This document describes a research project comparing the metabolism of human CD4+ and CD8+ T cells. The project found that both cell types rely primarily on aerobic glycolysis for energy during proliferation, even in the presence of oxygen. CD4+ cells were found to depend more on fatty acid oxidation and have a higher reliance on glycolysis compared to CD8+ cells. Inhibiting different metabolic pathways showed which pathways each cell type utilizes. This provides insight into manipulating T cell metabolism to potentially increase their anti-cancer activity.

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SCHOOL OF MEDICINE
YSGOL FEDDYGAETH
PM304 – Biomolecular Research Project 2016
Single Honours in Biochemistry
Name: Yasmin Agnew (789209)
Title: Metabolic Comparison of Human CD4+ and CD8+
T cells
Word Count: 6765

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SCHOOL OF MEDICINE
Biomolecular Research Project
DECLARATION OF ORIGINALITY
Project Title: Metabolic Comparison of Human CD4+ and
CD8+ T Cells
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Declaration: I confirm that I understand the term plagiarism and the
University’s regulations regarding the consequences of plagiarism. I have
generated this report myself, and the work described is my own, except where
otherwise acknowledged. It has not been copied from any other person’s work
(published or unpublished) and has not previously been submitted for
assessment.
SIGNATURE
OF STUDENT:.......................................................................................
STUDENT’S NAME (PRINTED):YASMIN AGNEW
DATE OF SUBMISSION: ……………………15/04/2016………………………………

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Contents
Single Honours in Biochemistry ............................................................................................1
Declaration of Originality........................................................................................................2
Summary...................................................................................................................................4
Introduction...............................................................................................................................5
Materials and Methods ...........................................................................................................8
Results ....................................................................................................................................10
Discussion..............................................................................................................................17
References .............................................................................................................................21

4. 4
Metabolic Comparison of Human CD4+
and CD8+ T cells
Summary
CD4+ and CD8+ T cells are highly important cells involved in the adaptive immune
system. They provide a more specific response to foreign agents that infect the
body, whether that is bacterial, viral or fungal, however they each have different
functions to perform as part of this response. Previous studies have shown they may
also have a role in cancer treatment, due to their ability to attack tumour cells. This
project looked at the differences between the different metabolic pathways each type
of cell used by inhibiting different pathways and analysing the cell proliferation as
well as their production of the cytokine interferon-gamma. It was found that both
CD4+ and CD8+ T cells use aerobic glycolysis as their main source of energy during
proliferation, even when oxygen is present, in a phenomenon known as the Warburg
Effect. CD4+ cells were found to have more of a reliance on fatty acid oxidation than
CD8+ cells did, as well as a heavier reliance on glycolysis as shown by a higher
expression of the glucose transporter GLUT1. Knowledge of these metabolic
differences could lead to research into the manipulation of metabolism in these cells
in order to increase their cytotoxicity to cancer.

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Introduction
The immune system is a highly important barrier that all organisms have in order to
protect the host from infectious agents. In humans especially the system has many
levels and sections, which overall combine to form the barrier, innate and the
adaptive immune systems, which link together (Turvey et al., 2010). These systems
help to prevent infectious agents from entering the body, as well as having the ability
to destroy these agents if they do get past the initial barriers. A key element of this
system, particularly the adaptive system, is the ability to make memory cells,
therefore if there is a recurring infection, the immune response is far more effective
and much faster. The innate immune system focuses mainly on phagocytosis,
through the use of phagocytes such macrophages and neutrophils engulfing
infectious agents. From this, the phagocytes can destroy the infectious agent using
lysozymes and present its antigen on the phagocyte cell surface (Janeway et al.,
2001). This allows for recognition by cells involved in the adaptive immune system.
The adaptive immune system involves two different types of cells, B cells and T
cells. B cells are formed in the bone marrow and are associated with the formation of
antibodies to destroy infectious agents. These antibodies can also remain in the
body for a long time in order to facilitate a very rapid response if the human is
infected with the same agent again (Klinker et al., 2012). T cells are created in the
thymus and can present different glycoproteins on their cell surface, in order to give
them different functions. In particular, some cells present CD4 and some present
CD8 (Koretzky, 2010). These are also known as T-Helper cells and T-Killer cells
respectively (Cao et al., 2014). These cells have different roles within the immune
response that help the removal of infectious agents. CD4+ cells bind to the Major
Histocompatibility Complex Class II (MHC II) ligand of antigen-presenting cells
(APCs) whereas CD8+ cells bind to the MHC Class I ligand (Miceli et al., 1991). This
differential binding allows for each type of cell to act differentially upon the cell.
CD4+ cells respond mainly to fungal and bacterial infections and can differentiate
into two types: regulatory (Treg) and effector (Teff). As with most things in the human
body, an element of homeostasis must be present. Teff cells are used to stimulate
an immune response and are particularly involved in the inflammatory response,
whereas Treg cells are used to decrease an immune response, therefore preventing
autoimmune effects (Macintyre et al., 2014). This balance helps to prevent the
immune system attacking the healthy cells within the host (Corthay, 2009). CD4+
cells are also able to assist B cells with the production of antibodies, as well as help
CD8+ cells with their functions (Chapman et al., 2010). The importance of the activity
of CD4+ cells on CD8+ cells is demonstrated in past research. In patients with a low
number of CD4+ cells, the number and function of CD8+ cells tend to deteriorate
over time when compared to patients with higher numbers of CD4+ cells (Sun et al.,
2004). CD8+ T cell function is far more direct than that of CD4+ T cells, as CD8+ T
cells are cytotoxic and will destroy virally-infected cells immediately using cytokines
such as interferon gamma (IFN-γ) (Koretzky, 2010). IFN-γ is involved with
lymphocyte attraction (Schroder et al., 2004) and is therefore pivotal in increasing
the number of T cells that are attacking the foreign species, resulting in a more
efficient destruction of the infecting cells. CD8+ cells can also differentiate into
memory cells, which are found more commonly in the blood than naïve CD8+ cells.
Their presence allows for a much faster response to any recurring infection due to
their increased number of mitochondria, allowing for more aerobic glycolysis and

6. 6
OXPHOS to occur at a faster rate (van der Windt et al., 2013). The different
functions of these two types of T cell implies that there may be a difference between
their metabolic functions, such as the different pathways they may use.
The metabolism of T cells is very different from when they are naïve and when they
are activated (Cao et al., 2014). Normal cell metabolism begins with glycolysis (GLY)
in order to produce pyruvate, which feeds into the Citric Acid Cycle (TCA Cycle).
This is where biosynthesis of a lot of necessary molecules for the cell takes place.
Electrons from this cycle are then taken to the electron transport chain, where
oxidative phosphorylation (OXPHOS) takes place to form ATP (Chang et al., 2013).
OXPHOS is usually regarded as the most efficient pathway for the synthesis of ATP
when oxygen is present, however ATP is also formed during GLY where oxygen is
not present. When T cells are activated by binding to their respective MHC, their
main function is to proliferate very rapidly. In order to do this, T cells undergo a very
different type of respiration, as they have a preference for using GLY as their main
energy source during proliferation as shown in Figure 1, even though they have
plenty of access to oxygen (Gubser et al., 2013). This phenomenon is known as the
Warburg Effect and is common in both T cells and cancer cells (Cao et al., 2014).
This method is very unusual for normal cells, which implies there must be a specific
reason that T cells behave in this way. One theory is based upon speed. GLY is a
much faster pathway than OXPHOS, therefore the use of GLY would allow the cells
to proliferate at a much faster rate than if they were undergoing OXPHOS. CD8+
cells in particular want to be able to very rapidly increase their numbers before
attacking an infectious agent, in order to increase their chance of success. GLY
allows for a small amount of ATP to be formed without using too many substrates
(Pearce, 2010), therefore these substrates are saved and can be used during the
CD8+ cell cytotoxicity functions later in the cascade. Another study that focused on
cancer cell proliferation suggested that this glycolytic switch leads to more
stimulation of the pentose phosphate pathway (PPP) (Preter et al., 2015). This
pathway is associated with rapid growth of cancer cells due to it giving the cells more
nutrients such as ribose-5-phosphate (Jiang et al., 2014). As shown in Figure 1, the
need for biosynthetic precursors is increased after T cell activation. This would
further support the theory behind the need for the increase in the rate of the PPP, as
Figure 1: The different metabolic pathways used by inactive and activated T cells. The differences are
based on their needs at their stage of function (MacIver et al., 2013).

7. 7
it would provide the T cells with these precursors. One glucose molecule can provide
a cell with 6 carbon atoms for use in macromolecule biosynthesis if it is directed to
the PPP, which would prove very useful to a dividing cell that needs to duplicate all
of its contents (Vander Heiden et al., 2009). However, this research is based on
cancer cells rather than T cells, but this use of the Warburg Effect is mimicked in
both cancer and T cells, therefore implying that this theory could be correct for both
types of cell.
There is also evidence to suggest that some types of T cell use fatty acid oxidation
(FAO) in order to grow and proliferate, particularly with CD4+ Treg cells (Cao et al.,
2014). This is another metabolic pathway that is not as efficient as OXPHOS,
however the use of FAO seems to be used for regulation in CD4+ cells and in the
generation of memory functions for differentiated CD8+ T cells (Pearce, 2010). This
implies the use of FAO tends to occur post-T cell activation and action.
Glutaminolysis has also been shown to be an important part of T cell metabolism,
particularly after activation (Wang et al., 2012). This pathway is responsible for the
production of α-ketoglutarate (α-KG), which is used in the TCA cycle. This allows the
TCA cycle to continue for the synthesis of macromolecules (MacIver et al., 2013).
T cells are proving to have potential in the treatment of cancers. Recent experiments
have involved using T cells from the blood of patients, mutating the cells to be
specific to the patients’ cancer, then re-inserting the T cells to combat tumours
(Porter et al., 2011). This is pivotal research as it involves using the patients’ own
cells to combat their own tumours, resulting in a much more specific response
against the tumour compared to current treatments such as chemotherapy or
radiotherapy, as well as reducing the risk of rejection by the host as the cells are not
foreign bodies. The specificity of their anti-tumour activity arises as a result of each
CD8+ T cell only having certain receptors for specific tumour cell antigens (Shanker
et al., 2010). This level of specificity has its advantages, as it reduces the risk of
healthy cells being targeted by the immune system, therefore causing less side
effects. Other research has shown that there is potential in reducing CD8+ cells’
ability to undergo pure aerobic glycolysis, therefore forcing them to undergo
OXPHOS, which increases their anti-tumour function drastically (Sukumar et al.,
2013). However, these results have been contested, as it has been shown that
aerobic glycolysis is required for optimal IFN-γ production (Chang et al., 2013),
therefore reducing glycolysis would reduce this cytokine production.
This project aims to measure the differences in metabolism between CD4+ and
CD8+ cells, primarily based on which pathways are used be each type, as well as
when each pathway is used within the individual cell activation cycle. This was
conducted by measuring basal metabolic rate of each type of cell before and after
activation, as well as using different respiratory inhibitors on each type of cell to see
which pathways had a larger effect on each type of cell. Four different respiratory
inhibitors were used to inhibit four different pathways. 2-deoxy-D-glucose prevents
GLY from occurring through the inhibition of hexokinase (Zhong et al., 2009).
OXPHOS was inhibited by treating the cells with oligomycin, which acts upon ATP
Synthase, also known as Complex V in the electron transport chain (Shchepina et
al., 2002). This slows or stops the production of ATP via OXPHOS, therefore the cell
needs to source ATP from other pathways such as GLY. Etomoxir was used to
inhibit FAO, by blocking carnitine palmitoyl transferase I, thus preventing fatty acids
from being able to enter the mitochondria itself (Samudio et al., 2010). The final

8. 8
respiratory inhibitor was 6-Diazo-5-oxo-L-norleucine (DON) which inhibits
glutaminase, the enzyme that converts glutamine to glutamate during glutaminolysis
(Thomas et al., 2014). This inhibition results in glutaminolysis not being able to
continue, therefore there is a reduction in the production of α-ketoglutarate. The
levels of glycolytic enzymes present in each type of T cell were also measured,
including the glucose transporter GLUT1. The production of the main cytokine
produced by both CD4+ and CD8+ cells, IFN-γ, was also analysed for each type of
cell.
Materials and Methods
Cell Isolation
Blood samples were taken from healthy, consenting adults over the age of 18. These
samples were mixed with heparin to avoid coagulation. The samples were layered
onto Histopaque (1.077g/mL) and centrifuged (5810 Centrifuge, Eppendorf) in order
to separate the samples into different layers of cells based on their density. The
mononuclear cells were isolated, washed and topped up with RPMI media and re-
centrifuged. The desired CD4+ and CD8+ cells were then isolated using the
AutoMACS procedure, using Microbeads (Miltenyi Biotec) to label all blood cells in
the sample other than the desired CD4+ or CD8+ cells. The cells were separated
using the AutoMACS Pro Separator (Miltenyi Biotec), with each sample being run
twice in order to increase the purity of the cell samples for further experimentation.
The number of cells was then estimated using a Trypan blue stain (Thermofisher
Scientific) to dye non-viable cells and counted using a Countess automated cell
counter (Invitrogen). In order to avoid the samples being contaminated, all stages of
this procedure were conducted in a sterile Mars hood (Scanlaf).
Seahorse Extracellular Flux Assay
The Seahorse Bioscience XF Analyser (XFe24 Extracellular Flux Assay Kit) was
used to measure the rate of glycolysis (GLY) and oxidative phosphorylation
(OXPHOS). The isolated cells were suspended in the Seahorse-specific media,
which was supplemented with glucose and glutamine to ensure there was enough
substrate for glycolysis and glutaminolysis to occur. Cell-Tak was applied to each
well of the 16-well seahorse plate in order to ensure the 250 000cells/100μl were
stuck to the bottom of the well and were therefore able to be analysed. Oxygen
Consumption Rate (OCR) measured the rate of OXPHOS and Extracellular
Acidification Rate (ECAR) measured the rate of GLY over a time period of 4.5 hours.
The cells were activated using an injection of CD3 and CD28. Glycolysis was
stopped by the injection of 2-DG. The purity of the samples were measured using
flow cytometry.

9. 9
Cell Culture
The isolated cells were resuspended in RPMI media with GlutaMAX, then cultured
with different respiratory inhibitors. The two controls were unstimulated cells and
cells activated with CD28.
Table 1: The different metabolic inhibitors used during the cell culture
Inhibitor Concentration
(μl/500μl)
Pathway Inhibited
2-deoxy-D-glucose (2-DG) 0.025 Glycolysis
Oligomycin 0.004 Oxidative
Phosphorylation
Etomoxir 0.02 Fatty Acid Oxidation
6-Diazo-5-oxo-L-norleucine
(DON)
0.005 Glutaminolysis
The inhibitors were 2-DG, oligomycin, etomoxir and DON, which all inhibit different
metabolic pathways as shown in Table 1. As the DON was dissolved in methanol, a
methanol vehicle was also tested as another control, to ensure any effects observed
by those cells exposed to DON were created by the DON exposure rather than the
exposure to methanol. After a 24 hour incubation at 37°C and 5% CO2, the cells
were spun and stained with DRAQ7 and CD29 before being analysed using the flow
cytometer. This analysis allows the different pathways of each type of T cell to be
assessed and observe any differences between the CD4+ and CD8+ cells. DRAQ7
staining was used to measure cell death.
Flow Cytometry
Using a flow cytometer (BD Biosciences FACS Aria III), 10’000 ‘events’ were
recorded for each sample, in order to profile the individual cells that pass through the
cytometer. This allowed for observations to be made about the number of CD4+ and
CD8+ cells that were present in each sample that was exposed to a respiratory
inhibitor. If there was a high cell death rate, this implies that the cells were
particularly vulnerable to that specific inhibitor, therefore showing which pathways
are most used by each type of T cell. The fluorophores used were DRAQ7 on the
APC channel, and CD69 on the PE channel.
Western Blot
Some of the samples were used in an immunoblot, where they were tested for
different glycolytic enzymes: hexokinase I, pyruvate kinase (PKM2),
phosphofructokinase (PFKP), glucose transporter 1 (GLUT1) and glyceraldehyde 3-
phosphate dehydrogenase (GAPDH). β-actin was used as a control to highlight the
correct molecular weights of the proteins. In order to give a comparison of molecular
weights, a protein standard ladder was used (Dual Colour Bio-Rad). 5μl of protein

10. 10
already dissolved in Laemmili buffer was added to each well of the gel. The
secondary antibody was a rabbit antibody. The gels were run under electrophoresis
for 40 minutes at 200V and transferred to the semi-dry apparatus Trans-Blot Turbo
system (Bio-Rad).The blot was blocked and washed using 5% Bovine Serum
Albumin (BSA)/Tris-buffered saline (TBS)/0.1% Tween. 1/1000 of primary antibody
was added, followed by the secondary rabbit antibody after an overnight incubation
at 4°C. The secondary antibody was a rabbit antibody. When the blots were
complete, they were developed and visualised under ultraviolet (UV) light using the
ChemiDoc Touch Gel and Western Blotting Imaging System (Bio-Rad).
ELISA
The supernatants of the cell samples were also analysed using an Enzyme-Linked
Immunosorbent Assay (ELISA), testing for which cytokines were released by each
type of cell. The main cytokine tested for was IFN-γ. This cytokine has been shown
to be produced by T cells, but the ELISA allows for any differences to be observed.
The blocking buffer used was 1% Bovine Serum Albumin (BSA) in Phosphate
Buffered Saline (PBS).
Results
Seahorse XF Analyser Results
CD4+ T cells
Upon activation at 60 minutes, there is a slight increase in the rate of OXPHOS,
however the increase is very minimal when compared to the naïve cells. The rate of
OXPHOS also seems to decrease over time with both the naïve and activated cells.
There is a slight increase in the rate of OXPHOS at 320 seconds where the 2-DG
injection is added to stop glycolysis.
0
5
10
15
20
25
30
35
40
45
50
0 50 100 150 200 250 300 350 400
OCR
Time (mins)
CD4 CD4 Act
Figure 2: The measurement of OCR showing the rate of OXPHOS over time for naïve and activated
CD4+ cells

11. 11
Upon activation at 60 minutes, there was a very large increase in the rate of GLY,
which slowly decreased over time. Despite this, the rate of GLY remained higher
than the naïve cells until the injection of 2-DG after 320 minutes, where both
decreased to the same rate.
CD8+ T cells
Upon activation at 60 minutes, there is a very small initial increase in the rate of
OXPHOS, however by 200 minutes the rate of OXPHOS was very similar to that of
the naïve cells, with the rate of OXPHOS even being slower than the naïve cells at
230 minutes. The rate of OXPHOS increases again at 330 minutes, where the 2-DG
injection occurred, however the rate in both the activated and naïve cells dropped to
the ‘normal’ level again by 340 minutes.
0
2
4
6
8
10
12
14
16
18
0 50 100 150 200 250 300 350 400
ECAR
Time (mins)
CD4 Act CD4
Figure 3: Measurement of rate of GLY over time on naive and activated CD4+ cells
0
10
20
30
40
50
60
0 50 100 150 200 250 300 350 400
OCR
Time (mins)
CD8 CD8 Act
Figure 4: The measurement of OCR over time for naive and activated CD8+ cells

12. 12
Upon activation at 60 minutes, the rate of GLY rapidly increases, although prior to
this the activated cells had a higher GLY than the naïve cells to start with. This rate
decreased over time in both the activated and naïve cells, until both rapidly
decreased to a similar rate from 320 minutes, when the 2-DG injection took place.
Cell Culture Flow Cytometry Results
CD4+ Cells
The cultured CD4+ cells were initially incubated for 24 hours with different treatments
in the form of respiratory inhibitors. When analysing the CD4+ T cells, the aim for the
purity level was at least 90%, which was achieved in the 3 samples analysed. From
the initial SSC/FSC plot, the cluster of cells corresponding to lymphocytes were
gated and analysed further. The activated cells were then separated from the dead
cells due to the DRAQ7 staining. The histograms formed for each treatment were
then overlaid (as shown in Figure 6) and the medians for each sample calculated.
0
2
4
6
8
10
12
14
16
18
0 50 100 150 200 250 300 350 400
ECAR
Time (mins)
CD8 Act CD8
Figure 5: Measurement of GLY over time on naive and activated CD8+ cells
Figure 6: An example of the overlaying histograms from the flow cytometry data. The activated, uninhibited cells were used as the control. PE-A
represents CD69

13. 13
As well as the samples shown in Figure 6, inactivated cells and the methanol DON
vehicle were also analysed as further controls, however they have been omitted from
this data as the results were very similar to the activated cells that were used as a
control. This similarity was expected as they were not exposed to any treatments.
The methanol vehicle was analysed as it was used as a solvent for DON. Analysis of
methanol showed that any effect seen by the cells exposed to DON was as a result
of DON rather than any effects from the methanol vehicle.
CD4+ cells from three different donors were analysed in this way. The medians for
each treatment were averaged, giving the bar graph shown in Figure 7.
The biggest decrease in the number of activated cells can be seen in those cells
treated with 2-DG. The smallest decrease can be seen in the cells treated with DON.
The decrease in activated cells in the samples treated with oligomycin and etomoxir
are very similar in number.
0
10000
20000
30000
40000
50000
60000
70000
Act 2-DG Oligomycin Etomoxir DON
CD69PositiveCells
CD4+ cells with inhibitor
Figure 7: The number of CD4+ cells shown to be active when exposed to different respiratory inhibitors

14. 14
CD8+ Cells
After a 24 hour incubation, the cultured CD8+ cells were analysed using flow
cytometry. The purity of CD8+ cells in each sample was over 90% in all 3 samples
analysed. The cells were gated and histograms were created in the same way as the
CD4+ samples.
As with the CD4+ cells, other controls in the form of unstimulated cells and the
methanol vehicle were analysed. Again they showed very similar results to those
seen in the inactivated cells and were therefore omitted from the final results.
From the 3 different CD8+ donor samples, the medians were averaged in order to
give an overview of CD69 expression, as shown in Figure 9.
The most striking result shown by this dataset is the large increase in CD69
expression after treatment with DON. As with the CD4+ cells, the largest decrease
was found in the samples treated with 2-DG. The sample treated with etomoxir had a
smaller reduction than the CD4+ samples, however the CD8+ cells treated with
oligomycin had a larger reduction than the CD4+ samples.
Figure 8: An example of the overlaid histograms from CD8+ cells after treatments with respiratory inhibitors. Inactivated
cells were used as the control. PE-A represents CD69
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
Act 2-DG Oligomycin Etomoxir DON
CD69PositiveCells
CD8+ cells with inhibitor
Figure 9: The number of CD8+ cells positive for CD69 after treatments with respiratory inhibitors

15. 15
Western Blot
Figure 10: UV scans from 3 different Western immunoblots testing for glycolytic enzymes: PFPK, hexokinase, PKM2,
GAPDH, GLUT1 and β-actin was used as a control.
Three separate gels were run, all using β-actin as a reference protein. When testing
for hexokinase, PKM2, GAPDH and GLUT1, 4 samples for both CD4+ and CD8+
cells were able to be recorded. When testing for PFPK, only 3 samples for both
CD4+ and CD8+ were able to be recorded as the concentration of protein in 2 of the
samples was too low to give an accurate reading.
All 8 samples seem to show a relatively similar expression of GAPDH and PKM2,
regardless of whether they are CD4+ or CD8+ T cells. GLUT1 appears to be
expressed more in CD4+ cells when compared to CD8+ cells. The expression of
hexokinase is more sporadic, as it appears to be highly expressed in one CD4+
sample and one CD8+ sample, with no other obvious pattern. However, it is
expressed to some extent in every sample.

16. 16
ELISA
CD4+ IFN-γ expression
Figure 11: Expression of the cytokine IFN-γ in CD4+ cells after exposure to respiratory inhibitors
After exposure to each respiratory inhibitor, the supernatants of the cell samples
were analysed using an ELISA. Initially, the level of IFN-γ expression was measured
in CD4+ cells. With all the samples, the expression dropped dramatically, particularly
when the cells were exposed to 2-DG. The vehicle sample had a very similar level of
expression as the control, therefore the methanol the DON was dissolved in had no
effect on the IFN-γ expression. Exposure to DON reduced the expression
dramatically, but with a smaller difference than the cells exposed to 2-DG. The
smallest reduction in expression was observed in the cells exposed to oligomycin.

17. 17
CD8+ IFN-γ Expression
As with the CD4+ cell samples, exposure to each of the respiratory inhibitors
reduces the expression of IFN-γ dramatically. The overall expression of the cytokine
in the control sample is higher than the expression in the control sample of CD4+
cells. Again, similarly to the CD4+ cell samples, the largest decrease in expression
was observed in the sample exposed to 2-DG. The smallest decrease was seen in
the sample exposed to oligomycin, however this decrease was much larger than the
decrease in the CD4+ cell samples exposed to oligomycin. The decrease in
expression of IFN-γ in the samples exposed to etomoxir and DON was more
prominent than the reduction of expression in the oligomycin sample. Despite this,
the reduction in expression in the etomoxir and DON samples was less than the
reduction seen in the CD4+ cells exposed to the same inhibitors.
Discussion
From looking at the results for the basal metabolic rates and pathways between the
CD4+ and CD8+ T cells, a distinct difference between the pathways used before and
after activation is apparent. Upon activation, both cells show a very rapid increase in
their rate of GLY, as is expected with this type of cell. CD4+ cells increase their GLY
rate by approximately 4mpH/min whereas CD8+ cells increase their rate by
approximately 5mpH/min. The rate of GLY before activation appears to be slightly
lower in CD8+ cells, however after activation the rate reaches around 16mpH/min,
which is very similar to the rate of GLY in activated CD4+ cells. This implies that the
CD8+ cells do not rely on GLY to the same extent as CD4+ cells when they are
naive. The initial rate of OXPHOS for both CD4+ and CD8+ is at the similar rate of
45pmol/min, which then decreased slowly over time. There was not a big difference
after activation in the rate of OXPHOS in either type of cell, indicating that both types
of cell rely more heavily on GLY after activation, as is expected from prior research.
Despite this, there was a small increase in the rate of OXPHOS upon activation,
however this quickly dropped to the ‘normal’ level. This may be due to the cells
wanting to proliferate efficiently, however the speed of proliferation became more of
Figure 12: The expression of IFN-γ in the supernatants of CD8+ cells exposed to different respiratory inhibitors

18. 18
a priority very shortly after, hence the switch to GLY. This clearly shows the
effectiveness of the Warburg Effect in action.
To further support this use of GLY over OXPHOS, the presence of different glycolytic
enzymes were measured using immunoblots. In all 3 immunoblots, β-actin was used
as a control, and was consistently highly expressed in all the samples, including both
CD4+ and CD8+ cells. PFKP is the enzyme responsible for phosphorylating
fructose-6-phosphate (Kloos et al., 2015). The expression of PFKP in both the CD4+
and CD8+ cells was present, however 2 of the CD4+ cell samples appeared to have
a higher expression than in CD8+ cells. Hexokinase I is responsible for directly
phosphorylating glucose sugars into glucose-6-phosphate, the first step of GLY
(Harrington et al., 2003). The expression of this was inconclusive as it appeared that
one CD4+ sample and one CD8+ sample showed a rather high expression of
hexokinase I, whereas the other 6 samples showed very little expression. This meant
that there was no significant difference in hexokinase I expression between CD4+
and CD8+ T cells. PKM2 is involved with phosphorylating pyruvate and has been
shown to regulate aerobic glycolysis (Yang et al., 2014). The immunoblot again
showed expression of PKM2 in both sets of cells, however there may be a slightly
higher expression in CD8+ cells compared to CD4+ cells. This may be because
CD8+ cells, as shown in previous literature, have a slightly higher rate of aerobic
GLY due to their need to proliferate rapidly without using many substrates. GAPDH
is used to convert glyceraldehyde-3-phosphate into 1,3-bisphosphate. It was
relatively evenly expressed across all the samples, due to both types of cell using
GLY. The final enzyme tested for was GLUT1, the glucose transporter which
appears to be specific for the activation of CD4+ T cells (Macintyre et al., 2014),
although is also expressed in CD8+ cells. The immunoblot conducted showed a
higher expression of GLUT1 in CD4+ cells than CD8+ cells, as is expected, although
the difference was not as significant as predicted. This may be due to errors during
the blot itself or due to low concentrations of cells being added to the wells. It was
often difficult to isolate high numbers of each type of cell as there was variation
between donors.
The cell culture and flow cytometry data showed a few interesting results, as well as
some that were expected. The CD4+ cells showed a reliance on glycolysis for their
activation, as shown by the large decrease in activation when they are treated with
2-DG. This supports the research found in the literature, as CD4+ cells rely on GLY
for their proliferation. The CD4+ cells exposed to oligomycin also showed a decrease
in activation, however not to the same extent as the 2-DG samples. This implies that
OXPHOS is still used by these cells, however at a much lower amount than the use
of GLY. The use of different respiratory inhibitors allowed other metabolic pathways
associated with T cells to be analysed. The CD4+ cells exposed to etomoxir, which
inhibits FAO, showed a similar decrease in CD69 expression as the cells exposed to
oligomycin, therefore implying that CD4+ cells rely on OXPHOS and FAO a similar
amount. Contrary to this, the CD4+ cells exposed to DON, the molecule used to
inhibit glutaminolysis, still showed a relatively high expression of CD69 compared to
the other respiratory inhibitors. This implies that CD4+ cells have a very low reliance
on glutaminolysis during their activation and proliferation.
Some of the flow cytometry results for the CD8+ cells were very different to the
results from the CD4+ cells. As with the CD4+ cells, the largest reduction in CD69
expression was found in the cells exposed to 2-DG. This was as expected, as the

19. 19
literature states that CD8+ cells rely on aerobic glycolysis. Unlike the CD4+ cell
samples, the CD69 expression in the CD8+ cell samples exposed to oligomycin and
etomoxir did not have similar values. Instead, the cells exposed to oligomycin had a
much lower CD69 expression than those exposed to etomoxir. This could suggest
that CD8+ cells have more reliance on OXPHOS than FAO. This shows a difference
between the metabolism of CD4+ and CD8+ T cells based on the pathways they rely
on. The largest difference shown between the two types of T cell was observed in
the cells treated with DON. The CD4+ cells showed a slight decrease in their CD69
expression when compared to the control cells, however the opposite effect was
observed in the CD8+ cells. Overall this suggests that neither type of T cell heavily
relies on glutaminolysis for their activation, however CD4+ cells require this pathway
to some extent. Based on the flow data collected in this particular project, it appears
that inhibiting glutaminolysis is beneficial to the activation of CD8+, however this
disagrees with literature mentioned previously. This particular aspect of metabolism
could be looked at in further detail in order to decipher whether this effect is accurate
or whether it was specific to this project, as there may have been errors in the
procedure leading to these results.
The expression of IFN-γ in both types of cell allowed for more comparisons to be
drawn. In CD4+ cells exposed to 2-DG, the expression of IFN-γ is very minimal, to
almost no expression at all. This is most likely due to the fact that 2-DG is the main
inhibitor of glycolysis, the pathway responsible for a high production of IFN-γ. This
inhibition has resulted in glycolysis not being able to take place at its normal rate,
therefore less IFN-γ is being produced. When compared to CD8+ cells, the amount
of IFN-γ produced by the uninhibited cells is higher in CD8+ cells than CD4+ cells. In
both the CD4+ and CD8+ cells, the expression of IFN-γ was reduced in those
exposed to oligomycin. However, there was a much larger reduction in the CD8+
cells expression when compared to CD4+ cells. This could imply that CD8+ cells
more heavily rely on OXPHOS for their IFN-γ production and expression whereas
CD4+ cells use more GLY. The cells exposed to etomoxir and DON showed a larger
decrease in IFN-γ expression than those exposed to oligomycin. This was common
in both CD4+ and CD8+ T cells, with the reduction in the DON exposed cells being
greater in the CD4+ cells than the CD8+ cells. This implies that CD4+ cells are more
reliant on the action of glutaminolysis than CD8+ cells to produce IFN-γ. Another
study looking at the effect of glutaminolysis on proliferation and cytokine production
has found that it can play a larger role than OXPHOS in some cases, due to its
ability to create biomass (Wahl et al., 2012). This corresponds with the data found for
CD4+ T cells in this project, therefore prompting the further analysis of glutaminolysis
within activated CD8+ cells.
This research could be applied to cancer research, as the metabolism of T cells can
have an effect on their activity, as has been shown from this data. An increase in
knowledge about the differences in T cell metabolism would allow for further
research to be focused on how to manipulate this metabolism in order to make the
cells more cytotoxic or produce more cytokines. From the data collected in this
project, the activity of GLY appears to have the largest effect on proliferation and
cytokine production, particularly with CD4+ T cells. If there were future developments
that allowed for GLY in CD4+ to be upregulated, there would be more CD4+ cells
activated and proliferating, as well as an increase in IFN-γ production which would
attract more T cells to the cancerous cells. This cascade effect would then lead to
the activation and increased function of CD8+ cells which would destroy the

20. 20
cancerous cells directly. The main problem with this type of treatment would be the
lack of specificity, however current research is underway which focuses on mutating
these T cells to increase specificity (Porter et al., 2011).
Overall, there are metabolic differences between CD4+ and CD8+ T cells,
predominantly based around which metabolic pathways they are more reliant on for
their function. Both types of cell use the Warburg Effect, where they continue to use
GLY as their main metabolic pathway even when they have access to oxygen. This
allows CD8+ cells in particular to proliferate at a much faster rate. The use of
respiratory inhibitors further supports this theory as both types of cell were mostly
reliant on the activity of GLY for activation as well as their production of IFN-γ.
Despite this, both types of cell also used other respiratory pathways in order to
maintain their ATP levels for survival, such as glutaminolysis and FAO. Both types
appeared to be more reliant on FAO than glutaminolysis, however CD4+ cells are
more reliant of FAO than CD8+ cells.

21. 21
References
Cao, Y., Rathmell, J.C., and Macintyre, A.N. (2014) Metabolic reprogramming
towards aerobic glycolysis correlates with greater proliferative ability and resistance
to metabolic inhibition in CD8 versus CD4 T cells. PLoS One 9: e104104
Chang, C.-H., Curtis, J.D., Maggi, L.B., Faubert, B., Villarino, A.V., O’Sullivan, D., et
al. (2013) Posttranscriptional Control of T Cell Effector Function by Aerobic
Glycolysis. Cell 153: 1239–1251
Chapman, T.J., and Topham, D.J. (2010) Identification of a unique population of
tissue-memory CD4+ T cells in the airways after influenza infection that is dependent
on the integrin VLA-1. J Immunol 184: 3841–3849
Corthay, A. (2009) How do regulatory T cells work? Scand J Immunol 70: 326–336
Gubser, P.M., Bantug, G.R., Razik, L., Fischer, M., Dimeloe, S., Hoenger, G., et al.
(2013) Rapid effector function of memory CD8+ T cells requires an immediate-early
glycolytic switch. Nat Immunol 14: 1064–1072
Harrington, G.N., and Bush, D.R. (2003) The bifunctional role of hexokinase in
metabolism and glucose signaling. Plant Cell 15: 2493–2496
Heiden, M.G. Vander, Cantley, L.C., and Thompson, C.B. (2009) Understanding the
Warburg effect: the metabolic requirements of cell proliferation. Science 324: 1029–
1033
Janeway, C., Travers, P., Walport, M., and Shlomchik, M.J. (2001) Immunobiology:
The immune system in health and disease. 5th ed., Garland Publishing, New York.
pp 45-53
Jiang, P., Du, W., and Wu, M. (2014) Regulation of the pentose phosphate pathway
in cancer. Protein Cell 5: 592–602
Klinker, M.W., and Lundy, S.K. (2012) Multiple mechanisms of immune suppression
by B lymphocytes. Mol Med 18: 123–137
Kloos, M., Brüser, A., Kirchberger, J., Schöneberg, T., and Sträter, N. (2015) Crystal
structure of human platelet phosphofructokinase-1 locked in an activated
conformation. Biochem J 469: 421–432
Koretzky, G.A. (2010) Multiple roles of CD4 and CD8 in T cell activation. J Immunol
185: 2643–2644
Macintyre, A.N., Gerriets, V.A., Nichols, A.G., Michalek, R.D., Rudolph, M.C.,
Deoliveira, D., et al. (2014) The glucose transporter Glut1 is selectively essential for
CD4 T cell activation and effector function. Cell Metab 20: 61–72
MacIver, N.J., Michalek, R.D., and Rathmell, J.C. (2013) Metabolic Regulation of T
Lymphocytes. Annu Rev Immunol 31: 259–283

22. 22
Miceli, M.C., and Parnes, J.R. (1991) The roles of CD4 and CD8 in T cell activation.
Semin Immunol 3: 133–141
Pearce, E.L. (2010) Metabolism in T cell activation and differentiation. Curr Opin
Immunol 22: 314–320
Porter, D.L., Levine, B.L., Kalos, M., Bagg, A., and June, C.H. (2011) Chimeric
antigen receptor-modified T cells in chronic lymphoid leukemia. N Engl J Med 365:
725–733
Preter, G. De, Neveu, M.-A., Danhier, P., Brisson, L., Payen, V.L., Porporato, P.E.,
et al. (2015) Inhibition of the pentose phosphate pathway by dichloroacetate
unravels a missing link between aerobic glycolysis and cancer cell proliferation.
Oncotarget 7: 2910–2920
Samudio, I., Harmancey, R., Fiegl, M., Kantarjian, H., Konopleva, M., Korchin, B., et
al. (2010) Pharmacologic inhibition of fatty acid oxidation sensitizes human leukemia
cells to apoptosis induction. J Clin Invest 120: 142–156
Schroder, K., Hertzog, P.J., Ravasi, T., and Hume, D.A. (2004) Interferon-gamma:
an overview of signals, mechanisms and functions. J Leukoc Biol 75: 163–189
Shanker, A., Buferne, M., and Schmitt-Verhulst, A.-M. (2010) Cooperative action of
CD8 T lymphocytes and natural killer cells controls tumour growth under conditions
of restricted T-cell receptor diversity. Immunology 129: 41–54
Shchepina, L.A., Pletjushkina, O.Y., Avetisyan, A. V, Bakeeva, L.E., Fetisova, E.K.,
Izyumov, D.S., et al. (2002) Oligomycin, inhibitor of the F0 part of H+-ATP-synthase,
suppresses the TNF-induced apoptosis. Oncogene 21: 8149–8157
Sukumar, M., Liu, J., Ji, Y., Subramanian, M., Crompton, J.G., Yu, Z., et al. (2013)
Inhibiting glycolytic metabolism enhances CD8+ T cell memory and antitumor
function. J Clin Invest 123: 4479–4488
Sun, J.C., Williams, M.A., and Bevan, M.J. (2004) CD4+ T cells are required for the
maintenance, not programming, of memory CD8+ T cells after acute infection. Nat
Immunol 5: 927–933
Thomas, A.G., O’Driscoll, C.M., Bressler, J., Kaufmann, W., Rojas, C.J., and
Slusher, B.S. (2014) Small molecule glutaminase inhibitors block glutamate release
from stimulated microglia. Biochem Biophys Res Commun 443: 32–36
Turvey, S.E., and Broide, D.H. (2010) Innate immunity. J Allergy Clin Immunol 125:
S24–32
Wahl, D.R., Byersdorfer, C.A., Ferrara, J.L.M., Opipari, A.W., and Glick, G.D. (2012)
Distinct metabolic programs in activated T cells: opportunities for selective
immunomodulation. Immunol Rev 249: 104–115
Wang, R., and Green, D.R. (2012) Metabolic checkpoints in activated T cells. Nat
Immunol 13: 907–915

23. 23
Windt, G.J.W. van der, O’Sullivan, D., Everts, B., Huang, S.C.-C., Buck, M.D., Curtis,
J.D., et al. (2013) CD8 memory T cells have a bioenergetic advantage that underlies
their rapid recall ability. Proc Natl Acad Sci U S A 110: 14336–14341
Yang, L., Xie, M., Yang, M., Yu, Y., Zhu, S., Hou, W., et al. (2014) PKM2 regulates
the Warburg effect and promotes HMGB1 release in sepsis. Nat Commun 5: 4436
Zhong, D., Xiong, L., Liu, T., Liu, X., Liu, X., Chen, J., et al. (2009) The glycolytic
inhibitor 2-deoxyglucose activates multiple prosurvival pathways through IGF1R. J
Biol Chem 284: 23225–23233
Acknowledgements
Many thanks to Dr Nigel Francis for supervising me through this project, to Dr James
Cronin for all his help with the immunoblots, to Silvia Panetti for helping me in the lab
and always offering me support, but above all to Nick Jones for working tirelessly in
and out of the lab to help me throughout the project.