The Roles of Diet and SirT3 Levels in Mediating Signaling Network Changes in
Insulin Resistance
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By
Nina Louise Lee
B.S. Chemical Engineering
Johns Hopkins University, 2009
SUBMITTED TO THE DEPARTMENT OF BIOLOGICAL ENGINEERING IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE IN BIOLOGICAL ENGINEERING
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
JUNE 2013
Signature of Author:
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Department of Biological Engineering
March 20, 2013
Certified by:
Forest White
Professor of Biological Engineering
Thesis Supervisor
Accepted by: K. Dane Wittrup
Professor of Chemical and Biological Engineering
Chair, Graduate Admissions Committee
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The Roles of Diet and SirT3 Levels in Mediating Signaling Network Changes in
Insulin Resistance
By
Nina Louise Lee
Submitted to the Department of Biological Engineering
on March 20, 2013 in Partial Fulfillment of the
Requirements for the Degree of Master of Science in
Biological Engineering
ABSTRACT
The goal of my research is to understand the mechanism by which high fat diets mediate insulin
sensitivity and the role SirT3 plays in high fat diet-induced insulin resistance. Insulin resistance is
defined as the inability of cells and tissues to respond properly to ordinary amounts of insulin and
is a precursor to many metabolic diseases such as diabetes and cardiovascular disease. Obesity,
brought on in large part by caloric excess from high fat diet feeding, is a major contributor to
insulin resistance. The recent drastic increase in the prevalence of obesity makes it imperative
that steps are taken to more effectively treat and cure obesity-linked diseases such as diabetes. To
identify optimal therapeutic targets, it is crucial to first gain a mechanistic understanding of
obesity-induced insulin resistance, and understand how specific changes in the signaling network
affect insulin sensitivity.
Previous work has demonstrated that levels of SirT3, a mitochondrial protein deacetylase, are diet
dependent. Additionally, SirT3 expression levels have been shown to mediate insulin and glucose
tolerance in animals in a diet-dependent manner. Perturbations in SirT3 levels also alter the levels
of phosphorylation on several canonical insulin signaling proteins. In my research, I further
investigated the link between SirT3, diet and insulin resistance from a signaling network
perspective. Using mouse liver as a model system, I analyzed liver tissue from mice fed a normal
diet (insulin sensitive) or mice fed a high fat diet, thus inducing insulin resistance. Quantification
of phenotypic and network events in response to insulin and utilization of computational
techniques revealed activated pathways and nodes mediating insulin response, some of which had
not been previously associated with the canonical insulin signaling network. I extended the study
to analyze the role SirT3 plays in diet-mediated insulin sensitivity by perturbing the level of
SirT3 in mice on both normal chow and high fat diets. The results of this research are useful for
designing more efficacious therapies to treat insulin resistance-induced diseases.
Thesis Supervisor: Forest White
Title: Professor of Biological Engineering
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I. Introduction
The overall goal of this project is to gain a better understanding of the mechanisms by which diet
and SirT3 levels modulate insulin resistance. High fat diets are becoming increasingly
commonplace in modem society and are major contributors to obesity. Obesity is associated with
insulin resistance and increased risk for diseases such as diabetes and cardiovascular disease.
Obesity-induced diabetes is becoming a serious medical issue because the prevalence of obesity
has rapidly increased over the last half-century and the number of affected individuals is
predicted to more than double in the next twenty years [1, 2].
A. Physiological effects of insulin and intracellular insulin signaling
Insulin is an important hormone for regulating blood-glucose homeostasis between feeding and
fasting states. In response to a post-prandial increase of blood glucose, pancreatic islet
p-cells
secrete insulin, which circulates and exerts effects on peripheral tissues, mainly the liver, adipose
tissue and skeletal muscle. In these tissues, insulin binds its cognate receptor and initiates a
signaling cascade mediated by phosphorylation events that activate downstream processes such as
glucose uptake, glycogen synthesis, protein synthesis and transcriptional regulation of gene
expression [3, 4, 5]. Insulin resistance is defined as an insufficient response of target tissues, in
terms of these processes, to normal levels of circulating insulin. Insulin resistance is involved in
the pathogenesis of many diseases such as type 2 diabetes, atherosclerotic heart disease, and
nonalcoholic fatty liver disease [6]. These disease states are characterized by reduced insulin
sensitivity, hyperglycemia and hyperlipidemia. Many metabolic and cardiovascular ailments are
affected by diets, and it has been found that obesity highly predisposes individuals to becoming
insulin resistant and afflicted by one or multiple insulin resistance-based diseases [7, 8]. Obesityinduced insulin resistance is becoming a major health issue for several reasons. First, the rate of
obesity in the United States has increased dramatically in the past few decades. This increase has
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major implications because historically, obesity has been confined to the adult population;
however, data has shown that obesity is now affecting a large proportion of American children as
well [9]. Secondly, the rise in the occurrence of obesity across the nation is paralleled by a
concomitant rise in type 2 diabetes, which is an obesity- and insulin resistance-linked disease [2].
Lastly, complications arising from obesity and the resulting insulin resistance in multiple organs
contribute to the morbidity and mortality associated with obesity-linked diseases [8].
Insulin resistance is defined as the inability of cells and tissue to properly respond to
physiological amounts of insulin. The lack of sensitivity in insulin resistant individuals is widely
attributed to defects in insulin signaling initiated by activation of the insulin receptor. The
canonical insulin signaling cascade starts at the cell surface, where insulin binds to the
extracellular subunits of the insulin receptor protein, a receptor tyrosine kinase. The resultant
conformational change in the intracellular subunits leads to autophosphorylation of key residues
in the catalytic domain and recruitment of secondary proteins such as the insulin receptor
substrate (IRS) family of proteins, in addition to Shc, Gab-1 and Cbl/CAP, which promote an
array of insulin responses. The most widely studied and understood portion of the pathway goes
through IRS 1/2, scaffold/adaptor proteins which are phosphorylated by the insulin receptor on
residues that recruit the regulatory subunit of phosphatidylinositol-3-kinase (PI3K), p85a.
Phosphorylated p85a activates its catalytic counterpart, p110, which phosphorylates the
membrane phospholipid phosphatidylinositol 4,5-phosphate (PIP2) to phosphatidylinositol 3,4,5phosphate (PIP3). PIP3 recruits the protein kinases PDK1 and PKB/Akt to the plasma membrane,
where PDK1 activates PKB/Akt by phosphorylation. Akt, in turn, activates several substrates by
phosphorylation. These substrates include glycogen synthase kinase 3P (GSK3p), which
promotes glycogen synthesis; AS 160, which promotes GLUT4 translocation and thus glucose
uptake into the cells; as well as forkheadbox family 0 (FOXO) transcription factors, which
regulate gene expression [3, 5, 10]. There are multiple isoforms of the aforementioned signaling
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proteins that differ with respect to certain aspects such as expression levels in different tissues or
affinities for substrates. These differences do affect the insulin response, but a detailed discussion
of the effect of isoform differences at nodes in the insulin signaling pathway is beyond the scope
of this chapter; the interested reader can follow up in other reviews [11].
Several negative regulatory mechanisms are intrinsic to the insulin signaling pathway, and are
upregulated in states of insulin resistance. One such negative regulatory mechanism is
phosphorylation of serine and threonine residues on insulin signaling proteins. The most studied
example is the serine phosphorylation of IRS proteins, which inhibits their activity by inducing
dissociation of the proteins from the insulin receptor, prevents tyrosine phosphorylation at
activating sites and induces degradation of the IRS proteins [12]. Many IRS kinases are actually
downstream insulin signaling proteins, including mammalian target of rapamycin (mTOR), S6
kinase 1 (S6K1) and protein kinase C ( (PKC (). These kinases are activated by inducers of
insulin resistance such as tumor necrosis factor a (TNF-a), free fatty acids and cellular stress.
Additionally, c-Jun N-terminal kinase (JNK) is a well-established serine/threonine kinase that is
activated by insulin and proinflammatory cytokines [13]. Serine phosphorylation of IRS- 1 by
JNK is correlated with decreased tyrosine phosphorylation on insulin signaling proteins and
diminished glucose uptake [14]. Furthermore, mouse studies have shown that IkB kinase
p
(IKKp), a PKC ( target, disruption prevents diet-induced insulin resistance [15]. On a molecular
level, it has been shown that overexpression of IKK decreases insulin signaling protein tyrosine
phosphorylation by phosphorylating inhibitory serine residues on these proteins [16]. Although
traditionally correlated with inflammatory responses, JNK and IKK serine/threonine kinases are
highly active in insulin resistant states, many of which have been associated with increased
inflammation.
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Another regulatory mechanism to attenuate insulin signaling is the activation of phosphatases,
PTP1B, PTEN and PP2A. PTP1B, a member of the protein tyrosine phosphatase family, is
known to decrease phosphorylation of activating residues on the insulin receptor as well as
decrease tyrosine phosphorylation of IRS proteins [17]. PTEN is a dual specificity lipid- and
protein- phosphatase whose primary role appears to be dephosphorylation of the 3' position in
PIP3; thus PTEN directly counteracts the activity of PI-3-K, regulating the intracellular levels of
PIP2 and PIP3 [18]. PP2A has been found to decrease phosphorylation of Akt [19]. The
combined effect of these phosphatases is to diminish the propagation of insulin-mediated signal
to downstream effectors. Mutation, inhibition or decreased levels of these proteins are known to
improve insulin sensitivity in model systems. Inhibitors of these proteins are being developed as
treatments for diabetes and other insulin resistance-based diseases [20, 21].
Aside from insulin stimulation through the insulin receptor, canonical insulin responses can be
modulated by other cellular proteins. For instance, AMP-activated protein kinase (AMPK) is an
important regulator of metabolism in the cell. AMPK exerts control of metabolic processes in
response to AMP/ADP/ATP ratios in the cell in order to maintain ATP level homeostasis. AMP
binding to AMPK promotes phosphorylation of AMPK, which is necessary for its enzymatic
activity, as well as decreases phosphatase binding to AMPK [22]. Generally, activation of AMPK
stimulates catabolic processes that generate ATP and inhibits anabolic processes that consume
ATP. In the insulin target tissues, AMPK activation induces responses that are similar to insulin
stimulation. For example, in skeletal muscle, both insulin stimulation and AMPK activation
increase glucose uptake by increasing translocation of GLUT4 proteins to the plasma membrane.
In hepatic tissue, insulin stimulation and AMPK activation inhibit gluconeogenesis by repressing
expression of genes such as phosphoenolpyruvate carboxylase (PEPCK) and glucose-6phosphatase. Interestingly, AMPK inhibits lipolysis in adipose tissue and increases fatty acid
oxidation by upregulating PGC 1a and mitochondrial biogenesis [23].
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Our knowledge of the insulin signaling network is far from complete, as new proteins and
pathways associated with the insulin signaling network are continually emerging. New causes of
decreased insulin sensitivity are being discovered and their key mediators and mechanism are still
being elucidated. Although insulin signaling and metabolism are generally studied separately,
they are very closely related fields. It is well established that insulin resistance in higher
organisms leads to many metabolic diseases, and also very clear that the canonical cellular
response to insulin is to provide one of the most utilized energy sources for cells. Appropriate
regulation of the cellular insulin response is crucial for metabolic homeostasis and proper cell
function. Therefore, research linking changes in insulin response on a signaling network level to
changes in cellular metabolic processes is necessary. Results from these types of studies will
provide an interesting perspective by which to view insulin resistance. The research reported in
this thesis provides information to help increase our coverage of the insulin signaling network, as
well as how changes in this network interact with metabolic nodes.
B. Background on SirT3 and its metabolic effects
SirT3, a member of the sirtuin family, plays a role in multiple metabolic processes and has
recently been shown to be implicated in insulin signaling. Sirtuins are sometimes referred to as
class III histone deacetylases, although the targets of this deacetylase family extend far beyond
histones, so they may be more properly referred to as NAD*-dependent protein deacetylases.
Members of this protein family share an evolutionarily conserved catalytic core and NAD*binding domain. The seven mammalian sirtuins have different subcellular localization; SirTI, 6
and 7 are predominantly nuclear, SirT2 is cytoplasmic and SirT3-5 are mitochondrial [24]. SirT3
has been regarded as the major mitochondrial deacetylase, since SirT3 deficiency, but not SirT4
or SirT5 deficiency, results in hyperacetylation of mitochondrial proteins [25].
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The mitochondria are important organelles for maintaining energy homeostasis within a cell. It is
the site of several metabolic processes, including glycolysis, TCA cycle, electron transport chain,
urea cycle and fatty acid oxidation. Mitochondria integrate cues from the environment, such as
oxygen and nutrient availability, as well as from the cell itself, such as intracellular stress levels
and point in the cell cycle, to coordinate metabolic processes to best fit the cell's current needs.
Changes in mitochondrial function are often associated with metabolic diseases such as fatty liver
disease and diabetes [26]. Although protein deacetylases, sirtuins, have been identified in the
mitochondria, there has been no concrete evidence of protein acetyltransferases to acetylate the
proteins. In the absence of acetyltransferases, theories have been proposed that excess amounts of
mitochondrial acetyl-CoA, a consequence of conditions such as chronic high fat diet, is stored by
chemical, non-enzymatic, conjugation onto proteins, a post-translational modification known as
acetylation [27].
Recently work has identified several substrates of SirT3. These proteins are involved in a range of
metabolic processes and are regulated by their acetylation status. For example, acetyl-CoA
synthetase 2, which converts acetate to the important metabolite acetyl-CoA, is deacetylated at
K635 for activity [28]. On the other hand, mitochondrial ribosomal protein L10, which is
essential for mitochondrial protein synthesis, is deacetylated for activity [29]. Other proteins that
are deacetylated by SirT3 include long chain acyl dehydrogenase, important in fatty acid
oxidation [30], isocitrate dehydrogenase, which regulates a key step in the TCA cycle [31],
ornithine transcarbamoylase of the urea cycle [32] and superoxide dismutase, one of the main
antioxidant proteins needed to quench reactive oxygen species within the cell [33]. It has been
found that mice fed a high fat diet or that have excessive ethanol consumption exhibit increased
mitochondrial protein acetylation, indicating an interesting link between SirT3 activity and
metabolic function [34, 35, 36].
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Several studies have characterized the effects of SirT3 in mice models. Interestingly, SirT3 levels
are diet dependent. SirT3 expression is elevated in calorie restricted mice, while diabetic mouse
models and high fat diet-fed mice have decreased levels of SirT3 [37]. SirT3 knockout mice
exhibit several changes in metabolism that are similar to the effects of a high fat diet regimen.
Work by Hallows et al showed altered levels of mitochondrial metabolic intermediates. They
found increased levels of urea cycle intermediates, such as aspartate and ornithine, decreased
levels of amino acids, including glutamine, tryptophan and argininosuccinate. Additionally, they
found increased levels of acylcarnitines, intermediates in fatty acid oxidation [32]. The increase
in acylcarnitines, especially long chain acylcarnitines, has been corroborated by a separate study
by Hirschey et al. The trend of increased acylcarnitines and decreased amino acid intermediates is
found in mice fed a high fat diet as well as Zucker diabetic fatty rats (rodent model of diabetes).
The Hirschey study also found decreased basal ATP levels in SirT3 knockout mice compared to
the control, and decreased levels of hydroxybutyrate during fasting [30]. Furthermore, the
researchers found increased hepatic mitochondrial protein acetylation levels in SirT3 knockout
mice compared to the acetylation levels seen in wildtype mice, supporting the idea of acetylationbased regulation of metabolism. Defects in metabolism could possibly be reversed by altering
acetylation status of important proteins.
In addition to the changes in mitochondrial function or metabolism, SirT3 has been implicated in
insulin resistance. A recent study by the Kahn group showed that SirT3 knockdown muscle cells
had decreased phosphorylation on several canonical insulin signaling proteins, such as IRS-1, Akt
and Erk. Additionally, these cells also exhibited increased phosphorylation on negative regulators
of insulin signaling, such as p38 and JNK, as well as increased inhibitory serine phosphorylation
on IRS-1 [37]. SirT3 knockout mice are hyperglycemic following a glucose challenge and have
impaired insulin sensitivity compared to their wild type counterparts [34]. Converse to the
established phenotypes from other studies, glucose uptake has been found to be increased in
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mouse embryonic fibroblast cells derived from SirT3 knockout mice compared to cells derived
from wildtype mice. With respect to the cellular usage of the glucose, the researchers found that
levels of glycolytic intermediates increased while TCA cycle intermediates decreased in SirT3
knockout mice compared to wildtype mice. This increase in glycolysis is paralleled by a decrease
in electron transport and mitochondrial function, demonstrating a metabolic shift from oxidative
phosphorylation to anaerobic respiration based on SirT3 level [38].
The above evidence supports the idea that SirT3 induces metabolic changes in the cell by
modulating the function of several proteins within the mitochondria. This would result in altered
levels of certain intermediates and metabolites, which can interact with important nodes in the
signaling cascade and change normal receptor-mediated responses. It remains to be determined
exactly how phosphorylation events are tied with the metabolic changes. Additionally, the
directionality of the cause and effect (for example: whether perturbations in cellular metabolism
change phosphorylation response, or whether aberrant phosphorylation signaling alters metabolic
function) is not well defined. The results of this study will help us characterize and understand
these changes.
C. High fat diet-induced insulin resistance
Several theories exist as to the mechanism by which high fat diets can induce insulin resistance.
To understand these hypotheses and also understand how SirT3 may play an active role in
regulating these mechanisms, one must consider the fate of the fat ingested by a subject on a
chronic high fat diet regimen. Obese subjects have increased fatty acid uptake and metabolism.
Fatty acids, which are activated as fatty acyl-CoAs upon entry into cells, can be metabolized for
energy, stored as triglycerides or used to make other biomolecules such as sphingomyelin. The
general effect of increased fatty acid flux into cells is to upregulate all of the processes by which
fatty acids can be used.
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When fatty acyl-CoAs are oxidized via fatty acid oxidation in the mitochondria, they generate
acetyl-CoA and shorter chain fatty acids. In obese subjects, there will be an abundance of acetylCoA from the fatty acid oxidation process, which may exceed the amount able to be utilized by
the TCA cycle. To counter this overproduction of acetyl-CoA, the mitochondria can incompletely
metabolize fatty acid chains. This results in a buildup of acylcarnitines, which are fatty acid
oxidation intermediates. These species, when in excess, can inhibit insulin sensitivity by a
currently unknown mechanism [39, 40]. Another option for minimizing the accumulation of
mitochondrial acetyl-CoA is to conjugate the acetyl group to other proteins in the mitochondria,
known as an acetylation post-translational modification [27]. Another effect of abundant acetylCoA is increased TCA cycle activity. An increase in TCA cycle activity results in the
overproduction of TCA cycle intermediates, as well as increased generation of electron carriers.
These electron carriers then deposit electrons into an inefficient electron transport chain, which
results in increased reactive oxygen species production, and eventually inflammatory response
[41].
When an excess of nutrients exceeds cell needs for metabolism for energy, most of it will be
stored. Fatty acids are predominantly stored as trigylcerides. Increased fatty acid intake will
promote continual triglyceride synthesis. Excessive triglyceride synthesis results in steatosis in
liver and adipose deposits in muscle cells, as well as enlarged adipose tissue. Adipose tissue
secretes free fatty acids into the bloodstream. An enlarged adipose tissue mass will elevate levels
of circulating fatty acids, also known as hyperlipidemia, which can inhibit glucose uptake in the
liver and muscle by a mechanism outlined in the "glucose-fatty acid cycle", better known as the
Randle cycle [42]. Glucose and fatty acids regulate the utilization of the other as a fuel source
based on their relative abundances in a variety of insulin-responsive tissues. Utilization of glucose
inhibits fatty acid oxidation, and fatty acid (or ketone body) oxidation impairs glycolysis. The
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latter occurs predominantly at the phosphofructokinase (PFK) and pyruvate dehydrogenase
(PDH) steps. Fatty acid oxidation causes an increase in acetyl-CoA to SH-CoA ratio as well as an
increase in NADH/NAD* ratio, which inhibits PDH. Furthermore, the accumulation of citrate,
derived from acetyl-CoA in the mitochondria and exported to the cytoplasm when levels are
excessive, in the cytosol cause the inhibition of PFK. Decreased activity of PFK leads to an
increase in glucose-6-phosphate, which inhibits hexokinase activity. Combined, the imbalance of
intermediates can cause dysregulation of the normal insulin response [43].
The endoplasmic reticulum is the site of lipid, triglyceride and lipoprotein synthesis. High fat
diets are known to cause endoplasmic reticulum stress, which results in an inflammatory response
in insulin sensitive tissue. Inflammation recruits macrophages which then infiltrate the tissue and
release pro-inflammatory cytokines. The pro-inflammatory cytokines, such as TNFa, IL1 P and
IL-6, induce inflammation and stress-response pathways within the liver and adipose tissue, such
as the JNK or IKK/NFKB pathways [39]. The endoplasmic reticulum is also the site of
sphingomyelin synthesis, which occurs through an important intermediate, ceramide. Ceramide
accumulation has been associated with insulin resistance and is known to inhibit PKC-mediated
IRS phosphorylation. Ceramide also decreases Akt phosphorylation by activating protein
phosphatase 2A (PP2A) that remove activating phosphorylation modifications. Furthermore,
ceramide can induce the JNK/IKK pathways [44].
D. SirT3 connection to mechanisms of high fat diet-induced insulin resistance
The results from the studies characterizing SirT3 described above illustrate many ways by which
altered protein expression or activation of SirT3 may regulate insulin resistance. For instance, the
evidence for SirT3 regulation of p-oxidation in the mitochondria is overwhelming.
Hyperacetylation of mitochondrial proteins and elevated levels of acylcarnitines in SirT3
knockout mice compared to their wild type counterparts, SirT3 deacetylation LCAD for activity,
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SirT3 correlation with increased stearoyl-CoA desaturase activity have all been experimentally
established. Whether by post-translationally modifying proteins or inducing changes in gene
expression, SirT3 modulation of fatty acid metabolism intermediates can affect insulin signaling.
When these intermediates are exported to the cytosol, they can physically interact with and inhibit
important proteins such as PKB/Akt. These intermediates could also activate negative regulators
such as IKK/JNK directly or indirectly, such as through a reactive oxygen species-mediated stress
response pathway.
Evidence also supports SirT3-dependent regulation of the triglyceride synthesis process. SirT3
knockout mice have increased steatosis, serum lipid content, and predisposition to obesity
compared to wild type mice when on a high fat diet. Another mechanism by which SirT3
regulates insulin resistance could be by mediating the levels of circulating free fatty acids.
Circulating free fatty acids would then inhibit insulin sensitivity of important target tissues by
enlarging adipose tissue mass or inducing inflammation through the effects of adipose deposits in
liver and muscle tissue.
One of the most well characterized effects of SirT3 is its mediation of reactive oxygen species
generation in cell and tissue systems. Studies have established that SirT3 affects the efficiency of
the electron transport chain, the source of many reactive oxygen species. Inflammatory and stress
responses induced by cytokines as a result of chronic high fat diet or obesity results in inducible
nitric oxide synthase (iNOS) activation. In addition to excessive lipid molecules that can help
propagate damaging reactive oxygen species, sustained high levels of nitric oxide from iNOS can
generate reactive nitrogen species such as peroxynitrite when combined with reactive oxygen
species. Together, they can lead to post-translational modifications that interfere with normal
protein signaling, such as S-nitrosylation and tyrosine nitration [8]. The effect of these
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modifications on insulin sensitivity can be very significant, as the canonical insulin response
heavily on signaling events.
Lastly, a new study provided evidence that perturbations in the levels of SirT3 in cells can alter
insulin-stimulated phosphorylation events. SirT3 knockdown muscle cells were shown to have
decreased phosphorylation of key insulin response signaling proteins and increased
phosphorylation of negative regulatory mechanisms [37]. Though this report was very focused on
canonical signaling proteins, a broader study on changes in phosphorylation due to changes in
SirT3 levels would provide even greater support for the hypothesis of SirT3-mediation of insulin
response. While many independent mechanisms have been elucidated, a systems-wide approach
will be able to increase the general understanding of how many SirT3-mediated changes in
cellular state act in concert to affect biological pathways and processes.
E. Systems Biology Approaches
The development of high throughput data acquisition methods and instrumentation in biology has
enabled researchers to gather large data sets when analyzing certain species, such as mRNA or
proteins. Using these methods and instruments, researchers can profile the changes that occur,
like changes in expression, phosphorylation, or localization, over different perturbations, such as
dose ranges or stimulation times, in one experiment. Specific examples include the use of mass
spectrometry for proteomics and the use of microarrays for transcriptional profiling. The result of
more efficiently gathering large sets of data has led to the necessity for computational methods to
properly analyze the data.
Depending on the goal of the study, different computational techniques can be employed to
organize the data set by trends, reduce the complexity of the data set to fewer variables, or even
correlate events in the data set with outcomes, such as phenotypic responses. To organize the data
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set by trends, clustering methods are often employed. These methods include hierarchical
clustering, k-means clustering and affinity propagation. Hierarchical clustering is a method for
organizing the data based on distance of each vector to another. The result can be readily
visualized in a heatmap and dendrogram representing the distances between each vector [45]. kmeans clustering is another clustering method that divides the data set into a predefined set of k
clusters, while minimizing the variance of the clusters [46, 47]. A new method, affinity
propagation, has been developed as another method for clustering, which does not need a priori
knowledge of the number of clusters to be generated. Instead, similarity values and the correlation
coefficient can be altered at the user's discretion [48].
To reduce the complexity of the data set, a linear algebra technique, termed principle component
analysis, is often employed. While signals from the data set may vary according to the
perturbation, such as time or dose, they can also vary with each other. Principle component
analysis combines variables in a way that emphasizes signals that have high co-variance, thereby
decreasing the weighting of signals with little co-variance with other signals. In this way,
principle component analysis compresses the data set to a few super-components that can help
differentiate different conditions from each other. This approach may be informative for
discerning which signals contribute most to differentiating or grouping together different
conditions or perturbations [49, 50]. Lastly, to correlate events in the data set with phenotypic
outcomes, regression modeling techniques have been used. Partial least squares regression
modeling requires a matrix of independent variables, such as signaling events, and a matrix of
dependent variables, or phenotypes. The partial least squares regression algorithm will
simultaneously compress independent variables into principle components, since many will be
collinear, with a strong focus on the relationships between the variables in the independent and
dependent variable matrices [51]. The emphasis in this model is on how accurately measurements
from the independent variable matrix, meaning it is highly predictive. The advantage of this
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model is that it enables researchers to generate hypotheses concerning the signaling-based
mechanism of action for a certain phenotype.
F. Directions for the future
While many specific effects in insulin resistant states are known within the insulin signaling
network, global signaling changes in model systems have not been very well characterized.
Additionally, the focus on canonical signaling events downstream from the insulin receptor in
many studies can be limiting. Important mediators of insulin resistance may not have been
identified or connected to the established network. It is worth noting that previous work on
insulin signaling has largely focused on particular sites on selected proteins in given pathways.
However, pathways rarely operate in isolation. The selected proteins can be activated by several
stimuli through activation of one of several upstream regulator proteins. Crosstalk via these
proteins will integrate diverse pathways into one response [52]. Additionally, it has also been
found that proteins in these known pathways have multiple sites for regulatory post-translational
modifications, which can alter the activity of the protein, the binding affinity to substrates or cofactors, and even hinder or enhance secondary post-translational modifications [53]. The nature of
the interaction of different post-translational modifications has not been well established for the
majority of known proteins. Analyzing the network in a global, unbiased manner by profiling the
changes that occur upon different perturbations and stresses, representative of disease states, will
enable us to determine the key proteins or pathways responsible for mediating the physiological
response.
Using cutting-edge analytical tools and computational techniques for data analysis, my research
has attempted to address this gap in knowledge by profiling changes in phosphorylation events
and phenotypic responses as mediated by diet and SirT3 levels. The data provides insight into
pathways and processes not previously implicated in mediating insulin resistance or connected
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with the canonical insulin signaling pathway. These novel pathways and important nodes could
be possible targets for therapeutic intervention. The project has resulted in a better understanding
of how perturbations in diet alter global phosphotyrosine levels in the insulin response; this new
information can be used in the effort to treat and cure insulin resistance-based diseases.
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H1. Diet-induced perturbations in insulin signaling
Introduction
Insulin is an important hormone in mediating blood glucose homeostasis. The main effects of
circulating insulin are to promote nutrient uptake into target tissues, such as liver, muscle and
adipose tissue, and to promote their metabolism or conversion into storage macromolecules. The
way in which insulin exerts these effects is by transmitting signals from the activated insulin
receptor to downstream effectors through a phosphorylation-mediated signaling cascade. Glucose
uptake, glycogen synthesis and lipid synthesis are examples of processes regulated by insulin
signaling [3, 4, 5]. Insulin resistance is defined as the inability of insulin target tissues to respond
appropriately to normal levels of circulating hormones and is widely attributed to defects in
insulin signaling initiated by activation of the insulin receptor. Insulin resistance can be induced
in a variety of ways, but one notable cause of insulin resistance is obesity.
It has been widely established that obesity predisposes individuals to insulin resistance and
insulin resistance-based diseases such as diabetes [7, 8]. Obesity is predominantly caused by
chronic consumption of a high fat diet. The mechanism by which obesity causes insulin resistance
is multi-faceted and not fully elucidated. It has been shown that obese individuals exhibit
increased inflammatory and stress responses due to enlarged adipose tissue and adipose deposits
in secondary tissues. It has also been observed that hyperlipidemia, or increased circulating free
fatty acids, decreases glucose uptake in insulin responsive tissues. Lastly, high fat diets cause
aberrant metabolism, especially in terms of fatty acid oxidation, which result in the accumulation
of intermediate metabolite species that negatively regulate the insulin signaling pathway. These
effects have been widely accepted as the changes that occur upon high fat diet feeding and
understood to result in insulin resistance.
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Since insulin resistance is based on aberrant signaling, it would be helpful to better understand the
global network changes that occur in insulin resistant states. The work presented in this thesis
examines the effect of high fat diet feeding on the insulin response. The results help improve our
knowledge by uncovering insulin responsive phosphorylation sites not previously known to be
associated with insulin signaling, characterizing their temporal response to insulin stimulation,
and profiling the modulation of phosphorylation dynamics due to a high fat diet.
In order to better understand the mechanisms by which diet influences the insulin signaling
network and the phenotypic responses, mice were fed different diets, tested for insulin sensitivity,
injected with insulin and sacked for tissue harvesting. A mouse model system is more
physiologically relevant than cell lines, and it is difficult to recapitulate high fat diet conditions in
cultured cells. I used mass spectrometry in combination with phosphorylation enrichment
techniques, such as immunoprecipitation and immobilized affinity metal chromatography, to
perform 'discovery-mode' experiments in which I gathered network data on the relative
abundances of many different phosphorylation sites on proteins from the mouse liver tissue. Since
the insulin stimulated signaling network has not been fully elucidated, this methodology is
important for obtaining an unbiased profile of the effects of diet on the phosphotyrosine network
response to insulin. To make sense of the data, I used computational techniques that enabled me
to reduce the complexity of the data and highlight important nodes and pathways. These tools
have also allowed me to make predictions concerning the mechanisms of insulin sensitivity as
mediated by diet, and also raised several questions for future work.
Materials & Methods
Model System: Cohorts of C57BL/6 mice were generated and fed different diets over their life
spans. Many studies use this strain to assess the effects of a high fat diet on weight and changes in
insulin sensitivity. One cohort of mice was fed a normal diet for 24 weeks, another cohort of mice
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was started on a high fat diet regimen 8 weeks into its lifespan (16-week HFD), and the last
cohort of mice was started on a high fat diet regimen 18 weeks into its lifespan (6-week HFD).
The high fat diet is 55% calories from fat, compared to the normal diet, which is 14% calories
from fat. The mice were taken for phenotypic testing at 23 weeks of age, including body weight
and composition measurements, glucose tolerance tests and insulin tolerance tests. The data from
these measurements can be seen in Table 1. At 24 weeks of age, the mice were injected with
insulin for a predefined amount of time and subsequently killed. The livers were harvested, flashfrozen and pulverized. The frozen tissue was processed for proteomic analysis.
Phenotypic response: Insulin tolerance tests and glucose tolerance tests were performed on the
mice a week prior to sacking [54]. Other measurements, such as body weight and composition,
were taken the day of sacrificing the mouse.
Signaling network measurements: In order to obtain a complete profile of the basal and
stimulated states of the insulin signaling network, I used a mass spectrometry based method to
identify and quantify levels of specific tyrosine phosphorylation sites on different proteins. The
mice were injected with 10 mU/g insulin for 0, 15, 30, and 60 minutes. It is important to note that
10 mU/g insulin is a very high dose that may induce stress responses. However, preliminary
experiments at 1 mU/g insulin injections indicated that the dose needed to be increased for the
purposes of obtaining adequate signal on the mass spectrometer (data not shown). Insulin
stimulation time points than span an hour after injection were designed to capture the dynamics of
the response for each phosphosite from "early" to "late" time points. Flash frozen and pulverized
livers from the mice were homogenized in ice-cold 8M urea with 1 mM sodium orthovanadate to
minimize phosphatase activity during processing. A bicinchoninic acid (BCA) protein
quantification assay was performed on 1:15 and 1:30 dilutions of the homogenates. The
concentration was adjusted to 3.33 mg/mL in 3 mL 8M urea. The resulting homogenates were
22
subsequently reduced with 10 mM dithiothretiol at 560C for 1 hour, alkylated with 55 mM
iodoacetamide for 1 hour in the dark at room temperature, and digested overnight with trypsin at
a concentration of 20 pg trypsin/l mg protein. The peptides from the tryptic digest were desalted
and fractionated on a C18 Sep-pak cartridge (Waters) and eluted in 70% acetonitrile in a
background of 0.1% acetic acid. The elution was lyophilized into powder form, from which each
sample was differentially labeled with iTRAQ (isobaric tag for relative and absolute quantitation)
reagent, according to manufacturer's protocol.
The combined labeled sample was resuspended in 100 mM Tris with 0.3% NP-40, pH 7.4 and
immunoprecipitated for phosphotyrosine-containing residues using a cocktail of pan specific antiphosphotyrosine antibodies (PT-66, P-Tyr-100 and 4G10). After an overnight incubation, they
were eluted off the beads with 100 mM glycine pH 2. The elution was passed through an IMAC
(immobilized metal affinity chromatography) column for a second phosphopurification step and
finally loaded into a C18 reverse phase precolumn (100 micron inner diameter). Peptides in the
eluate were separated by reverse phase HPLC and subject to ESI/MS-MS on an Orbitrap Elite
(Thermo Scientific) mass spectrometer. The instrument was operated in a data-dependent mode;
MS/MS scans were performed on the top 10 most abundant species in the preceding MS scan.
The resulting .RAW data file of spectra generated from the experiment was converted to .mgf file
using DTA Supercharge and the MS/MS peak lists were searched against a mouse protein
database using MASCOT for sequence and protein identification information. Relative
quantification for the identified peptides was obtained by measuring peak area of the reporter ions
from the iTRAQ tag in each scan. As a loading control, a sample of the supernatant from the
immunoprecipitation step was analyzed in a similar manner. From the supernatant data, protein
averages for each iTRAQ channel were taken and averaged to yield a normalizing factor (mean
normalized across the normalizing factors for all 8 channels) with which to normalize all the
iTRAQ values for the peptides in the elution data. As an additional clean up step, scans with a
23
MASCOT score less than 25 or having contaminated precursor spectra (a contaminating peak
greater than 25% of the base peak within ± 1 m/z of the precursor ion) were eliminated.
Additionally, scans in which a zero was reported for any iTRAQ channel were excluded [55, 56].
Computational Analysis: Obtaining a complete profile of signaling events in basal and stimulated
states for mice on both diet regimes requires amassing a large set of phosphorylation data. To
develop biological meaning to the data, I used computational techniques to aid my analysis. I
used affinity propagation to cluster the trends into related groups, principle component analysis to
reduce the data set to smaller sets of correlated signaling variables and differential analyses to
determine which sites had significantly altered phosphorylation levels between conditions. By
doing these types of analyses, key mediators and pathways for diet-dependent insulin resistance
can be made apparent amidst a complex data set. Additionally, enriching groups of differentially
phosphorylated sites or different affinity propagation clusters for Gene Ontology terms helped
identify and characterize processes and pathways activated by insulin and their alterations due to
a high fat diet regimen. Details of the analyses will be described in further depth in the following
results sections.
Results
Three cohorts of mice were generated. The first cohort was fed a normal diet for its 24-week
lifespan. The second cohort of mice was fed a 6-week high fat diet starting 18 weeks into its
lifespan. The mice from this cohort gained more weight than the mice on a normal diet and were
also insulin resistant. The third cohort of mice was fed a 16-week high fat diet starting 8 weeks
into its lifespan. The mice from this cohort gained even more weight than the mice on a 6-week
high fat diet, and were insulin resistant. Average phenotypic data for each cohort is shown in
Figure 1. Different mice from each cohort were analyzed to generate quantitative
phosphotyrosine signaling network data under basal conditions or insulin stimulated conditions.
24
niet
Insulin
Injection
ITime
unsca b
Weiaht tl
A waiaht ial
:
ual
Aftfai
an Mla
Jal
A lpan Jal
ITT IAUCI
GTT
lAui
Starved
Blood
Glucose
(me/dL)
16 HFD
16 KFD
16 HFD
0
15
30
269
313
314
52.5
47.1
47.7
28
27.3
27.7
23.6
16.98
19.12
21.55
14.44
17.37
26.22
27.15
25.5
5.76
11.33
9.01
123.09
102.67
117.14
33015
40110
33600
141
167
138
16 HFD
30
315
35.6
17.5
10.1
8.84
22.84
8.1
83.17
28763
104
Table 1. Phenotypic data for mice used in this study. Diet regimes include normal diet (NC), 6-week high fat diet (6HFD) and 16-week
high fat diet (16 HFD).
Fig 1b. Weight
Fig la. Glucose Tolerance Test
450
50 -
400
45 -
50
350
300
40 35 -
250
'0 30
19
C 200
8 150
0
25 20 -
015
50
0
_________
0
30
10
90
60
5 -
120
Time (min)
-- on- Normal Chow
6-week High Fat Diet
m 16-week High Fat Diet
0
Normal Chow 6-week high
fat diet
16-week high
fat diet
Figure 1. Phenotypic data from mice cohorts. (a) Glucose tolerance test results by cohort. The
average value for blood glucose concentration across all mice in the cohort following glucose
injection was plotted ± SEM. (b) Ending weights by cohort. The average weight across all mice in
the cohort prior to sacking was plotted ± SEM.
Replicates were performed for many of the quantitative analyses. In the following section, I will
describe the results generated from the comparison of different diets relative to the normal diet.
Part A: Analysis of normal diet vs. 6-week high fat diet insulin response from 0 to 60 minutes.
The study analyzing normal diet insulin response and 6-week high fat diet insulin response
resulted in 246 peptides quantified across 3 mass spectrometric analyses. Only those
phosphorylation sites identified in 2 or more runs were included in the full data analysis. The
mice representative of each time point in each replicate are displayed in Table 2.
Run#0
1
2
3
NC0
958
960
958
NC15
959
961
959
NC30
974
974
974
NC60
988
985
988
6HFD0
816
817
816
6HFD15
815
815
815
6HFD50
806
808
806
6HFD60
835
836
835
Table 2. Mouse identifier numbers for each sample included in the replicates for the analysis of
normal diet vs. 6-week high fat diet insulin response from 0 to 60 minutes.
26
0
Z
0
M
Z
0
Z
I
0
0
0
U
.
U
Figure 2. Heatmap of 77 phosphorylation sites identified in 2 or more separate experiments. The
first four columns represent the insulin stimulation time course in the normal diet and the second
four columns represent the insulin stimulation time course in the 6-week high fat diet.
Hierarchical clustering was used a first-pass check of the data for overall trends in
phosphorylation patterns. The phosphorylation intensity data were clustered by Euclidian distance
metrics and displayed on a heatmap (Figure 2). The distances between each column (condition)
and each row (phosphorylation data) can be displayed as a dendogram [57]. The visualization of
the data can make it easier to distinguish similar conditions without compressing the data. The
heatmap depicts 77 phosphosites identified in 2 or more separate experiments. For each run, the
relative abundance of each phosphopeptide was normalized to a common mouse present in all
experiments. Setting the relative abundance level of the overlapping phosphosites for the
common mouse to the same value (in this case, 1) in each run, the values used to generate the
heatmap were found by calculating the average relative abundance of the peptide for each
condition across the different runs. The data was mean normalized and log(2) transformed prior
to hierarchical clustering. The resulting clustergram shows basal differences, as well as
differences in temporal response, between phosphorylation levels in the normal diet and a 6-week
27
high fat diet. This indicates that the high fat diet regimen, even at 6 weeks, representative of
insulin resistance but prior to "obesity", causes network dysregulation.
To look at overall changes in phosphorylation, I used affinity propagation clustering techniques to
group together phosphorylation sites with similar temporal dynamics and diet dependence. In
short, affinity propagation finds centers, termed "exemplars", in the data set that best minimizes
the sum of the distances between all data points and their next closest centers. The algorithm
initially considers all data points to be a center, but then calculates similarity scores between all
points and their possible neighboring centers. The resulting scores for each point to be a center is
input for the next iteration of scoring. The iterations continue until an optimal set of centers and
their corresponding clusters is determined [48]. This algorithm for clustering was implemented in
MATLAB with a self-similarity "s" score = - 2 and Pearson correlation coefficient for the
distance metric. All 8 conditions, the two diets and 4 time points, were analyzed together. The
affinity propagation algorithm returned 5 clusters, shown below. To gain biological insight from
this analysis, the clusters were enriched for Gene Ontology (GO) terms. Assigning GO terms to
each cluster facilitated the association of phosphorylation dynamics to biological processes.
28
(a) Cluster 1
25 sites
mean similarity 0.79609
-41
Z
J
Z
Z
Z
II"
X
IA.
(c) Cluster 3
14 sites
mean similarity 0.69518
(b) Cluster 2
16 sites
mean similarity 0.74174
U.
X X
.41
Z
U.
X
(d) Cluster 4
11 sites
mean similarity 0.64337
Z
Z
I
.
0
X
I'-
0
I
U.
IL
Z
X
U9
U
Z
U
Z
UQ
Z
u
X
0
W~
z
0
U.
0
(e) Cluster 5
9 sites
mean similarity 0.58923
2
4L.
.4'1
.
In
0''
U
U
Z
U
Z
U
Z
IL
X
0
0
0
X
X
X
U-
IL.
U.
0
Z
U
U
0
IL
I
0
I
IL
IU.
Figure 3. Clustering result from affinity propagation of data set from normal diet and 6-week high fat
diet comparison experiments. The first four points, with the NC- prefix, represent normal diet insulin
stimulation time points. The second four points, with the HFD- prefix, represent 6-week high fat diet
insulin stimulation time points.
Cluster #
1
2
3
4
5
Functional Annotation
Carboxylic acid, oxoacid, organic acid, amino acid and
ketone metabolic process
Response to chemical stimulus
Mitochondrial localization
Adherens junction proteins
Insulin receptor binding, protein kinase and protein kinase
binding
Urea cycle, amine and amino acid metabolic process
Focal adhesion, cell-substrate junction proteins
Insulin receptor signaling pathway
Regulation of carbohydrate biosynthesis and glucose
metabolic process
MAPK activity, phosphorylation and protein kinase activity
Phosphorous metabolic process
Protein modification
Nuclear localization
Protein kinase and phosphorylation activity, ATP binding
Positive reaulation of DNA binding
Proteins Includ ed
Aldhlal, Aldh6 a1, Assi, Bcarl, Carkd, Eno, Fah,
Fbpl, Gludi, G px1,Hrspl2, Hspel, Ptk2b, Pxn,
Prose, ShcI
Aldh I11, Arg, Ceacam I, Ctnnd I, Cps I, Gprc5c,
Gstml, Gstpl, I lpd, IdhI, Irs2, Rbck 1,Sorbsl, Tjp1,
Tlnl .Tnsl, Was
Boarl, Dok, Gmnt, Gsk3b, Insr, rs2, Vel
Bcarl, Dyrk lb, Fpha2. Mapk 1, Mapk 12, Mapk3,
Ptpn 11, Ttn. Yes1
Egfr, Hl-ipkl, Hi Ak3,Prpf4b, Ptk2, Tencl
Table 2. Significant GO term enrichment for each cluster in Figure 3.
29
Each of the clusters has a unique exemplar trend (Figure 3). Cluster 1 depicts a sharp increase in
phosphorylation at 30 minutes for the normal diet, while the phosphorylation levels increase at a
quicker pace (by minute 15) and is sustained into 30 minutes prior to decreasing in the high fat
diet. This cluster is enriched for a wide range of metabolic processes and many of the proteins are
localized within the mitochondria. Based on the differential analysis performed in the previous
section and our knowledge of the dysfunctional metabolic pathways upon high fat diet feeding or
in obese individuals, the diet-dependent phosphorylation trends represented by this cluster may
suggest decreased phosphatase activity or premature activation of these enzymes in response to
insulin due to the increased nutrient load.
Cluster 2 contains phosphorylation sites that have a similar normal diet profile to the first cluster,
but a much attenuated phosphorylation response in the high fat diet. These sites are on proteins
are involved in forming adherens junctions, binding to the insulin receptor and other protein
kinases, and urea/amino acid metabolism. The selective decrease in phosphorylation of some but
not all metabolic processes provides insight into which processes may be more sensitive to high
fat diet-induced rewiring of the network. The urea cycle is highlighted here. The urea cycle
proteins have no significant phosphorylation dynamics in response to insulin in the high fat diet
state compared to the temporal dynamics in the normal diet state.
Cluster 3 shows a very different temporal response to insulin stimulation: one where it is highly
phosphorylated at 30 minutes in the high fat diet condition but otherwise no significant change in
phosphorylation in the normal diet. This cluster is enriched for focal adhesion proteins, as well as
those that regulate carbohydrate biosynthesis and glucose metabolism in response to the insulin
stimulus. Proteins in these pathways could be phosphorylated due to a change in the normal
insulin signaling pathway; when insulin signaling is operating normally, these proteins are not
modified, however, when there is a decrease in insulin signaling, these proteins are activated to
30
compensate, such as the members of the integrin signaling pathway. Like AMPK regulation of an
insulin response, glucose uptake, signals from these proteins could also affect the insulin
response.
Cluster 4 contains phosphorylation sites that are hyperphosphorylated in the basal state in mice
fed a high fat diet, but are lower in all other conditions. This group is enriched for MAPK activity
and signal transduction via phosphorylation. This highlights another branch of signaling that can
be induced by insulin stimulation, the MAPK pathway. The high fat diet regimen may make
certain proteins involved in MAPK signal transduction more active. Since they are not activated
in the normal diet state, these proteins may lead to negative regulation of RTK-induced signaling
pathways, like p38 and JNK. The last cluster does not show any clear temporal response in either
diet, but interestingly clusters transcription factors with nuclear-localized proteins, indicating a
transcriptional response to insulin that can be modulated by diet.
To summarize, the results from the affinity propagation analysis highlighted several pathways
that were differentially regulated based on diet. Some are known signaling pathways, such as
MAPK and insulin response; however, others have not been previously known to be regulated by
phosphorylation, such as the metabolism pathways. Follow up studies focusing on the mechanism
for phosphorylation of these enzymes (especially those that are localized to the mitochondria)
would be extremely insightful in terms of understanding how the signaling insulin response is
connected to the metabolic insulin response, and how both are affected by diet.
The clustering algorithm in the previous section puts an emphasis on the similarities between the
trends than magnitudes of phosphorylation levels. However, absolute changes in levels of
phosphorylation of certain sites can also play a major role in mediating signaling. Therefore, I
performed a simple 'differential analysis' of these sites to analyze the data with an emphasis on
31
magnitude differences in phosphorylation between any given time point and the basal level. The
procedure for this analysis is as follows: the data from the phosphorylation experiments are in a p
x m matrix, where there are p phosphorylation sites and m conditions. In this case, the m
conditions include:
0
Normal Diet
15
30
PJ,0,N
P1,15,N
P2,0,N
P2,15,N
-P,30,N
P2,30,N
60
P1,60,N
P2,60,N
0
_P,0,H
P2,0,H
High Fat Diet
15
30
P1,15,H
P2,15,H
60
Pf,60,H
-P,30,H
P2,30,H
P2,60,H
Pn,60,H
Pn,30,H
Pn,15,H
Pn,OH
Pn,60,N
Pn,30,N
Pn,15,N
Pn,0,N
where the numbers in the second row indicate the amount of time the mice are injected with
insulin prior to sacrificing. Each phosphorylation site has 8 intensity values that correspond to
each diet regime and insulin time point. For each diet, sites were included in the analysis if the
phosphorylation level fold change compared to the unstimulated (time = 0 min) sample was
above or below a threshold value (here: below 0.67 or above 1.5) at any time point. The next step
was to compare the peptides that have high phosphorylation magnitude changes between the two
diets. An analysis of the biological annotation of sites that are differentially phosphorylated in
only one diet regime is informative of processes that are differentially regulated by diet. For the
peptides differentially phosphorylated in both diets, a comparison of the insulin response
dynamics allowed me to determine the nature of the way in which a high fat diet alters the
response. For example, a phosphorylation increase on a particular site in the mice fed a high fat
diet could be delayed in comparison to mice on a normal diet, indicating changes in the efficiency
by which upstream proteins transmit signals. A decreased phosphorylation signal on another site
in high fat diet-fed mice could indicate activation of inhibitory feedback mechanisms.
The results of the differential analysis revealed 41 sites on 38 proteins that had high fold changes
in terms of phosphorylation in the normal diet regime and 29 sites on 28 proteins that had high
fold changes in terms of phosphorylation in the 6-week high fat diet regime. The two groups were
32
enriched for Gene Ontology terms. Several terms were common between both the normal diet and
the 6-week high fat diet group, including amine and amino metabolic process, insulin receptor
signaling, mitochondrial localization, regulation of glucose import, transport and metabolism, and
regulation of glycogen, glycan and carbohydrate metabolism. Terms that were present in only the
6-week high fat diet group included aldehyde dehydrogenase activity, carboxylic and organic acid
metabolism, ketone metabolism and oxidoreductase activity. This result is very interesting as
ketone bodies are the end products of lipid metabolism for energy; the abundance of fatty acids
from a high fat diet could upregulate this process. Terms that were only present in the normal diet
group included cell-substrate adherens junction, focal adhesion, protein complex binding, the urea
cycle and nitrogen cycle metabolic process.
I first took a closer look at the sites that had high fold changes only in the normal diet. Three sites
on proteins involved in the urea cycle and nitrogen metabolism were identified. Two, carbamoylphosphate synthetase 1 and arginase 1, are graphed below in Figure 4. The blue bar represents
the normal diet trend while the red bar represents the high fat diet trend. All relative intensity
values were calculated with respect to the 30 minute insulin stimulation, normal diet condition
(this sample has a relative intensity value of 1 for all graphs). These proteins are very interesting
because they are necessary for proper entry into the urea cycle (Figure 5); they each catalyze the
formation of the two necessary molecules to begin the cycle, carbamoyl-phosphate and ornithine.
Carbamoyl-phosphate synthetase activity, and thus proper urea cycle functioning, is known to be
regulated by acetylation status [58]. Arginase is responsible for converting arginine into ornithine,
because otherwise, arginine would be metabolized to nitric oxide by iNOS [59]. It would be
interesting to understand the consequences of the phosphorylation modification on this protein. If
it altered its activity, aberrant arginase activity may shift arginase breakdown towards the iNOS
route, creating elevated levels of nitric oxide, which can then activate stress and inflammatory
33
responses. The resultant effect would be to increase negative regulatory mechanisms of insulin
signaling.
The data shows that these urea cycle proteins have decreased phosphorylation levels in response
to insulin stimulation in high fat diet-fed mice compared to the normal diet-fed mice. The data
indicates that an effect of the high fat diet on the urea cycle is to reduce phosphorylation of these
proteins at these specific sites. This result is of particular interest, because it has not been
demonstrated that the insulin signaling pathway permeates to within the mitochondria, where the
urea cycle occurs. The sites on both enzymes have been previously identified by high throughput
screens [60]. However, there have been no corresponding functional annotations, though their
phosphorylation states are clearly regulated by diet. Follow-up studies regarding the effect of
phosphorylation of these urea cycle and nitrogen metabolism proteins would shed insight into
multiple mechanisms of regulation of mitochondrial processes. It has already been established
that acetylation affects the activity of several metabolic proteins and thus the proper functioning
of the biological process they are involved in. Phosphorylation could also regulate activity; this is
the case of other proteins involved in metabolism like glycogen synthase and pyruvate kinase [61,
62]. The effect of high fat diet and resultant metabolic dysfunction could be to attenuate signaling
transduction to metabolic centers of the cell or impose negative feedback regulatory mechanisms
that target metabolic enzymes. An interesting study to pursue would be to discern active
mitochondrial kinases that could alter phosphorylation levels of proteins localized to the
mitochondria. There have been reports of oncogenic receptor tyrosine kinases and a few
cytoplasmic kinases localizing to the mitochondria membrane and matrix [63]. Their activity has
yet to be comprehensively studied and their substrates have yet to be determined, but insulininduced mitochondrial tyrosine phosphorylation may act through a similar mechanism.
34
Taln 2
ECDySDGINR
arginase 1
EGLYITEEyNK
carbamfoy-phosphte synthetase 1
VPA4yGVDTR
1
0.8
0.8
0.6
0.4
0.6
o3
.4
0.2
0
K
15
30
Time (min)
0.2
60
30
15
Time (min)
0
I
I
.3
60
30
heat shock protein 1 (chaperonin 10)
TFGENyVQELLEK
VLLPEyGGTK
1.4
Ii
0.8
% 0.6
A 0.4
WO
0.2
0
0
0
15
30
Time(min)
60
3. 5
2.5
3
2
0
5i
30
Time (mlin)
60
1.5
I
'0
1
0.5
0
is
30
Time (min)
1.2
W
US
60
proline synthetase co-transcribed Isoform 1
A 0.5
APCSCSGDNDQyVLMISSPVGR
I 50Time (mi)
.1
insufn Receptor Substrate 2
DWETDYW
1-
15
30
Insuin Receptor
2
0
15
0
Time (min)
1. 5 -
Time (min)
1.5
0
60
2.
0.8
0.6
0.4
0.2
0
0.4
.2
am
0.2
0
30
15
0
1
0.5
Time (min)
g
0.8
em0.6
C
60
1.4
1.2
1
30
I
fumryhcetoacetate hydrolase
DIQQWEyVPLGPFLGK
1.2
1s
S
0
Hpd
FWSVDDTQVHTEySSLR
0
2
em
1.5
S0.8
0.6
OA
0.2
0
0
2.5
1A
1.2
1.2
1.2
Tensin 2
VGEEGHEGCSyAVCSEGR
60
0
is
30
60
Time (min)
Figure 4. Dynamics for sites identified with high
phosphorylation level fold change (<0.67x or > 2x) at any given
time point with respect to basal levels in response to insulin. The
blue bar represents the normal diet trend while the red bar
represents the high fat diet trend. All relative intensity values
were calculated with respect to the 30 minute insulin stimulation,
normal diet condition (this sample has a relative intensity value
of 1 for all graphs) ± SEM.
N-Acetygulate gynthOae
3730)
(ECIIC
... : U
COON
I
(CHA~
CH-NH?
co
COONi
I
C=O
I
NH,
NHn**" HCO,
2 ATP
Oco
1
(oH
RA"s
1SI
2 ATP
V
CH-NH-C-OH
OOA
CPS
Cao
OE
n4Ie2
M
C
2 AP
1
2 Pi
COONAX
-C
"M44
HN-COPOH
CIIWI
NNH
NH,
HI)
(CH)
Omh.IC
O ( H-NH2
OTC - om*Wne
transcaibwyIe.
(EC2.1.3.3aMIS so)
CO
NH
I
g~nift
NH,
SCHI
HaN-C-NH
mrso)IC
U(EC3.S.3.1MIaims
N
ATP
NH?
C=NH
AMP+2P4
NH
It
A
OH-NH2
COON
AL
Argnunoecinate LySe
(SC43±1I; " 29MhO)
CON
COON
NH
0CMO
C-N -COH
I
I
NH
Om,
I
I
(CH
COOH
W
CH-NH,
COON
ArgtnknomiccnMO
Syn"Wea.
(EC .. 4: - 21P)
ODON
Figure 5. Schematic of the urea cycle. Phosphorylation sites on carbamoyl phosphate synthetase
and arginase were identified as having high fold changes compare to basal levels upon insulin
stimulation in the normal diet but not in the high fat diet. Adapted from www.ncbi.nlm.nih.gov.
Another group of functional annotations present in the normal diet samples but not the 6-week
high fat diet samples are adherens junction and focal adhesion proteins. The phosphorylation
response to insulin on Talin 2 and Tensin 2 are graphed in Figure 4. Tensin 2 regulates cell
motility and proliferation but also interferes with Akt phosphorylation and downstream signaling.
Talin is a cytoskeletal protein involved in actin filament assembly. It is known that cytoskeletal
rearrangement is part of the glucose uptake process and enables GLUT4 translocation to the
plasma membrane [64, 65]. Furthermore, it has been found that focal adhesion kinase, an
interacting partner of tensin 2 and talin [66], regulates glucose uptake and glycogen synthesis by
mediating cytoskeletal rearrangement [67]. Other binding partners, such as paxillin and vinculin,
have also been detected in my experiments, indicating the contribution of integrin signaling to
insulin response. Further work to explore the exact signaling mechanism from insulin stimulation
36
to cytoskeletal rearrangement-mediated glucose uptake would be very useful for expanding the
map of the insulin signaling network. Understanding how phosphorylation of the focal adhesion
and adherens junction proteins at these specific sites affects protein activity would also be an
important follow up study.
The phosphorylation temporal response to insulin of sites on 4-hydroxyphenylpyruvic acid
dioxygenase, Hpd, and fumarylacetoacetate hydrolase, FAH, are graphed in Figure 4. Both of
these enzymes are involved in tyrosine catabolism, shown in the Figure 6. Mutations in either of
these enzymes cause different levels of tyrosinemia with the FAH mutation being more severe
(type I). It has been found that Hpd can be phosphorylated [68] and both sites have been
identified in many large-screen studies of phosphorylation sites; however, no current functional
annotations are provided for either site. Although the dynamics for these enzymes are similar in
the normal diet state, they are very different in the high fat diet state. For Hpd, the
phosphorylation barely changes, and for FAH, the phosphorylation is more rapidly induced than
in the normal diet. FAH is the last enzyme necessary for tyrosine catabolism. It generates
fumarylacetoacetate as a byproduct, which can be used for reactions that produce energy or is
otherwise excreted by the kidneys. It has been found that reduced levels of FAH lead to
alterations in gene expression in the liver [69]. Aberrant activity of the protein, due to
phosphorylation at specific residues, may have a similar effect. The products of FAH include
acetoacetate and fumarate, which can accumulate in the cell and cause changes in other metabolic
pathways that use these molecules or generate them as a byproduct, such as the urea cycle, the
TCA cycle and fatty acid metabolism. Thus, phosphorylation can have an effect on a much larger
scale just by targeting one process, such as tyrosine metabolism. For example, excess acetoacetate
could be converted into acetyl-CoA, which inhibits pyruvate dehydrogenase and activates
pyruvate carboxylase, inducing upregulation of gluconeogenesis. It would also be interesting to
study the effects of high fat diet on tyrosine metabolism.
37
V iA
1
-
..
1 1 1 1 1M
m
" mW" 60
Figure 6. Tyrosine catabolism pathway. The proteins identified in this study are in the red boxes.
Figure adapted from http://en.wikipedia.org/wiki/Tyrosine.
Members of the insulin signaling pathway were also identified in this study, including insulin
receptor (shown in Figure 4). In agreement with studies on the insulin receptor, it is clear there
are diet-dependent changes in phosphorylation of the activation loop of the insulin receptor [70].
The muted response in the high fat diet state could be indicative of the insulin-resistant
phenotype.
Mitochondrial localization was functional annotation term that overlapped between the two diets.
The phosphorylation trends of proline synthetase and heat shock protein 1 are graphed below.
The proline synthetase protein is not very well characterized, but heat shock protein 1 is a
mitochondrial enzyme essential for mitochondrial protein biogenesis. The synthesis of heat shock
protein 1 has been found to be increased in cells subject to heat shock or amino acid challenge. It
has been found to be the eukaryotic homolog of CpnlO, a bacterial enzyme necessary for protein
folding. It was initially discovered to be essential for the folding of ornithine transcarbamoylase,
another mitochondrial protein [71]. It is interesting to note that in these cases, although the
phosphorylation sites are differentially phosphorylated in both diets, their temporal profiles are
different. The phosphorylation events on these proteins are more quickly activated in the high fat
diet state, suggesting either more rapid activation of upstream kinases or different route of signal
transduction. It would be very interesting to discern the effect of the phosphorylation
modification on the protein's activity and on overall mitochondrial protein synthesis. If it was
involved in the folding of ornithine transcarbamoylase, a urea cycle protein, in bacteria, perhaps it
38
helps activate other urea cycle or other metabolic pathway proteins in eukaryotes in response to
nutrient overload.
The results from this analysis highlight several new and interesting sites with diet-dependent
differential temporal dynamics in response to insulin stimulation. The data from this study can be
used to help extend our knowledge of the network response to insulin, as it contains a variety of
sites whose phosphorylation statuses are previously unknown to be modulated by high fat diet.
Further work investigating the consequence of these phosphorylation modifications on these
proteins would increase our understanding of the functional changes on enzymes that take place,
and the role these changes have in mediating the global response.
Part B: Analysis of basal level phosphorylation between normal diet, 6-week high fat diet and 16week high fat diet mouse liver tissue.
To follow up on the observation that there seem to be significant differences in basal
phosphorylation between the different diets, 2 separate experiments were performed to analyze
differences in basal phosphorylation between the 3 cohorts representing normal, 6-week high fat
and 16-week high fat diets. The analyses are listed in Table 3.
113
114
115
116
117
118
119
121
Condition
Mouse ID #
Condition
NC 0
958
NC 0
NC 0
960
NC 0
6HFD 0
816
NC 0
6HFD 0
817
6HFD 0
6HFD 0
818
6HFD 0
16HFD 0
245
16HFD 0
16HFD 0
261
16HFD 0
16HFD 0
267
16HFD 0
Mouse ID #
954
958
960
--
--
245
261
267
iTRAQ
channel
Table 3. Mouse identifier numbers for each sample included in the experiments for the analysis
of basal level phosphorylation in normal diet, 6-week high fat diet and 16-week high fat diet
mouse liver tissue.
The supernatant correction for the 116 and 117 channels in the second run were too large to
include the data in the analyses; the peptides were processed in a separate batch than the other
39
samples and may have been subject to fewer losses in the sample preparation procedure or
aliquoted differently. The data analysis was performed on the data from the first experiment,
including the data for the 954 mouse in the second run by normalizing the values to a common
mouse (the 958 mouse). This combined data set had 3 mice per diet regime. I analyzed 46
peptides that were detected in both experiments to find phosphorylation sites that were
differentially phosphorylated at the basal state between diets. For each phosphorylation site, I
performed a t-test between the values for each combination of two diets. The significant results,
with p < 0.05, are shown below in Table 4. The average phosphorylation intensity for each diet
regime was plotted for selected peptides in Figure 6.
t-test between normal diet and 6-week high fat diet
Protein Name
SEC14-like 2
glutathione S-transferase, pi 1
clathrin, heavy polypeptide (Hc)
protein tyrosine phosphatase, receptor type, A
P-value
Sequence
0.00339
ENVQDVLPTLPNPDDyFLLR
0.01088
FEDODLTLyQSNAILR
0.01332
TSIDAyDNFDNISLAQR
0.02125
vVQEYIDAFSDyANFK
t-test between normal diet and 16-week high fat diet
Sequence
Protein Name
dual-specificity tyrosine-(Y)-phosphorylation regulated kinase lb isoform a IYQyIQSR
FEDGDLTLyQSNAILR
glutathione S-transferase, pi 1
ADDyFLLR
SEC14 (S. cerevisiae)-like 2
t-test between 6-week high fat diet and 16-week high fat diet
Protein Name
viral oncogene yes homolog
solute carrier family 25, member 5
protein tyrosine phosphatase, receptor type, A
acyl-Coenzyme A dehydrogenase family, member 11
SEC14-like 2
P-value
0.00018
0.00110
0.00237
P-value
Sequence
0.00316
LIEDNEyTAR
0.01320
AAyFGIYDTAK
0.01491
VVQEYIDAFSDyANFK
0.02343
LAGISQGVyR
0.04848
ENVQDVLPTLPNPDDyFLLR
Table 4. Phosphorylation sites with significant changes (p < 0.05) in phosphorylation levels
between normal diet and 6-week high fat diet, normal diet and 16-week high fat diet, or 6-week
high fat diet and 16-week high fat diet.
The basal phosphorylation levels for each diet regime of glutathione-S-transferase (GST) are
displayed in Figure 7. GST is an important protein in regulating oxidative stress within the cell.
There are several isoforms, many of which can have multiple subcellular localizations. GST is
synthesized outside the mitochondria but is unidirectionally transported into the mitochondria. It
has been found that certain conditions, such as an increase in cholesterol content of the inner
40
mitochondrial membrane, make GST transport into the mitochondria more difficult [72]. A high
fat diet could increase cholesterol levels since fatty acids have been found to increase the
enzymatic activity of enzymes involved in cholesterol biosynthesis in the liver [73]. Additionally,
mitochondrial GST status is closely associated with reactive oxygen species production and redox
state [72]. The decrease in phosphorylation seen here on this GST isoform, could be reflective of
a decrease in transport of GST to the mitochondria and subsequent activation in the high fat diet
samples compared to the normal diet samples.
Notably, many of the sites identified in this analysis did not have the same trend as the site on
GST. In many cases, such as in sites on acyl-CoA dehydrogenase and protein tyrosine
phosphatase (receptor type A), the phosphorylation levels decreased in the 6-week high fat diet
mice compared to the normal diet mice, but returned to normal diet levels at 16-weeks of high fat
feeding (Figure 7). This data could be representative of a long-term attenuation response to
changes in signaling or phosphorylation levels due to a high fat diet regime. Acyl-CoA
dehydrogenase plays a very important role in fatty acid oxidation. There are several types of acylCoA dehydrogenase that preferentially utilize different fatty acids depending on their carbon
chain length [74]. The phosphorylation status, and possibly activity, of this enzyme over 16
weeks of high fat diet feeding may not be very surprising. After 16 weeks of high fat diet feeding,
the levels of different length fatty acid intermediates within the cell could be very different than
the levels of the same intermediates at 6 weeks. Other acyl-CoA dehydrogenases, such as the
short chain or medium chain acyl-CoA dehydrogenases, may be activated to reduce the
accumulation of the resultant species from incomplete
p-oxidation. The phosphorylation site
identified here has been previously discovered in high throughput screens of mouse tissue and
tyrosine kinase signaling. However, the role of this modification is unknown.
41
The differential phosphorylation of protein tyrosine phosphatase (receptor type A) is very
interesting because of its role in Src-family kinase activation and interaction with several other
focal adhesion proteins with differential phosphorylation identified in this study such as focal
adhesion kinase (Fak) and p13OCAS (Bcar 1). These proteins are regulated by nitric oxide species
generated by inducible nitric oxide synthase activation upon stimulation [75]. Additionally,
protein tyrosine phosphatases have been implicated in mediating interferon resistance associated
with insulin resistance [76]. The site on PTPalpha identified in this study is a Grb2 binding site
and can be phosphorylated by c-Src [77]. The protein tyrosine phosphatase members are often
activated by other phosphatases and therefore, the decrease in basal level phosphorylation at 6
weeks of high fat diet feeding could be activating. The change in basal level phosphorylation
could indicate changes in Src family kinase-mediated signaling due to elevated levels of reactive
species like NO in the cell.
The site identified on Yes, a Src-family kinase, is conserved between many members of the
family (Figure 7). While the phosphorylation of the PTPalpha site decreases at six weeks on a
high fat diet, the phosphorylation of this Src-family kinase increases. Src-family kinases have
been demonstrated to control docking protein phosphorylation and their activation due to changes
such as high fat diet feeding may reroute signaling towards Grb2 and Shp2 mediated events [78].
The data from this analysis highlight the basal level changes based on high fat diet feeding that
can then alter resultant signaling from hormonal stimuli such as insulin. Also, Src-family kinases
like Src, Yes and Fyn, have been found to be necessary for adipogenesis and the early steps of
insulin signaling, further linking their activity to high fat diet-induced insulin resistance [79].
42
glutathione S-transferase, pi1
FEDGDLTLyQSNALR
1.2
1
-
-
-
-
-
-
-- -
- -
- -
-
-1.8
-
14
- 0.6
0.4
acyl-CoA dehydrogenase
LAGISQGVyR
-
1.8
0.6
--
0.4
0.2
0.2
0
0
Yes
protein tyrosin. phosphatase, receptor type A
WQEYlDAFSDyANFK
UEDNEyTAR
1.2
1.8
0.8
1.6
1.4
1.2
0.6
0.8
0.4
0.2
0.6
0.2
--~~0.4
0.2
0
0
Figure 7. Selected peptides with significantly different (p < 0.05) phosphorylation levels
between two diets. The blue bar represents the normal diet, the red bar represents the 6-week
high fat diet and the green bar represents the 16-week high fat diet.
The basal phosphorylation analysis demonstrates that there are multiple nodes for dysregulation
of phosphorylation-mediated signaling due to a high-fat diet. Further investigation of the stability
of these phosphorylation changes would be useful to understand how these events mediate
phenotypes, such as insulin resistance or body weight. For instance, it would be very interesting
to conduct a study profiling phosphorylation changes on mice put on a high fat diet regimen and
then converted back to a normal diet.
Clustering by affinity propagation was performed on this data set; however, no significant
enrichment of Gene Ontology terms could be found for any of the clusters. If the peptide list was
extended, the analysis could be performed again with more interesting results. However, the fact
that no significant enrichment of Gene Ontology terms resulted for any of the clusters suggests
43
the biological variability between mice, even at the basal state, could be large enough to pose
problems for statistical testing.
Part C: Analysis of normal diet vs. 16-week high fat diet insulin response from 0 to 60 minutes.
The final portion of this study was to compare the phosphorylation response to insulin between
mice fed a normal diet and mice fed a 16-week high fat diet. The results from this analysis
provide information concerning which trends in diet-induced phosphorylation differences are
sustained for "long" time points, as demonstrated by the phenotypic data shown in Figure 1. 16
weeks of high fat diet feeding results in obesity in mice, as well as sustained insulin resistance.
The final data set for this study contained 216 sites quantified across 3 mass spectrometric
analyses. Only those phosphorylation sites identified in 2 or more runs were included in the full
data analysis and are displayed in Figure 8. I excluded the 5 minute time point analyzed in these
experiments as there were no matched samples for them in the analysis from Part A (Table 5).
The heat map was generated in the exact same fashion as the heat map generated in Part A.
Similar to the analysis comparing the normal diet response to the response of the mice fed a high
fat diet for 6 weeks, the two groups here show different temporal responses as well as basal
differences between the two groups. The sites clustered in the bottom half of the heat map
highlight high fat diet-induced increases in phosphorylation.
Run #
1
2
3
NCO
958
954
960
NC 5
970
968
970
NC 15
959
961
962
NC 30
975
975
975
16HFD 0 16 HFD 5 16HFD 15 16HFD 30
314
313
266
267
315
313
268
269
314
313
244
261
Table 5. Mouse identifier numbers for each sample included in the replicates for the analysis of
normal diet vs. 16-week high fat diet insulin response from 0 to 60 minutes.
44
IS~
05
0
-0S
I
Figure 8. Heatmap of 60 phosphorylation sites identified in 2 or more separate experiments. The
first three columns represent the insulin stimulation time course in the normal diet and the second
three columns represent the insulin stimulation time course in the 16-week high fat diet.
A similar analysis was performed on the 60 peptides identified in 2 or more separate experiments.
Clustering by affinity propagation was performed with subsequent GO term enrichment, as
outlined in Part A. The resulting clusters are shown in Figure 9.
1 O
(a) C iuster
15 Sies
mnuanmlarity 0.82828
29 sites
me=n similarity 0.77209
o
7
(c) Ckl r 3
15 Sits
a nulaity 0.55727
(b) Chaer 2
'~)
U
z
U
a
a
7
16
7
I?
U
?
T
z
~
U
2 2
0
L
7
.
X
Figure 9. Clusters resulting from affnity propagation of data set fotm nonnal diet and 16-week high fat diet comparison
experiments. The first three points, with the NC- preft, represent normal diet insulin stimulation time points. The
second three points, with the HFD- prefix, represent 16-week high fat diet insulin stmulation time points.
45
Cluster #
I
2
I unctional
Annotation
Focal adhesion, adherens junction
Response to peptide hormone stimulus and insulin receptor
signaling pathway
Protein kinase binding
Regulation of carbohydrate, metabolic processes
Amino acid and S-adenosylhomocysteine metabolism
Cellular metabolic process (triglyceride, acylglycerol,
neutral lipid, phosphate, fatty acid beta-oxidation, glycerol
ether, ketone, organic ether, and fructose metabolic
processes)
3
Regulation of kinase activity, signal transduction by
phosphorylation
Purine nucleotide binding, ATP binding
Transmembrane receptor protein tyrosine kinase signaling
pathway
Positive regulation of transport, cellular component
movement
Protein phosphorylation,
Cellular metabolic process
Purine ribonucleotide/ribonucleoside triphosphate binding
EGFR/IGF-IR binding
Proteins Included
Afapll2, Ahey, Bcarl, Cltc, Dak, Gnmt, Gstpl, Irs2,
Lpp, Pxn, Rbekl, Sorbs l, Tln2, Uox, Vel,
Acadl 1,Aldh Ia7, Ass1, Carkd, Crkl, Dbi, Dyrk Ib,
Egfr, Fbpl, Gpxl, Gsk3b, H2-KI, Hipk3, Hrspl2,
Hspel, Irs2, Mapk14, Mapk3, Nck, Nipsnap, Psmb4,
Ptk2, Ptpnl 1, Ptpra, Slc25a5, Sord, Til, Tnsl, Yes
CdkI5, Cth, Ctnndl, GludI, Gnmt, Gpcr5c, Grlf, Insr,
Mapkl4, Prpf4b, Ptpn18, Shel, Tencd, Yes
Table 6. Significant GO term enrichment for each cluster in Figure 9.
Each cluster has a unique profile. The first cluster contains sites that show a steady increase in
phosphorylation from 0 to 30 minutes in the high fat diet. In the normal diet, however, these
peptides have relatively constant levels of phosphorylation until 15 minutes, and exhibit a slight
increase at 30 minutes. The peptides in this cluster are on proteins involved in focal adhesion
signaling or proteins that serve as adaptor or scaffold proteins for protein-protein interaction for
signal transduction downstream of insulin stimulation; several of them are localized to the plasma
membrane. The trend of increased phosphorylation in the high fat diet case could indicate either a
different set of signaling events initiated by high fat diet, or that hyperphosphorylation results in
altered activities of these proteins.
The temporal responses of the peptides in the second cluster are characterized by a slight decrease
in phosphorylation at 15 minutes in the normal diet and a general increase in phosphorylation in
the high fat diet that plateaus at 15 minutes into 30 minutes. Most of the peptides analyzed reside
in this cluster, which has a diverse range of GO term enrichments. Many of the proteins on which
these phosphorylation sites lie are involved in phosphorylation, especially in signal transduction
to negative regulatory mechanisms of insulin signaling, such as p38 or JNK activation. This is in
46
line with other observations of protein phosphorylation in a stress or inflammatory response
inducing scenario, such as high fat diet feeding. Additionally, many of the identified proteins are
localized in the mitochondria. An interesting follow up study would be to examine the effects of
phosphorylation on mitochondria proteins.
The general trend for the peptides in the third cluster is an overall increase in phosphorylation in
the normal diet at 15 minutes. The high fat diet peptides generally exhibit increased
phosphorylation at 15 minutes but to a slightly lesser extent than in the normal diet trend. GO
terms enriched in this cluster overlapped with enrichments from other clusters; however, an
interesting enrichment seen was binding of receptors different than the insulin receptor such as
EGFR and IGF- IR. An interesting follow up study would be to discern the effects on the fate of
other receptors upon non-cognate ligand binding. IGF- 1R and insulin receptor share like domains
but have different affinities for insulin or IGF 1 and thus activate different cellular processes.
Feedback mechanisms downstream of insulin-stimulated pathways may regulate other receptors,
possibly due to conserved sequences on activating domains that can be targeted by negative
regulatory kinases.
To discern which proteins have altered phosphorylation in the 16-week high fat diet mouse livers
compared to the control, I performed a fold-change analysis according to the procedure outlined
in Part A. A total 12 sites on 12 proteins had increased phosphorylation in the normal diet, while
12 sites on 12 proteins had decreased phosphorylation in the normal diet. In the high fat diet, 33
sites on 34 proteins had increased phosphorylation and 4 sites on 4 proteins had decreased
phosphorylation at any given time point compared to the basal. Selected sites from the differential
analysis are displayed in Figure 10. The normal diet trends are represented by the blue bars and
the 16-week high fat diet trends are represented by the green bars.
47
Insulin Receptor
DWYETDYR
1.6
1.2
1.4
1
I. 0.8
I
1.2
1
-
0.8
I
4.5
81
4
3.5
3
25
7
0.6
5~2
0.4
1
I
S0.4
0.2
0.2
0
I
0.5
0
0
0
15
30
0
Time (min)
rho-GTPase activating proteIn3S
NEEENIySVPHDSTQGK
15
0
30
6
5.
S 4.
C
0.6
15
3.
0
30
3.5
3
2.5
2.5
3
2.5
2
2
Ii
2
2
I
1.5
1.5
1.5
I
I
1
0.5
0.5
0.5
0
0
15
0
15
Time (min)
30
Time(min)
Taln 1
LLGEIAQGNENyAGIAAR
1.2
2
1
I.
I
I
0.8
1.5
0.6
1
.5
0.5
0.4
0.2
0
0
15
Time (mlin)
1.5
0.5
0
15
Time (min)
30
0
15
30
Time (min)
Tensin 1
GPLDGSPyAQVQR
2.5
0
30
0
0
30
15
Time (min)
Fructose bisphosphatase
GNIYSLNEGyAK
Time (min)
Argininosuccinate synthetase
FELTCySLAPQIK
Time (min)
NIPSNAP1
GWDENVyYTVPLVR
2.5
00
p38
HTDDEMTGyVATR
EGFR
GPTAENAEyR
Shc
ELFDDPSyVMQNLDK
30
0
is
Time(min)
Figure 10. Dynamics for sites identified with high
phosphorylation level fold change (< 0.67x or > 2x) at any
given time point with respect to basal levels in response to
insulin. The blue line represents the normal diet trend while
the green line represents the high fat diet trend. All relative
intensity values were calculated with respect to the 30 minute
insulin stimulation, normal diet condition (this sample has a
relative intensity value of 1 for all graphs) ± SEM.
Similar to the trends seen in Results Part A, the phosphorylation of the insulin receptor is muted
in the 16-week high fat diet samples compared to the normal diet samples (Figure 10). The extent
of the increase in phosphorylation on the activation loop is not as great in the high fat diet
condition as in the normal diet condition. Shc, an insulin receptor interacting partner, shows
different temporal dynamics between the two diets. It seems to increase up to 30 minutes in the
normal diet, but the initial increase in phosphorylation at 15 minutes is not sustained to 30
minutes in the high fat diet. The temporal dynamics seen here may be indicative of attenuated
signaling the canonical pathway in insulin resistant states.
An interesting trend that emerged from the analysis was an increase in phosphorylation of EGFR
in the high fat diet sample, which contrasts the decrease in phosphorylation of EGFR in normal
diet samples (Figure 10). This is very interesting because it has been found that EGF can
stimulate GLUT4 translocation to the plasma membrane [80]. The EGFR and insulin receptormediated signaling cascades activate some of the same nodes [81], making it feasible that when
the insulin receptor is not being activated properly, EGFR can be activated to compensate for the
decreased signaling through the insulin receptor. Here, activation of EGFR could provide a
secondary backup mechanism for glucose transport. A negative regulatory protein of insulin
signaling, p38a, was much more highly phosphorylated in the 16-week high fat diet fed mice
compared to their normal diet counterparts (Figure 10). The increase seen here could be due to
the upregulation of stress and inflammatory pathways characteristic of insulin resistant states.
Intriguingly, activation of p38 MAPK has a role in cytoskeletal rearrangement [82] and also been
implicated in GLUT4 synthesis and mitochondrial biogenesis [83]. The activation of p38 could
be another compensatory mechanism for the decreased glucose uptake in high fat diet-induced
insulin resistance via cytoskeletal rearrangements for increased glucose transporter translocation.
49
Other proteins involved in signaling, such as the rho GTPase-activating protein, have altered
temporal response to insulin after 16 weeks of high fat diet feeding (Figure 10). The rho GTPaseactivating protein (also known as p190 rho GAP) is necessary for formation of stress fibers and
focal adhesions [84]. The data shows decreased phosphorylation of the site on p190 rhoGAP in
obese mice. This trend adds to the focal adhesion story line hypothesized in Parts A and B. The
rho GTPase-activating protein is phosphorylated at the site identified in this study by PTK6 to
induce association with RASAl, which converts Ras into its inactive GDP-bound form, and
subsequent Ras activation [85]. The fact that the protein is more highly phosphorylated in the
normal diet indicates that MAPK signaling may be more slowly activated in the obese animals
fed a high fat diet.
Nipsnap1 is protein that has been shown to have increased phosphorylation across all time points
in the 16-week high fat diet compared to the normal diet (Figure 10). This site has been identified
with the same phosphorylation trends in other work from our lab. It has been shown to be
localized to the mitochondria [86], and while research has been conducted it on it in several
neurological systems [87], its function in the liver is largely unexplored. Follow up studies
pursuing the role of this protein and its modification with respect to a high fat diet and insulin
response would be insightful to broaden our understanding of mitochondrial changes in insulin
resistant states.
Sites on proteins involved in metabolism also had different phosphorylation dynamics after 16
weeks of high fat diet feeding. Argininosuccincate synthetase is an important enzyme in the urea
cycle. Like the other urea cycle enzymes identified in Part A, the phosphorylation dynamics of
the enzyme is muted in the high fat diet in comparison with the normal diet (Figure 10). Unlike
the trends found in Part A, though, the level of phosphorylation is elevated in the high fat diet
compared with the normal diet. Tyrosine phosphorylation may be inhibitory on this enzyme, such
as in the case of PKM2, also a metabolic enzyme [62].
50
Another metabolic protein, fructose bisphosphatase, also has increased phosphorylation in the
high fat diet state compared with the normal diet (Figure 10). While there seem be little changes
in phosphorylation over the insulin stimulation time course in the normal diet, there is a sharp
increase in phosphorylation in the high fat diet state. Fructose bisphosphatase catalyzes the
reverse reaction as phosphofructokinase, the first step in glycolysis. Thus, it is a key regulator of
glucose metabolism within the cell. The site identified in this study has been previously identified
in high throughput screens of phosphorylation, but has no known functional annotation. Fructose
bisphosphatase has been found to be phosphorylated in vivo, and the phosphorylated enzyme has
a higher enzymatic activity than its dephosphorylated counterpart [88]. Therefore, an interesting
follow up study would be to examine the effect of this phosphorylation site on enzymatic activity.
Another interesting aspect of the phosphorylation site is that it lies in close proximity to a lysine
residue, which is known to be modified by either acetylation or ubiquitination. It is possible that
the modification of this residue changes the modification of the other, or vice versa. Further
investigating cross talk on this protein could shed light on methods of regulation of this important
node in glucose metabolism.
In terms of focal adhesion proteins, similar proteins were identified as in Part A. The levels of
phosphorylation of these sites are very different between the two diets. In Tensin 1, the normal
diet phosphorylation is much greater than the phosphorylation in the high fat diet. However, in
Talin 1, the normal diet phosphorylation is much less than that of the high fat diet. The
phosphorylation site in Talin 1 has been reported in a phosphoproteomic study examining the
global effects of oncogenic Src-family kinases [89]. Combined with the data from Part B, it
shows that differential activation of Src-family kinases can alter the phosphorylation status of
their interacting factors and proteins further downstream the signaling network.
51
Summary
Overall, the data shows that there are proteins in multiple biological processes that had not been
previously reported to be differentially phosphorylated upon insulin stimulation. In addition, the
high fat diet, both at 6 and 16 weeks, induces network dysregulation of key processes, such as
glucose metabolism, the urea cycle, and focal adhesion-mediated cytoskeletal rearrangements.
Further investigation of the phenotypic effects of differential phosphorylation of proteins in these
processes in more isolated systems would be immensely helpful in understanding the changes that
occur upon high fat diet feeding and obesity. Additionally, many metabolic proteins were found
to be phosphorylated, and several of these had differential phosphorylation dynamics upon high
fat diet feeding. This indicates that the insulin signaling network can reach beyond canonical
signaling pathways, for example: from the membrane through the cytosol to the nucleus, and
affect processes in other compartments such as the mitochondria. Follow up studies concerning
the effects of differential phosphorylation on these mitochondrial enzymes, and studies
investigating the effect of phosphorylation on other prominent modifications such as acetylation
or ubiquitination, would help us better understand the metabolic consequences of insulin
resistance.
52
11. Effect of SirT3 levels on diet-induced perturbations in insulin signaling
Introduction
High fat diets are major contributors to obesity. Obesity highly predisposes individuals to
becoming insulin resistant and increases their susceptibility to insulin resistance-based diseases
such as type 2 diabetes. High fat diet regimen can induce insulin resistance through a variety of
mechanisms, such as by causing aberrant metabolism or upregulating inflammatory and stress
responses that negatively feedback to attenuate insulin signaling [8, 39, 40, 44].
Recently, SirT3, a member of the sirtuin family, has been implicated in insulin signaling. SirT3
knockdown muscle cells were shown to have decreased phosphorylation of key insulin response
signaling proteins, such as IRS 1 and Akt, and increased phosphorylation of negative regulatory
mechanisms, such as p38 and JNK [37]. SirT3 has already been established as the main
mitochondrial deacetylase [25] and a major regulator of multiple metabolic processes, including
the urea cycle and fatty acid beta oxidation, by regulating acetylation status of the important
enzymes involved in these processes [30, 32, 34]. Several studies have shown that SirT3 levels
are diet dependent. SirT3 expression is elevated in calorie restricted mice, while mice fed a high
fat diet have decreased levels of SirT3 [37]. This has been hypothesized to be an effect of
inducing metabolic pathways that promote the conversion of NAD to NADH, thus decreasing the
levels of NAD necessary for sirtuin activity [90]. Additionally, SirT3 knockout mice are
hyperglycemic following a glucose challenge and have impaired insulin sensitivity compared to
their wild type counterparts [34].
The evidence from the literature supports the idea that SirT3 induces metabolic changes in the
cell by modulating the function of several proteins within the mitochondria. The effect of these
changes would be altered levels of certain metabolic intermediates, which in turn could interact
with important nodes in the insulin signaling cascade and change normal receptor-mediated
53
responses. To determine exactly how phosphorylation events are tied with changes in SirT3
levels, I analyzed liver and muscle tissue from mice with either wild type or knockout levels of
SirT3, fed either a normal or high fat diet, for their phosphorylation response to insulin
stimulation.
Materials and Methods
Model System: Cohorts of C57BL/6 mice were generated with wildtype or knockout levels of
SirT3 (Eric Bell) and fed different diets over their lifespan. Half of the mice with each genotype
were started on high fat diet regimen (same diet as in Part A of the previous chapter) 18-weeks
into their life, resulting in a 6-week high fat diet. The other half of the mice were kept on a
normal diet. The mice were weighed on the day of sacrificing, prior to insulin injection. Mice
from each genotype and diet were injected with phosphate buffered saline as a control or insulin
at 10 mU/g for 15 minutes. Two minutes prior to sacrificing, they were injected with sedative
(Avartin). The mice were subsequently opened and their tissues were harvested and flash frozen
in liquid nitrogen.
Phenotypic response: Ending weight measurements were taken prior to sacrificing.
Signaling network measurements: I used the same mass spectrometry-based method to identify
and quantify levels of specific tyrosine phosphorylation sites on different proteins as in the
previous chapter. Flash frozen muscle and liver tissue from the mice were homogenized in icecold 8 M urea with 1 mM sodium orthovanadate to minimize phosphatase activity during
processing. A bicinchonic acid (BCA) protein quantification assay was performed on 1:15 and
1:30 dilutions of the homogenates. The concentration was adjusted to 1.67 mg/mL in 3 mL 8 M
urea. The resulting homogenates were subsequently reduced with 10 mM dithiothretiol at 56*C
for 1 hour, alkylated wit 55 mM iodoacetamide for 1 hour in the dark at room temperature, and
54
digested overnight with trypsin at a concentration of 20 pg trypsin/1 mg protein. The peptides
from the tryptic digest were desalted and fractionated on a C18 Sep-pak cartridge (Waters) and
eluted in 70% acetonitrile in a background of 0.1% acetic acid. The elution was lyophilized in
powder form, from which each sample was differentially labeled with iTRAQ (isobaric tag for
relative and absolute quantitation, ABSciex) reagent, according to manufacturer's protocol.
The combined labeled sample was resuspended in 100 mM Tris with 0.3% NP-40, pH 7.4 and
immunoprecipitated for phosphotyrosine-containing residues using a cocktail of pan specific antiphosphotyrosine antibodies (PT-66, P-Tyr-100 and 4G10). After an overnight incubation, they
were eluted off the beads with 100 mM glycine pH 2. The elution was passed through an IMAC
(immobilized metal affinity chromatography) column for a second phosphopurification step and
finally loaded into a C18 reverse phase precolumn (100 micron inner diameter). Peptides in the
eluate were separated by reverse phase HPLC and subject to ESI/MS-MS on an Orbitrap Elite
(Thermo Scientific) mass spectrometer. The instrument was operated in a data-dependent mode;
MS/MS scans were performed on the top 10 most abundant species in the preceding MS scan.
The resulting .RAW data file of spectra generated from the experiment was converted to .mgf file
using DTA Supercharge and the MS/MS peak lists were searched against a mouse protein
database using MASCOT for sequence and protein identification information. Relative
quantification for the identified peptides was obtained by measuring peak area of the reporter ions
from the iTRAQ tag in each scan. As a loading control, a sample of the supernatant from the
immunoprecipitation step was analyzed in a similar manner. From the supernatant data, protein
averages for each iTRAQ channel were taken and averaged to yield a normalizing factor (mean
normalized across the normalizing factors for all 8 channels) with which to normalize all the
iTRAQ values for the peptides in the elution data. As an additional clean up step, scans with a
MASCOT score less than 25 or having contaminated precursor spectra (a contaminating peak
greater than 25% of the base peak within ± 1 m/z of the precursor ion) were eliminated.
Additionally, scans in which a zero was reported for any iTRAQ channel were excluded [55, 56].
55
Results
One cohort of mice was generated for this study. The ending body weights for each mouse are
listed in the table below (Table 6).
Table 6. Conditions for each mouse generated for the study. For each mouse, the genotype was
either SirT3 wildtype (WT) or SirT3 knockout (KO); the diet was either normal diet (NC) or 6week high fat diet (HFD); the injection was either insulin (INS) or a mock injection with
phosphate buffered saline (PBS). Ending weights were also recorded.
Mouse ID #
Genotye
Diet
Weight
KO
HFD
36.3
INS
4602
KO
NC
31.2
PBS
4610
KO
NC
30.2
INS
4612
WT
NC
26.8
INS
4600
4600
Inection
Figure 11. Ending weight of mice used in this study. The average weight for each diet and
genotype combination was plotted i SEM.
Ending Weight (g)
50
45
40
35
30
25
20
15
10
5
0
-
---
l-
f
tMITt
WT NC
WT HFD
KO NC
KO HFD
Genotype and Diet
Part A: Analysis of mouse liver tissue from SirT3 wildtype and knockout mice fed either a
normal diet or high fat diet.
The study analyzing the effect of SirT3 levels on normal diet insulin response and 6-week high
fat diet insulin response resulted in 25 sites across two mass spectrometric analyses. All
56
phosphorylation sites identified were used in the analysis. The mice representative of each time
point are displayed in Table 7.
Table 7. Mouse identifier numbers for each sample included in the replicates for the analysis of
SirT3 wildtype or SirT3 knockout animals on a normal diet or high fat diet in response to insulin.
Run#
1
2
WTNC4611
4611
WTNC+
4612
4612
WTHFD4601
4601
WTHFD+
4609
4609
KONC4602
4602
KONC+
4613
4610
KOHFD4598
4598
KOHFD+
4600
4600
Figure 12. Heat map of 25 phosphorylation sites identified in this study. The conditions for each
mouse are labeled as SirT3 wildtype (WT) or SirT3 knockout (KO); normal diet (NC) or 6-week
high fat diet (HFD); insulin stimulated (+) or mock injection with phosphate buffered saline (-).
protein
12
haa*responsive
*
s1 *
one8-ransferase,
Lekt394)
-
.gpcogen
2
C14(, cereislae)-tke
T9110ya
syn.has.
kinass
3 beta
57
Sec1412
ADDyFLLR
Shp2
VyENVGLMQQQR
Insulin Receptor
DIYETDyYR
2.5
16
14-
6
2
12 10
- -
--
1.5
*
PBS
"INS
4
I
4
I
a Pas
3 -
" PBS
0.5
INS
2
BINS
2
0
WTNC
WTHFD KONC KOHFD
Genotype and Diet
WT NC WT HFD KONC KO HFD
Genotype and Diet
WT NC WT HFD KO NC KOHFD
Genotype and Diet
Lck (y394)
UEDNEyTAR
Lek (y505)
SVLDDFFTATEGQyQPQP
Nipsnapl
GWDENVyYTVPLVR
25
4
3.5
3
-
20
-- -
2.5
- -
--
-
-
_
_ _ _
2 -
2.5
PBS
15
BINS
OSIN
0.5
@5
0
KO NC
KOHFD
WT NC WT HFD
Genotype and Diet
KONC KO HFD
WT NC WT HFD
Genotype and Diet
p-actin
1.2
4 3.5 -
1
TI
3 -
0.8
-
0.6
"PBS
BPBS
A 1.5
BINS
1
1
1O.5
de0
0.4
BINS
0.2
0
WT NC WT HFD KONC KO HFD
Genotype and Diet
KONC
KOHFD
Genotype and Diet
delta catenin
SLDNNySTLNER
DLyANTVLSGGTTMYPGIADR
00
"BINS
-
WT NC WT HFD
~2.52
1
1-
WT NC
WT HFD KONC
Genotype and Diet
KOHFD
Figure 13. Phosphorylation levels for selected sites.
Conditions for each mouse are labeled as SirT3
wildtype (WT) or SirT3 knockout (KO); normal diet
(NC) or 6-week high fat diet (HFD). Blue bars
represent basal phosphorylation levels and red bars
represent insulin-stimulated phosphorylation levels.
All values are normalized to the wildtype normal
diet basal condition (set to I for all graphs).
Although the analysis of liver tissue from this cohort resulted in fewer phosphotyrosinecontaining peptides than the tissue from the previous chapter, the results were very interesting. I
first examined phosphorylation levels on known insulin responsive sites. The relative levels of
phosphorylation of the insulin receptor (yl 168) and Shp2 (y584) are graphed in Figure 13. In
accordance with literature and the findings from the previous chapter, high fat diet in wild type
mice reduces phosphorylation of the insulin receptor in both the basal and insulin stimulated
states. SirT3 knockout mice have decreased phosphorylation on the insulin receptor following
insulin stimulation compared to wildtype mice. The increase in tyrosine phosphorylation upon
insulin stimulation in SirT3 knockout mice fed a high fat diet is very similar to that of the
wildtype mice fed a normal diet. This is intriguing as it had been previously thought that
decreased levels of SirT3 decreased insulin receptor phosphorylation. However, the data from this
experiment seems to indicate that SirT3 actually plays a protective role in guarding against
possible cellular signaling changes due to high fat diet feeding.
Shp2 is a protein tyrosine phosphatase that targets residues on the insulin receptor and IRS
proteins. It is actively involved in positively or negatively mediating insulin resistance depending
on the context [91, 92], and has been connected to Akt and Erk activation. It has been found that
phosphorylation of the y584 residue, the site identified in this study, results in enhanced Shp2
activity [93]. The data here shows that high fat diet feeding induces higher levels of
phosphorylated Shp2 in wildtype mice, which agrees with previous research on insulin resistant
states. Interestingly, SirT3 knockout mice on both diets exhibited a greater increase in insulinstimulated phosphorylated Shp2 compared with the wildtype mice. The loss of SirT3 makes Shp2
phosphorylation more insulin responsive, possibly by inactivating upstream kinases or activating
phosphatases that act on the y584 residue.
The phosphorylation levels of Sec 14-like 2 (SEC 14L2) exhibited interesting trends across
genotypes and diets (Figure 13). Phosphorylation of the site identified in the study increases
59
markedly upon insulin stimulation. The increase in phosphorylation is slightly diminished upon
high fat diet feeding, indicating the effect of diet on the phosphorylation of the protein. However,
the basal level of phosphorylation in a normal diet is extremely elevated in SirT3 knockout mice
compared to its wildtype counterparts to the point where insulin stimulation elicits a very small
response. To my knowledge, the site on this protein identified in the study has not been
previously discovered. Sec1412, or alpha-tocopherol-associated protein, is not very well
characterized except for its Vitamin E binding abilities. It has been postulated to be involved in
cholesterol synthesis, which could be a clue as to why its phosphorylation levels seem to be
inversely proportional to SirT3 levels and fat intake. Additionally, follow up studies investigating
its upstream regulators and the nature of the phosphorylation modification would be insightful to
expanding the scope of the insulin signaling network.
Notably, Nipsnap 1 exhibited similar phosphorylation response to insulin stimulation, diet, and
SirT3 levels as Sec1412 (Figure 13). Basal levels of phosphorylation in the wildtype mice were
higher in the high fat diet-fed mouse than the mouse fed a normal diet. Although these mice were
only fed a high fat diet for 6 weeks, the data is in agreement with the findings in the previous
chapter, where mice on a 16-week high fat diet had higher levels of basal phosphorylation than
those on a normal diet at this site in wild type mice. However, mice lacking SirT3 have much
higher levels of phosphorylated Nipsnapl and a muted insulin response. The lack of SirT3 may
cause changes in the cell that resemble a high fat diet-like state, which then causes the levels of
Nipsnap phosphorylation to increase. Thus, the site is no longer insulin responsive. Work still
needs to be done to understand the function of the protein and the nature of this post-translational
modification.
Two sites on Lck, a Src-family kinase, were identified in this study and are graphed in Figure 13.
One is y394, a site known to induce enzymatic activity upon phosphorylation, and the other is
y505, a site known to inhibit enzymatic activity upon phosphorylation. The y505 site is known to
60
be phosphorylated by another Src-family kinase, Csk [94]. In wildtype mice, the phosphorylation
trends are different between the two sites. Upon insulin stimulation in normal diet mice, the
activating site y394 exhibits a large increase in phosphorylation. In high fat diet mice, basal levels
of phosphorylation of this site increases more than two-fold, but the site becomes unresponsive to
insulin stimulation. On the other hand, the inhibitory y505 site shows little change in response to
insulin in the wildtype and normal diet conditions. On high fat diet regimen, the basal level of
phosphorylation increases, but the response to insulin stimulation is a marked decrease in
phosphorylation level.
The phosphorylation changes also seem to be SirT3 dependent. The basal levels of y394
phosphorylation are not significantly altered in knockout mice compared to the wildtype on a
normal diet, but the increase in basal phosphorylation of y505 is more than three-fold in the
knockout on a normal diet compared to the wildtype on a normal diet. Clearly, decreased SirT3
levels cause cellular changes that induce hyperphosphorylation of y505. The activity of Srcfamily kinases such, as Lyn or Yes, are activated in response to oxidative stress [95], which could
be brought about by increased reactive oxygen species production due to decreased levels of
SirT3. Along with data from the previous chapter showing increased basal level phosphorylation
of Yes after 6 weeks of high fat diet feeding, this data suggests a role of aberrant Src-family
kinase signaling in high fat diet and SirT3 level-induced dysregulation of the insulin signaling
network.
The phosphorylation levels of sites on beta actin and delta catenin across the different genotypes
and diets are graphed in Figure 13. Actin is very well known cytoskeletal protein and involved in
multiple processes including cell migration, motility, and intracellular transport [96]. Catenin is
part of the cadherin protein complex that makes up adherens junctions and is part of the Wnt
signaling pathway. The data from this study indicates that SirT3 also affects phosphorylation of
cytoskeletal proteins. In wildtype mice, regardless of diet, the response to insulin stimulation is a
61
three-fold increase in phosphorylation. However, SirT3 knockout mice have higher levels of basal
beta actin phosphorylation compared to the wildtype mice and exhibit a decrease in
phosphorylation upon insulin stimulation. For the delta catenin site, the phosphorylation
dynamics are exactly the opposite between wildtype mice fed a normal diet and wildtype mice
fed a high fat diet. The overall effect of decreased levels of SirT3 is to significantly decrease the
phosphorylation level of this site and mute the insulin-induced phosphorylation response. Delta
catenin is also known to be phosphorylated by GSK3 [97], as well as Src, and interacts with a
variety of kinases and phosphatases [98], which extends the consequences of SirT3 regulation of
Src family kinase activity. SirT3 regulation of Src family kinases could have widespread effects,
including affecting cytoskeletal rearrangments necessary for insulin response phenotypes such as
glucose uptake.
The results from this portion of the study demonstrate that SirT3 levels affect the phosphorylation
status of multiple sites. Pertubations in SirT3 levels alter basal level phosphorylation levels and
the insulin response, indicating its role in modulating the effects of high fat diet feeding. The
directionality of the changes in phosphorylation mediated by SirT3 levels depend on the protein
involved. Different pathways seem to be involved in the dysregulation of the insulin response,
based on the variety of proteins on which the phosphorylation sites identified in this study reside.
To link these pathways together and discern important nodes central to the SirT3-mediated
response, more experiments, like the one performed for this portion of the study, should be
conducted. The replicates will increase the number of phosphorylation events quantified, which
will increase our coverage of the insulin signaling network while simultaneously providing
information about how SirT3 mediates the effect of high fat diets on insulin signaling. It would
also be useful to include more mice representative of each condition to enhance the statistical
power of the data and enable computational techniques to be employed to better understand the
biological meaning of the global network changes that occur due to perturbations in SirT3 levels.
62
Part B: Analysis of mouse muscle tissue from SirT3 wildtype and knockout mice fed either a
normal diet or high fat diet.
The study analyzing the effect of SirT3 levels on normal diet insulin response and 6-week high
fat diet insulin response in muscle tissue resulted in 48 sites across one mass spectrometric
analysis. The mice representative of each time point are displayed in Table 8.
Table 8. Mouse identifier numbers for each sample included in the analysis of SirT3 wildtype or
SirT3 knockout animals on a normal diet or high fat diet in response to insulin.
Run#
1
I WTNC- I WTNC+
4612
4611
WTHFD-
4601
I WTHFD+ I KONC4602
4609
I KONC+ I KOHFD- I KOHFD+
4600
4598
4610
Figure 14. Heat map of 48 phosphorylation sites identified in this study. The conditions for each
mouse are labeled as SirT3 wildtype (WT) or SirT3 knockout (KO); normal diet (NC) or 6-week
high fat diet (HFD); insulin stimulated (+) or mock injection with phosphate buffered saline (-).
bet
7
0~spfto4ase (y 32)
dehydmogenase
phosphate
8(V163)
nse ubtquinone)
1betasubcomplex
A
NIMNSAI We99 a..
9Iso
(y227)
nspo4faJsttllhI*
Of the 48 phosphorylation sites identified, 6 sites were on myosin isoforms, 4 sites were on actin,
19 sites were on Titin, and one site was on nebulin. Myosin and actin are the predominant
proteins in muscle tissue, as bundles of them comprise the myofibril subunits that make up
muscle fibers. Titin and nebulin are myofibril-associated proteins and contribute to their structure
and stability. It has been found that the stiffness of muscle fibers is greatly increased upon titin
phosphorylation. Upstream kinases include protein kinase C (PKC), protein kinase G (PKG), Erk
and Ca 2+/calmodulin-dependent protein kinase (CaMKII). The effect of tension increase in
63
muscle due to titin phosphorylation is exacerbated when basal levels of titin are dephosphorylated
by protein phosphatase 1 (PP 1). Heart failure and cardiac dysfunction are characterized by
hypophosphorylation of titin [99, 100]. Although the overall phosphorylation level of titin stayed
relatively constant across all genotype, diet, and stimulation combinations, the individual sites
had different phosphorylation trends, indicating regulation of upstream kinases targeting specific
sites on titin by SirT3 levels and diet regimen.
Besides muscle fiber proteins, sites on metabolic enzymes were also identified. The
phosphorylation levels of selected sites are graphed in Figure 15. Phosphorylation levels of two
sites on glycogen phosphorylase were quantified in the study. Glycogen phosphorylase catalyzes
the degradation of glycogen and generation of glucose 1-phosphate, which can be used for ATP
synthesis [101]. The two sites have similar responses to insulin stimulation and high fat diet
feeding in the mice with wildtype levels of SirT3. In the wildtype mice fed a normal diet, the
phosphorylation of these sites increase in response to insulin stimulation about two-fold. Basal
phosphorylation levels increase in the wildtype mice fed a high fat diet, but do not change
significantly in response to insulin. However, in mice with decreased levels of SirT3, the
phosphorylation levels and response to insulin on these sites are quite different. In the SirT3
knockout mice on a normal diet, the increase in phosphorylation of y732 upon insulin stimulation
is much less than that seen in the wildtype mice on a normal diet. On the other hand, the basal
phosphorylation level of y227 is much greater in the SirT3 knockout mouse fed a normal diet
compared to the wildtype mouse on a normal diet. In the SirT3 knockout mice on a high fat diet,
basal phosphorylation levels of y732 are lower than wildtype mice on a high fat diet, but the
insulin response seems to be an increase in phosphorylation level. However, the basal
phosphorylation level of y227 is increased as a result of high fat diet, and the response to insulin
on y227 is a slight decrease in phosphorylation.
64
This result is interesting because demonstrates that SirT3 levels affects two sites on the same
enzyme in different ways. While diet seems to have a strong effect on altering basal
phosphorylation levels and attenuates the insulin response of both sites, decreased levels of SirT3
ameliorates the change in basal levels for y732, and changes the insulin response for y227. This
result indicates that the two sites serve different functions, possibly co-factor binding, as
glycogen phosphorylase is regulated by AMP levels [102], or conformation, and are regulated by
different mechanisms. Y732 seems to be part of an activation loop (YX 4YY motif) while y227 is
closer to the substrate binding pocket. Decreased levels of SirT3 could have a variety of effects.
Loss of SirT3 may alter phosphorylation levels of one site by increasing flux through glycolysis
[38], which would increase levels of glucose 6-phosphate, an allosteric glycogen phosphorylase
inhibitor, and alter the phosphorylation levels of another by increasing reactive oxygen species
production and generating a stress response that could affect upstream kinases that act on
glycogen phosphorylase such as protein kinase A (PKA). This result indicates enzymes may be
regulated by both signaling events and global changes in metabolism, highlighting the intricacy
of the effects of loss of SirT3 in both areas.
Another tyrosine phosphorylation site on muscle glycogen phosphorylase has been found to be
inhibited by nitration due to peroxynitrite, a byproduct of reactive nitrogen species. SirT3
regulates reactive oxygen species production within the mitochondria [103] and therefore could
produce inhibitory species such as peroxynitrite in combination with reactive nitrogen species
from the upregulation of stress or inflammatory pathways due to high fat diet feeding. Lower
levels of phosphorylated y227 in response to insulin stimulation in the mice with knockout levels
of SirT3 could be due to increased tyrosine nitration. Interestingly, the enzymatic activities of
other glycolytic enzymes such as aldolase and glyceraldehyde 3-phosphate dehydrogenase
(GAPDH) have been found to be inhibited upon nitration of tyrosine residues [104, 105]. Sites on
both of these enzymes were also identified in this study.
65
muscle glycogen phosphorylase (y732)
GYNAQEyYDR
muscle glycogen phosphorylase (y227)
WVDTQVVLAMPyDTPVPGYR
2.5
2.5
I2.0
a PBs
a PBS
.0
* INS
0.5
a
0.5
0.0
INS
0.0
WT NC
WT HFD
KO NC
KO HFD
WT NC
WT HFD
KO NC
Genotype and Diet
Genotype and Diet
Aldolese A(y364)
YTPSGQSGAAASESLFISNHAy
GAPDH
USWYDNEYGySNR
KOHFD
1.2
1.6
1.0
1.4
0.8
1.2
1.0
0.8
0.6
0.4
a PBS
I
0.6
"INS
" PBS
" INS
0.2
0.2
0.0
WT NC
WT HFO
KONC
KOHFD
Genotype and Diet
2
Dyrk1b (y 73)
IYQylQSR
3.5
- -
2.s5-
2.0
1.s5
-
-
-
-
-
=PBS
-
8 INS
1.0
a
0.5
0.0
WT NC
WT HFD KONC KOHFD
Genotype and Diet
Figure 15. Phosphorylation levels of selected
peptides across different conditions. The
conditions for each mouse are labeled as SirT3
wildtype (WT) or SirT3 knockout (KO);
normal diet (NC) or 6-week high fat diet
(HFD). Insulin stimulated mice are represented
by the red bars while mice mock injected with
phosphate buffered saline are represented by
the blue bars. All values are normalized to the
wildtype normal diet basal condition (set to I
for all graphs).
Aldolase A is a key enzyme in glycolysis that catalyzes the conversion of fructose bisphosphate
into dihydroxyacetone phosphate and glyceraldehyde-3-phosphate. The site identified in this
study is the C-terminal tyrosine residue, which is critical for its enzymatic activity [105]. The data
from this study for this phosphorylation site (graphed in Figure 15) demonstrate that there are
only small changes between basal and insulin-stimulated phosphorylation levels at this site
between wildtype mice fed normal diets and high fat diets. However, the effect of diet on
phosphorylation levels of this site is extremely pronounced in SirT3 knockout mice. This result
66
demonstrates that SirT3 is an important regulator of maintaining robustness in a working
glycolysis pathway despite changes in fatty acid flux. The reduction in SirT3 levels causes
cellular changes, perhaps in the form of accumulated metabolic byproducts that inhibit glycolytic
enzymes when exported to the cytoplasm, like the inhibitory effect of citrate on phosphofructose
kinase [106], which in turn would affect insulin response. Despite previous identification of this
site, the effect of phosphorylation of this site is not well understood.
GAPDH is the next enzyme in glycolysis after aldolase and converts glyceraldehyde-3-phosphate
into 3-phosphoglycerate. GAPDH is known to be phosphorylated by PKC, the same enzyme that
phosphorylates titin [108]. The data from this study (graphed in Figure 15) show that basal
phosphorylation levels of this site is not affected by diet but more by SirT3 level; there lower
levels of phosphorylation in SirT3 knockout mice than wildtype. The site does not seem to be
highly responsive to insulin stimulation except in the high-fat diet state, where phosphorylation
markedly decreases. The site identified in the study was y318, which has been previously
identified but currently has no functional annotation. Like the case for many other phosphorylated
enzymes, the effect of the post-translational modification is unclear.
Lastly, phosphorylation site y273 on dual specificity tyrosine phosphorylation regulated kinase
(Dyrkl) was identified in this study and graphed in Figure 15. Dyrkl is a transcription factor that
co-activates FOXO- 1a and regulates glucose 6-phosphatase gene expression [109]. Basal levels
of Dyrkl phosphorylation at this site increase dramatically upon high fat diet feeding in wildtype
mice, indicating a strong transcriptional response for increased metabolic machinery. However,
the increase in basal phosphorylation upon high fat diet feeding is not seen in SirT3 knockout
mice. This result is interesting because the basal and insulin stimulated phosphorylation levels of
this site in mice fed a normal diet does not change between SirT3 wildtype and knockout mice.
With respect to phosphorylation of Dyrklb, the effect of decreased SirT3 levels may be
compensated for by another mechanism in mice fed a normal diet. It also seems as if lack of
67
SirT3 is protective against the high fat diet-induced hyperphosphorylation of Dyrk1 y273.
Although this phosphorylation of this site has been previously identified, the effect of
phosphorylation has yet to be determined.
The results from this part of the study demonstrate that there are phosphorylation changes on
several phosphorylation sites in muscle tissue. The modified proteins are involved in musclespecific processes such as contraction, but also main cellular processes such as glycolysis,
metabolite shuttling and transporting, as well as transcriptional activity. By furthering the study
with more mass spectrometric analyses, we can increase the coverage of the insulin response
network. The increased coverage of the insulin response network will help us gain a better
understanding of how diet-induced changes in these different processes are affected by
perturbations in SirT3 levels. Additionally, the expanded data set will help us dig deeper into the
underlying mechanisms of change in the insulin response due to diet and SirT3 levels, and will
help us understand which changes are tissue-specific and which are more universal.
Summary
Overall, data from this portion of the study show that decreases in SirT3 have intruiging effects
on multiple phosphorylation events, indicating that SirT3 levels do perturb the diet-induced
change in insulin response. In some instances, loss of SirT3 alters basal levels of phosphorylation
and the high fat diet insulin response. Several of the sites identified in this portion of the study
have not been very well characterized. The ones that are characterized are involved in a variety of
cellular processes, suggesting that SirT3 may mediate more pathways than we are aware of. The
data from these experiments help expand the map of the insulin signaling network since several
of the proteins on which sites were identified have not been previously linked to insulin response.
Analyzing muscle tissue in addition to liver was also insightful. Although the insulin responsive
organs have some shared pathways, they have different primary functions, which will influence
68
the phosphorylation events that occur within them and how they change based on diet regimen or
SirT3 levels. Being able to profile the similarities and differences between phosphorylation
changes and phenotypes in the different insulin responsive organs will help increase our
understanding of the insulin response network. The next steps in this project would be to generate
a larger cohort of mice to be able to repeat the experiment over a larger number of mice for each
genotype and diet and also to take more phenotypic measurements, such as insulin and glucose
tolerance, for the representative mice. The results from the expanded study will help highlight the
sites that change significantly between conditions and will give us a clearer picture of which
nodes are regulated more by diet, which nodes are regulated more by SirT3 levels, and which
nodes are affected by the combination.
69
IV. Future Directions
The results from the work completed so far give an interesting perspective on effects of diet and
SirT3 levels on insulin response. The compiled data shows that the effects of diet or SirT3 levels
on insulin responsive phosphorylation events are diverse, and that there is not one overlying
global effect (i.e. "global increase in phosphorylation upon SirT3 knockout"). The results indicate
that we need to examine each site within its pathway and clearly understand the regulation
mechanisms for phosphorylation as well as the consequences for the post-translational
modification. The unbiased approach for identifying phosphorylation sites within the model
systems enabled me to find many sites that have not been previously shown to be dynamically
phosphorylated in response to insulin. The phosphorylation sites identified in this study are on a
wide range of proteins involved in a variety of processes. The results from the study highlight on
groups of proteins involved in processes not previously linked to insulin response, but may be
worth further investigation because of their dysregulation due to high fat diet and SirT3 level.
The most obvious group, due to the metabolic effects of high fat diet and loss of SirT3, are
metabolic enzymes. Many of them, however, are localized to the mitochondria. Whether these
proteins are phosphorylated prior to translocation to the mitochondria or phosphorylated inside
has yet to be determined. There is limited information on mitochondrial kinases, but there have
been reports of mitochondrial MAPK kinases, Akt, and PKC [110], in addition to literature with
evidence supporting translocation of receptor tyrosine kinases to the mitochondria [111]. Many
cytoplasmic metabolic enzymes are phosphorylated for activity, so it would not be surprising if
mitochondrial enzymes were as well. However, the activities of several mitochondrial metabolic
proteins are regulated by acetylation status, so it would be worth conducting studies to understand
how the two post-translational modifications interact, and if there are post-translational
modifications that may also determine functionality of these proteins.
70
Aside from metabolic proteins, cytoskeletal proteins are also differentially phosphorylated in
response to insulin and this response is altered based on high fat diet as well as SirT3 levels.
Although these proteins are abundant in cells, they are related to insulin response because glucose
uptake requires cytoskeletal rearrangements for translocation of glucose transporters to the cell
membrane [64, 65, 67]. Understanding their regulatory mechanisms in terms of phosphorylation
status and the consequence of the protein modification will be extremely informative for finding
targets that will help improve glucose uptake in insulin resistant individuals. In another recurring
theme throughout the study, members of the Src kinase family are differentially phosphorylated
in response to insulin, diet and SirT3 levels. They have been implicated in phosphorylating other
insulin responsive proteins like Akt [112], and are known to be activated by nitric oxide-related
species. Their activation may play an important role in regulating the overall cellular response to
stresses induced by diet and reduction in SirT3 levels. Lastly, attention should be paid to proteins
like Nipsnap 1 that do not have a defined function but whose phosphorylation levels are clearly
regulated by diet and SirT3 level. The most important mediators of diet-induced insulin resistance
may not already have been identified. Since the trends in phosphorylation response of these
proteins to insulin are so strong, they should be investigated to understand their function,
interacting partners, and role in mediating phenotypes.
To further this study, I would expand the mice cohorts to have several representative mice to
conduct the same type of study as presented in Chapter 2 in both SirT3 wildtype and SirT3
knockout mice. This way, I could capture as many dimensions as possible with respect to insulin
stimulation and diet regimen. I would harvest all the insulin responsive tissues from these mice
for phosphotyrosine analyses. Having a complete profile of liver, muscle and adipose tissue
across the genotype and diet conditions would help me understand how different overt insulin
response phenotypes would be affected by perturbations in the diet or SirT3 levels. In the
expanded study, I would also include insulin tolerance and glucose tolerance tests for all the
71
mice. With the combined phenotype and phosphorylation data, computational techniques can be
employed to link phosphotyrosine signaling events to phenotype, and will be extremely
informative for pinpointing new therapeutic targets for diabetes and other insulin resistance-based
diseases. Finally, I would also pursue the interesting targets from this study, such as some of the
sites involved in the pathways mentioned in the above paragraphs of this section. It is critical to
understand the role of phosphorylation on these enzymes that are necessary for important
biological processes. As I stated before, perhaps the most important regulator has not yet been
characterized. It is also possible that to improve insulin sensitivity, the best therapy would be a
combination of drugs that will both modulate signaling events as well as preserve proper
metabolic function.
In conclusion, the approach to globally profile phosphorylation events in response to insulin has
enlightened us to the fact that the insulin response network is even more far-reaching than
currently established. The results from this study have emphasized the effects of high fat diet on
insulin response are diverse and very site-specific, and these effects are regulated by SirT3 levels
in an even more site-specific way. To firmly understand the interplay between metabolism and
signaling will require many more studies on the specifics of many of these events, but the
information gained from these studies will enable us to identify the most effective targets to
combat insulin resistance and obesity-linked diseases.
72
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