculations to determine metabolic parameters of the mitochondrial stress test (138, 139).
Metabolic Parameter
Calculation
Non-mitochondrial Respiration
minimum rate measurement after antimycin-A injection
Basal Respiration
(last rate measurement before first injection) – (nonmitochondrial respiration)
Maximal Respiration
(maximum rate measurement after FCCP injection) – (nonmitochondrial respiration)
Proton Leak
(minimum rate measurement after oligomycin injection) –
(non-mitochondrial respiration)
ATP-linked Respiration
(last rate measurement before oligomycin injection) –
(minimum rate measurement after oligomycin injection)
Spare Respiratory Capacity
(maximal respiration) – (basal respiration)
Spare Respiratory Capacity (%)
maximal respiration
x 100
basal respiration
Coupling Efficiency (%)
ATP-linked respiration rate
x 100
basal respiration rate
Baseline OCR
last OCR rate measurement before oligomycin injection
Baseline ECAR
last ECAR rate measurement before oligomycin injection
Stressed OCR
maximum OCR rate measurement after FCCP injection
Stressed ECAR
maximum ECAR measurement after oligomycin injection
42
Data for MCF7 was obtained from two biological samples run on two separate XF plates
(n=2) and data for MDA-MB-231 was obtained from four biological samples run on two
separate XF plates (n=4). There were 6-10 replicate wells per sample. Presented data has
been normalised to µg protein (pmol/min/µg protein or mpH/min/µg protein).
MCF7 cells had significantly higher basal respiration than MDA-MB-231 cells (1.15 vs
0.77, respectively; p=0.005). MCF7 cells also had higher maximal respiration than
MDA-MB-231 cells (2.00 vs 1.51, respectively; p=0.011) (Figure 3.5: A). Assessment
of oxygen consumption following inhibition of the ATP synthase with oligomycin
indicates that the MCF7 cells are generating higher amounts of ATP than MDA-MB-231
cells (0.88 vs 0.66, respectively; p=0.012). MCF7 cells however, had higher indication
of proton leak than MDA-MB-231 cells (0.28 vs 0.11, respectively; p=0.001). In line
with this, the coupling efficiency of MDA-MB-231 cells was significantly higher than
MCF7 cells (0.85 vs 0.76, respectively; p<0.001) (Figure 3.5: D). Non-mitochondrial
respiration did not differ between MCF7 cells and MDA-MB-231 cells (0.18 vs 0.22,
respectively; p=0.700). There was no significant difference in spare respiratory capacity
between MCF7 and MDA-MB-231 cells (0.84 vs 0.74, respectively; p=0.137) (Figure
3.5: B). Spare capacity can also be measured as a percentage of basal respiration; MDAMB-231 cells had a higher spare capacity as a percentage of basal respiration than MCF7
cells (197 vs 173, respectively; p=0.021) (Figure 3.5: C).
43
A)
2.5
OCR (pmol/min/µg protein)
MCF7
MDA-MB-231
2
1.5
1
0.5
0
0.00
20.00
40.00
60.00
80.00
Time (minutes)
100.00
120.00
140.00
B)
OCR (pmol/min/µg protein)
2.5
MCF7
MDA-MB-231
*
2
1.5
**
*
1
**
0.5
0
Basal
Respiration
*
200
180
160
140
120
100
80
60
40
20
0
Maximal
Respiration
Spare
Respiratory
Capacity
Non Mito
Respiration
ATP-linked
respiration
D)
100
Coupling Efficiency (%)
Spare Respiratory Capacity (%)
C)
Proton Leak
**
80
60
40
20
0
MCF7
MDA-MB-231
MCF7
MDA-MB-231
Figure 3.5: Comparing oxidative metabolic parameters of MCF7 and MDA-MB-231 breast cancer cell
lines.
A) OCR graph of MCF7 (n=2) and MDA-MB-231 (n=4). B) Various oxidative metabolic parameters of
MCF7 (n=2) and MDA-MB-231 (n=4). C) Spare respiratory capacity as a percentage of basal respiration
between MCF7 (n=2) and MDA-MB-231 (n=4). Basal respiration indicated by dashed line at 100%. D)
Coupling efficiency of MCF7 (n=2) and MDA-MB-231 (n=4).
All data normalised to protein content and presented as mean ± SD as analysed by independent twosample t-test. Significance denoted as * p<0.05, ** p<0.01, *** p<0.001.
44
The data from a mitochondrial stress test can also be displayed as an energy graph (Figure
3.6). This is generated by the Seahorse Energy Phenotype report generator from
measurements of baseline and stressed OCR and ECAR. Baseline is used to refer to the
OCR or ECAR before any drug injection. Stressed is used to refer to OCR directly after
FCCP injection and ECAR directly after oligomycin injection. The graph is designed to
inform the general energy phenotype of the cells being tested
Figure 3.6: Energy graph displaying cells in their baseline and stressed phenotypes. Obtained from (139).
45
MCF7 cells showed a significant increase in OCR between baseline and stressed
conditions (1.34 vs 2.18, respectively; p=0.005). MDA-MB-231 cells also had higher
OCR in stressed conditions than baseline conditions (1.73 vs 0.99, respectively;
p<0.001). Baseline OCR rate was higher in MCF7 cells than MDA-MB-231 cells (1.34
vs 0.99, respectively; p=0.016). MCF7 cells also had a higher stressed OCR rate
compared to MDA-MB-231 (2.18 vs1.73, respectively; p=0.012) (Figure 3.7: A).
MCF7 cells showed no difference in ECAR rate between baseline and stressed conditions
(0.10 vs 0.18, respectively; p=0.153); although an upward trend was observed. In
comparison MDA-MB-231 cells had a significant increase in ECAR rate between
baseline and stressed conditions (0.06 vs 0.11, respectively; p<0.001). There was no
difference in baseline ECAR rate between MCF7 and MDA-MB-231 (0.10 vs 0.06,
respectively; p=0.281). MCF7 cells had a higher stressed ECAR rate than MDA-MB231 (0.18 vs 0.11, respectively; p=0.039) (Figure 3.7: B).
When this data is visualised on the energy graph both cell lines showed a similar energy
phenotype; MDA-MB-231 cells move from quiescent to aerobic and MCF7 cells move
from a low level of aerobic to highly aerobic. Both cell lines remained quiescent in terms
of glycolytic ability (Figure 3.7: C).
46
B)
A)
**
2.5
0.25
**
**
*
2
1.5
ECAR (mpH/min/µg protein)
OCR (pmol/min/µg protein)
*
*
1
0.5
0
0.2
0.15
0.1
0.05
0
Baseline
Stressed
Baseline
MCF7
Stressed
MDA-MB-231
C)
2.5
aerobic
MDA-MB-231
energetic
MCF7
Baseline
2
OCR (pmol/min/µg protein)
Stressed
1.5
1
0.5
quiescent
glycolytic
0
0
0.2
0.4
0.6
ECAR (mpH/min/µg protein)
0.8
1
Figure 3.7: Comparing energy phenotypes of MCF7 and MDA-MB-231 breast cancer cell lines using OCR
and ECAR in baseline and stressed conditions.
A) OCR in baseline and stressed conditions for MCF7 (n=2) and MDA-MB-231 (n=4). B) ECAR in
baseline and stressed conditions for MCF7 (n=2) and MDA-MB-231 (n=4). C) Energy graph of MCF7 and
MDA-MB-231.
All data normalised to protein content and presented as mean ± SD as analysed by independent two-sample
t-test. Significance denoted as * p<0.05, ** p<0.01, *** p<0.001.
47
3.1.5 Metabolic characterisation of MCF7 breast cancer cells grown alone
and in adipocyte co-culture
Data for MCF7 was obtained from two biological samples run on two separate XF plates
(n=2) and data for MCF7 co-cultured with adipocytes was obtained from two biological
samples run on two separate XF plates (n=2). There were 6-10 replicate wells per sample.
Presented data has been normalised to µg protein.
There was no significant difference between MCF7 cells grown alone or in adipocyte coculture for any of the oxidative metabolic parameters (Figure 3.8: B). The MCF7 cells
co-cultured with adipocytes tended towards a lower OCR for all of the parameters
measured (Figure 3.8: A). Basal respiration was unchanged between MCF7 cells and cocultured MCF7 (1.10 vs 0.98, respectively; p=0.379), as was maximal respiration (1.91
vs 1.55, respectively; p=0.322). MCF7 and co-cultured MCF7 cells did not differ in
proton leak (0.25 vs 0.17, respectively; p=0.172). They also had no difference in ATPlinked respiration (0.85 vs 0.81, respectively; p=0.453). Non-mitochondrial respiration
was unaltered between MCF7 and co-cultured MCF7 (0.22 vs 0.15, respectively;
p=0.169). MCF7 and co-cultured MCF7 showed no change in spare respiratory capacity
(0.81 vs 0.56, respectively; p=0.273), or spare capacity measured as percentage of basal
respiration (174 vs 157, respectively: p=0.245) (Figure 3: C). There was no difference in
coupling efficiency between MCF7 and co-cultured MCF7 cells (0.77 vs 0.83,
respectively; p=0.661) (Figure 3.8: D).
48
A)
2.5
ECAR (mpH/min/µg protein)
MCF7
MCF7 co-culture
2
1.5
1
0.5
0
0.00
20.00
40.00
60.00
80.00
Time (minutes)
100.00
120.00
140.00
B)
OCR (pmol/min/µg protein)
2.5
MCF7
MCF7 co-culture
2
1.5
1
0.5
0
Basal
Respiration
Proton Leak
Spare
Respiratory
Capacity
Non Mito
Respiration
ATP-linked
respiration
D)
200
100
Coupling Efficiency (%)
Spare Respiratory Capacity (%)
C)
Maximal
Respiration
160
120
80
40
80
60
40
20
0
0
MCF7
MCF7 coculture
MCF7
MCF7 coculture
Figure 3.8: Comparing oxidative metabolic parameters of MCF7 and MCF7 co-cultured with adipocytes.
A) OCR graph of MCF7 (n=2) and co-cultured MCF7 (n=2). B) Various oxidative metabolic parameters
of MCF7 (n=2) and co-cultured MCF7 (n=2). C) Spare respiratory capacity as a percentage of basal
respiration between MCF7 (n=2) and co-cultured MCF7 (n=2). Basal respiration indicated by dashed line
at 100%. D) Coupling efficiency of MCF7 (n=2) and co-cultured MCF7 (n=2).
All data normalised to protein content and presented as mean ± SD as analysed by independent twosample t-test.
49
MCF7 cells showed an increase in OCR between baseline and stressed conditions (1.34
vs 2.18, respectively; p=0.005), whereas co-cultured MCF7 cells showed no change (0.89
vs 1.28, respectively; p=0.505). There was no difference in baseline OCR between MCF7
and co-cultured MCF7 cells (1.34 vs 0.89, respectively; p=0.340). Stressed OCR also
showed no difference between MCF7 and co-cultured MCF7 cells (2.18 vs 1.28,
respectively; p=0.307). Although no significant difference was observed, there was a
noticeable trend toward decreased OCR in co-cultured MCF7 cells compared to MCF7
cells grown alone (Figure 3.9: A).
Co-cultured MCF7 cells had a significant increase in ECAR between baseline and
stressed conditions (0.04 vs 0.10, respectively; p=0.012), whereas MCF7 cells grown
alone showed no change in ECAR between baseline and stressed conditions (0.10 vs
0.18, respectively; p=0.153) (Figure 3.9: B). Adipocyte co-cultured MCF7 cells showed
greater variation in OCR than in ECAR whilst MCF7 cells grown alone showed greater
variation in ECAR than OCR.
MCF7 cells cultured alone had a more energetic phenotype than their adipocyte cocultured counterparts; starting in a low level aerobic state and moving to a high level
aerobic state whilst the co-cultured cells started in a quiescent state and moved into a low
level aerobic state (Figure 3.9: C).
50
B)
A)
2.5
0.25
**
ECAR (mpH/min/µg protein)
OCR (pmol/min/µg protein)
*
2
1.5
1
0.5
0
0.2
0.15
0.1
0.05
0
Baseline
Stressed
Baseline
MCF7
Stressed
MCF7 co-culture
C)
2.5
aerobic
MCF7
energetic
MCF7 co-culture
OCR (pmol/min/µg protein)
Baseline
2
Stressed
1.5
1
0.5
glycolytic
quiescent
0
0
0.2
0.4
0.6
0.8
1
ECAR (mpH/min/µg protein)
Figure 3.9: Comparing energy phenotypes of MCF7 and adipocyte co-cultured MCF7 using OCR and
ECAR in baseline and stressed conditions.
A) OCR in baseline and stressed conditions for MCF7 (n=2) and co-cultured MCF7 (n=2). B) ECAR in
baseline and stressed conditions for MCF7 (n=2) and co-cultured MCF7 (n=2). C) Energy graph of MCF7
and co-cultured MCF7.
All data normalised to protein content and presented as mean ± SD as analysed by independent two-sample
t-test. Significance denoted as * p<0.05, ** p<0.01, *** p<0.001.
51
3.1.6 Fatty acid oxidation stress test optimisation
The fatty acid oxidation stress test began optimisation as a protocol for measuring
alteration in β-oxidation when breast cancer cells were co-cultured with adipocytes. This
stress test is capable of calculating the respiration attributable to either exogenous or
endogenous fatty acids (131). This is by addition of a free fatty acid, palmitate (SigmaAldrich), to half of the wells via a protein vehicle, bovine serum albumin (BSA). BSA is
required for cells to be able to utilise the palmitate. A BSA control is applied to the other
half of the wells. On top of this, the CPT1 inhibitor, etomoxir (Sigma-Aldrich), is applied
to half the wells from each of the BSA and BSA:palmitate conditions. Etomoxir prevents
fatty acid oxidation by stopping palmitate from entering the mitochondria (131). This
gives four well conditions which can be used to calculate the metabolic parameters of
fatty acid oxidation (Figure 3.10). The calculations required to work out the final values
can be seen in Table 3.2.
Figure 3.10: Representation of a typical fatty acid oxidation stress test, identifying the fractions
attributed to exogenous or endogenous oxidation (140).
52
Table 3.2: Calculations to determine metabolic parameters of the fatty acid oxidation stress test (131).
Metabolic Parameter
Calculation
Oxygen Consumption due to
uncoupling by free fatty acids
(oligomycin BSA:palmitate –etomoxir rate) – (oligomycin BSA
–etomoxir rate)
Basal Respiration due to utilization
of exogenous fatty acids
(basal BSA:palmitate –etomoxir rate) – (basal BSA –etomoxir
rate) – (OCR due to uncoupling by free fatty acids)
Maximal Respiration due to
utilization of exogenous fatty acids
(maximal BSA:palmitate –etomoxir rate) – (maximal BSA –
etomoxir rate) – (OCR due to uncoupling by free fatty acids)
Basal Respiration due to utilization
of endogenous fatty acids
(basal BSA –etomoxir rate) – (basal BSA +etomoxir rate)
Maximal Respiration due to
utilization of endogenous fatty acids
(maximal BSA –etomoxir rate) – (maximal BSA +etomoxir rate)
3.1.6.1 BSA:palmitate conjugation
A key component for a FAO stress test is the BSA:palmitate conjugate which allows the
fatty acid, palmitate, to be taken up by the cell via the vehicle, BSA. These can be
conjugated together through a series of heating steps (132). Multiple attempts were made
to conjugate the BSA to palmitate. In the first attempt, the instructions were correctly
followed at a 1/10 scaled down version. Palmitate required dissolving and gradually
heating to 70°C at which point the solution is supposed to transition from cloudy to clear.
The solution did not reach a clear state and was not continued with. Further attempts were
made, heating the palmitate solution to 80-100°C. The solution remained cloudy at higher
temperatures but did appear to be slightly less cloudy than at 70°C. These attempts were
then repeated with a 1/5 scaled down version and a full size version with no apparent
changes observed. Conjugation was eventually carried out using the original protocol,
with the observation that there had not been a state change from cloudy to clear noted. It
was assumed that the conjugation was successful.
3.1.6.2 Optimisation of FCCP for fatty acid oxidation stress test
Cell lines that have undergone FCCP optimisation for the mitochondrial stress test have
to repeat this in the presence of BSA. This is because BSA can bind FCCP and so greater
concentrations are often required (131). FCCP optimisation in the presence of BSA was
very variable and no concentration could be accurately selected (Figure 3.11). OCR
values were at similar levels for both BSA and BSA:palmitate optimisations (Figure
3.12).
53
350
300
OCR (%)
250
200
150
100
50
0
0
1
2
3
4
5
6
FCCP concentration (µM)
Figure 3.11: FCCP optimisation of MCF7 cells in the presence of BSA. Data was obtained from two
separate experiments and combined to cover the range presented.
350.0
Palmitate:BSA
BSA
OCR (pmol/min/µg protein)
300.0
250.0
200.0
150.0
100.0
50.0
0.0
0.00
20.00
40.00
60.00
Time (minutes)
80.00
100.00
120.00
Figure 3.12: MCF7 OCR graph during FCCP optimisation with BSA or BSA:palmitate. Data presented
as mean ± SD from 10 wells.
54
3.2 Analysis of proteins involved in metabolism using Western blotting
Western blotting was used to investigate levels of CPT1A, ACC1, phospho-ACC and
GPD2 in MCF7 and MDA-MB-231 cells and their adipocyte co-cultured counterparts.
CPT1A is located in the mitochondrial membrane and is involved in the translocation
of fatty acids from the cytoplasm into the mitochondrial matrix where they can be used
in β-oxidation (105). ACC catalyses the conversion of acetyl CoA into malonyl CoA
which is an inhibitor of CPT1A. The phosphorylated form of ACC is inactive and does
not catalyse this reaction (95). GPD2 is a protein in the glycerophosphate shuttle that
has been associated with changes in cellular metabolism and upregulation in prostate
cancer (110, 111). Data is calculated as relative density to β-actin.
3.2.1 CPT1A
CPT1A was detected in both cell lines at 88kDa (Figure 3.13: A). MCF7 cells had
significantly more CPT1A protein than MDA-MB-231 cells (1.11 vs 0.46, respectively;
p<0.001) (Figure 3.13: B). There was no difference in CPT1A protein level between cocultured MCF7 and MCF7 grown alone (0.64 vs 0.66, respectively; p=0.538) (Figure
3.13: C, D). There was also no significant difference in CPT1A between co-cultured
MDA-MB-231 and MDA-MB-231 grown alone (0.60 vs 0.57, respectively; p=0.768)
(Figure 3.13: C, E).
55
B)
MCF7
A)
1
Relative Density to β-actin
1.4
MDA-MB-231
2
3
1
2
3
CPT1A β-actin
88kDa
64kDa
42kDa
37kDa
***
1.2
1
0.8
0.6
0.4
0.2
0
MCF7
MDA-MB-231
C)
CPT1A
MCF7
64kDa
β-actin
37kDa
β-actin
37kDa
MCF7
0.8
E)
MDA-MB-231
0.9
0.8
0.7
Relative Density to β-actin
Relative Density to β-actin
D)
MDA-MB-231
CPT1A
64kDa
0.6
0.5
0.4
0.3
0.2
0.1
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
Control
Co-culture
Control
Co-culture
Figure 3.13: Comparison of CPT1A protein level in MCF7 and MDA-MB-231 cells compared to their
adipocyte co-cultured counterparts.
A) Western blot comparison of CPT1A in MCF7 and MDA-MB-231. B) CPT1A in MCF7 (n=3) and
MDA-MB-231 (n=3). C) Western blot comparison of CPT1A in MCF7 and co-cultured MCF7 and in
MDA-MB-231 and co-cultured MDA-MB-231. D) CPT1A MCF7 (n=5) and co-cultured MCF7 (n=6). E)
CPT1A in MDA-MB-231 (n=5) and co-cultured MDA-MB-231 (n=6).
Data normalised to β-actin and presented as mean ± SD as analysed by independent two-sample t-test.
Significance denoted as * p<0.05, ** p<0.01, *** p<0.001.
56
2.3 ACC1 and phospho-ACC
ACC1 was detected in both cell lines at 265kDa (Figure 3.14: A). There was no
difference in ACC1 level between MCF7 cells and MDA-MB-231 cells (0.63 vs 0.65,
respectively; p=0.889) (Figure 3.14: B). There was no difference in ACC1 protein levels
between co-cultured MCF7 and MCF7 grown alone (0.50 vs 0.69, respectively;
p=0.061); although there was an observed trend downward in co-cultured MCF7
compared to MCF7 grown alone (Figure 3.14: C, D). ACC1 protein level did not differ
between co-cultured MDA-MB-231 and MDA-MB-231 grown alone (0.97 vs 1.05,
respectively; p=0.380) (Figure 3.14: C, E).
Phospho-ACC was detected in both cell lines at 285kDa (Figure 3.15: A). Phospho-ACC
level was not different between MCF7 cells and MDA-MB-231 cells (0.89 vs 0.90,
respectively; p=0.866) (Figure 3.15: B). There was a significant increase in phosphoACC in co-cultured MCF7 compared to MCF7 grown alone (1.03 vs 0.50, respectively;
p=0.013) (Figure 3.15: C, D). Phospho-ACC was also significantly increased in cocultured MDA-MB-231 compared to MDA-MB-231 grown alone (1.11 vs 0.56,
respectively; p=0.007) (Figure 3.15: C, E).
57
A)
MCF7
MDA-MB-231
1
2
1
2
ACC1
265kDa
250kDa
β-actin
50kDa
42kDa
Relative Density to β-actin
B)
1
0.8
0.6
0.4
0.2
0
MCF7
MDA-MB-231
C)
ACC1
250kDa
MCF7
β-actin
39kDa
β-actin
MDA-MB-231
ACC1
250kDa
39kDa
D)
MCF7
0.8
E)
1.4
Relative Density to β-actin
Relative Density to β-actin
0.9
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
MDA-MB-231
1.2
1
0.8
0.6
0.4
0.2
0
Control
Co-culture
Control
Co-culture
Figure 3.14: Comparison of ACC1 protein level in MCF7 and MDA-MB-231 cells compared to their
adipocyte co-cultured counterparts.
A) Western blot comparison of ACC1 in MCF7 and MDA-MB-231. B) ACC1 in MCF7 (n=3) and MDAMB-231 (n=4). C) Western blot comparison of ACC1 in MCF7 and co-cultured MCF7 and in MDA-MB231 and co-cultured MDA-MB-231. D) ACC1 MCF7 (n=5) and co-cultured MCF7 (n=6). E) ACC1 in
MDA-MB-231 (n=5) and co-cultured MDA-MB-231 (n=6).
Data normalised to β-actin and presented as mean ± SD as analysed by independent two-sample t-test.
58
B)
1
MDA-MB-231
2
1
2
P-ACC
280kDa
250kDa
β-actin
50kDa
42kDa
Relative Density to β-actin
1.2
MCF7
A)
1
0.8
0.6
0.4
0.2
0
MCF7
MDA-MB-231
C)
MCF7
P-ACC
250kDa
β-actin
39kDa
β-actin
MDA-MB-231
P-ACC
250kDa
39kDa
D)
MCF7
E)
1.4
*
MDA-MB-231
**
1.4
Relative Density to β-actin
Relative Density to β-actin
1.6
1.2
1
0.8
0.6
0.4
0.2
0
1.2
1
0.8
0.6
0.4
0.2
0
Control
Co-culture
Control
Co-culture
Figure 3.15: Comparison of phospho-ACC protein level in MCF7 and MDA-MB-231 cells compared to
their adipocyte co-cultured counterparts.
A) Western blot comparison of phospho-ACC in MCF7 and MDA-MB-231. B) Phospho-ACC in MCF7
(n=4) and MDA-MB-231 (n=3). C) Western blot comparison of phospho-ACC in MCF7 and co-cultured
MCF7 and in MDA-MB-231 and co-cultured MDA-MB-231. D) Phospho-ACC MCF7 (n=5) and cocultured MCF7 (n=6). E) Phospho-ACC in MDA-MB-231 (n=5) and co-cultured MDA-MB-231 (n=6).
Data normalised to β-actin and presented as mean ± SD as analysed by independent two-sample t-test.
Significance denoted as * p<0.05, ** p<0.01, *** p<0.001.
59
3.2.4 GPD2
GPD2 was detected in both cell lines. A double band was observed in MCF7 cells at
81kDa and 68kDa. In MDA-MB-231 cells GPD2 was only visualised as a single band at
68kDa (Figure 3.16: A). There was no difference in GPD2 protein level between MCF7
and MDA-MB-231 (1.13 vs 0.66, respectively; p=0.071) (Figure 3.16: B).
There was no difference in overall GPD2 protein level between MCF7 grown alone and
co-cultured MCF7 (0.70 vs 0.57, respectively; p=0.533) (Figure 3.17: A, B). GPD2
81kDa protein increased in MCF7 grown alone compared to co-cultured MCF7 (0.21 vs
0.09, respectively; p=0.013) (Figure 3.17: D). However, GPD2 68kDa protein was not
significantly different between MCF7 grown alone and co-cultured MCF7 (0.15 vs 0.29,
respectively; p=0.127); although there was a trend toward increased protein in the cocultured MCF7 samples compared to MCF7 grown alone (Figure 3.17: E). For MDAMB-231 there was no difference in GPD2 68kDa protein between cells grown alone and
co-cultured cells (0.20 vs 0.01, respectively; p=0.069); although protein in the cocultured MDA-MB-231 samples had a decreasing trend compared to MDA-MB-231
grown alone (Figure 3.17: A, C).
B)
MCF7
A)
1
42kDa
37kDa
3
1
2
3
GPD2 β-actin
81kDa
68kDa
2
MDA-MB-231
Relative Density to β-actin
1.6
1.2
0.8
0.4
0
MCF7
MDA-MB-231
Figure 3.16: Comparison of GPD2 protein level between MCF7 and MDA-MB-231.
A) Western blot comparison of GPD2 in MCF7 and MDA-MB-231. B) GPD2 in MCF& (n=3) and MDAMB-231 (n=3).
Data normalised to β-actin and presented as mean ± SD as analysed by independent two-sample t-test.
60
A)
GPD2
64kDa
β-actin
37kDa
Relative Density to β-actin
1.2
MCF7 total
C)
0.4
Relative Density to β-actin
B)
1
0.8
0.6
0.4
0.2
Control
0.3
0.2
0.1
Co-culture
MCF7 81kDa
Control
E)
0.5
**
Relative Density to β-actin
Relative Density to β-actin
0.3
MDA-MB-231
0
0
D)
MDA-MB-231
GPD2
64kDa
MCF7
β-actin
37kDa
0.25
0.2
0.15
0.1
0.05
Co-culture
MCF7 68kDa
0.4
0.3
0.2
0.1
0
0
Control
Co-culture
Control
Co-culture
Figure 3.17: Comparison of GPD2 protein level in MCF7 and MDA-MB-231 cells compared to their
adipocyte co-cultured counterparts.
A) Western blot comparison of GPD2 in MCF7 and co-cultured MCF7 and in MDA-MB-231 and cocultured MDA-MB-231. B) Total GPD2 in MCF7 (n=5) and co-cultured MCF7 (n=6). C) 81kDa GPD2
in MCF7 (n=5) and co-cultured MCF7 (n=6). D) 68kDa GPD2 in MCF7 (n=5) and co-cultured MCF7
(n=6). E) 81kDa GPD2 in MDA-MB-231 (n=5) and co-cultured MDA-MB-231 (n=6).
Data normalised to β-actin and presented as mean ± SD as analysed by independent two-sample t-test.
Significance denoted as * p<0.05, ** p<0.01, *** p<0.001.
61
3.3 Analysis of glycerol in conditioned media
There was no difference in glycerol level between the conditioned media conditions for
MCF7 (ANOVA (3,22)=0.42, p=0.744). There was also no difference for MDA-MB-231
(ANOVA (3,14)=0.44, p=0.727). Overall there was no significant difference in glycerol
levels between MCF7 and MDA-MB-231 for any of the conditioned media conditions
(ANOVA (7,36)=0.37, p=0.912) (Figure 3.18). When glycerol levels for post co-culture
samples and 24 hours post co-culture samples were combined and compared between
MCF7 and MDA-MB-231 there was no difference (34 µM vs 44 µM, respectively;
p=0.077). It was observed that co-cultured samples from different biological adipocyte
sources showed differing patterns of glycerol levels across the conditioned media
conditions and between breast cancer cell lines (Supplementary Figure 5.5).
140
MCF7
MDA-MB-231
120
Glycerol (µM)
100
80
60
40
20
0
Pre-adipocyte
Mature adipocyte
Post co-culture
24hr post co-culture
Figure 3.18: Comparison of glycerol levels in various conditioned media samples from MDA-MB-231
and MCF7 adipocyte co-cultures. A) Glycerol in MCF7 (n=7) and MDA-MB-231 (n=3) co-culture
conditioned media. B) Glycerol in MCF7 and MDA-MB-231 co-culture conditioned media from seven
separate adipocyte samples. Data presented as mean ± SD as analysed by one-way ANOVA.
62
4 Discussion
In this project it was hypothesised that breast tumour cells are capable of activating lipid
release by nearby adipocytes, and may metabolise the resulting glycerol and fatty acids
to fuel migration and invasion. This was based on recent literature which has found that
breast cancer cell interactions with adipocytes causes formation of cancer-associated
adipocytes (CAA), which result in more aggressive and invasive breast cancer and
resistance to radiotherapy and chemotherapy (15-17). Additionally to this, adipocytes
have been observed to transfer lipids to neighbouring ovarian cancer cells where they are
metabolised by β-oxidation (17). The overall aim was to observe metabolism in breast
tumour cells and look at potential changes when the breast tumour cells are co-cultured
with adipocytes.
4.1 Analysis of metabolism in breast cancer cells and breast cancer cells cocultured with adipocytes using the Seahorse Extracellular Flux Analyser
The objective of these experiments was to characterise tumour cell metabolism in breast
cancer cell lines MCF7 and MDA-MB-231. Further to this, tumour cell metabolism
would be investigated in MCF7 cells co-cultured with adipocytes to see if breast cancer
cells are altering metabolism; potentially utilising glycerol and fatty acids from adipocyte
lipolysis. Firstly, it was hypothesised that MDA-MB-231 cells would be more
metabolically active than MCF7 cells due to their more metastatic phenotype. Secondly,
that MCF7 cells grown in co-culture would be more metabolically active than MCF7
cells grown alone, as they are metabolising glycerol and free fatty acids.
Optimisation of the Seahorse protocols to the chosen cell lines was a very important part
of the project. If optimisation steps are not completed then the data gathered may not
show the true metabolic behaviour of the cells and cannot be validly interpreted. The
decision to not use Cell-Tak, although it showed good adhesion properties, was based on
observed abnormal cell morphology observed in MCF7 and MDA-MB-231 cells when
cultured on Cell-Tak. It was thought that, therefore, their phenotype might be altered and
experimental results would not be valid. At the optimised seeding density cells looked
morphologically similar to when they were grown in a culture flask. They also behaved
within the sensitivity parameters specified by Seahorse Biosciences which would aid
accuracy of readings and reliability of data from the ensuing experiments. This density
was further supported with an MTT assay which ensured that cell viability was not being
63
reduced at this density. Seeding conducted in co-cultured cells had to be altered between
experiments and a final seeding density of 6x104 was confirmed by the third co-culture
experiment. Unfortunately this meant that the seeding density varied across co-culture
experiments and 85% confluence was only achieved in the last experiment.
It was generally observed that MCF7 cells were more metabolically active than MDAMB-231 cells. They had higher basal and maximal respiration, and higher ATP-linked
respiration. This was an unexpected result and did not support the initial hypothesis.
MDA-MB-231 cells are typically described as a more migratory breast cancer cell line
(119, 141). As such, it was predicted that they would be more metabolically active than
MCF7 cells. Although MDA-MB-231 cells had lower respiration, they had a higher spare
capacity as a percentage of their basal respiration; almost doubling their respiration
levels. This showed the superior ability of MDA-MB-231 cells to increase their OCR in
response to stressful conditions compared to MCF7 cells. It has been proposed that
vitality and survival of cells may be dependent on the maintenance of bioenergetic
capacity during conditions of maximal physiological stress (121). This is an important
survival advantage for MDA-MB-231 (74-76).
MCF7 cells had a higher proton leak than MDA-MB-231 cells, and in line with this,
MDA-MB-231 cells had a higher coupling efficiency. Coupling is the mechanism by
which ATP is generated (142). This is by the proton gradient being coupled to ATP
synthesis through ATP synthase (142). This means that MDA-MB-231 cells were more
efficient at synthesising ATP. This is important as cancer cells need to maximise
resources and this feature may give MDA-MB-231 cells an advantage over MCF7 cells.
In combination with the ability to greatly increase OCR under stress, MDA-MB-231 cells
have two important survival advantages over MCF7 cells.
Proton leak is suggested to be a mechanism that helps prevent reactive oxygen species
(ROS) formation (143). The presence of ROS is part of a fine balance; increased ROS
can promote tumour development, but high levels of ROS can result in cell death (87,
88). It is possible that the low proton leak in MDA-MB-231 cells has led to higher levels
of ROS and that this is indicative of the aggressive nature of these cells. This is supported
by research from Pelicano et al. (2014), who have observed increased ROS in triple
negative breast cancer cell lines (144).
64
When looking at the energy graph, in baseline (non-metabolically stressed) conditions,
MDA-MB-231 cells were displayed as quiescent and MCF7 cells as low level aerobic.
Once stressed by the drug injections the cells moved into mid-high level aerobic state;
their glycolysis however remained quiescent. The Seahorse generated energy phenotype
depicts both MCF7 and MDA-MB-231 as non-glycolytic, whether in baseline or stressed
conditions. This infers that both cell lines are not subject to the modern interpretation of
Warburg’s Theory, which details that in aerobic conditions cells will sustain oxidative
phosphorylation and become glycolytic (76, 80). However, as described below, the
MDA-MB-231 cells did show a significant increase in ECAR, a measure of glycolysis,
between baseline and stressed conditions. The energy graph generated by the Seahorse
Energy Phenotype Report Generator provides no explanation for the quadrant divisions
and overall scale. It is probable that the cells originally used to develop these quadrants
created divisions that are not applicable to a wide range of cell types. This would mean
that the definition of MCF7 and MDA-MB-231 as quiescent, energetic, aerobic, or
glycolytic is somewhat arbitrary. Therefore, observations of aerobic and glycolytic
changes should be interpreted from changes in cellular OCR and ECAR, not from where
the cells are depicted on the Seahorse generated energy graph.
Both cell lines increased in oxidative respiration between baseline and stressed
conditions, but MCF7 cells, in line with other calculations of oxidative respiration, had
significantly higher OCR rates than MDA-MB-231 in both conditions. MDA-MB-231
cells, however, had an increase in glycolysis when stressed, whilst MCF7 cells showed
no change. This upregulation of glycolysis in MDA-MB-231 cells is in agreement with
Warburg’s theory, and correlates with MDA-MB-231 being considered a more
aggressive cell line. This is also supported by Gupta and Tikoo (2013), who have
observed increased viability and proliferation in MDA-MB-231 cells, but not MCF7
cells, exposed to high glucose levels (145). This may be due to the ability of MDA-MB231 to increase glycolysis and take advantage of this fuel.
MCF7 cells did, conversely, have a higher ECAR than MDA-MB-231 cells in
metabolically stressed conditions, but were unchanged between baseline and stressed
conditions. This was seen in oxidative spare capacity as well and shows a greater ability
of MDA-MB-231, compared to MCF7, to adapt under stressed conditions. This adaptive
ability is beneficial for cancerous growth as it allows for survival in changing
65
environments that may stress the cells (74-76). As mentioned previously, maintaining
bioenergetic capacity may be important for vitality and survival of cells (121). This is
supported by the literature which describes MDA-MB-231 and other triple negative cell
lines as more aggressive than MCF7 and other luminal cell lines (119, 141).
Previous literature using the Seahorse Extracellular Flux Analyser to look at MCF7 and
MDA-MB-231 cell lines did not compare the two and data is difficult to compare across
papers due to differences in protocols and normalisation procedures (134, 146, 147). This
characterisation of MCF7 and MDA-MB-231 cell lines in our lab is useful for future
work. Environmental changes can now be introduced, and alternative stress tests run to
see how cellular metabolism reacts.
It was observed that MCF7 cells grown in adipocyte co-culture were not significantly
different from cells grown alone, for most standard measures of oxidative respiration.
The only difference seen was an increase in OCR from baseline to stressed conditions in
MCF7 cells grown alone but not in co-cultured cells. However, the MCF7 cells cocultured with adipocytes had a significant increase in glycolysis from baseline to stressed
conditions, not seen in cells grown alone. This partially supported the hypothesis, as it
was anticipated that metabolism would increase in co-cultured MCF7 cells; glycolysis
was observed to be upregulated but oxidative respiration was not.
During co-cultures, lipid loss and morphological changes, associated with transition of
adipocytes
to
cancer-associated
adipocytes
(CAA),
were
visually observed
(Supplementary Figure 5.1). It was predicted that fatty acids were being released by CAA
into the media where they would become available to the breast cancer cells. It was
expected that growing MCF7 with CAA would result in observations of increased
oxidative respiration as cells took advantage of the available fatty acids. Nieman and
colleagues (2011) have seen transfer, and subsequent β-oxidation, of fatty acids in an
analogous ovarian cancer model (17). This metabolic research does not yet support this
in a breast cancer model. It is probable that changes in β-oxidation would not be visible
in a standard mitochondrial stress test and that fatty acid oxidation stress tests will be
necessary to determine if this is occurring. Fatty acids are not provided during
mitochondrial stress tests. Therefore, if the β-oxidation pathway was upregulated by
adipocyte co-culture the MCF7 cells may not be able to show any oxidative advantages
66
post co-culture (during the mitochondrial stress test) if there is no substrate available for
this pathway.
Although unexpected, the changes observed in co-culture glycolysis are supported by the
Warburg effect (76, 80). When CAA undergo lipolysis they release not only fatty acids
but also glycerol. Glycerol can be metabolised through the glycolysis pathway (148).
Glycolysis may be upregulated in co-cultured MCF7 cells in response to this glycerol
availability. This is also supported by studies of two-compartment tumour metabolism,
which hypothesise the release of metabolites from stromal cells being utilised by cancer
cells (19, 46, 63). Unlike the β-oxidation pathway, the glycolytic pathway would still
have fuel available during the mitochondrial stress test as the assay media is
supplemented with glucose. This would allow the observation of glycolysis upregulation
that potentially couldn’t be made in β-oxidation.
The analysis of energy phenotype in co-cultured MCF7 cells detailed the cells as
quiescent to low level aerobic. As mentioned in the comparison of MCF7 and MDAMB-231 cells, this energy phenotype graph is not valid for this research. The aim is to
see changes in comparison to the control sample, not in comparison to the cell types that
were used to design the energy phenotype graph. In addition to this, the co-cultured
MCF7 cells are grown in serum-free conditions as FBS can affect the adipocytes. This
means that the cells will not be operating at their optimum performance.
An issue with the interpretation of co-culture results is the MCF7 control sample. The
controls used for comparison in this project were MCF7 cells grown in standard culture
conditions. The co-culture cells, in comparison, were grown on inserts and in serum free
media for 3 days. A relevant MCF7 control sample would be grown in identical
conditions. This was intended, but not completed, due to time restrictions and equipment
issues. Therefore, it must be kept in mind that all MCF7 co-culture data was compared
to MCF7 cells grown in standard conditions., Although, previous research in our lab
group observed little change in viability, migration rates, or resistance to chemotherapy
agents between MCF7 cells grown on inserts and in serum free media compared to those
grown in standard co-culture conditions (Supplementary Figure 5.6), it is still possible
that metabolism could be affected. However, baseline readings conducted in this study
between the measured MCF7 control and a serum free media MCF7 control showed no
difference in baseline metabolism. During attempts to grow MCF7 controls in serum free
67
media for 3 days, high levels of cell death were observed and there was difficulty in
obtaining sufficient cells. It is promising that, although adipocyte co-cultured MCF7 cells
are also in serum free media for 3 days, they show increased viability compared to MCF7
cells cultured in serum free media. This suggests a protective effect of adipocytes in the
co-culture which may be partly due to metabolic changes.
Final sample sizes for Seahorse experiments were small; 2-4 biological replicates per
condition. This was due to time restraints, equipment issues, and restricted availability
of adipose tissue samples for co-culture. Adipose samples are very variable and difficult
to differentiate to a workable mature state. Because of this, the low intake of adipose
samples can be further restricted by sample loss. A Seahorse 24-well plate provides 20
active wells for measurements. The breast cancer cell lines, MCF7 and MDA-MB-231,
had reliable results with low deviation within and between plates. For this reason, it is
thought that the data obtained from the Seahorse experiments, although from small
sample sizes, is representative of these breast cancer cell lines. It must also be taken into
account that breast cancer cell lines were only investigated within a narrow passage
range.
Optimisation of the Seahorse fatty acid oxidation test was started during this project. This
test will allow analysis of changes in β-oxidation between breast cancer cells grown alone
or in adipocyte co-culture. Issues in the BSA:palmitate conjugation slowed progress and
consequently, optimisation is still ongoing. It was not apparent that BSA:palmitate
conjugation had been successful. FCCP optimisation for MCF7 was also incomplete.
During FCCP optimisation it was observed that when MCF7 cells were exposed to BSA
or BSA:palmitate the OCR after FCCP addition appeared unchanged. This was
unexpected as it was anticipated that the MCF7 cells would have increased OCR in the
BSA:palmitate condition as this was depicted in the protocol example (131). It is possible
that MCF7 cells do not have active β-oxidation in baseline conditions and so when
supplied with palmitate cannot take advantage of it. However, it is equally possible that
the BSA:palmitate conjugation was unsuccessful and that the palmitate was therefore not
available for the cells during the stress test. To check the BSA:palmitate conjugation two
experiments were proposed: a fatty acid oxidation test on HepG2, the cell line shown in
the protocol; and to run the BSA and the BSA:palmitate on a gel to see if any differences
are observed. Neither of these were carried out in this project due to time constraints.
68
4.2 Analysis of proteins involved in β-oxidation using Western blotting
The objective of these experiments was to investigate if β-oxidation of fatty acids is
upregulated in adipocyte co-culture. This was analysed by determining the levels of
specific tumour cell cytosolic and mitochondrial proteins; CPT1A (generates
acylcarnitine to aid fatty acid transfer into the mitochondrial matrix), ACC1 (catalyses
conversion of acetyl-CoA to malonyl-CoA), and phosphorylated ACC (inactive ACC)
(86, 95, 96). Additionally, GPD2 was looked at, as some studies have shown associations
between GPD2 and changes in cancer cellular metabolism (110, 111, 114). It was
hypothesised that CPT1A levels would increase in cells co-cultured with adipocytes
compared to those grown alone. Secondly, that ACC1 levels would not change, but
phosphorylated ACC levels would increase in adipocyte co-cultured cells compared to
cells grown alone.
MCF7 cells showed a higher level of CPT1A than MDA-MB-231 cells. This is supported
by Kim (2015), who also observed increased CPT1A in MCF7 cells compared to MDAMB-231 cells (149). Higher protein levels of CPT1A have been shown to result in
increased β-oxidation (150, 151). This suggests that MCF7 cells have an increased ability
to oxidise fatty acids compared to MDA-MB-231 cells.
There was no difference in CPT1A protein level between co-culture with adipocytes, or
cells grown alone, for MCF7 or MDA-MB-231. This was unexpected as it was predicted
that CPT1A would increase in the adipocyte co-cultured breast cancer cells. Increased
CPT1A has been associated with upregulated β-oxidation and oncogenic effects in breast
cancer cells (104, 150, 151). Studies in mice have shown an increase in CPT1A protein
is associated with prevention of obesity (152-154). This has only just begun to be
replicated in humans and it has been observed that different variants of CPT1 can
influence adiposity levels (155, 156). CPT1A can be transcriptionally regulated (157),
but this does not appear to be occurring. It is possible that there is CPT1A regulation
occurring at a level that doesn’t change total protein level but rather protein availability;
this is through malonyl-CoA inhibition. CPT1A is primarily regulated by concentration
of malonyl-CoA (157). Malonyl-CoA competitively binds with high affinity to the
CPT1A carnitine site, and it binds non-competitively with low affinity to the CPT1A
palmitate site (158, 159). When malonyl-CoA binds CPT1A it causes reversible
inhibition of CPT1A activity (105, 157, 158). If the concentration of malonyl-CoA is
69
altered between adipocyte co-culture and breast cancer cells grown alone this could be
indicative of altered β-oxidation. Malonyl-CoA concentration was not directly measured
in this project. Instead levels of ACC1 and phosphorylated ACC were measured. ACC
catalyses the conversion of acetyl-CoA to malonyl-CoA, and when phosphorylated is
inactive (95).
There was no difference in ACC1 or phospho-ACC level between MCF7 and MDA-MB231 cells. Also, neither MCF7 nor MDA-MB-231 cells showed a change in the level of
ACC1 between adipocyte co-cultured cells and cells grown alone. ACC1 level was not
expected to change and so this result was in support of the hypothesis. It was expected
that any changes in ACC would be seen as inhibition of ACC via phosphorylation rather
than a transcriptional change. Phosphorylated ACC was significantly lower in cells
grown alone compared to those grown in adipocyte co-culture for both MCF7 and MDAMB-231 cell lines. This is an important result and is supported by the literature on
phosphorylated ACC function. When ACC is phosphorylated it is inhibited and cannot
catalyse the acetyl-CoA to malonyl-CoA reaction; this leads to lower malonyl-CoA
concentrations, increased availability of CPT1A, and increased β-oxidation (96, 102).
This means that the adipocyte co-cultured cells have more available CPT1A which can
be used to move fatty acids into the mitochondria for β-oxidation. It appears that the coculture of adipocytes with breast cancer cells is altering the cell phenotype so that the
MCF7 and MDA-MB-231 cells can increase β-oxidation to take advantage of the fatty
acids provided by CAA.
One of the issues in ACC detection is that only ACC1 was probed for. It is thought that
malonyl-CoA produced by ACC1 is primarily involved in the lipogenesis pathway,
whilst malonyl-CoA from ACC2 is involved in β-oxidation (160, 161). ACC1 is found
in the cytosol, but ACC2 is mostly located on the exterior of the mitochondria which is
geographically advantageous for involvement in the β-oxidation pathway (162). Studies
in mice have shown ACC2 knockouts have continuous β-oxidation (163). This finding is
beginning to be replicated in humans with similar results (164). Both ACC1 and ACC2
possess the phosphorylation site detected by the anti-phospho-ACC antibody used in this
study. Thus, both were taken into account in phosphorylated form but only ACC1 was
detected in active form.
70
Through Western blotting, there have been changes observed in the β-oxidation pathway
of MCF7 and MDA-MB-231 breast cancer cells during adipocyte co-culture. This is
supported by previous work from Liu who detected increased β-oxidation in prostate
cancer and even more closely by Nieman et al. (2011) who observed transfer of lipids to
the mitochondrial matrix for β-oxidation in ovarian cancer cells co-cultured with
adipocytes (17, 82). This is also supported by the wider research on breast cancer cell
interactions with CAA. The increase in invasion and migration seen by Dirat et al. (2011)
in breast cancer adipocyte co-culture could be partly due to an increase in β-oxidation
(15).
The glycerophosphate shuttle connects the cytosolic and mitochondrial metabolic
pathways, allowing glycolytic NADH to contribute to oxidative metabolism (109). GPD2
is a regulatable component of this glycerophosphate shuttle (110). GPD2 was observed
in MCF7 cells at 81 kDa and 68 kDa, whereas in MDA-MB-231 cells it was only
observed at 81 kDa. There is no literature which describes the difference between the
GPD2 isoforms or why this might be observed. Both cell lines had a decrease in GPD2
at 81kDa in adipocyte co-cultured cells compared to cells grown alone; this was observed
as a non-significant trend in MDA-MB-231 cells. MCF7 cells did not show a change in
total GPD2 level between cells co-cultured with adipocytes or grown alone. As
mentioned above, the 81 kDa isoform was decreased in adipocyte co-cultured cells; the
opposite trend was observed in the levels of the 68 kDa isoform. The 68 kDa isoform
showed an increasing trend in adipocyte co-cultured cells compared to those grown
alone. It appears that in MCF7 the two isoforms are differentially expressed during
adipocyte co-culture. Recent literature has found GPD2 to be upregulated in glycolysis
and in cancer, including breast cancer (111, 114). Due to the lack of literature around
GPD2 isoforms it is difficult to interpret the findings any further. Thus far, it is seen that
there were differences in GPD2 level in adipocyte co-cultured MCF7 cells.
4.3 Analysis of glycerol in conditioned media
The final objective of this project was to measure adipocyte-derived glycerol and free
fatty acids in adipocyte-breast tumour cell co-culture media. This would allow analysis
of whether adipocytes have been induced to undergo lipolysis by co-culture with breast
cancer cells. Due to resource and time constraints, only glycerol levels were measured
in this project. It was hypothesised that glycerol levels would be lower in pre-adipocyte
71
samples compared to mature adipocyte, post co-culture, and 24 hour post co-culture
samples. Also, that glycerol levels would be highest in post co-culture and 24 hour post
co-culture samples. Lastly, it was hypothesised that glycerol level would not differ
between cell lines for any of the conditioned media conditions.
There were no significant changes in glycerol level observed across the different
conditioned media samples between MCF7 and MDA-MB-231 cells. The other two
hypotheses, however, were not supported. Glycerol levels tended to be lower, but were
not significantly lower, in pre-adipocyte conditioned media compared to other
conditioned media samples. Also, glycerol levels were not higher in post co-culture and
24 hour post co-culture conditioned media compared to other conditioned media
samples. Levels of glycerol showed high variability between individual adipocyte
samples and made comparisons difficult.
As mentioned previously, glycerol is released during lipolysis in adipocytes (165); visual
observations during co-culture showed loss of lipid content (Supplementary Figure 5.1).
This lipid loss should therefore result in increased glycerol in the conditioned media.
Glycerol was detected at a similar level in mature adipocyte and post co-culture
conditioned media. If breast cancer cells are both causing increased glycerol release and
consuming glycerol, this would be an expected result. In the mitochondrial stress test it
was seen that MCF7 cells in adipocyte co-culture have upregulated glycolysis; it is
suspected that this is to take advantage of the glycerol released by CAA. If the cells are
utilising this glycerol this would account for the lack in glycerol level change between
these conditions. The glycerol assay and mitochondrial stress test results support each
other in a model of upregulated glycolysis. This interpretation is also supported by
current Warburg theory and two-compartment tumour metabolism models (63, 80). It
notes the co-cultured cells upregulating glycolysis, without diminishing oxidative
respiration, and concedes that this is made possible by the release and subsequent uptake
of glycerol from neighbouring adipocytes.
72
4.4 Future Work
One of the key strengths in this study was the availability of a wide range of resources,
knowledge, and previously optimised protocols. The combination of the Seahorse
Extracellular Flux Analyser with the availability of human breast adipose samples and
the adipocyte co-culture system allowed for a unique angle on metabolism in breast
cancer cells.
The main limit in the scope of this study, however, was the amount of work planned.
After beginning optimisations it became clear that not all experiments would be
completed due to time restrictions. This led to different sections of the study being
completed to different extents and ended in the results not overlapping for
interpretation between sections. There were no clear links between the results obtained
from the Extracellular Flux Analyser and the results from Western blotting. The
optimisation of procedures and the preliminary data gathered, nevertheless, has set up
the capability to continue researching this area to complete the experiments originally
planned. With further work, the Extracellular Flux Analyser can be used to observe βoxidation in MCF7 and MDA-MB-231 cells and measurements of free fatty acids can
made in conditioned media. The results from this will be directly comparable with the
preliminary observations of upregulated β-oxidation in adipocyte co-cultured MCF7
and MDA-MB-231 cells that was observed using Western blotting.
Another limiting factor was the use of the Transwell co-culture. This system has no
physical contact between cell populations and only allows for transfer of soluble factors
between the adipocytes and the breast cancer cells. This means that all further analysis
was based on the effects of secreted factors and therefore was not representative of
physiological interactions in vivo. This allows for easier analysis post-co-culture and
can elucidate the contribution of soluble factor secretions on cellular behaviour. It is
unable to include potential changes due to cell-cell contact dependent communication.
To get a more accurate interpretation of metabolic changes occurring in adipocyte cocultured MCF7 cells future work must include an appropriate control. When compared
to a relevant control it may be apparent that the adipocytes have provided the co-cultured
MCF7 cells with observable metabolic advantages. Only MCF7 cells were investigated
in adipocyte co-culture, which gives a narrow view of metabolic changes in breast cancer
cells. To expand the understanding of changes other cell lines should be examined. This
73
can begin with the MDA-MB-231 cell line which has already been metabolically
characterised and provides a contrast to MCF7 and it is typically viewed as a more
aggressive breast cancer cell line.
The optimisation of the Seahorse fatty acid oxidation stress test has begun paving the
way for investigation of this metabolic pathway on the Seahorse. This is the most
important assay to finish optimising to assist in answering the hypothesis. This assay
will allow further investigation to see if the signs of β-oxidation upregulation during
adipocyte-breast cancer cell co-culture in MCF7 and MDA-MB-231 cells observed in
Western blotting, are supported by more direct observation of metabolic alterations.
This will help determine if adipocyte co-culture is upregulating β-oxidation in the
breast cancer cells to take advantage of the fatty acids released by adipocytes.
The observation that co-culture with adipocytes may be causing cancer cells to
upregulate glycolysis should be further explored. This can be by adding the glycolysis
stress test to the panel of Seahorse experiments that will be conducted in the future.
This stress test provides a more detailed look at glycolysis and measures parameters of
glycolysis, glycolytic capacity, glycolytic reserve, and non-glycolytic acidification
(166). By undertaking further analysis of glycolysis it will be possible to provide a
well-rounded view of metabolism during adipocyte-breast cancer cell co-culture.
The results from Western blotting are promising and already show alterations in the βoxidation pathway during adipocyte co-culture with breast cancer cells. The changes
shown so far in the β-oxidation pathway support the need for further investigation of
this metabolic pathway during adipocyte-breast cancer cell co-culture. The proteins
investigated already were chosen due to their key roles in the pathway. There are
however other proteins in this pathway that can be observed to give a more detailed
view of what is changing during adipocyte-breast cancer cell co-culture. AMPK is the
primary cause of ACC phosphorylation, and as such, can be probed (96). Western
blotting can also be carried out on malonyl-CoA, to directly measure levels of the key
inhibitory protein in this β-oxidation pathway (97). ACC2 is a CPT1A inhibitor
involved in the β-oxidation pathway (160, 161). It has been observed that ACC2 -/mice have continuous β-oxidation (163) and as such, ACC2 would be interesting to
probe. CPT1C is a different CPT1 isoform that has been shown to be upregulated in
cancer (167). This was also tested this in MCF7 cells and it was found that upregulation
74
of CPT1C caused upregulated β-oxidation and increased ATP synthesis. It is possible
that although literature has focused on CPT1A there is a role for CPT1C in cancer as
well. The remaining proteins in the CPT system can also be probed; this includes
carnitine:acyl-carnitine translocase (CACT) and carnitine-palmitoyl transferase 2
(CPT2). CACT is located on the intermembrane side of the mitochondrial inner
membrane and is responsible for moving fatty acids across the inner membrane into the
matrix space (168). CPT2 is located on the matrix side of the inner membrane and
prepares the fatty acids to be oxidised by removing carnitine (168). Both of these
proteins do not appear to limit the rate of mitochondrial fatty acid uptake (157) but may
still be altered in adipocyte co-culture if the β-oxidation pathway is upregulated. Lastly,
long-chain acyl-CoA synthetase (LCAS) is another protein involved in B-oxidation. It
prepares fatty acids in the cytosol for translocation by CPT1A (157). This is another
important step that can be investigated.
Although phosphorylated ACC was probed for, this antibody is not specific to ACC1 or
ACC2 and can bind either. To elucidate which ACC isoform is being phosphorylated
during co-culture with adipocytes, both ACC1 and ACC2 antibodies can be used to
purify out these proteins by immunoprecipitation. These samples can then be probed
with the anti-phospho-ACC antibody to determine which is phosphorylated, or if both
are.
Glycerol is only one of the metabolites that is released in lipolysis; another is fatty
acids. Fatty acids were not measured in this project and are a key component to
measure. This will allow linking of changes observed in Western blotting and Seahorse
stress tests, with the visual observations of lipid loss which was made in co-culture.
4.5 Conclusion
In this study two key observations were made; adipocyte co-cultured MCF7 cells showed
upregulated glycolysis, and both co-cultured MCF7 and MDA-MB-231 cells displayed
signs of upregulated β-oxidation. This study supports previous work that suggests
adipocytes in the breast tumour microenvironment are capable of providing energetic
advantage to metastasizing breast cancer cells. This has implications in the treatment,
and outcomes, of overweight and obese breast cancer patients. If the mechanism(s)
through which excess adiposity is having an adverse effect on patients can be determined,
then this pathway can be used in development of targeted treatments.
75
5 Appendices
5.1 Media
5.1.1 Pre-adipocyte growth media
Gibco® DMEM/F-12 medium [+] L-Glutamine [+] 2.438g/L sodium bicarbonate
(Thermo Fisher Scientific) supplemented with 10% foetal bovine serum (FBS) and 1%
Antibiotic Antimycotic Solution (100X) (Sigma-Aldrich).
5.1.2 Serum free media
Gibco® DMEM/F-12 medium [+] L-Glutamine [+] 2.438g/L sodium bicarbonate
supplemented with 1% Antibiotic Antimycotic Solution (100X).
5.1.3 Adipocyte differentiation media
Made in serum free Gibco® DMEM/F-12 medium [+] L-Glutamine [+] 2.438g/L sodium
bicarbonate supplemented with 1% Antibiotic Antimycotic Solution (100X).
0.5 mM
3-Isobutyl-1-methylxanthine (IBMX)
100 nM
Insulin
100 nM
Dexamethasone
2 nM
3,3′,5-triiodo-l-thyronine (T3)
1 µM
Rosiglitazone
33 µM
Biotin
17 µM
Pantothenic acid
10 µg/mL Transferrin
Alternative differentiation media –IBMX contains same final concentrations of above
components minus IBMX.
(Differentiation media is sterile filtered (Millex®-GV filter unit PVDF 0.22µm, Merck
Millipore Ltd.) before use)
5.1.4 Adipocyte maintenance media
Made in serum free Gibco® DMEM/F-12 medium [+] L-Glutamine [+] 2.438g/L sodium
bicarbonate supplemented with 1% Antibiotic Antimycotic Solution (100X).
10 nM
Insulin
10 nM
Dexamethasone
(Maintenance media is sterile filtered before use)
76
5.1.5 Breast cancer cell media
Gibco® RPMI 1640 medium [+] L-Glutamine (Thermo Fisher Scientific) supplemented
with 10% FBS and 1% Antibiotic Antimycotic Solution (100X).
5.1.6 MTT assay media
Gibco® RPMI 1640 medium [+] L-Glutamine [-] Phenol Red (Thermo Fisher Scientific).
0.5 mg/ml
3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT)
5.1.7 Mitochondrial stress test assay media
Made in XF assay medium (Seahorse Biosciences).
2 mM
Pyruvate
0.4%
Glucose
Prepared fresh.
Warmed to 37°C and adjusted to pH 7.35 using 0.1M sodium hydroxide.
Sterile filtered before use.
5.1.8 Substrate limited media
Made in Gibco® DMEM (-glutamine, -glucose, -HEPES) (Thermo Fisher Scientific).
0.5 mM
Glucose
1 mM
GlutaMAX
0.5 mM
L-carnitine
1%
FBS
5.1.9 FAO assay media (1X)
Made in H2O.
20%
FAO assay media 5X (Appendix 5.1.10)
2.5 mM
Glucose
0.5 mM
L-carnitine
5 mM
4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES)
Prepared fresh.
Warmed to 37°C and adjusted to pH7.35 using 0.1M sodium hydroxide.
Sterile filtered before use.
77
5.1.10 FAO assay media (5X)
Made in distilled H2O.
555 mM
Sodium chloride
23.5 mM
Potassium chloride
6.25 mM
Calcium chloride
10 mM
Magnesium sulfate
6 mM
Disodium phosphate
5.2 Buffers and solutions
5.2.1 Phosphate buffered saline (PBS)
Made in distilled H2O.
1 PBS tablet/200ml (Sigma-Aldrich)
Autoclaved and stored at room temperature.
5.2.2 Cell-Tak solution
Made in sterile filtered pH8 100mM sodium bicarbonate solution.
22.4 µg/ml
Cell-Tak™ (Corning)
5.2.3 Solubilisation solution
Made in isopropanol.
10%
Triton-X
1%
Hydrochloric acid
5.2.4 Lysis buffer
Made in RIPA (Appendix 5.2.5).
10%
Phosphatase inhibitor (10X) (PhosSTOP, Roche)
2%
Protease inhibitor (50X) (cOmplete tablets, Roche)
5.2.5 RIPA buffer
Made in distilled H2O.
150 mM
Sodium chloride
5%
Tris
1%
Octylphenoxypolyethoxyethanol (nonidet P-40)
0.5% w/v
Sodium deoxycholate
0.1%
Sodium dodecyl sulfate (SDS)
78
50%
5.2.6 Sample loading buffer
LDS sample loading buffer (4x) (Thermo Fisher Scientific)
20%
Dithiothreitol (DTT) (1M)
30%
dH2O
5.2.7 Running buffer
Made in distilled H2O.
5%
3-(N-morpholino) propanesulfonic acid (MOPS) SDS NuPage (20X)
5.2.8 Transfer buffer
Made in distilled H2O.
10%
Methanol
5%
Bolt transfer buffer (20X)
5.2.9 TBS-T
Made in distilled H2O.
10%
10X TBS
0.1%
Tween-20
5.2.10 Mild stripping buffer
Made in distilled H2O.
1.5% w/v
Glycine
1%
Tween-20
0.1%
SDS
Adjusted to pH2.2 using 12M hydrochloric acid.
79
Post co-culture adipocytes
Mature adipocytes
Pre-adipocytes
5.3 Supplementary Figures
Figure 5.1: Representative photographs of adipocyte samples before and after co-culture with breast cancer
cells.
A) 20X AT047 pre-adipocyte. B) 10X AT062 and AT063 mature adipocytes, C) 10X AT062 and AT063
adipocytes post co-culture.
80
MDA-MB-231
1x104
2x104
1x105
5x105
MCF7
Figure 5.2: Representative photographs of cell seeding for MCF7 and MDA-MB-231 from 5x105-1x104
in an XF 24-well plate. Photographs are 10X from the centre of the well.
81
MCF7
MDA-MB-231
1x105 edge
1x105 centre
A)
1x104 edge
1x104 centre
B)
Figure 5.3: Comparison of seeding at the centre and edge of XF 24-well plate for MCF7 and MDA-MB231 cells using 10X representative photographs.
A) Seeding at 1x105 for MCF7 and MDA-MB-231. B) Seeding at 1x104 for MCF7 and MDA-MB-231.
Top photographs from the centre of the well, bottom photographs from the edge of the well.
82
1
MCF7
0.9
MDA-MB-231
0.8
Absorbance
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
5 x 10⁵
1 x 10⁵
5 x 10⁴
2 x 10⁴
1 x 10⁴
1 x 10³
1 x 10²
0
Density (cells/well)
Figure 5.4: MTT assay showing viability of MCF7 and MDA-MB-231 cells at varying densities.
83
AT053
25
25
Glycerol (µM)
Glycerol (µM)
20
15
10
5
20
15
10
5
0
0
Mature
adipocyte
Mature
adipocyte
100
50
150
100
50
0
0
Mature
adipocyte
Mature
adipocyte
Post co- 24hr post
culture co-culture
AT065
30
25
20
15
10
5
Post co- 24hr post
culture co-culture
AT062
80
Glycerol (µM)
Glycerol (µM)
Post co- 24hr post
culture co-culture
AT056
200
Glycerol (µM)
Glycerol (µM)
Post co- 24hr post
culture co-culture
AT055
150
60
40
20
0
0
Mature
adipocyte
Post co- 24hr post
culture co-culture
Mature
adipocyte
Post co- 24hr post
culture co-culture
AT063
50
Glycerol (µM)
AT054
30
40
30
MCF7
20
MDA-MB-231
10
0
Mature
adipocyte
Post co- 24hr post
culture co-culture
Figure 5.5: Glycerol levels in varying stages of individual adipocyte co-cultures with MCF7 and MDAMB-231 cells.
84
A)
B)
MCF7
MDA-MB-231
Figure 5.6: MTT assays of MCF7 and MDA-MB-231 cells grown on inserts or not on inserts as
controls to adipocyte co-cultured cells. A) Comparison of MCF7 controls grown on, or not on, inserts
(p=0.119). B) Comparison of MDA-MB-231 controls grown on, or not on, inserts (p=0.634).
85
References
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Organization WH. Obesity and overweight [Internet]: World Health
Organization; 2015 [updated Jan 2015; cited 2015 May 26]. Available from:
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