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System biology approaches to cancer and diabetes metabolism
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System biology approaches to cancer and diabetes metabolism
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Content
System Biology Approaches to Cancer
and Diabetes Metabolism
by
Dongqing Zheng
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(CHEMICAL ENGINEERING)
August 2021
Copyright 2021 Dongqing Zheng
ii
Acknowledgments
I would like to express my wholehearted appreciation to my research advisor, Dr. Nicholas
Graham. His patience, warmness, and knowledge epitomize a good scientist, and guided me
throughout my Ph.D. career. His mentorship has had made invaluable impact on my life.
I sincerely with to thank my committee, Dr. Stacey Finley, Dr. Malancha Gupta, Dr. Kate White,
Dr. Katherine S-F Shing Wang, and Dr. Ted Lee for supporting my research, Qualifying Exam
and Dissertation.
I am grateful to having the opportunity to collaborate with Dr. Jennifer Rohrs, Dr. Stacey Finley,
Dr. Pin Wang, Dr. Marcelo Coba, Dr. Scott Fraser, Dr. Barak Raveh, Dr. Liping Sun, Dr. Kate
White, Dr. Andrej Sali, Dr. Raymond Stevens and the great people from the Pancreatic Beta Cell
Consortium
I would like to thank my co-workers and friends in the Graham Lab, Dr. Alireza Delfarah, Dr.
James Joly, Nicolas Hartel, Jonathan Sussman, Matthew Jeon, Kylie Burdsall, Melanie
MacMullan, Belinda, Sydney Parrish, as well as my friend Luke Lin.
Finally, and most importantly, I would like to express my gratitude to my parents, and my loving
wife Cici Chen, whose love and guidance are with me in whatever I pursue.
iii
Table of Contents
Acknowledgments ......................................................................................................................... ii
List of Figures ................................................................................................................................ v
Abstract ....................................................................................................................................... vii
1. Chapter 1. Introduction ............................................................................................................ 1
1.1. Mass spectrometry-based proteomics and metabolomics as tools to understand the
mechanisms of disease ................................................................................................................ 1
1.2. Cancer metabolism, oncogenes, and the effect of galactose on cellular metabolism ........... 2
1.3. Diabetes and metabolism ...................................................................................................... 4
2. Chapter 2: AKT but not MYC promotes reactive oxygen species-mediated cell death in
oxidative culture ............................................................................................................................ 8
2.1. Objective .............................................................................................................................. 8
2.2. Materials and Methods ...................................................................................................... 8
2.3. Results ................................................................................................................................ 16
1.1.1. AKT but not MYC temporarily induces cell death in oxidative culture. .............. 16
1.1.2. mAKT-expressing cells exhibit extensive metabolic adaptation in galactose
culture. 19
1.1.3. Short-term galactose culture increases glutathione metabolism in mAKT-
expressing cells. ..................................................................................................................... 23
1.1.4. Proteomic analysis reveals enriched nonsense-mediated mRNA decay (NMD) in
mAKT-expressing cells in short-term galactose culture. ...................................................... 24
1.1.5. mAKT but not RFP- or MYC-expressing cells exhibited oxidative stress in short-
term galactose culture. ........................................................................................................... 27
1.1.6. Galactose culture-induced cell death can be rescued by ROS scavengers. ........... 32
2.4. Discussion and Conclusion ............................................................................................... 33
3. Chapter 3: 2. .......... Metabolomics reveals that aspartate and asparagine couple to insulin
secretion in pancreatic β-cells ..................................................................................................... 38
3.1. Objective ............................................................................................................................ 38
3.2. Materials and Methods .................................................................................................... 38
3.3. Results ................................................................................................................................ 42
1.1.7. Glucose and Exendin-4 stimulate insulin secretion response in INS1E cells. ...... 42
1.1.8. Glucose but not Ex-4 induces a global shift in the metabolomics profile of INS1E.
44
1.1.9. Functional metabolites groups correlated with insulin secretion. ......................... 46
1.1.10. GAPDH inhibition blocks glycolytic flux and reduces insulin secretion .............. 47
iv
1.1.11. Aspartate and asparagine supplementation increases insulin secretion in INS1E
cells. 48
1.1.12. Malate-aspartate shuttle inhibitor AOA blocks aspartate induced insulin ............ 52
3.4. Discussion and Conclusion ............................................................................................... 55
References .................................................................................................................................... 60
Supplemental Figures .................................................................................................................. 69
v
List of Figures
Figure 2.1. mAKT but not MYC negatively affects proliferation in galactose culture.
Figure 2.2. Stable isotope tracing metabolomics reveals differential usage of glutamine and
galactose in mAKT-expressing cells.
Figure 2.3. Glutathione metabolism is enriched in mAKT- but not RFP- or MYC-
expressing MCF-10A cells in short-term galactose culture.
Figure 2.4. Proteomic profiling reveals that mAKT-expressing cells exhibit upregulation of
nonsense-mediated mRNA decay (NMD) proteins in short-term galactose culture.
Figure 2.5. mAKT- but not RFP- or MYC-expressing cells exhibited increased ROS upon
switching from glucose to galactose.
Figure 2.6. ROS are required for galactose-induced cell death.
Figure 3.1. Insulin secretion response of INS1E cells to glucose and Exendin-4.
Figure 3.2. Bioinformatic analysis identifies Glycolysis and TCA cycle intermediates and
amino acids positively and negatively correlated with insulin secretion, respectively.
Figure 3.3. GAPDH inhibition by koningic acid (KA) blocks glycolysis flux and negatively
affects insulin secretion.
Figure 3.4. Aspartate and asparagine addition positively affect insulin secretion in INS1E
Figure 3.5. Malate-aspartate shuttle inhibitor AOA blocks aspartate induced insulin
secretion.
Supp. Figure S2.1. Effects of galactose culture on cell growth and viability, and AKT
activation.
Supp. Figure S2.2. Quantitation of phospho-AKT and AKT in glucose and galactose
culture for mAKT-expressing cells
Supp. Figure S2.3. Experimental overview
Supp. Figure S2.4. Atom transition maps for [U-
13
C]-L-glutamine and [U-
13
C]-galactose
stable isotope labeling.
Supp. Figure S2.5. Differential usage of glutamine in mAKT-expressing cells.
Supp. Figure S2.6. Metabolite set enrichment analysis of glutathione metabolism for RFP-
and MYC-expressing MCF-10A cells.
Supp. Figure S2.7. LC-MS proteomics Experiment 2 demonstrates enrichment of nonsense-
mediated mRNA decay (NMD) proteins in mAKT cells switched to galactose.
Supp. Figure S2.8. Density plots of DCF-DA time course.
vi
Supp. Figure S3.1. Glucose and Ex-4 stimulate secrtion and a metabolite shift in INS1E
cells.
Supp. Figure S3.2. Bioinformatic analysis identifies that glycolysis and TCA cycle
intermediates and amino acids are positively and negatively correlated with insulin
secretion,
Supp. Figure S3.3 .GAPDH inhibition blocks glycolysis flux and reduces insulin secretion.
Supp. Figure S3.4 . Inhibition of the malate-aspartate shuttle reduces aspartate-induced
insulin secretion.
vii
Abstract
The emergence of liquid chromatography-mass spectrometry-based (LC-MS) proteomics and
metabolomics provide a systematic approach to uncover the complex and dynamic protein and
metabolite networks regulating human disease. Metabolomics is the study of global metabolite
concentrations or fluxes in cells, tissues, and organisms. Metabolites represent not only the output
of gene and protein networks but also play important role in regulating various biological functions.
The dysregulation of metabolism leads to various diseases, including cancer and diabetes.
Many cancers preferentially use glycolysis for survival and proliferation, even in the presence of
oxygen, a phenomenon known as aerobic glycolysis or the Warburg effect. Increased glycolytic
activity is thought to help satisfy the rapacious demands of highly proliferative cancer cells for
biosynthetic precursors, including lipids, proteins, and nucleic acids. Understanding the interplay
between oncogenes and metabolism is essential to understand how to design therapeutic strategies
targeting tumor metabolism. Diabetes is a worldwide pandemic, and type 2 diabetes (T2D) is the
most common form of the disease. Effectively using insulin in the human body is critical for
maintaining calorigenic nutrient homeostasis. Understanding the underlying pathways that
regulate insulin production, secretion, and storage in pancreatic beta cell (β-cells) can provide a
basis for rational T2D therapy. Taken together, this work highlight the use of LC-MS
metabolomics and proteomics approach to uncover oncogene-dependent metabolic vulnerabilities
in cancer cells, and the mechanism underlying glucose stimulated insulin secretion (GSIS)
synergizing with amino acids suggesting appropriate therapies targeting disease associated with
abnormal metabolism.
1
1. Chapter 1. Introduction
1.1. Mass spectrometry-based proteomics and metabolomics as tools to understand
the mechanisms of disease
The emergence of liquid chromatography-mass spectrometry-based (LC-MS) proteomics
and metabolomics provides a systematic approach to uncover the complex and dynamic protein
and metabolite networks regulating human disease. Metabolomics is the study of global metabolite
concentrations or fluxes in cells, tissues, and organisms. Metabolites represent not only the output
of gene and protein networks but they also regulate biological functions. Although the diverse
chemical nature of metabolites presents a challenge to global analysis, advances in LC-MS have
enabled system-wide insight into cellular function and disease. MS is the preferred metabolite
detection technology thanks to its unmatched sensitivity and specificity (1–3) .
Proteomics is the study of the protein complement of the genome. Proteomics can refer to
1) when and where proteins are expressed, 2) rates of protein production, degradation, and steady-
state abundance; 3) how proteins are modified (eg, post-translational modifications (PTMs) such
as phosphorylation); or 4) how proteins interact with one another (eg, protein-protein interactions)
(1–3). Notably, within an organism, the proteome can differ from cell to cell, or can vary within a
single cell over time. This is in contrast to the genome, which is very stable (even in cancer)
throughout an organism and over time. Using LC-MS, thousands of proteins and/or phospho-
proteins from a complex cell lysate can be identified and quantified routinely. Using these data,
we can explore cellular functions to generate great insight into the complex and dynamic nature of
diseases with altered metabolism including cancer and type-2 diabetes (T2D). By coupling
proteomic and metabolomic analysis, I propose to explain how metabolism regulates disease and
permit discovery of therapeutic targets for suppressing tumorigenic or diabetic activities.
2
1.2. Cancer metabolism, oncogenes, and the effect of galactose on cellular
metabolism
Cancer metabolism
Many cancers preferentially use glycolysis for survival and proliferation, even in the
presence of oxygen, a phenomenon known as aerobic glycolysis or the Warburg effect. Increased
glycolytic activity is thought to help satisfy the rapacious demands of highly proliferative cancer
cells for biosynthetic precursors including lipids, proteins, and nucleic acids. However, this altered
metabolism can leave tumors vulnerable to metabolic disruptions such as starvation of substrates
including glucose, asparagine, glutamine, methionine, serine, and others (4–10). Therefore,
understanding the interplay between oncogenes and metabolism is essential to understand how to
design therapeutic strategies targeting tumor metabolism (2,11).
Oncogenes
The altered metabolism of tumor cells has been directly linked to the same oncogenes that
drive tumorigenesis. In breast cancer, both the PI3K/AKT signaling pathway and the transcription
factor MYC are frequently hyperactivated (12). Deregulated PI3K/AKT signaling can result from
multiple mechanisms including PIK3CA mutation, PTEN loss/mutation, or high AKT3 expression.
Hyperactivated PI3K/AKT signaling, which is prominent in the luminal subtype of breast cancer,
results in increased glycolytic flux by altering the localization and activity of glycolytic enzymes
including glucose transporters, hexokinase, and phosphofructokinase (13). The oncogenic
transcription factor MYC is typically hyperactivated by high-level DNA amplification, especially
in the basal subtype of breast cancer. Like AKT, MYC also exerts broad effects on the metabolism
of tumor cells including roles in glycolysis, glutamine metabolism, nucleotide biosynthesis, and
3
other metabolic processes (14). However, although both AKT and MYC promote aerobic
glycolysis, these oncogenes can exert differential effects on metabolism in prostate cancer and pre-
B cells (15,16).
Oxidative culture
The galactose culture system is a classic technique for shifting the metabolism of
mammalian cells from glycolysis to aerobic respiration. In glucose-containing media, mammalian
cells can utilize both glycolysis and oxidative phosphorylation (OXPHOS) to generate ATP.
However, when galactose is substituted for glucose, cells must rely on OXPHOS for energy
generation. Most mammalian cells exhibit a flexible metabolic state that can dynamically shift
between glycolysis and OXPHOS. Because galactose culture forces oxidative metabolism, it has
proved useful in the identification of inborn errors of metabolism (17,18), drugs that redirect
metabolism to glycolysis (19), genes essential for OXPHOS (20), and the role of aerobic glycolysis
in T cell effector functions (21). However, to our knowledge, the galactose culture system has not
been used to investigate the metabolic vulnerabilities induced by oncogenes.
Here, we report that non-tumorigenic, immortalized mammary epithelial cells expressing
constitutively active AKT (i.e., myristoylated AKT1, or mAKT) undergo rapid cell death when
switched from glucose to galactose culture. In contrast, cells expressing either RFP (i.e., negative
control) or MYC readily proliferated in galactose. Cell death in AKT-expressing cells was,
however, short-lived, and cells expressing AKT recommenced cell growth after ~15 days in
galactose culture. To understand the differential phenotype of AKT-expressing cells, we
performed metabolomic and proteomic analyses using mass spectrometry and found evidence that
4
AKT-expressing cells in galactose were dying due to oxidative stress. We therefore used the
reactive oxygen species (ROS) scavenger catalase to validate that ROS were required for
galactose-induced death of AKT-expressing cells. Importantly, we tested breast cancer cell lines
with constitutively active PI3K/AKT signaling and found that these cells also exhibited ROS-
mediated cell death in galactose culture. Taken together, our multi-omic analysis has identified a
metabolic state-dependent lethality, namely the reduced ability of cells with constitutive activation
of PI3K/AKT to switch between glycolysis and respiration.
1.3. Diabetes and metabolism
Pancreatic beta-cell and Glucose-stimulated insulin secretion (GSIS)
Diabetes is a worldwide pandemic, and type 2 diabetes (T2D) is the most common form of
the disease. Effectively using insulin in the human body is critical for maintaining calorigenic
nutrient homeostasis. Understanding the underlying pathways that regulate insulin production,
secretion, and storage in β-cells can provide a basis for rational T2D therapy (22,23). When β-cells
are exposed to glucose, the glucose transporter 2 (GLUT2) expressed on the membrane of the β-
cell eables the import of glucose. Increased glycolytic activity and oxidative phosphorylation then
result in elevated adenosine triphosphate (ATP) production (24,25). Rapid insulin release then
occurs through an ATP-sensitive potassium [K
+
] channel (KATP) dependent mechanism (26).
Higher ATP concentration leads to a net outflux of K
+
from cytoplasm, which depolarizes the β-
cell membrane and opens voltage-dependent calcium [Ca
2+
] channel (VDAC). Opening of VDAC
allows Ca
2+
ions influx into cytoplasm potentiating insulin granules exocytosis (27). This
phenomenon is known as glucose-stimulated insulin secretion (GSIS).
5
Secretagogues are hormones that stimulate secretion for biological functions. Insulin
secretagogues are available for T2D treatment. One type, e.g. Metformin, can temperately reduce
blood glucose level by preventing the conversion of glycogen to glucose (28). Glucagon-like
peptide-1 (GLP-1) is a type of incretin hormone that acts on β-cells and stimulates insulin secretion
by increasing cellular cyclic adenosine monophosphate (cAMP) (26). Exendin-4 (Ex-4) is a
peptide and it was first discovered in the saliva of Glia Monster. It was later characterized as an
analog to GLP (29–32). In β-cells, both GLP1 and Ex-4 bind to the class E GPCR glucagon-like
peptide receptor (GLP-1R), upregulate cAMP, and triggers the PKA signaling cascade. Activated
PKA signaling activates transcription factors including cAMP response element-binding protein
(CREB) and pancreatic and duodenal homeobox 1 (PDX-1). CREB and PDX-1 exert broad effects,
including potentiating insulin production and regulating the intracellular calcium gradient for
insulin secretory granule exocytosis (33–35). The exocytosis of insulin granules has been directly
linked to the cytoplasmatic Ca
2+
ions influx and activated calcium calmodulin kinase (CAMK)
downstream of PKA signaling (36,37).
Besides glucose and incretins, recent studies have demonstrated that amino acids also play
critical roles in enhancing GSIS via the NADH shuttles. Notably, aspartate-glutamate carrier 1
(Aralar1) and aspartate aminotransferases (AST1/2) belong to the malate-aspartate shuttle are
highly expressed in β-cells (38–40). It has been shown that inhibition of AST1/2 by AOA abolishes
the cAMP/PKA dependent incretin-induced insulin secretion in MIN6 mouse β-cell line but did
not affect GSIS (39). Inhibition of malate-aspartate shuttle diminishes the cytosolic glutamate
production by incretin stimulation (39). In addition, it has also been demonstrated that
overexpression of Aralar1 on INS1E cells is directly associated with elevated NAD(P)H turnover
6
rates, mitochondria activation, and insulin secretion (40). However, the links between other
malate-aspartate shuttle intermediates, such as aspartate, insulin production and secretion remain
elusive.
Pancreatic islets and insulin-secreting insulinoma INS1E cell model
Islet of Langerhans comprises a plethora of acinar and ductal pancreatic cell types.
Including α cell (secreting glucagon), β cell (secreting insulin), ∂ cell (secreting somatostatin), γ
cell (pancreatic polypeptide), ε cell (secreting ghrelin), and others. Each cell type has a unique
biomarker and functional roles. This heterogeneity of pancreatic cells poses a challenge in
establishing a proper cell model to study β cell biology (41,42). Using tissue samples from T2D
patient ensure the most physiological relevance. However, isolating β-cells from the pancreatic
islet is challenging, and patient variation is another obstacle. To address these challenges, several
immortalized rat and human pancreatic β cell lines are used represent a stable β cell model for
exploratory research.
1.1B4 was generated by electrofusion of human pancreatic β-cells and the immortalized
human PANC-1 cell lines. 1.1B4 tested positive for insulin, GSIS pathway-specific proteins like
PC1/3, PC2, Glut1, glucokinase, and KATP channel complex by Western blot, RT-PCR, and
immunohistochemistry (43). INS1E cells are a rat pancreatic β-cell line isolated from the parental
radiation-induced rat insulinoma cells, based on the high insulin content and secretory response to
glucose. INS1E cell line was tested for insulin secretion in 116 passages over two years and
demonstrated no significant insulin secretion difference (44,45). Recently, EndoC-βH1 cell line
was also made available by genetically modifying from human fetal pancreatic buds. The EndoC
7
cell line was tested, expressing many β specific markers but not other pancreatic cell types specific
proteins (46). Although none of the β-cell lines can fully recapitulate the physiology of the primary
β-cell, they offer advantages in LC-MS based metabolomics for investigative β-cell functions. β-
cell lines require simpler nutrients, in contrast to primary cells, to culture, and can proliferate
indefinitely. Together, these cell lines can serve as models of the pancreatic β-cells.
8
2. Chapter 2: AKT but not MYC promotes reactive oxygen species-
mediated cell death in oxidative culture
2.1. Objective
The altered metabolism of tumors has long been proposed as a therapeutic target. Defining
the metabolic vulnerabilities induced by specific oncogenes is crucial for the design and
stratification of therapeutics targeting tumor metabolism. Increased glycolytic activity is thought
to help satisfy the rapacious demands of highly proliferative cancer cells for biosynthetic
precursors including lipids, proteins, and nucleic acids. However, this altered metabolism can
leave tumors vulnerable to metabolic disruptions such as starvation of substrates including glucose,
asparagine, glutamine, methionine, serine, and others. Therefore, understanding the interplay
between oncogenes and metabolism is essential to understand how to design therapeutic strategies
targeting tumor metabolism.
2.2. Materials and Methods
Cell culture:
MCF-10A human mammary epithelial cells were obtained from American Type Culture
Collection (ATCC, obtained in November 2016). Cells were cultured in DMEM/Ham’s F-12
supplemented with 5% horse serum, 100 ng/ml chole ra toxin, 20 ng/ml epidermal growth
factor, 10 µg/ml insulin, 500 ng/ml hydrocortisone, 10,000 units/ml penicillin G, 10 mg/ml
streptomycin sulfate, and 25 ug/ml amphotericin B, and either 25 mM glucose or galactose. MDA-
MB-436 (MB436) and Hs578t cells were a gift from Dr. Michael Press (USC Department of
Pathology, obtained in January 2019). MB436 and Hs578t cells were cultured in DMEM
supplemented with 10% FBS, 10,000 units/ml penicillin G, 10 mg/ml streptomycin sulfate, and 25
9
ug/ml amphotericin B. Dialyzed FBS was used for all experiments where MB436 and Hs578t cells
were cultured with galactose. All cells were grown in a 5% CO2, 37˚C, and humidified incubator
and were used within 30 passages of thawing. Cell counting and viability were assessed using
trypan blue staining with a TC20 automated cell counter (BioRad).
Retroviral infection:
RFP, myristoylated AKT1 (mAKT), and MYC were cloned into the pDS-FB-neo retroviral
vector and verified by Sanger sequencing. Retrovirus was prepared in 293T cells by co-
transfection with viral packaging plasmids. Following infection, MCF-10A cells were selected
with 1 mg/ml G418. Following selection, cells were maintained in media with 500 µg/ml G418.
Reactive oxygen species measurement:
MCF-10A were treated in respective media for 3 h. For total ROS determination, cells were
incubated with 5 µM of DCF-DA (Biotium #10058) for 30 min at 37˚C MCF-10A prior to
trypsinization. The cells were washed with PBS twice, lifted with TrypLE, and resuspended in
PBS for an approximate final concentration of 1 million cells/ml. For mitochondrial ROS
determination, cells were assessed by MitoSOX
TM
Red Mitochondrial Superoxide Indicator
(ThermoFisher M36008) according to the manufacturer’s instruction. Samples were analyzed on
a Miltenyi Biotec MACSQuant flow cytometer with FITC channel (488 nm excitation/520 nm
emission) or PE channel (585 nm exication/578 nm emission) to measure fluorescence, and data
were processed and analyzed with flowCore (1.48.1) R package.
Annexin V and Propidium Iodide (PI) staining:
10
mAKT- and MYC-expressing MCF-10A cells were switched from glucose culture to
galactose culture for 3 hours. Cells were then subjected to trypsinization and pelleting by
centrifugation (500g, 3 min). Cell pellets were resuspended in 500 μl of binding buffer, then
stained with 5 μl of Annexin V-FITC and 5 μl of PI stock (BioVision 10013-436) for 5 min in
darkness according to the manufacturer’s instruction. The probed samples were analyzed on a
Miltenyi Biotec MACSQuant flow cytometer with FITC channel (488 nm excitation/520 nm
emission) or APC channel (652 nm exication/660 nm emission) to measure fluorescence, and data
were processed and analyzed with FlowJo
TM
(v.10.6.1).
Phospho-flow measurement of AKT and phospho-AKT:
MCF-10A were treated in respective media for 3 h before trypsinization. The suspended
cells were centrifuged (3000 g, 2 min) and washed twice with 1 ml of PBS. Cell pellets were gently
mixed with 100 ul of Citofix/Cytoperm (BD Bioscience #554714) and incubated at 4˚C for 15 min
for membrane permeabilization. Then cell suspensions were diluted with 100 ul of wash buffer
(BD Bioscience #554714) and pelleted. An additional of 150 ul of wash buffer was added and
pellet cells by centrifugation for washing. Cells were then resuspended in 50 ul of wash buffer
containing 1:50 anti-phosphoSer473-AKT1 APC-fluorochrome (eBioscience
TM
, #17971542) or
1:50 anti-total-AKT (Cell Signaling Technology 9272) and incubated for 30 min at 4 ˚C in
darkness. The anti-total-AKT stained cells were further stained with 1:100 ratio secondary
antibody (ThermoFisher Sientific, #A-10931)) in wash buffer for 10 min at 4 ˚C in darkness. Cells
were washed twice with 150 ul of PBS and analyzed on a Miltenyi Biotec MACSQuant flow
cytometer with APC channel (652 nm excitation/660 nm emission) to measure fluorescence. Data
were processed and analyzed with FlowJo
TM
(v.10.6.1).
11
Western blotting:
Cells were lysed in modified RIPA buffer (50 mM Tris–HCl (pH 7.5), 150 mM NaCl, 50
mM β-glycerophosphate, 0.5 mM NP-40, 0.25% sodium deoxycholate, 10 mM sodium
pyrophosphate, 30 mM sodium fluoride, 2 mM EDTA, 1 mM activated sodium vanadate, 20 µg/ml
aprotinin, 10 µg/ml leupeptin, 1 mM DTT, and 1 mM phenylmethylsulfonyl fluoride). Whole-cell
lysates were resolved by SDS–PAGE on 4–15% gradient gels and blotted onto nitrocellulose
membranes (Bio-Rad). Membranes were blocked for 1 h and then incubated with primary
overnight and secondary antibodies for 2 h. Blots were imaged using the Odyssey Infrared Imaging
System (Li-Cor). Primary antibodies used for Western blot analysis were: 1:500 anti-AKT (Cell
Signaling Technology 9272), 1:500 anti-phospho-Ser473-AKT (Santa Cruz Biotechnology sc-
7985-R), 1:500 anti-c-MYC (Cell Signaling Technology 9402), 1:1000 anti-β-actin (Proteintech
66009-1-lg), IRDye 800CW Goat anti-Mouse IgG (VWR 926-32210), and IRDye 680RD Goat
anti-Rabbit IgG (926-68071). Band intensities were quantified using Li-Cor Image Studio
TM
Lite
(V.5.0).
LC-MS Metabolomics:
MCF-10A cells were plated on 6-well plates at the density of 7,333 cells/cm
2
. After 24 h,
media was removed, cells were washed twice with 2 mL of PBS, and 1 mL of media was added to
cells. Media contained either [U-
13
C]-L-glutamine or [U-
13
C]-galactose (Cambridge Isotope
Laboratories). After 24 h, the culture plates were cooled on ice, media was aspirated, and the cells
were washed with 1 mL of cold ammonium acetate. Upon aspirating the ammonium acetate,
metabolites were extracted with 1 mL of -80˚C methanol. The methanol cell suspension was
scraped and transferred to Eppendorf tubes, and the cell suspension was centrifuged at 4˚C. The
12
supernatants was transferred to a new Eppendorf tubes, and the pellet was re-extracted with another
350 μL of -80˚C methanol. The second methanol extraction was spun down, and the supernatant
was pooled with the first extraction. Metabolites were speed-vac dried, resuspended in LC-MS
grade water, and sent for LC-MS analysis.
Samples were randomized and analyzed on a Q Exactive Plus hybrid quadrupole-Orbitrap
mass spectrometer coupled to an UltiMate 3000 UHPLC system (Thermo Scientific). The mass
spectrometer was run in polarity switching mode (+3.00 kV/-2.25 kV) with an m/z window ranging
from 65 to 975. Mobile phase A was 5 mM NH4AcO, pH 9.9, and mobile phase B was acetonitrile.
Metabolites were separated on a Luna 3 µm NH2 100 Å (150 × 2.0 mm) column (Phenomenex).
The flowrate was 300 µL/min, and the gradient was from 15% A to 95% A in 18 min, followed by
an isocratic step for 9 min and re-equilibration for 7 min. All samples were run in biological
triplicate. Metabolites were detected and quantified as area under the curve based on retention time
and accurate mass (≤ 8 ppm) using the TraceFinder 3.3 (Thermo Scientific) software. Raw data
were corrected for naturally occurring
13
C abundance (47). Intracellular data was normalized to
the cell number at the time of extraction.
LC-MS Proteomics:
MCF-10A cells dishes were placed on ice and washed with PBS. Cells were then scraped
and pelleted by centrifugation. The cell pellets were lysed by probe sonication in 8 M urea (pH
7.5), 50 mM Tris, 1 mM activated sodium vanadate, 2.5 mM sodium pyrophosphate, 1 mM β-
glycerophosphate, and 100 mM sodium phosphate. The above procedures were performed in 0-
4˚C. Insoluble cell debris were filtered by 0.22 um syringe filter. Protein concentration was
13
measured by BCA assay (Pierce, PI23227). Lysates were reduced with 5 mM DTT, alkylated with
25 mM iodoacetamide, quenched with 10 mM DTT, and acidified to pH 2 with 5% trifluoracetic
acid. Proteins were then digested to peptides using a 1:100 trypsin to lysate ratio by weight. Tryptic
peptides were desalted by reverse phase C18 StageTips and eluted with 30% acetonitrile. The
eluents were vacuumed dried, and 250 ng/injection was submitted to LC-MS. We performed two
independent biological replicates, and each experiment were subjected to two technical LC-MS
injections.
The samples were randomized and injected into an Easy 1200 nanoLC ultra high-
performance liquid chromatography coupled with a Q Exactive quadruple orbitrap mass
spectrometry (Thermo Fisher). Peptides were separated by a reverse-phase analytical column
(PepMap RSLC C18, 2 µm, 100Å, 75 µm X 25 cm). Flow rate was set to 300 nL/min at a gradient
from 3% buffer B (0.1% formic acid, 80% acetonitrile) to 38% B in 110 min, followed by a 10-
minute washing step to 85% B. The maximum pressure was set to 1,180 bar and column
temperature was maintained at 50˚C. All samples were run in technical duplicate. Peptides
separated by the column were ionized at 2.4 kV in the positive ion mode. MS1 survey scans were
acquired at the resolution of 70k from 350 to 1800 m/z, with maximum injection time of 100 ms
and AGC target of 1e6. MS/MS fragmentation of the 14 most abundant ions were analyzed at a
resolution of 17.5k, AGC target 5e4, maximum injection time 65 ms, and normalized collision
energy 26. Dynamic exclusion was set to 30 s and ions with charge +1, +7, and >+7 were excluded.
MS/MS fragmentation spectra were searched with Proteome Discoverer SEQUEST
(version 2.2, Thermo Scientific) against in-silico tryptic digested Uniprot all-reviewed Homo
sapiens database (release Jun 2017, 42,140 entries) plus all recombinant protein sequences used in
this study. The maximum missed cleavages was set to 2. Dynamic modifications were set to
14
oxidation on methionine (M, +15.995 Da) and acetylation on protein N-terminus (+42.011 Da).
Carbamidomethylation on cysteine residues (C, +57.021 Da) was set as a fixed modification. The
maximum parental mass error was set to 10 ppm, and the MS/MS mass tolerance was set to 0.02
Da. The false discovery threshold was set strictly to 0.01 using the Percolator Node validated by
q-value. The relative abundance of parental peptides was calculated by integration of the area
under the curve of the MS1 peaks using the Minora LFQ node.
Data analysis and statistics:
The Proteome Discoverer peptide groups abundance values were normalized to the
corresponding samples’ median values. After normalization, the missing values were imputed
using the K-nearest neighbor algorithm (48). The optimized number of neighbors was determined
to be n = 10. The protein copy numbers were assessed using intensity-based absolute quantification
(iBAQ) (49). Proteomics data analysis was performed in Microsoft Excel, R (version 3.4.2), and
Perseus (version 1.6.2.2).
Metabolite Set Enrichment Analysis:
MCF-10A intracellular pool sizes were ranked based on log2 fold change, and enrichment
analysis was run with unweighted the statistic using the Broad Institute’s GSEA java applet against
all KEGG metabolic pathways. Statistical significance was assessed by 5,000 permutations of the
ranked list.
15
DATA AVAILABILITY
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium
via the PRIDE partner repository (50) with the dataset identifier PXD015122 (reviewer username:
reviewer74490@ebi.ac.uk, reviewer password: X1dvWm5c).
16
2.3. Results
1.1.1. AKT but not MYC temporarily induces cell death in oxidative culture.
To investigate the effect of oncogenes on the ability of human cells to survive and
proliferate in oxidative culture, we expressed either constitutively active AKT (i.e., myristoylated
AKT1, or mAKT), the transcription factor MYC, or the negative control red fluorescent protein
(RFP) in immortalized but non-tumorigenic MCF-10A human mammary epithelial cells. We then
switched these cells from glucose culture to galactose culture, which forces mammalian cells to
rely on oxidative phosphorylation rather than glycolysis for energy generation (17). Following the
switch to galactose culture, cells expressing either RFP or MYC grew ~2-fold slower than in
glucose culture but did not exhibit significant cell death (Fig. 2.1.A and Supp. Fig. 1.1.A-B). In
contrast, cells expressing mAKT exhibited significant cell death and declining cell numbers for
three passages in galactose culture. In the first five days following the switch from glucose to
galactose, we confirmed that cells expressing mAKT but not RFP or MYC declined in both viable
cell number and overall viability (Supp. Figs. 2.1C-E). Staining with annexin V and propidium
iodide (PI) additionally revealed that mAKT- but not MYC-expressing cells underwent necrotic
cell death after 3 h of galactose culture (Supp. Fig. 2.1.F).
Interestingly, after three passages (~15 days) in galactose culture, mAKT-expressing cells
reversed this phenotype and recommenced proliferation. However, even after resuming
proliferation, mAKT-expressing cells exhibited significantly slower growth in galactose relative
to glucose than did RFP- or MYC-expressing cells (Fig. 2.1.B). Cell viability remained high in
mAKT cells proliferating in galactose (Supp. Fig. 2.1.B). Next, using liquid chromatography-mass
spectrometry (LC-MS) metabolomics, we assessed the effect of galactose culture on aerobic
17
glycolysis by measuring extracellular lactate secretion. We found that galactose culture
significantly reduced the abundance of secreted lactate in both the short (24 h) and long term (~5
passages or ~25 days) for all three cell types (Fig. 2.1.C). Notably, mAKT-expressing cells in
glucose exhibited the highest rate of lactate secretion, consistent with reports that mAKT can
increase aerobic glycolysis (15,51). Next, we tested whether galactose culture affected the
expression of the exogenous oncogenes and found that galactose culture induced a slight decrease
in expression of AKT and phospho-serine-473 AKT (Fig. 2.1.D). Conversely, expression of MYC
was unaffected by galactose culture. Quantitation of the ratio of phospho-serine 473-AKT to total
AKT by both Western blot and phospho-flow demonstrated a ~20% reduction in mAKT-
expressing MCF-10A cells in galactose culture (Fig. 2.1.E and Supp. Fig. 2.2.). Taken together,
these data demonstrate that the oncogenes mAKT and MYC exert opposite effects on the ability
of non-tumorigenic MCF-10A cells to proliferate in oxidative culture conditions (i.e., galactose)
at short times (< 3 passages) but that cells expressing either oncogene can proliferate in oxidative
culture at longer times.
We next tested whether MCF-10A-mAKT cells that had acquired the ability to proliferate
in galactose would retain this ability if temporarily removed from galactose. We thus switched
RFP- and mAKT-expressing cells from long-term galactose culture to glucose culture for 5
passages (~25 days) and then compared the growth of these cells in galactose against cells which
had never been exposed to galactose (i.e., naïve). RFP-expressing cells grew similarly regardless
of whether they had been previously cultured in galactose (Fig. 2.1.F). In contrast, mAKT-
expressing cells that had been previously cultured in galactose exhibited an enhanced ability to
18
grow in galactose. This demonstrates that MCF-10A-mAKT cells selected for growth in galactose
retain the ability to proliferate in galactose even after removal from galactose.
Figure 2.1. mAKT but not MYC negatively affects proliferation in galactose culture. A)
Constitutively active (myristoylated) AKT (mAKT) but not MYC negatively affected the
proliferation of MCF-10A cells grown in media containing galactose. MCF-10A cells expressing
red fluorescent protein (RFP, negative control), mAKT, or MYC were switched from media
containing glucose to media containing galactose and cultured for 7 passages. Galactose culture
forces cells to use OXHPHOS instead of glycolysis for energy generation. For 3 passages, mAKT-
expressing cells exhibited cell death and declining cell number, but afterwards, mAKT-expressing
cells recommenced proliferation. Data are shown as log2 viable cell number. Viability was
19
measured by trypan blue staining (n=2 biological replicates). Error bars represent standard
deviation. B) mAKT-expressing cells experienced a proliferative disadvantage in long-term
galactose culture. A boxplot of the ratio of log2 fold-change for growth in glucose compared to
galactose at passages 5, 6, and 7 from panel A) demonstrated that mAKT-expressing cells
exhibited a growth disadvantage in galactose culture relative to RFP- or MYC-expressing cells. *
denotes p-value less than 0.05 by Student’s t-test, and n.s. denotes not significant (n=2 biological
replicates). Error bars represent standard deviation. C) Lactate secretion was suppressed in
galactose culture. MCF-10A cells expressing RFP, mAKT, or MYC were cultured in glucose,
switched from glucose to galactose for 24 h, or cultured in galactose for ~5 passages. Extracellular
lactate abundance was measured by liquid chromatography-mass spectrometry (LC-MS)
metabolomics. All cell types exhibited significant reductions in secreted lactate when cultured in
galactose. mAKT-expressing cells exhibited the highest secretion of lactate in glucose culture. **
denotes p-value less than 0.01 by Student’s t-test (n=5 biological replicates). Error bars represent
standard deviation. D) Expression of mAKT and MYC was not altered by long-term culture in
galactose. MCF-10A cells expressing RFP, mAKT, or MYC were cultured in glucose, switched
from glucose to galactose for 24 h, or cultured in galactose for ~5 passages, and then lysed. Protein
expression was measured by Western blotting using antibodies against phospho-serine473-AKT,
total AKT, or MYC. Actin was used as an equal loading control. See Supp. Fig. 2.S2 for
quantitation. E) The ratio of phospho-Ser473-AKT to total AKT was slightly reduced in galactose
culture. Levels of total AKT and pS437-AKT were measured by phospho-flow-cytometry in MCF-
10A cells expressing mAKT in glucose, short-term galactose (3 h), or long-term galactose culture
(~5 passages). Error bars represent standard deviation. See Supp. Fig. 2.S2B for histograms of the
AKT and pSer473-AKT levels. F) MCF-10A cells selected for proliferation in galactose retain the
ability to proliferate in galactose even when removed from the selective pressure of galactose.
Long term galactose cultured MCF-10A cells expressing RFP (negative control) and mAKT were
cultured in glucose culture for 5 passages and then switched to media containing galactose. The
growth of cells previously cultured in galactose was compared to that of naïve cells that had never
been cultured in galactose. RFP-expressing cells grew similarly regardless of whether they had
been previously cultured in galactose, but mAKT-expressing cells that had been previously
cultured in galactose exhibited an enhanced ability to grow in galactose. The viable cell number at
each passage was measured by trypan blue staining. ** denotes p-value less than 0.01 by Student’s
t-test, and n.s. denotes not significant (n=2 biological replicates). Error bars represent standard
deviation.
1.1.2. mAKT-expressing cells exhibit extensive metabolic adaptation in galactose
culture.
To elucidate the metabolic mechanisms regulating the differential phenotypes of RFP-,
mAKT-, and MYC-expressing cells in galactose culture, we profiled cells using stable isotope
tracing LC-MS metabolomics (Supp. Fig. 2.S3). Because cells in galactose culture upregulate
glutamine anaplerosis (19,52), we first labeled cells with [U-
13
C]-L-glutamine for 24 h in either
20
glucose, short-term galactose culture (24 h), or long-term galactose culture (~5 passages) followed
by LC-MS metabolomics (Supp. Fig. 2.S4A and Supp. Table S1). Relative to RFP-expressing cells,
mAKT- but not MYC-expressing cells cultured in glucose exhibited an increased percentage of
M0 isotopomers for the TCA cycle intermediates citrate/isocitrate, aconitate, α-ketoglutarate,
succinate, fumarate, and malate (Fig. 2.2.A-B and Supp. Fig. 2.S5), indicating that mAKT-
expressing cells were utilizing less glutamine to fuel the TCA cycle. Upon switching to galactose
culture, all cells exhibited increased percentages of fully labeled isotopomers (e.g., M6
citrate/isocitrate), indicating increased flux of glutamine-derived carbon through the TCA cycle.
Additionally, in galactose culture at both short and long times, all MCF-10A cells exhibited an
increased percentage of M5 isotopomer in citrate/isocitrate, indicating increased reductive
carboxylation flux. Taken together, [U-
13
C]-L-glutamine stable isotope tracing indicated that all
cells increased glutamine oxidation and reductive carboxylation upon switching from glucose to
galactose, but that mAKT-expressing cells experienced larger changes than either RFP- or MYC-
expressing cells because of their more aerobic glycolytic basal state in glucose.
Next, we tested how MCF-10A cells expressing oncogenes adapt to galactose culture by
labeling cells with [U-
13
C]-galactose in short-term (24 h) and long-term galactose culture (~5
passages) followed by LC-MS metabolomics (Supp. Fig. 2.S4B and Supp. Table S2). Examining
the fractional incorporation of
13
C, we found that mAKT-expressing cells exhibited a small but
significant reduction in
13
C fractional incorporation compared to RFP- and MYC-expressing cells
in the glycolytic intermediates 3-phosphoglycerate (3PG) and phosphoenolpyruvate (PEP),
indicating slower flux from galactose into glycolysis (Fig. 2.2.C-D). At longer times, however,
mAKT cells reversed this difference and exhibited
13
C fractional incorporation to levels similar to
21
RFP- and MYC-expressing cells, indicative of increased flux from galactose into glycolysis. In
addition, we found that mAKT-expressing cells significantly increased the
13
C fractional
incorporation from galactose into the amino acids glutamate and alanine (Fig. 2.2.E-F), TCA cycle
intermediates including citrate/isocitrate and fumarate (Fig. 2.2.G-H), and the redox buffering
molecules reduced (GSH) and oxidized glutathione (GSSG) (Fig. 2.2I-J).
In contrast, RFP- and MYC-expressing cells exhibited smaller increases in
13
C fractional
incorporation in glutamate, alanine, citrate/isocitrate, and fumarate, and decreased
13
C fractional
incorporation into GSH and GSSG. Overall, the changes in galactose-derived
13
C fractional
incorporation for mAKT-expressing cells were much greater than for RFP- or MYC-expressing
cells. Taken together, this demonstrates that AKT-expressing cells exhibited significantly larger
adaptations to galactose culture than do RFP- or MYC-expressing cells, consistent with the
observation that AKT-expressing cells initially die in galactose culture before recommencing
proliferation.
22
23
Figure 2.2. Stable isotope tracing metabolomics reveals differential usage of glutamine and
galactose in mAKT-expressing cells. A-B) Isotopomer distributions for citrate-isocitrate and α-
ketoglutarate (αKG) following [U-
13
C]-L-glutamine labeling. MCF-10A cells expressing RFP,
mAKT, or MYC were cultured in glucose, switched from glucose to galactose for 24 h, or cultured
in galactose for ~5 passages. Cells were labeled with [U-
13
C]-L-glutamine for 24 h and then
analyzed by LC-MS metabolomics. Isotopomer abundances were normalized to total abundance
to calculate the percentage of each isotopomer. mAKT-expressing cells in glucose exhibited a
larger percentage of M0 isotopomer than either RFP- or MYC-expressing cells. All cell types
exhibited more reductive carboxylation (e.g., M5 citrate-isocitrate) and more glutamine
anaplerosis (e.g., M6 citrate-isocitrate) in galactose culture. Error bars represent standard deviation
(n=3 biological replicates). C-J) Fractional contribution for MCF-10A cells labeled with [U-
13
C]-
galactose for selected metabolites from glycolysis (C, D), amino acid metabolism (E, F), the TCA
cycle (G, H), and glutathione metabolism (I, J). MCF-10A cells expressing RFP, mAKT, or MYC
were switched from glucose to galactose for 24 h or cultured in galactose for ~5 passages. Cells
were labeled with [U-
13
C]-galactose for 24 h and then analyzed by LC-MS metabolomics. 3PG
denotes 3-phosphoglycerate, PEP denotes phosphoenolpyruvate, GSH denotes reduced
glutathione, and GSSG denotes oxidized glutathione. mAKT-expressing cells exhibited larger
changes in
13
C fractional incorporation than either RFP- or MYC-expressing cells. * denotes p-
value less than 0.05, ** denotes p-value less than 0.01, *** denotes p-value less than 0.001 by
Student’s t-test, and n.s. denotes not significant (n=3 biological replicates). Error bars represent
standard deviation.
1.1.3. Short-term galactose culture increases glutathione metabolism in mAKT-
expressing cells.
To further understand the metabolic profile that occurs in MCF-10A cells expressing
oncogenes when switched from glucose to galactose, we next analyzed metabolite pool sizes (Supp.
Table S3). Unsupervised hierarchical clustering of metabolite pool sizes segregated samples based
on the culture condition (glucose, short-term galactose, and long-term galactose culture) rather
than by proteins (RFP, mAKT, and MYC) (Fig. 2.3.A). No obvious grouping of functionally
related metabolites was apparent from the hierarchical clustering of metabolites. Therefore, to
identify metabolic pathways affected by galactose culture in each cell type, we employed
Metabolite Set Enrichment Analysis (MSEA) (15,53,54). First, we analyzed the relative
enrichment of all KEGG pathways comparing short-term galactose to glucose culture. To identify
differentially enriched pathways, we plotted the MSEA results on a volcano-style plot (Fig. 2.3.B).
24
This analysis revealed that glutathione metabolism was significantly enriched in short-term
galactose mAKT- but not RFP- or MYC-expressing cells (Fig. 2.3.C and Supp. Fig. 2.S6A,C).
Next, we conducted a similar analysis comparing metabolite pool sizes in short-term and long-
term galactose cultured cells. Again, we found that glutathione metabolism was significantly
enriched in short-term galactose mAKT- but not RFP- or MYC-expressing cells (Fig. 2.3.D-E and
Supp. Fig. 2.S6B,D). Taken together, this metabolite set enrichment analysis suggests that
glutathione metabolism is specifically upregulated in mAKT-expressing cells when initially
switched to galactose culture.
1.1.4. Proteomic analysis reveals enriched nonsense-mediated mRNA decay (NMD)
in mAKT-expressing cells in short-term galactose culture.
Next, to further characterize the mechanisms underlying the differential phenotype of mAKT-
expressing cells in galactose culture, we performed label-free quantitative LC-MS proteomics. We
analyzed two independent biological experiments of MCF-10A cells expressing either RFP,
mAKT, or MYC cultured in glucose, short-term galactose, and long-term galactose culture. Across
both experiments, we identified and quantified 2,460 proteins, 1,356 of which were quantified in
both biological replicates (Fig. 2.4.A and Supp. Table S4). To identify global differences between
samples, we conducted principal component analysis (PCA) using the proteins identified in both
biological replicates. PCA revealed a clear separation across samples and consistent trends across
the two experiments. Notably, in both replicates, short-term galactose culture induced a positive
shift on PC1 (48.4% of variation for Experiment 1) for all cell types (Fig. 2.4.B and Supp. Fig.
2.S7A). Long-term galactose culture, in contrast, generally exhibited a negative shift on PC1
relative to short-term galactose culture. Notably, the PC1 shift for mAKT-expressing cells was
25
significantly larger than for either RFP- or MYC-expressing cells.
26
Figure 2.3. Glutathione metabolism is enriched in mAKT- but not RFP- or MYC-expressing
MCF-10A cells in short-term galactose culture. A) Hierarchical clustering of metabolite pool
sizes separated samples by media type but not oncogene. MCF-10A cells expressing RFP, mAKT,
or MYC were cultured in glucose (Glc), switched from glucose to galactose for 24 h (24 h Gal),
or cultured in galactose for ~5 passages (Gal), and metabolite pool sizes were measured by LC-
MS metabolomics. Metabolite pool sizes were filtered for ANOVA p-value < 0.5, and clustered
using one minus the Pearson correlation and average linkage. Samples clustered by media type
(e.g., Glc) rather than by different proteins (e.g., RFP). Metabolites are colored at right of the
heatmap according to their metabolic pathways. Red and blue denote higher and lower abundance,
respectively, from samples run in biological triplicate. B, C) Metabolite set enrichment analysis of
metabolite pool sizes from short-term galactose culture (24 h Gal) relative to glucose culture (Glc).
All KEGG metabolic pathways were analyzed. B) Volcano plot of -log2(false discovery rate, FDR)
vs. the normalized enrichment score (NES). The glutathione metabolism pathway (GSH pathway)
for cells expressing RFP, mAKT, and MYC is highlighted. All other pathways are shown in light
gray. C) Mountain plot of KEGG Glutathione Metabolism demonstrating that glutathione
metabolism was significantly enriched in mAKT-expressing cells following 24 h of culture in
galactose. The green line denotes the enrichment score, and the black tick marks denote
metabolites that belong to glutathione metabolism. D, E) Metabolite set enrichment analysis of
metabolite pool sizes from short-term galactose culture (24 h Gal) relative to long-term galactose
culture (~5 passages, Gal). All KEGG metabolic pathways were analyzed. D) Volcano plot of -
log2(FDR) vs. the normalized enrichment score (NES). The glutathione metabolism pathway (GSH
pathway) for cells expressing RFP, mAKT, and MYC is highlighted. All other pathways are shown
in light gray. C) Mountain plot of KEGG Glutathione Metabolism demonstrating that glutathione
metabolism was significantly enriched in mAKT-expressing cells following 24 h of culture in
galactose. The green line denotes the enrichment score, and the black tick marks denote
metabolites that belong to glutathione metabolism.
To understand the proteins driving separation on PC1, we analyzed the PC1 loadings vector
using 1D annotation enrichment using the Reactome Pathway Database (55,56). Among the
enriched pathways, we found that two nonsense-mediated mRNA decay (NMD) pathways were
enriched in positive PC1, in addition to several other mRNA-related pathways (Fig. 2.4.C and
Supp. Fig. 2.S7B). To better understand how these pathways were regulated by galactose culture,
we examined members of Nonsense-mediated mRNA decay (NMD) enhanced by the Exon
Junction Complex (EJC), a surveillance pathway that eliminates mRNAs containing premature
translation stop codons (57). Visualization of these proteins on a heatmap revealed that their basal
expression was low in mAKT-expressing cells cultured in glucose and that short-term galactose
27
induced higher expression (Fig. 2.4.D and Supp. Fig. 2.S7C). In long-term galactose culture, the
levels of NMD proteins were again reduced to low expression in mAKT-expressing cells. Taken
together, this proteomic analysis suggests that mAKT-expressing cells in short-term galactose
culture dramatically upregulated NMD.
1.1.5. mAKT but not RFP- or MYC-expressing cells exhibited oxidative stress in
short-term galactose culture.
In mammalian cells, activation of NMD can sensitize cells to oxidative stress (58). Because
our metabolomic analysis (Figs. 2.3., 2.4.) demonstrated that mAKT-expressing cells in short-term
galactose culture upregulated glutathione metabolism, we hypothesized that AKT-expressing cells
were dying from increased oxidative stress following the switch from glucose to galactose culture.
We thus measured the levels of reactive oxygen species (ROS) using the fluorescent ROS probe
DCF-DA. MCF-10A cells expressing either RFP, mAKT, or MYC were switched from glucose to
galactose culture for 3 h and ROS levels were measured by flow cytometry.
28
Figure 2.4. Proteomic profiling reveals that mAKT-expressing cells exhibit upregulation of
nonsense-mediated mRNA decay (NMD) proteins in short-term galactose culture. A) Overlap
in quantified proteins by LC-MS proteomics from two independent biological replicates. MCF-
29
10A cells expressing RFP, mAKT, or MYC were cultured in glucose (Glc), switched from glucose
to galactose for 24 h (24 h Gal), or cultured in galactose for ~5 passages (Gal), and protein
expression was measured by label-free LC-MS proteomics. Experiments 1 and 2 identified and
quantified 1,686 and 2,130 proteins, respectively. 1,356 proteins were quantified in both
experiments in technical duplicate. B) Principal component analysis score plots (PC1 vs. PC2) of
proteomic data from Experiment 1 segregated samples by proteins and media type. Only proteins
identified in both experiments were used. Color denotes proteins, and shape denotes media type.
Each sample was analyzed in technical duplicate. Short-term galactose culture (24 h Gal) induced
a positive shift on PC1 (48.4% of variation) for all cell types relative to glucose culture (Glc).
Long-term galactose culture (Gal), in contrast, exhibited a negative shift on PC1 relative to short-
term galactose culture for mAKT- and RFP-expressing cells. The PC1 shift for mAKT-expressing
cells was significantly larger than for either RFP- or MYC-expressing cells. Similar trends were
seen in Experiment 2 (Supp. Fig. 2.S6A). C) Enrichment analysis identified Reactome pathways
enriched in the PC1 loadings vector from Experiment 1. Nonsense Mediated Decay (NMD)
enhanced by the Exon Junction Complex (EJC) and Nonsense Mediated Decay (NMD)
independent of the Exon Junction Complex (EJC) are highlighted. Similar trends were seen in
Experiment 2 (Supp. Fig. 2.S6B).D) mAKT-expressing cells significantly upregulated NMD
proteins in short-term galactose culture. A heatmap of protein expression from Experiment 1 for
the Nonsense Mediated Decay (NMD) enhanced by the Exon Junction Complex (EJC) pathway
(Reactome R-HSA-975957) demonstrated that mAKT-expressing cells dramatically upregulated
NMD protein expression in short-term (24 h) galactose culture. Similar trends were seen in
Experiment 2 (Supp. Fig. 2.S6C).
In RFP- and MYC-expressing cells, the switch to galactose culture did not significantly
alter ROS levels (Fig. 2.5.A, C). In mAKT-expressing cells, however, 3 h of galactose culture did
significantly increase the levels of ROS (Fig. 2.5.B). Next, we investigated whether the increase
in ROS was sustained following the switch from glucose to galactose. In MCF-10A-mAKT cells,
we found that ROS levels were consistently elevated in mAKT cells for at least 48 h after exposure
to galactose (Fig. 2.5.D and Supp. Fig. 2.S8). To further understand the sources of ROS generation,
we next used mitoSOX, a fluorogenic dye that measures superoxide production in the
mitochondria. Similar to DCF-DA staining, we observed that 3 h of galactose culture increased
mitochondrial ROS in mAKT but not MYC cells (Fig. 2.5.E, F). We did not measure levels of
mitochondrial ROS in RFP-expressing cells because of the overlap between the fluorescent signals
30
of mitoSOX and RFP (both ~580 nm). Taken together, this data supports that constitutively active
AKT but not MYC induced elevated ROS in short-term galactose-cultured MCF-10A cells.
Figure 2.5. mAKT- but not RFP- or MYC-expressing cells exhibited increased ROS upon
switching from glucose to galactose. A-C) MCF-10A cells expressing RFP, mAKT, or MYC
were cultured in glucose or switched from glucose to galactose for 3 h (3 h Gal). ROS levels were
measured by flow cytometry using the fluorescent ROS probe DCF-DA. Only mAKT-expressing
cells exhibited increased levels of ROS following galactose culture. D) MCF-10A cells expressing
mAKT were cultured in glucose or switched from glucose to galactose for 1, 3, 24, and 48 h. ROS
levels were measured by flow cytometry using the fluorescent ROS probe DCF-DA. mAKT-
expressing cells exhibited consistently increased levels of ROS by switching to galactose culture.
E-F) MCF-10A cells expressing mAKT or MYC were cultured in glucose or switched from
glucose to galactose for 3 h (3 h Gal). Mitochondrial ROS levels were measured by flow cytometry
using probe MitoSOX. mAKT- but not MYC-expressing cells exhibited increased levels of
mitochondrial ROS upon switching to galactose culture.
31
Figure 2.6. ROS are required for galactose-induced cell death. A) The ROS scavenger catalase
rescued mAKT-expressing cells from galactose-induced cell death. MCF-10A cells expressing
RFP, mAKT, or MYC were cultured in glucose, without glucose, or in galactose with or without
200 U/ml of the ROS scavenger catalase for 32 h. Cell viability was measured by trypan blue
staining. ** denotes Student’s t-test p-value < 0.01 compared to glucose culture, and ‡ denotes
Student’s t-test p-value < 0.01 compared to galactose culture without catalase (n=2-4 biological
replicates). Error bars represent standard deviation. B) M436 and Hs578t breast cancer cells exhibit
constitutively active PI3K/AKT signaling. MCF-10A cells expressing either RFP or mAKT and
M436 and Hs578t breast cancer cell lines were serum starved for 16 h and then lysed. Western
blotting with antibodies against total AKT and phosphoSer473-AKT demonstrated that MCF-10A
expressing mAKT and the breast cancer cell lines exhibited constitutively active AKT. Actin was
used as an equal loading control. C) The ROS scavenger catalase rescued MB436 and Hs578t
breast cancer cells from galactose-induced cell death. MB436 and Hs578t cells were cultured in
glucose, without glucose, or in galactose with or without 200 U/ml of the ROS scavenger catalase
for 32 h. Cell viability was measured by trypan blue staining for 24 h, and cell viability was
measured by trypan blue staining. ** denotes Student’s t-test p-value < 0.01 compared to glucose
culture, and ‡ denotes Student’s t-test p-value < 0.01 compared to galactose culture without
catalase (n=3-4 biological replicates). Error bars represent standard deviation.
32
1.1.6. Galactose culture-induced cell death can be rescued by ROS scavengers.
Having identified that mAKT-expressing cells experience oxidative stress in short-term
galactose culture, we next tested whether ROS were functionally involved in the cell death caused
by galactose culture. We cultured MCF-10A cells expressing either RFP, mAKT, or MYC in
media without glucose, media with galactose, or media with galactose plus the ROS scavenger
catalase. For MCF-10A cells expressing either RFP or mAKT, glucose starvation resulted in
significant cell death (Fig. 2.6.A and Supp. Fig. 2.S9A). Interestingly, cells expressing MYC were
protected from glucose starvation-induced cell death, mirroring observations in normal human
fibroblasts and glioma cells (9,10). In galactose culture, however, only MCF-10A cells expressing
mAKT exhibited cell death. In these mAKT-expressing cells, supplementation with the ROS
scavenger catalase rescued cells from galactose culture-induced cell death. Thus, ROS induced by
galactose culture are required for galactose culture-induced cell death.
Next, we sought to determine if our results in MCF-10A expressing mAKT were also
reflected in breast cancer cell lines. We chose two cell lines reported to exhibit constitutively active
AKT signaling, MDA-MB-436 (MB436) and Hs578t (59,60). Western blotting confirmed that
both cell lines exhibited AKT activation even after serum starvation (Fig. 2.6.B). Next, we tested
the effect of glucose starvation and galactose culture on these cell lines and found that both cell
lines exhibited significant cell death upon glucose starvation and short-term galactose culture (Fig.
2.6.C and Supp. Fig. 2.S9B). In addition, for both cell lines, the ROS scavenger catalase rescued
cells from galactose culture-induced cell death. Taken together, these results show that breast
cancer cell lines with constitutively active AKT signaling experience ROS-mediated cell death
when cultured in galactose medium, similar to our MCF-10A-mAKT cells.
33
2.4. Discussion and Conclusion
The altered metabolism of tumors has long been proposed as a therapeutic target. Defining
the metabolic vulnerabilities induced by specific oncogenes is crucial for the design and
stratification of therapeutics targeting tumor metabolism (2,11). Here, using the galactose culture
system, which forces mammalian cells to rely on OXPHOS instead of glycolysis for energy
generation (17–21), we have uncovered a metabolic state-dependent lethality, namely the restricted
ability of cells with constitutively active PI3K/AKT signaling to switch between glycolysis and
OXPHOS. Through multi-omics analysis, we identified that the galactose-induced cell death
exhibited by mAKT-expressing cells was accompanied by increased glutathione metabolism (Fig.
2.3.) and increased expression of NMD proteins (Fig. 2.4.), which is a hallmark of sensitivity to
oxidative stress. Based on these results, we found that MCF-10A cells expressing mAKT cultured
in galactose exhibited increased ROS levels (Fig. 2.5.) and ROS-mediated cell death (Fig. 2.6.).
Importantly, we replicated this result in breast cancer cell lines with activated PI3K/AKT signaling.
Taken together, these results reveal a novel metabolic state vulnerability induced by PI3K/AKT
signaling.
Our findings in MCF-10A, a non-tumorigenic but immortalized breast cancer cell line,
confirm reports that constitutive activation of PI3K/AKT signaling forces cells to rely on aerobic
glycolysis (16,51). Stable isotope tracing with [U-
13
C]-L-glutamine demonstrated that these cells
exhibited less glutamine anaplerosis than either RFP- or MYC-expressing cells when cultured in
glucose (Fig. 2.2.A, B). Interestingly, MCF-10A cells expressing the negative control RFP were
sensitive to glucose deprivation, and mAKT expression did not further sensitize cells to glucose
starvation (Fig. 2.6.A). However, RFP-expressing cells were able to switch from glycolysis to
34
aerobic respiration, as evidenced by their survival and growth in galactose media (Figs. 2.1., 2.6.).
mAKT-expressing cells, in contrast, were initially unable to metabolize galactose, leading to ROS-
mediated cell death even though PI3K/AKT activation has been shown to increase resistance to
oxidative stress through upregulation of glutathione biosynthesis (61). In addition, our observation
that MYC did not sensitize MCF-10A cells to galactose culture supports previous reports that AKT
and MYC differentially alter metabolism, including the creation of metabolic vulnerabilities in
glycolysis and mitochondrial bioenergetics, respectively (15,16). Similarly, our data confirm that
MYC activation protects against glucose deprivation (9,10), though MYC expression in MCF-10A
cells did not increase oxidative metabolism relative to RFP-expressing cells as seen in other cell
types (62). Together, the differential sensitivity of AKT- and MYC-expressing cells suggests that
these oncogenes alter metabolism through distinct mechanisms even though both can upregulate
aerobic glycolysis (13,14,63).
Although mAKT-expressing cells initially died in galactose culture, these cells
recommenced proliferation after ~15 days. At present, it is not known if this represents metabolic
remodeling to process galactose (i.e., acquired resistance) or the emergence of a pre-existing sub-
population of galactose-metabolizing cells (i.e., clonal selection). Notably, mAKT cells that had
been selected in galactose retained the ability to proliferate in galactose even after an extended
period of glucose culture (~25 days) (Fig. 2.1.F). This suggests that a sub-population of galactose-
metabolizing cells could stably co-exist within a population of cells cultured in glucose. If a
galactose-resistant sub-population did exist, one could hypothesize that this sub-population would
exhibit less ROS generation upon the switch from glucose to galactose culture. However, using
flow cytometry, we failed to observe the emergence of any such sub-populations during the first
35
48 h of galactose culture (Fig. 2.5.D and Supp. Fig. 2.S9). It is notable, however, that the AKT-
expressing cells proliferating in galactose exhibited slower growth relative to glucose than either
RFP- or MYC-expressing cells (Fig. 2.1.B). Thus, even the “galactose-resistant” mAKT-
expressing cells exhibited a disadvantage in oxidative culture, which may explain why
proliferating mAKT cells exhibited a substantially different galactose metabolism than RFP- or
MYC-expressing cells (Fig. 2.2.E, F). Notably, because long-term culture in galactose only
slightly reduced AKT expression and phosphorylation (Fig. 2.1.D, E), the changes that enable
proliferation of mAKT-expressing cells in galactose likely occur downstream of AKT itself. These
changes could include increased mitochondrial content and aerobic metabolism, as has been
observed when human myotubes, which have low mitochondrial oxidative potential, are cultured
in galactose (64,65). Regardless, the fact that mAKT cells reliably and rapidly demonstrated
resistance to galactose suggests that eradication of tumors with constitutive PI3K/AKT signaling
will require the therapeutic targeting of another, compensatory pathway to prevent re-emergence.
Notably, at this time, we have been unable to generate galactose-resistant clones of either MB436
or Hs578t, suggesting that these breast cancer cells harbor oncogenic lesions in addition to
PI3K/AKT signaling that more permanently restrict the flexibility to utilize OXPHOS.
Another possible mechanism by which mAKT-expressing cells adapt to galactose culture
might be through suppression of NMD (Fig. 2.4.). In general, oxidative stress suppresses NMD in
order to enhance the ability of cells to survive ROS toxicity (57,58). However, when NMD is
activated, cells become more sensitive to oxidative stress (58). Thus, the galactose-induced cell
death of mAKT-expressing may be due to increased ROS levels coinciding with increased
sensitivity to oxidative stress due to NMD upregulation. In fact, there may exist a direct link
36
between AKT signaling and NMD upregulation, as insulin signaling in HeLa cells upregulates
NMD through increased binding of UPF1, the master regulator of NMD, to mRNA transcripts (66).
In our MCF-10A-AKT and breast cancer cell lines, the mechanisms by which AKT signaling in
galactose induces the upregulation of NMD proteins are currently under investigation.
Notably, the myristoylated, constitutively active form of AKT1 (mAKT) used here is not
found in tumors. Future studies using more realistic models of PI3K/AKT activation, including
PI3K point mutants and homozygous PTEN deletion, will be required to fully understand the role
of AKT-induced metabolic state inflexibility. However, it is interesting to note that MCF-10A-
mAKT cells and the breast cancer cell line MB436 exhibited very similar levels of AKT activation
and ROS-mediated cell death in galactose culture (Fig. 2.6.). Additionally, given the known
differences between AKT isoforms, future studies will be required to test whether AKT2 and
AKT3 can restrict metabolic flexibility similar to AKT1 (67). Finally, future integration of
transcriptomic and phospho-proteomic profiling with our proteomic and metabolomic profiling
may reveal additional mechanisms of metabolic adaptation to galactose culture.
In summary, the deregulation of PI3K/AKT and MYC in breast cancer motivates further
research into how these oncogenes generate oncogene-dependent metabolic vulnerabilities. Like
AKT and MYC, many other frequently altered oncogenes including BRAF (68), ERBB2 (69),
KRAS (70,71), and VHL (72) alter the balance between glycolysis and OXPHOS. However, it
remains to be tested whether these oncogenes will affect the flexibility of cells to shift between
glycolysis and OXPHOS as does AKT. Regardless of the oncogene, it will be crucial to understand
how oncogenic events define the response to fluctuations in nutrient availability, oxygen tension,
37
and pH within the tumor microenvironment (73). In addition, given the flexibility and adaptability
of cancer metabolism, inhibition of a single molecular target (e.g., glycolysis in tumors with
hyperactivated AKT signaling) may not prove sufficient for tumor eradication. As such,
combinational therapies that generate synthetic lethality in tumors also need to be investigated in
the context of oncogene dependence (74–77). Taken together, our findings highlight the
importance of oncogene-dependent metabolic vulnerabilities in cancer cells and suggest that
therapies targeting tumor metabolism will need to be appropriately paired with tumor genetic
profiles.
38
3. Chapter 3: 2. Metabolomics reveals that aspartate and asparagine
couple to insulin secretion in pancreatic β-cells
3.1. Objective
The dysregulation of metabolism in pancreatic β-cells (PBC) has been directly linked to
insulin resistance in T2D. Therefore, understanding the metabolomic mechanisms underpinning
glucose-stimulated insulin secretion (GSIS) is essential for the design of therapeutic strategies for
T2D. In this study, we sought to profile the metabolome of the β-cells model cell line INS1E to
understand the dynamic metabolic regulation of insulin secretion.
3.2. Materials and Methods
Cell culture
INS1E Rattus Norvegicus insulinoma cells , originated from Maechler lab (UNIGE), were
obtained from Cell Culture Core of Stevens lab in University of Southern California. Cells were
cultured in monolayer in modified RPMI 1640 supplemented with 5% horse serum, 1 mM sodium
pyruvate, 50 μM β-mecaptoethanol, 2 mM glutamine, 10 mM HEPES. All cells were grown in a
5% CO2, 37˚C, and humidified incubator and were used between 30-50 passages of thawing. Cell
counting and viability were assessed using trypan blue staining with a TC20 automated cell counter
(BioRad).
INS1E cells were plated on 6-well plates at the density of 7,000 cells/cm
2
. When cell
densities reached 70%, media was removed, cells were washed twice with 2 mL of PBS, and 5 mL
of KRBH buffer ( 29 mM NaCl, 5 mM NaHCO3, 4.8 mM KCl, 1.2 mM KH2PO4, 2.5 mM CaCl2,
39
1.2 mM MgSO4, and 10 mM HEPES, 0.1% BSA, 0 mM glucose) was added to cells for 30 min.
After starvation, KRBH buffer was removed, and the cells were subjected to indicated treatment
for 30 min. The 30 min starvation is a critical step allow the synchronization the INS1E cellular
pre-insulin content, and to ensure proper baseline before treatment. (44,78,79)
When indicated, the starved INS1E cells were treated with 1.1 mM, 2.8mM, 16.7 mM, or
25 mM of glucose without or with 10 nM of Exendin-4 for 30 min in KRBH buffer. When
indicated, the starved INS1E cells were treated with 100 μM of Konigic Acid (Cayman Chemical
14079), 180/1000 μM of L-aspartate (Sigma-Aldrich, A5474), 380/1000 μM of L-asparagine
(Sigma-Aldrich, A4159), 20 μM of L-proline (Sigma-Aldrich, P0380), and/or 5 μM (BOC-
aminooxy) acetic acid (Fisher Scientific, AC429690010) The supernatants were collected for
insulin content using ELISA. The cell pellets were subjected to metabolites extraction.
Insulin ELISA assay
INS1E cells supernatant were collected, spun-down (180 g, 3 min), and transferred into 96-
well plates. Secreted insulin was assayed by Mercodia Rat Insulin ELISA kit (10-1250-01)
according to the manufacturer’s protocol.
LC-MS Metabolomics
The treated culture plates were cooled on ice, media was aspirated, and the cells were
washed with 1 mL of cold ammonium acetate (150 mM, pH 7.3). Upon aspirating the ammonium
acetate, metabolites were extracted with 1 mL of -80˚C methanol. The methanol cell suspension
was scraped and transferred to Eppendorf tubes, and the cell suspension was centrifuged at 4˚C.
The supernatants were transferred to a new Eppendorf tubes, and the pellet was re-extracted with
40
another 350 μL of -80˚C methanol. The second methanol extraction was spun down, and the
supernatant was pooled with the first extraction. Metabolites were speed-vac dried, resuspended
in LC-MS grade water, and sent for LC-MS analysis.
Samples were randomized and analyzed on a Q Exactive Plus hybrid quadrupole-Orbitrap
mass spectrometer coupled to an ULTIMATE 3000 UHPLC system (Thermo Scientific). The mass
spectrometer was run in polarity switching mode (+3.50 kV/-2.25 kV) with an m/z window ranging
from 65 to 975. Mobile phase A was 5 mM NH4AcO, pH 9.9, and mobile phase B was acetonitrile.
Metabolites were separated on a Luna 3 µm NH2 100 Å (150 × 2.0 mm) column (Phenomenex).
The flowrate was 300 µL/min, and the gradient was from 15% A to 95% A in 18 min, followed by
an isocratic step for 9 min and re-equilibration for 7 min. All samples were run in biological
triplicate. Metabolites were detected and quantified as area under the curve based on retention time
and accurate mass (≤ 8 ppm) using the TraceFinder 3.3 (Thermo Scientific) software. Raw data
were corrected for naturally occurring
13
C abundance (41). Intracellular data was normalized to the
cell number at the time of extraction.
Data analysis and statistics
INS1E intracellular pool sizes were normalized to average cell counts. Pearson correlation
of the metabolite abundances were performed against insulin secretion profile for each condition.
The metabolite signatures were ranked based on Pearson correlation coefficients, and enrichment
analysis was run with unweighted statistic using the Broad Institute’s GSEA java applet against
all KEGG metabolic pathways. Statistical significance was assessed by 5,000 permutations of the
ranked list. Data analysis was performed in Microsoft Excel, and R (version 3.4.2).
41
Hierarchical clustering was performed using Morpheus (Broad Institute,
https://software.broadinstitute.org/morpheus). Metabolite abundances were median normalized
and mean-center transformed. Similarity measures were calculated based on Pearson correlation-
based distance.
Data Availability
The quantified INS1E intracellular pool sizes data and the processed data are available
within the supporting tables. The raw mass spectrometry data are available from the author upon
request.
42
3.3. Results
1.1.7. Glucose and Exendin-4 stimulate insulin secretion response in INS1E cells.
To investigate the metabolic pathways that mediate insulin secretion in pancreatic β-cells,
we used INS1E rat insulinoma cells which have a GSIS dose response similar to rat islets,
suggesting that their insulin secretion pathways are comparable (80). We stimulated INS1E cells
for 30 min with glucose (2.8, 16.7, or 25 mM) in the presence or absence of 10 nM exendin-4 (Ex-
4), a glucagon-like peptide-1 receptor (GLP-1R) agonist that potentiates GSIS (45) (Fig. 3.1.A).
When stimulated with low glucose (2.8 mM), regardless of the presence of Ex-4, there was
no significant insulin secretion (Fig. 3.1.B, Supp. Fig. S3.1.A). Upon stimulation with either 16.7
or 25 mM glucose, insulin secretion was increased 4- and 5-fold, respectively. Ex-4 treatment
further increased insulin secretion at both 16.7 and 25 mM concentrations of glucose. Next, we
analyzed the intracellular metabolite concentrations on the same samples by LC-MS metabolomics
(81). The intracellular concentration of glucose strongly tracked with glucose stimulation
concentrations. Notably, Ex-4 treatment significantly increased the intracellular concentration of
cyclic AMP (Supp. Figure S3.1.B), confirming the known response of the GLP1 receptor to
activate the PKA signaling pathway. Therefore, the metabolomic analysis of pancreatic β-cells
provides a framework to elucidate the connection of insulin secretion to the complex interplay of
molecular functions of glucose in conjunction with the regulatory layer of a diabetic drug (i.e., Ex-
4).
43
Figure 3.1. Glucose and Ex-4 stimulate insulin secretion and a metabolic shift in INS1E cells.
(A) Schematic showing overall experimental design. INS1E cells were cultured in 11.1 mM
glucose before switching to 0 mM glucose KRBH buffer for starvation. After 30 min, cells were
treated 30 min in 2.8 mM, 16.7 mM, 25 mM glucose with or without 10 nM Ex-4 supplementation
in KRBH buffer. The treated media were collected, spun-down, and the supernatants were
subjected to insulin ELISA measurement. (B) ELISA result showing INS1E insulin secretion was
potentiated by glucose. Addition of Ex-4 promoted higher insulin secretion in 16.7 mM and 25
mM glucose conditions. ** denotes Benjamini-Hochberg corrected two-tailed unequal-variance
Student’s t-test p-value < 0.01, and * denotes p-value <0.05 (n=3 biological replicates). (C)
Hierarchical clustering of metabolite abundances reveals a cluster of glycolysis metabolites up-
44
regulated in 16.7 mM and 25 mM glucose with or without 10 nM Ex-4 supplementation in KRBH
buffer. Metabolite abundances were filtered for one-way ANOVA p<0.50, and clustered using one
minus the Pearson correlation and average linkage. Metabolites are colored at the right of the
heatmap according to their metabolic pathways. Red and blue denote higher and lower abundance,
respectively, from samples run in biological triplicate. (D) Principal component analysis score
plots (PC1 versus PC2) of metabolite abundances segregated samples by insulin secreting (16.7
mM, 25 mM glucose +/- Ex-4) and non-insulin secreting group (Starved, and 2.8 mM glucose +/-
Ex-4). Color denotes glucose concentration, and shape denotes with or without Ex-4
supplementation.
1.1.8. Glucose but not Ex-4 induces a global shift in the metabolomics profile of
INS1E.
We identified and quantified 98 intracellular metabolites by LC-MS using exact mass and
retention time reference to authenticated standards library. Unsupervised hierarchical clustering of
metabolite abundances revealed a cluster of glycolysis and TCA cycle metabolites up-regulated in
16.7 mM and 25 mM glucose with or without Ex-4 supplementation (Fig. 3.1.C, Supp. Fig. S3.1.C).
In contrast, all amino acids except for L-glutamine exhibited reduced abundance in 16.7 mM and
25 mM glucose with or without Ex-4 supplementation. To identify global differences between
samples, we conduct principal component analysis (PCA) using the median normalized, mean-
centered scaling metabolite abundances in the three biological replicates. PCA reveals a clear
separation between insulin secreting samples (16.7 and 25 mM glucose +/- Ex-4) and non-insulin
secreting samples (starved, 2.8 mM glucose +/- Ex-4), with a negative shift on PC1 (80% variation)
by 16.7 glucose +/- Ex-4 relative to starved culture. Increase glucose concentration to 25 mM
further induced a 10% negative shift on PC1. In contrast, 2.8 mM glucose +/- Ex-4 stimulation did
not exhibit a significant shift relative to starved culture. No clear segregation between samples
with and without 10 nM Ex-4 treatment was observed for all three glucose concentrations. (Fig.
3.1.D Supp. Fig. S3.1.D).
45
Figure 3.2. Bioinformatic analysis identifies that glycolysis and TCA cycle intermediates and
amino acids are positively and negatively correlated with insulin secretion, respectively. (A)
Waterfall plot showing Pearson correlation coefficients between INS1E metabolite abundances
(n=98) and insulin secretion. The coefficients were ranked from the largest positive (left) to the
largest negative (right). Metabolites were colored and annotated according to their metabolic
pathways. Glycolysis (red), TCA cycle (blue), and PPP (green) metabolites were clustered at the
positive end, whereas the amino acids (purple) were grouped as the negatively correlated clusters.
46
(B–D) Metabolite set enrichment analysis (MSEA) of Pearson correlation coefficients between
INS1E metabolite abundances (n=98) and paired insulin secretion ranked from positive to negative
correlation. Mountain plots of Glycolysis (B) and TCA Cycle (C) pathway demonstrated that
upregulation of glycolysis and TCA Cycle metabolism were significantly correlated with insulin
secretion in INS1E cells. The mountain plot of Amino Acids (D) showed downregulation of Amino
Acids was significantly correlated with insulin secretion in INS1E cells. (E–H) Scatter plots of
secreted insulin (ng/million cells) relative to selected glycolysis pathway metabolite abundances
showing that glucose (E), fructose 1,6,–BP (F), 3PG (G), and G3P (H) were positively correlated
with insulin secretion. (I–L) Scatter plots of secreted insulin (ng/million cells) relative to selected
TCA Cycle metabolite abundances showing that citrate–isocitrate (I), fumarate (J), malate (K),
and α-ketoglutarate (L) were positively correlated with insulin secretion. (M–P) Scatter plots of
secreted insulin (ng/million cells) relative to selected amino acids showing that aspartate (M),
asparagine (N), alanine (O), and leucine–isoleucine (P) were negatively correlated with insulin
secretion.
1.1.9. Functional metabolites groups correlated with insulin secretion.
To identify metabolites and metabolic pathways correlated with insulin secretion, we
calculated and ranked the Pearson correlation coefficients for each metabolite abundance with
paired measurements of secreted insulin (Fig. 3.2.A, Supp. Fig. S3.2.A). This metric identified
glycolytic and the TCA cycle metabolites as the most positively correlated pathways to insulin
secretion. In addition, almost all amino acids clustered as negatively correlated to insulin
secretion. Interrogating of the Pearson correlation rank list by Metabolite Set Enrichment
Analysis (MSEA) confirmed that upregulation of glycolysis (Fig. 3.2.B, Supp. Fig. S3.2.B) was
significantly correlated with insulin secretion. By examination of the scatter plots of selected
glycolysis metabolites (Figs. 3.2.E-H, Supp. Fig. S3.2.E-H) revealed that the Pearson correlation
coefficient values for glucose (Fig. 3.2.E, Supp. Fig. S3.2.E), fructose 1,6–BP (Fig. 3.2.F, Supp.
Fig. S3.2.F), 3PG (Fig. 3.2.G, Supp. Fig. S3.2.G), and G3P (Fig. 3.2.H, Supp. Fig. S3.2.H) were
consistently greater than 0.8. A similar observation was made for the TCA cycle (Fig. 3.3.B,
Supp. Fig. S3.2.B), and scatter plots of selected TCA cycle metabolites (Fig. 3.2.I-L) showing
the citrate–isocitrate (Fig. 3.2.I, Supp. Fig. S3.2.I), fumarate (Fig. 3.2.J, Supp. Fig. S3.2.J),
47
malate (Fig. 3.2.K, Supp. Fig. S3.2.K), and α-ketoglutarate (Fig. 3.2.L, Supp. Fig. S3.2.L) were
positively correlated with insulin secretion. Upregulation of glycolysis and the TCA cycle are the
characteristics of GSIS in β-cells (80). In the same analysis, down-regulation of amino acids was
significantly correlated with insulin secretion by examination of the MSEA mountain plot (Fig.
3.2.C, Supp. Fig. S3.2.C). Scatter plots of secreted insulin (ng/million cells) relative to selected
amino acids abundance showed that aspartate (Fig. 3.2.M, Supp. Fig. S3.2.M), asparagine (Fig.
3.2.N, Supp. Fig. S3.2.N), alanine (Fig. 3.2.O, Supp. Fig. S3.2.O), and leucine–isoleucine (Fig.
3.2.P, Supp. Fig. S3.2.P) were negatively correlated with insulin secretion.
1.1.10. GAPDH inhibition blocks glycolytic flux and reduces insulin secretion
Our bioinformatic analysis demonstrated that glycolysis was correlated with insulin
secretion in INS1E cells. To investigate the dependency of insulin secretion on glycolysis, we
employed a selective GAPDH inhibitor koningic acid (KA) to disrupt glycolysis (82). INS1E cells
were thus treated with 16.7 mM glucose with or without 10/100 μM KA. The addition of the 10
μM or 100 μM KA significantly decreased the insulin secretion (Fig. 3.3.A, Supp. Fig. S3.3.A-B).
We next measured the intracellular metabolomic profiles by LC-MS. The intracellular glucose
abundance plot (Fig. 3.3.D) did not show significant alteration by KA treatment. However, we
observed that KA differentially changed the glycolytic intermediates (Fig. 3.3.B, Supp. Fig.
S3.3.C). Glycolytic metabolites upstream of GAPDH including hexose-6-P (Fig. 3.3.E), F1,6-BP
(Fig. 3.3.F), and G3P (Fig. 3.3.G) were increased in abundance upon 10/100 μM KA treatment. In
contrast, glycolytic metabolites downstream of GAPDH including 3PG (Fig. 3.3.H), PEP (Fig.
3.3.I), and lactate (Fig. 3.3.J) were decreased in abundance upon 10/100 μM KA treatment. In
addition, intracellular concentrations of aspartate (Fig. 3.3.K) and malate (Fig. 3.3.L) were
48
significantly increased and decreased by KA treatment, demonstrating that these changes are
dependent on glycolysis. Taken tother, we demonstrated that disruption of glycolysis reduced
insulin secretion in INS1E cells and that KA-treated cells exhibit a metabolomic profile consistent
with GAPDH inhibition.
1.1.11. Aspartate and asparagine supplementation increases insulin secretion in
INS1E cells.
Our bioinformatic analysis demonstrated that the intracellular levels of aspartate (Asp) and
asparagine (Asn) were negatively correlated with insulin secretion in INS1E cells. In contrast,
proline levels were not correlated with insulin secretion. To investigate the relationship of secreted
insulin on aspartate (Asp) and asparagine (Asn), we treated INS1E cells with 16.7 mM glucose
(Gluc) with or without 380 μM/1000 μM Asn, 180 μM/1000 μM Asp, or 20 μM proline. These
concentrations were chosen based on the concentrations of Asn and Asp in RPMI media (380 μM
and 180 μM, respectively). Interestingly, supplementation with 380 μM Asn or 180 μM Asp
enhanced insulin secretion by approximately 30% relative to 16.7 mM Gluc. Secreted insulin was
further increased by higher concentrations (1,000 μM) of Asn but not by 1,000 μM Asp (Fig.
3.4.A). Proline supplementation, in contrast, had no effect on GSIS. Combined supplementation
of 180 μM Asp and 380 μM Asn together did not further increase insulin secretion (Supp. Figure
S3.3.D).
49
Figure 3.3. GAPDH inhibition blocks glycolysis flux and reduces insulin secretion. INS1E
cells were cultured in 11.1 mM glucose full media before switching to 0 mM glucose KRBH buffer
for starvation. After 30 min, cells were treated 30 min in 16.7 mM glucose without or with 10/100
μM KA in KRBH buffer. The treated media were collected, spun-down, and the supernatants were
subjected to insulin ELISA measurement. The attached cells were subjected to ice-cold methanol
extraction, and speed-vac’d to dryness, and submitted to LC-MS. (A) ELISA result showing
INS1E insulin secretion was significantly reduced by the GAPDH inhibitor KA at both 10/100 μM.
(B) Volcano plot representing the comparison of 16.7 mM glucose with 100 μM KA in KRBH
buffer relative to 16.7 mM glucose showing fructose 1,6–bisphosphate and PEP were significantly
50
up- and down-regulated, respectively. Average of log2 (16,7 mM gluc +100 μM KA / 16.7 mM
gluc) vs. -log10 FDR p-value. Color denotes metabolites with FDR p-value < 0.05, and average
absolute log2 fold change > 1 (red) or others (black), and shape denotes glycolysis (triangle), TCA
(circle), or other (square). (C) Schematic representation of glycolysis pathway showing
involvement of GAPDH inhibitor KA affecting glycolytic flux. (D–J) Dot and box plots showing
that intracellular glucose (D) abundance was not affected by KA treatment. Glycolytic metabolites
upstream of GAPDH including hexose-6-P (E), F1,6-BP (F), and G3P (G) exhibited an increase
in abundance upon KA treatment. Glycolytic metabolites downstream of GAPDH including 3PG
(H), PEP (I), and lactate (J) exhibited a decrease in abundance upon KA treatment. (K) Aspartate
abundance was significantly increased by KA addition. (L) TCA cycle metabolite malate was
significantly decreased by KA addition. *p <0.05, and **p <0.01 compared to glucose (Benjamini-
Hochberg FDR corrected two-tailed unequal-variance Student’s t-test, n=3 biological replicates).
To investigate the effect of Asp and Asn on GSIS, we profiled cells using quantitative
metabolomics LC-MS and identified 108 metabolites. We confirmed that aspartate and asparagine
intracellular concentrations were increased by Asp or Asn treatment (Fig. 3.4.E, F). Of other
quantified metabolites, intracellular deoxyribose was significantly increased (Fig. 3.4B, J) and
asparagine was significantly decreased by 180 μM Asp treatment (Fig. 3.4.B, E). No obvious
grouping of functionally related metabolites was apparent from the volcano plot for 180 μM Asp
stimulation. When comparing Asn+Gluc relative to Gluc (Fig. 3.4.C), deoxyribose (Fig. 3.4.J),
phosphoribosyl pyrophosphate (PRPP) (Fig. 3.4.K), ribose (Fig. 3.4.L), 6-phosphogluconate (6PG)
(Fig. 3.4.M), ribulose 5-phosphate or Ru5P (Fig. 3.4.N), and ribulose 1,5–bisphosphate (Ribulose
1,5–BP) (Fig. 3.4.O) were significantly increased, suggesting upregulation of the pentose
phosphate pathway (PPP). The MSEA analysis of log2 fold-change for Asn+Gluc to Gluc
confirmed the PPP metabolism was significantly enriched in Asn treated INS1E cells (83,84) (Fig.
3.4.D).
51
52
Figure 3.4. Aspartate and asparagine supplementation increases insulin secretion in INS1E
cells. INS1E cells were cultured in 11.1 mM glucose full media before switching to 0 mM glucose
KRBH buffer for 30 min starvation. Next, cells were treated 30 min in 2.8 mM, 16.7 mM glucose
with 380 μM asparagine, 1000 μM asparagine 180 μM aspartate, or 1000 mM aspartate
supplementation in KRBH buffer. (A) ELISA result showing INS1E insulin secretion was
increased by asparagine or aspartate supplementation in 16.7 mM glucose in KRBH buffer. (B–C)
Volcano plots representing average of log2 (16.7 mM gluc +180 μM Asp / 16.7 mM gluc) vs. -
log10 FDR p-value (B) and average of log2 (16.7 mM gluc +380 μM Asn / 16.7 mM gluc) vs. -
log10 FDR p-value (C). Color denotes metabolites with FDR p-value < 0.05, and average absolute
log2 fold change > 1 (red) or others (black), and shape denotes glycolysis (triangle), PPP (circle),
or other (square). (D) Mountain plot of Pentose Phosphate Pathway (PPP) demonstrating that PPP
metabolism was significantly enriched in INS1E cells following asparagine supplementation.
Metabolite set enrichment analysis (MSEA) of metabolite abundance log2 fold change of 16.7 mM
gluc + 380 μM Asn relative to 16.7 mM gluc. (E–G) Intracellular asparagine (E), aspartate (F),
and glutamate (G) abundances were significantly increased upon asparagine or aspartate
supplementation in 16.7 mM glucose KRBH buffer. (H–I) Selected TCA cycle metabolites
abundance plots showing malate (H) level increased in asparagine or aspartate supplementation
conditions relative to 16.7 mM glucose, while αKG (I) exhibited an increase in aspartate
supplemented conditions relative to 16.7 mM glucose. (J–O) PPP intracellular metabolites
abundance plots demonstrate an increase in deoxyribose (J), PRPP (K), ribose (L), 6PG (M), R5-
P–Ru5P (N), and Ribulose 1,5–BP (O) in 380 μM and 1000 μM asparagine supplemented
conditions. *p <0.05, and **p <0.01 compared to glucose (Benjamini-Hochberg FDR corrected
two-tailed unequal-variance Student’s t-test, n=3 biological replicates).
Notably, aspartate addition significantly decreased the asparagine abundance (Fig. 3.4.B),
and differences observed in αKG (Fig. 3.4.I), deoxyribose (Fig. 3.4.J), 6PG (Fig. 3.4.M), Ru5P
(Fig. 3.4.N), and Ribulose 1,5–BP (Fig. 3.4.O) suggested that the insulin secretion enhanced by
Asp relied on a different mechanism rather than increased PPP metabolism in INS1E cells.
1.1.12. Malate-aspartate shuttle inhibitor AOA blocks aspartate induced insulin
Having identified that insulin secretion in INS1E cells was increased by Asp
supplementation, we next asked what was the underlying mechanism contributing to increased
insulin secretion. Previous studies (39,44,85) have demonstrated that glucose and amino acid
coupling in mitochondria plays a crucial role in insulin granules exocytosis. Additionally, the
malate–aspartate shuttle is one of the key regulators of cytosolic-mitochondrial NAD+/NADH
53
transport in pancreatic β-cells (40,86). To test the role of the malate-aspartate shuttle, we treated
INS1E cells with 16.7 mM glucose (Gluc) with and without 180 μM Aspartate (Asp) in the
presence or absence of the aspartate aminotransferase (AST) inhibitor AOA (5 μM). Importantly,
the addition of AOA eliminated aspartate-induced insulin secretion but did not affect GSIS (Fig.
3.5.A). Comparing Asp to Asp+AOA treatements, we identified that the intracellular aspartate
abundance was significantly lower without AOA (Fig. 3.5.B, D, Supp. Fig. S3.4.A-B.). The
intracellular aspartate abundances for Asp+AOA and AOA were similar to the 2.8 mM glucose
baseline condition, indicating the malate-aspartate shuttle activity of INS1E cells treated with
AOA was similar to physiological malate-aspartate shuttle dynamics of the non-insulin secreting
2.8 mM glucose cultured INS1E cells. Previous study has demonstrated that the
aspartate/glutamate carrier Aralar1, and the aspartate aminotransferases (AST1/2) are significant
for maintaining cellular nutrient β-cell metabolism (Fig. 3.5.C), and the malate-aspartate shuttle
derived glutamate acts as a signal in insulin granules exocytosis (38–40). Our result shows the
intracellular glutamate level was significantly higher only in Gluc+Asp (Fig.5 3.E, Supp. Fig.
S3.4.C), suggesting the combined aspartate and glucose culture induced insulin secretion was
associated with the enhanced malate-aspartate shuttle activity.
54
Figure 3.5. Inhibition of the malate-aspartate shuttle reduces aspartate-induced insulin
secretion. INS1E cells were cultured in 11.1 mM glucose full media before switching to 0 mM
glucose KRBH buffer for 30 min starvation. Next, cells were treated 30 min in 2.8 mM, 16.7 mM
glucose (Gluc), Gluc with 180 μM Aspartate (Asp), Gluc with Asp and 5 μM AOA (Asp + AOA),
and Gluc + 5 μM AOA (AOA). (A) Malate-aspartate shuttle inhibitor AOA blocked aspartate-
induced insulin secretion in INS1E cells. AOA had no effect on GSIS. Insulin levels were
55
measured by ELISA. (B) Volcano plot representing average of log2 (Asp+AOA / Asp) vs. -log10
FDR p-value. Red datapoints denote metabolites with FDR p-value < 0.05, and average absolute
log2 fold change > 0.5. (C) Schematic representation of malate-aspartate shuttle showing
involvement of aspartate and AOA. (E, F) Intracellular metabolite abundances in response to
aspartate and/or AOA for aspartate (E) and glutamate (F) *p <0.05, and **p <0.01 compared to
glucose;
†
p <0.05, and
‡
p <0.01 compared to glucose +aspartate (Benjamini-Hochberg FDR
corrected two-tailed unequal-variance Student’s t-test, n=3 biological replicates).
3.4. Discussion and Conclusion
The dysregulation of metabolism in pancreatic β-cells (PBCs) has been directly linked to
insulin resistance in T2D. The altered metabolism of β-cellsleads to insufficient insulin secretion
into the bloodstream in T2D patient. The altered metabolism state of diseased pancreatic β-cells
has long been proposed as a therapeutic target (87–90) Understanding the metabolomic
mechanisms underpinning glucose-stimulated insulin secretion (GSIS) is essential for the design
of therapeutic strategies targeting β-cells insulin resistance. Glucose and the anti-diabetic agent
Ex-4 have been reported to promote insulin secretion in INS1E cells (29). We thus sought to
investigate the functional metabolic nodes controlled by glucose and Ex-4 (Fig. 3.1.A). A robust
5-fold increase of insulin level was potentiated upon supplementing 16.7 mM or 25 mM of glucose.
Notably, insulin secretion further enhanced with the presence of 10 nM of Ex-4 was significantly
in 16.7 mM glucose but not 25 mM glucose (Fig. 3.1.B, Supp. Fig. S3.1.A). Treating β-cells with
25 mM glucose to induce GSIS is not uncommon practice (91–93). However, there is the concern
that sudden exposure of high level of glucose to β-cells previously starved in 0 mM glucose KRBH
buffer poses the risk of creating osmotic shock to the β-cells, disrupting the cytoplasm membrane
potential, and leading to unphysiological insulin secretion. Therefore, 16.7 mM glucose treatment
was chosen as the positive GSIS control in this study.
56
We profiled the metabolome of INS1E cells as β-cells model to understand the metabolic
regulation of insulin secretion. Global unsupervised analysis on the complete metabolic profile
using principal component analysis (PCA) (Fig. 3.1.D, Supp. Fig. S3.1.C) revealed an insulin
secreting cluster (16.7 and 25 mM glucose +/- 10 nM Ex-4) and a insulin non-secreting cluster
(starved, 2.8 mM glucose +/- 10 nM Ex-4) separated by a striking 90% variation along PC1,
suggesting our metabolic profile containing information underlying differential metabolites
change in association with insulin secretion in INS1E cells. In contrast, no significant segregation
was observed between 2.8 mM, 16.7 mM, and 25 mM glucose with and without 10 nM Ex-4
supplementation samples, indicating the limitation of our metabolomic profiles to explain Ex-4
induced insulin secretion. The kinetics of Ex-4 binds to GLPR triggering cAMP/PI3K signaling
cascades within seconds in INS1E cells (45,94). Therefore, shorter treatment times may be more
suitable for profiling metabolomics changes relevant to Ex-4 stimulation in INS1E cells. This is
suggested in the study of mapping INS1E cells insulin vesicles using soft X-ray tomography (SXT)
that significant higher insulin vesicles molecular densities were observed in INS1E cells treated
with 16.7 mM of glucose for 5 min relative to stimulation for 30 min. (95) The rapid acidification
of insulin within the readily available immature vesicles upon glucose and/or Ex-4 stimulation
pose less rate-limiting constraint compared to the biosynthesis of new vesicles. (95–97) The
biphasic insulin release upon glucose stimulation in INS1E cells were extensively studied, where
a transient first insulin release peaks at 4-5 min, and followed by a gradually increased second
phase after 15 min of stimulation. (78,98)
Next, visual inspection on unsupervised hierarchical clustering of metabolite abundances
identifying a cluster of glycolysis and TCA cycle metabolites exhibited an increased in abundances.
These observations are consistent with the GSIS dynamics in β-cells (26) We also observed that
57
the majority of amino acids exhibited reduced abundance in insulin secreting conditions.
Consistent with the above findings, the waterfall plot ranking the Pearson correlation coefficient
comparing each metabolite abundance to secreted insulin concentration revealed glycolysis and
TCA metabolites were correlated to insulin secretion, while amino acids were anti-correlated with
insulin secretion (Fig. 3.2.A, Supp. Fig. S3.2.A). Among all 20 quantified amino acids, the
coefficient values for the bottom 15 anti-correlated metabolites were smaller than -0.3, and all 15
are proteogenic amino acids. This finding suggests GSIS depletes proteogenic amino acids in order
to satisfy the demand for β cell insulin production and secretion activities. Among those amino
acids, leucine–isoleucine (Fig. 3.2.P, Supp. Fig. S3.2.P), and alanine (Fig. 3.2.O, Supp. Fig. S3.2.O)
are known to be important for the regulation of β cell membrane electrical potential for insulin
secretion (85,99). Aspartate (Fig. 3.2.M, Supp. Fig. S3.2.M) is the intermediate of the malate-
aspartate shuttle that plays an vital role in maintaining NAD+/NADH homeostasis (100). Cationic
transporters (CAT) on the β-cell plasma membrane allow arginine to enter the cytoplasm, and
arginine (Fig. 3.2.M) , Supp. Fig. S3.2.M, is converted to citrulline by inducible nitric oxide
synthase (iNOS), where citrulline is one of the precursors for fumarate synthesis (83). Taken
together, we hypothesized that altering the glycolytic metabolism and supplementing proteogenic
amino acids to INS1E cells would affect insulin secretion in INS1E cells.
We discovered GSIS in INS1E was reduced by inhibition of glyceraldehyde 3-phosphate
dehydrogenase (GAPDH) conversion of glyceraldehyde 3-phosphate (G3P) to 1,3-
bisphosphoglycerate (1,3-BP) using konigic acid (KA) (Fig. 3.3.A, Supp. Fig. S3.3.A-B). The
inhibition of the GAPDH blocks the conversion of G3P (Fig. 3.3.G) to 3PG (Fig. 3.3.H) the
decrease of the abundances other following glycolytic intermediates PEP (Fig. 3.3.I) and lactate
(Fig. 3.3.J), leading to a significant decrease of insulin secretion (Fig. 3.3.A, Supp. Fig. S3.3.A-
58
B). Thus, blocking the glycolysis metabolism by GAPDH inhibitor KA negatively affected GSIS
in INS1E cells. We also reported that intracellular levels of aspartate and asparagine were depleted
in INS1E cells when stimulated by glucose (Fig. 3.1.C, Supp. Fig. S3.1.C). We tested
supplementing 180 μM or 1000 μM aspartate, and 380 μM or 1000 μM asparagine in the presence
of 16.7 mM glucose. Significantly higher insulin secretion was observed upon asparagine and
aspartate in combination 16.7 mM glucose treatment (Fig. 3.4.A). We observed elevated
intracellular asparagine abundance in combined asparagine and glucose treated cells (Fig. 3.4.F).
Pentose phosphate pathway (PPP) was enriched in combined asparagine-glucose relative to
glucose only condition (Fig. 3.4.D). PPP mediates NADP+/NADPH homeostasis provide an
alternative metabolic route to regenerate GSSG from GSG by glutathione reductase that does not
require ATP (83,84). However, the PPP is relatively inactive in β-cells and is further
downregulated due to the rerouting of glycolytic flux toward ATP production as cells undergo
GSIS (83,84,101). Our result suggests there is an elusive link connecting asparagine uptake into
cytoplasm and upregulation of PPP activity with the presence of glucose in INS1E cells. We also
reported elevated intracellular aspartate abundance when treated with aspartate plus 16.7 mM
glucose (Fig. 3.4.F, Fig. 3.5.A, Supp. Fig. S3.4.A-B). The inhibition of the malate-aspartate shuttle
by AOA abolished the aspartate-induced insulin secretion in the presence of 16.7 mM of glucose.
However, shutting down the malate-aspartate shuttle did not affect GSIS (Fig. 3.5.A, Supp. Fig.
S3.4.A-B). Our findings are consistent with reports that supplementing the malate-aspartate shuttle
intermediate (i.e., aspartate) facilitates the transport of electrons into the mitochondria in the form
of NADH, and therefore enhances electron transport chain action to produce ATP (38–40,102).
Additionally, elevated cytoplasmic aspartate concentration enhances the aspartate
59
aminotransferases 1 (AST1) activity, allowing the higher catalytic conversion of αKG to glutamate
for insulin granule exocytosis (38–40).
In summary, we sought to discover therapeutic targets for Type-2 diabetes (T2D). Toward
this end, our result so far demonstrated the LC-MS based metabolomic analysis of INS1E cells
can provide a framework to elucidate the connection of insulin secretion to the complex interplay
of molecular functions of glucose in conjunction with the regulatory layer of a verity of
stimulants, (e.g., Ex-4, KA, and amino acids). Besides the work presented in this paper, our high-
content metabolic profiles are being corroborated with a collection of heterogeneous analyses
into a Bayesian meta-model to predict drugs perturbation on pancreatic β-cells. Overlaying
heterogeneous data types by meta-modeling approaches promotes the identification of
physiological and pathological biomolecular signals systematically and allow holistic
understanding of β-cell GSIS associated pathways. (51) We expect our LC-MS based high-
content metabolomics pipeline provide unique and important perspective to explain the β-cells
insulin secretion physiology that is complementary with other analyses. In addition, future work
in applying the same metabolomics framework to uncover the mechanism underlying in vivo
pancreatic islets insulin production and secretion to achieve higher clinical relevancy.
60
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Supplemental Figures
Supp. Figure S2.1. Effects of galactose culture on cell growth and viability, and AKT
activation. A-B) Growth curves (A) and cell viability (B) for MCF-10A cells expressing RFP,
mAKT, or MYC over seven passages in either glucose or galactose culture. Growth data are shown
70
as log2 viable cell number. Viability of adherent cells was measured by trypan blue staining.
Growth curves are shown using the log2 viable cell number. Viability data are not shown for
glucose culture in B because the values were universally high (>95%). Error bars denote standard
deviation from biological replicates (n=2). C-E) MCF-10A cells expressing RFP, mAKT, or MYC
were switched from glucose culture to galactose culture, and the total cell number (C), viable cell
number (D), and viability (E) were measured over 5 days (equivalent to one passage in panels A
and B). Here, both adherent and floating cells were collected for viability measurements. Error
bars denote standard deviation from biological replicates (n = 3). F) Annexin V and propidium
iodide (PI) staining of MCF-10A cells expressing either mAKT or MYC cultured in glucose or
galactose. Galactose cells were switched from glucose culture and exposed to galactose for 3 h
before staining. The percentage of cells determined as alive (lower left), necrotic (upper left),
apoptotic (lower right), or dead (upper right) is indicated.
Supp. Figure S2.2. Quantitation of phospho-AKT and AKT in glucose and galactose culture
for mAKT-expressing cells A) Quantitation of the ratio of phospho-Ser473-AKT to total AKT
from the Western blot in Fig. 2.1.D for MCF-10A cells expressing mAKT. The pAKT/AKT ratio
slightly decreased in short-term galactose culture. B) Histogram density plots of total AKT and
phospho-Ser473-AKT levels measured by phospho-flow for MCF-10A cells expressing mAKT
cultured in glucose (top, red), switched from glucose to galactose for 3 h (middle, blue), or long-
term cultured in galactose (bottom, green). The ratio quantitation of these proteins is shown in Fig.
2.1E. mAKT-expressing cells in long-term galactose culture are larger than mAKT cells in glucose
or short-term galactose, and therefore the per cell signal is increased for these cells.
71
Supp. Figure S2.3. Experimental overview MCF-10A cells expressing either RFP, mAKT, or
MYC were cultured in glucose before switching to glucose (Glc) or galactose media for 24 h (24
h Gal). Long-term galactose cultured MCF-10A RFP, AKT and MYC cells (>5 passages in
galactose) were cultured in galactose before switching to galactose (Gal) containing media for 24
h. For metabolomics, intracellular metabolites were extracted using cold methanol and then
analyzed using hydrophilic interaction liquid chromatography (HILIC)-mass spectrometry
(103,104). For proteomics, cells were lysed in 8M urea, followed by reduction, alkylation, and
trypsinization. We performed two independent biological replicates, and each experiment were
subjected to two technical LC-MS injections. Protein levels were calculated using label-free
quantitation and the iBAQ method (49).
72
Supp. Figure S2.4. Atom transition maps for [U-
13
C]-L-glutamine and [U-
13
C]-galactose
stable isotope labeling. A) [U-
13
C]-L-glutamine is converted to glutamate and then α-
ketoglutarate (αKG) to enter the TCA cycle. Solid arrows indicate forward TCA cycle activity and
73
dashed arrows indicate reductive carboxylation (105). B) [U-
13
C]-D-galactose enters cellular
metabolism by isomerization to α-D-galactose, conversion to galactose-1-phosphate (α-D-
galactose-1P) and UDP-galactose/UDP-glucose before conversion to glucose-6-phosphate (G6P).
G6P can then enter glycolysis and the TCA cycle (106).
74
Supp. Figure S2.5. Differential usage of glutamine in mAKT-expressing cells.
13
C isotopomer
distributions from MCF-10A cells labeled with [U-
13
C]-L-glutamine. Isotopomer abundances
were normalized to the sum of all isotopomers to calculate the percent abundance of each
isotopomer. A) aconitate; B) succinate; C) fumarate; and D) malate from the TCA cycle show that
AKT cells in glucose condition were more dependent on non-glutamine sources whereas MYC
cells were more dependent on glutamine-derived carbon. This suggests that MYC cells should
have less problem switching to galactose. Increasing incorporation of M5 citrate-isocitrate suggest
all three cells type were forced to utilize
13
C-glutamine to produce citrate-isocitrate through
reductive carboxylation
Supp. Figure S2.6. Metabolite set enrichment analysis of glutathione metabolism for RFP-
and MYC-expressing MCF-10A cells. Metabolite set enrichment analysis mountain plots for
glutathione metabolism for A) RFP-expressing MCF-10A cells comparing short-term galactose
culture to glucose culture (24 h Gal/Glc), B) RFP-expressing cells comparing short-term to long-
term galactose culture (24 h Gal/Gal), C) MYC-expressing MCF-10A cells comparing short-term
galactose culture to glucose culture (24 h Gal/Glc), and D) MYC-expressing cells comparing short-
term to long-term galactose culture (24 h Gal/Gal). The green line denotes the enrichment score,
and the black tick marks denote metabolites that belong to glutathione metabolism. The normalized
enrichment score (NES) and false discovery rate (FDR) are shown. In comparison to mAKT-
expressing cells (Fig. 2.3), RFP- and MYC-expressing cells did not exhibit upregulation of the
glutathione metabolism pathway.
75
Supp. Figure S2.7. LC-MS proteomics Experiment 2 demonstrates enrichment of nonsense-
mediated mRNA decay (NMD) proteins in mAKT cells switched to galactose. A) Principal
76
component analysis score plots (PC1 vs. PC2) of proteomic data from Experiment 2 segregated
samples by oncogene and media type. Color denotes oncogene, and shape denotes media type.
Each sample was analyzed in technical duplicate. Short-term galactose culture (24 h Gal) induced
a positive shift on PC1 (34.9% of variation) for all cell types relative to glucose culture (Glc).
Long-term galactose culture (Gal), in contrast, exhibited a negative shift on PC1 relative to short-
term galactose culture. The PC1 shift for mAKT-expressing cells was significantly larger than for
either RFP- or MYC-expressing cells. Similar trends were seen in Experiment 1 (Fig. 2.4B).
B) Enrichment analysis identified Reactome pathways enriched in the PC1 loadings vector from
Experiment 2. Nonsense Mediated Decay (NMD) enhanced by the Exon Junction Complex (EJC)
and Nonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC) are
highlighted. Similar trends were seen in Experiment 1 (Fig. 2.4C) D) mAKT-expressing cells
significantly upregulated NMD proteins in short-term galactose culture. A heatmap of protein
expression from Experiment 2 for the Nonsense Mediated Decay (NMD) enhanced by the Exon
Junction Complex (EJC) pathway (Reactome R-HSA-975957) demonstrated that mAKT-
expressing cells dramatically upregulated NMD protein expression in short-term (24 h) galactose
culture. Similar trends were seen in Experiment 1 (Fig. 2.4D).
Supp. Figure S2.8. Density plots of DCF-DA time course. MCF-10A cells expressing mAKT
were switched from glucose culture to either glucose or galactose culture. At the indicated times,
cells were labeled with the ROS probe DCF-DA, and ROS levels were measured by flow
cytometry. ROS levels were consistently upregulated in galactose cultured cells.
77
Supp. Figure S2.9. ROS are required for galactose-induced cell death. A) The ROS scavenger
catalase rescued mAKT-expressing cells from galactose-induced cell death. MCF-10A cells
expressing RFP, mAKT, or MYC were cultured in glucose, without glucose, or in galactose with
or without 200 U/ml of the ROS scavenger catalase for 32 h. Viable cell number was measured by
trypan blue staining and normalized to the glucose cultured cells. ** denotes Student’s t-test p-
value < 0.01 compared to glucose culture, and ‡ denotes Student’s t-test p-value < 0.01 compared
to galactose culture without catalase (n=2-4 biological replicates). B) The ROS scavenger catalase
rescued MB436 and Hs578t breast cancer cells from galactose-induced cell death. MB436 and
Hs578t cells were cultured in glucose, without glucose, or in galactose with or without 200 U/ml
of the ROS scavenger catalase for 24 h. Viable cell number was measured by trypan blue staining
and normalized to the glucose cultured cells. ** denotes Student’s t-test p-value < 0.01 compared
to glucose culture, and ‡ denotes Student’s t-test p-value < 0.01 compared to galactose culture
without catalase (n=3-4 biological replicates).
78
Supp. Figure S3.1. Glucose and Ex-4 stimulate secrtion and a metabolite shift in INS1E cells.
(A) ELISA result showing INS1E insulin secretion was potentiated by glucose. Addition of Ex-4
promoted higher inslin secretion in 16.7 mM and 25 mM glucose conditions. B) INS1E
intracellular cAMP response to glucose with or without 10 uM of Ex-4. (C) Principal component
analysis (PCA) score plots (PC1 versus PC2) of metabolite abundances segregated samples by
insulin secretion (16.7 mM, 25 mM glucose +/- Ex-4) and non-insulin secreting group (Starved,
1.1 mM glucose +/- Ex-4). Color denotes glucose concentration, and the shape denotes with or
without Ex-4 supplementation. (D) Hierarchical clustering of metabolite abundances reveals a
cluster of glycolysis metabolites up- 43 regulated in 16.7 mM and 25 mM glucose with or without
10 nM Ex-4 supplementation in KRBH buffer. Metabolite abundances were filtered for one-way
ANOVA p<0.50, and clustered using one minus the Pearson correlation and average linkage.
Metabolites are colored at the right of the heatmap according to their metabolic pathways. Red and
blue denote higher and lower abundance, respectively, from samples run in biological triplicate.
79
*p <0.05, and **p <0.01 compared to glucose (Benjamini- Hochberg FDR corrected two-tailed
unequal-variance Student’s t-test, n=3 biological replicates).
80
Supp. Figure S3.2. Bioinformatic analysis identifies that glycolysis and TCA cycle
intermediates and amino acids are positively and negatively correlated with insulin secretion,
respectively. (A) Waterfall plot showing Pearson correlation coefficients between INS1E
81
metabolite abundances (n=98) and insulin secretion. The coefficients were ranked from the largest
positive (left) to the largest negative (right). Metabolites were colored and annotated according to
their metabolic pathways. Glycolysis (red), TCA cycle (blue), and PPP (green) metabolites were
clustered at the positive end, whereas the amino acids (purple) were grouped as the negatively
correlated clusters. 45 (B–D) Metabolite set enrichment analysis (MSEA) of Pearson correlation
coefficients between INS1E metabolite abundances (n=98) and paired insulin secretion ranked
from positive to negative correlation. Mountain plots of Glycolysis (B) and TCA Cycle (C)
pathway demonstrated that upregulation of glycolysis and TCA Cycle metabolism were
significantly correlated with insulin secretion in INS1E cells. The mountain plot of Amino Acids
(D) showed downregulation of Amino Acids was significantly correlated with insulin secretion in
INS1E cells. (E–H) Scatter plots of secreted insulin (ng/million cells) relative to selected
glycolysis pathway metabolite abundances showing that glucose (E), fructose 1,6,–BP (F), 3PG
(G), and G3P (H) were positively correlated with insulin secretion. (I–L) Scatter plots of secreted
insulin (ng/million cells) relative to selected TCA Cycle metabolite abundances showing that
citrate–isocitrate (I), fumarate (J), malate (K), and α-ketoglutarate (L) were positively correlated
with insulin secretion. (M–P) Scatter plots of secreted insulin (ng/million cells) relative to selected
amino acids showing that aspartate (M), asparagine (N), alanine (O), and leucine–isoleucine (P)
were negatively correlated with insulin secretion.
82
Supp. Figure S3.3 .GAPDH inhibition blocks glycolysis flux and reduces insulin secretion.
INS1E cells were cultured in 11.1 mM glucose full media before switching to 0 mM glucose
KRBH buffer for starvation. After 30 min, cells were treated 30 min in 16.7 mM glucose with or
without 10 μM KA/20 uM proline in KRBH buffer. The treated media were collected, spun-down,
and the supernatants were subjected to insulin ELISA measurement. The attached cells were
subjected to ice-cold methanol extraction, and speed-vac’d to dryness, and submitted to LC-MS.
(A, B) ELISA result showing INS1E insulin secretion was significantly reduced by the GAPDH
inhibitor KA at both 10 μM. Insulin secretion was unaffected by proline addition. (C) Volcano plot
representing the comparison of 16.7 mM glucose with 10 μM KA in KRBH buffer relative to 16.7
mM glucose showing fructose 1,6–bisphosphate and PEP were significantly 49 up- and down-
regulated, respectively. Average of log2 (16,7 mM gluc +10 μM KA / 16.7 mM gluc) vs. -log10
83
FDR p-value. Color denotes metabolites with FDR p-value < 0.05, and average absolute log2 fold
change > 1 (red) or others (black), and shape denotes glycolysis (triangle), TCA (circle), or other
(square). (D) INS1E secreted insulin response to different amino acids supplementation with 16.7
mM glucose measured by ELISA intracellular cAMP response to glucose with or without 10 uM
of Ex-4. two-tailed unequal-variance Student’s t-test * denotes p-value <0.05 (n=3 biological
replicates).*p <0.05, and **p <0.01 compared to glucose (Benjamini- Hochberg FDR corrected
two-tailed unequal-variance Student’s t-test, n=3 biological replicates).
Supp. Figure S3.4 . Inhibition of the malate-aspartate shuttle reduces aspartate-induced
insulin secretion. INS1E cells were cultured in 11.1 mM glucose full media before switching to
0 mM glucose KRBH buffer for 30 min starvation. Next, cells were treated 30 min in 2.8 mM,
16.7 mM glucose (Gluc), Gluc with 180 μM Aspartate (Asp), Gluc with Asp and 5 μM AOA (Asp
+ AOA), and Gluc + 5 μM AOA (AOA). (A, B) Malate-aspartate shuttle inhibitor AOA blocked
aspartate- induced insulin secretion in INS1E cells. AOA had no effect on GSIS. Insulin levels
were measured by ELISA. (C, D) Intracellular metabolite abundances in response to aspartate
and/or AOA for glutamate (C), and aspartate (D) *p <0.05, and **p <0.01 compared to glucose;
84
†p <0.05, and ‡ p <0.01 compared to glucose +aspartate (Benjamini-Hochberg FDR corrected
two-tailed unequal-variance Student’s t-test, n=3 biological replicates).
Abstract (if available)
Abstract
The emergence of liquid chromatography-mass spectrometry-based (LC-MS) proteomics and metabolomics provide a systematic approach to uncover the complex and dynamic protein and metabolite networks regulating human disease. Metabolomics is the study of global metabolite concentrations or fluxes in cells, tissues, and organisms. Metabolites represent not only the output of gene and protein networks but also play important role in regulating various biological functions. ❧ The dysregulation of metabolism leads to various diseases, including cancer and diabetes. Many cancers preferentially use glycolysis for survival and proliferation, even in the presence of oxygen, a phenomenon known as aerobic glycolysis or the Warburg effect. Increased glycolytic activity is thought to help satisfy the rapacious demands of highly proliferative cancer cells for biosynthetic precursors, including lipids, proteins, and nucleic acids. Understanding the interplay between oncogenes and metabolism is essential to understand how to design therapeutic strategies targeting tumor metabolism. Diabetes is a worldwide pandemic, and type 2 diabetes (T2D) is the most common form of the disease. Effectively using insulin in the human body is critical for maintaining calorigenic nutrient homeostasis. Understanding the underlying pathways that regulate insulin production, secretion, and storage in pancreatic beta cell (β-cells) can provide a basis for rational T2D therapy. Taken together, this work highlight the use of LC-MS metabolomics and proteomics approach to uncover oncogene-dependent metabolic vulnerabilities in cancer cells, and the mechanism underlying glucose stimulated insulin secretion (GSIS) synergizing with amino acids suggesting appropriate therapies targeting disease associated with abnormal metabolism.
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Zheng, Dongqing
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System biology approaches to cancer and diabetes metabolism
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