1. Investigation of P38 and AKT Inhibitor
Effects on SPARC and PTEN-Induced
Signaling in Glioma Cells
Camryn R. Romph
Winter 2015
RESEARCH SUPERVISOR
Sandra A. Rempel, Ph.D.
VP, Research and
Senior Scientist,
Division of Neurosurgery
Department of Clinical Neurosciences
Spectrum Health System
Grand Rapids, MI
FACULTY SUPERVISOR
D. Blaine Moore, Ph.D.
Professor of Biology
Kalamazoo College,
Kalamazoo, MI
A paper submitted in partial fulfillment of the requirements
for the degree of Bachelor of Arts at Kalamazoo College
2015

3. ii
Acknowledgements
I would like to thank my supervisor, Dr. Sandra Rempel, for allowing me to work
in her laboratory and for her insight throughout the project and writing process. I greatly
appreciate all of the time and effort she put into helping me, and I have learned a lot
about both research and writing with her guidance. I am incredibly grateful that I worked
so closely with William Golembieski, who shared his bench and equipment, and provided
constant guidance, knowledge, and enthusiasm for glioblastoma research. I thoroughly
enjoyed all of the time, and conversations we shared. I would also like to thank Stacey
Thomas, Ph.D., for her help in the lab and especially in the writing process. She was a
dedicated mentor to me, who challenged and motivated me throughout my entire
experience. I am very happy I had her kind and understanding voice to guide me, and for
all the time and effort she spent helping me. I thank Stephanie Scott for all of the extra
time she put into helping me with the analysis and editing of the data, especially in the
end of the process. I am very thankful for her kind, positive attitude and willingness to
help throughout my entire experience in the lab. I would also like to thank Chad Schultz
for his constant help and support, for teaching me, and for spending so much of his time
explaining pathways, data and experiments to me. Additionally, I owe thanks to the
donors of the normal cell lines for their contributions: Oliver Bogler (Ast 11.9), Helene
Sage (MLF), and Henry Ford Hospital (SVARBEC). I also want to thank the Center for
Career and Professional Development and the Biology Department of Kalamazoo
College for generously funding my research opportunity with the Diebold Research
Fellowship. Finally, I am very grateful for all of the time and effort my SIP advisor, Dr.
Blaine Moore, and my entire peer review group put into editing and evaluating my work.

4. iii
Table of Contents
Acknowledgements .……………………………...……………………………………... ii
List of Figures ...………………………………………………………………………… iv
List of Tables. …………………………………………………………………………… v
Abstract ………………………………………………………………………………….. 1
Introduction ……………………………………………………………………………… 2
Materials and Methods .……………………………...…………………………………. 16
Results .……………………………...………………………………………………….. 22
Discussion ……………………………………………………………………………… 35
References ……………………………………………………………………………… 44

5. iv
List of Figures
Figure 1. Proposed signaling pathways for SPARC +/- and PTEN +/- cell lines………..11
Figure 2. Effects of AKT and P38 inhibitors on AKT and P38—MK2—HSP27 signaling
pathways in SPARC-negative glioma cells…………………………………….. 23
Figure 3. Effects of AKT and P38 inhibitors on AKT and P38—MK2—HSP27 signaling
pathways in SPARC-positive glioma cells……………………………………... 27
Figure 4. Western blot analysis of three non-cancerous cell lines probed for various
proteins downstream of SPARC………………………………………………... 29

6. v
List of Tables
Table I. Summary of results from Western blot analyses shows fold changes compared to
control in phosphorylated proteins with the eight treatment conditions ……….. 24
Table II. Summary of results shows the effects of treatment with AKT inhibitor IV alone,
P38 inhibitor alone, and a combination of AKT inhibitor IV and a P38 inhibitor
on phosphorylated proteins of SPARC-regulated pathways…..….…………….. 31

7. 1
Abstract
Glioblastoma (GBM), the most malignant type of adult brain cancer, affects
thousands of patients annually in the U.S. Though these tumors rarely metastasize, GBM
is characterized by its invasive phenotype, limiting survival to less than two years post-
diagnosis. While a myriad of signaling pathways regulate GBM, SPARC, an extracellular
matrix protein, is of particular interest due to its overexpression in GBM. Downstream
proteins of SPARC, such as AKT, P38, MK2, and HSP27, correlate with survival and
migration of glioma cells and are tested in the present study. PTEN, a tumor suppressor
generally lost in GBM patients, inhibits signaling through the SPARC-induced pathways.
Four established U87 malignant glioma (MG) clones and three non-cancerous cell lines
were used, all of which differ in SPARC and PTEN status. Cell lines were tested in eight
treatment conditions: an AKT inhibitor, three P38 inhibitors, and combinations of the
AKT inhibitor with each P38 inhibitor. Changes in levels of pAKT, pHSP27, pP38, and
pMK2 were detected by Western blot analysis. Results for HSP27 show that in SPARC-/
PTEN- cells, Ser82 pHSP27 is reduced with P38 and AKT inhibition together. In
SPARC-/ PTEN+ cells, inhibitors do not affect pHSP27 expression at any site, which
supports that PTEN may indirectly suppress pHSP27. In SPARC+/ PTEN- cells, Ser82
pHSP27 is reduced by P38 inhibition alone and combination treatment. SPARC+/PTEN+
cells showed that all inhibitor treatments shut down SPARC-induced Ser78 pHSP27
expression. Results provide mechanistic implications for signaling downstream of
SPARC. Future studies should compare cell lines on the same blot so quantifiable
amounts may be compared, and treatments should be tested in survival and migration
assays to determine which condition best prevents the invasive phenotype of GBM.

8. 2
Introduction
Glioblastoma (GBM) is the most common and malignant brain tumor in adults,
characterized by its invasive phenotype and consequent poor prognosis. In the United
States, over 10,000 GBM diagnoses are made annually (Harter et al., 2014). Median
survival time is only 14 months and very few patients survive for two years. Furthermore,
patients often suffer severe neurological problems and poor quality of life post-diagnosis,
even when undergoing the most aggressive treatments (Lefranc et al., 2009; Woodworth
et al., 2014). Due to its invasive nature and persistent recurrence, GBM tumors are not
usually eliminated with a single surgical procedur. Helseth and colleagues, however,
showed that a subsequent surgery, when appropriate, significantly increases overall
survival of GBM patients (2010). Nonetheless, multiple surgeries increase risk factors
and tend to decrease the quality of life in patients. Recent clinical studies show that the
overall survival of GBM patients increases with an age at diagnosis of < 60 years, an
Eastern Cooperative Oncology Group performance grade of 0-2, a Karnofsky
performance scale score of ≥ 60, a unilateral tumor, and a gross total removal of the
tumor as compared with subtotal removal and biopsy (Brown et al., 2008; Helseth et al.,
2010; Gutenberg et al., 2013; Lee et al., 2013).
The Origin of GBM
While the origin of glioblastoma is still uncertain, various theories have
developed over time, as outlined by Inda et al. (2014). Such theories are based on the
characteristic heterogeneity of GBM tumors, and the fact that recurring tumors are often
resistant to the different treatments available (Inda et al., 2014). It is thought that when
tumor cells divide they simultaneously acquire mutations. Of these resultant daughter

9. 3
cells, only those with mutations creating resistance to radiotherapy and chemotherapy
may survive and cause tumor relapse (Inda et al., 2014).
It is also believed that recurrent tumors may be the result of the activation of
cancer stem cells, a small population of cells existing in the original GBM tumor (Inda et
al., 2014). Such progenitor cells have been isolated from GBM tumors and are believed
to possess stem cell-like capabilities, such as self-renewal, differentiation to many
different types of cells, and proliferation (Van Meir et al., 2010). They are thought to
divide asymmetrically such that some daughter cells retain this stem cell population,
while others acquire mutations to resist therapy and differentiate into the cells that make
up the tumor mass itself (Inda et al., 2014).
Another theory suggests that mature brain cells may undergo certain mutations
that cause them to de-differentiate and acquire stem cell-like characteristics and
functions, thus, allowing these cells to further mutate and differentiate into therapy-
resistant and proliferative tumor cells (Van Meir et al., 2010). Conclusively, these
theories both suggest that progenitor cells and de-differentiated brain cells may be
responsible for not only therapy resistance, but also for the proliferation of invading
tumors.
Characterization of GBM
Depending on the onset of the tumor, glioblastoma is characterized as either
primary or secondary GBM, for which the average age of patients is 62 and 45 years,
respectively (Ohgaki and Kleihues, 2007). In primary GBM, the tumor arises without
prior evidence of any malignancy, whereas secondary GBM patients have some history
of a tumor (Louis et al., 2007). It is thought that secondary glioblastoma progression

10. 4
involves a gradual transformation from a lower-grade astrocytoma (World Health
Organization [WHO] grade I or II) to an anaplastic astrocytoma (WHO grade III) before
achieving the malignancy and proliferative tendencies of WHO grade IV GBM (Louis et
al., 2007).
Genetic Causes for GBM
Glioblastoma is directed by changes and mutations of various genes, indicating
that its pathogenesis originates at the transcriptional level (Reardon and Wen, 2006).
However, what makes treatment of the disease so complicated is the fact that individual
patients possess different combinations of these alterations, indicating the need for
personalized therapy (Reardon and Wen, 2006). By generalizing the different genetic
profiles of 500 primary GBM patients through DNA, mRNA, and microRNA analyses,
four subtypes of glioblastoma have been established: classical, mesenchymal, proneural
and neural (Van Meir et al., 2010). Each subtype is sensitive to different treatment
protocols and resistant to others, depending on the existence, mutation, or deletion status
of different molecular markers (Van Meir et al., 2010).
The most common molecular indicators of GBM are the overexpression of EGFR
(epidermal growth factor receptor), PDGFR (platelet-derived growth factor receptor),
VEGF (vascular endothelial growth factor) and certain integrin proteins; active MGMT
(O6
-methylguanine DNA methyltransferase); and loss of p16, p53, and PTEN
(phosphatase tensin homolog) (Reardon and Wen, 2006; Brown et al., 2008; Van Meir et
al., 2010; Ang et al., 2010; Harter et al., 2014). In general, these different changes lead to
the inhibition of tumor suppressor pathways and/or up-regulation of receptor tyrosine
kinase pathways (Van Meir et al., 2010). As previously noted, these modifications,

11. 5
among others, are present in a variety of combinations depending on the individual (Van
Meir et al., 2010). Given the heterogeneity of the disease at the transcriptional level, as
well as the natural diversity in patient health status, GBM treatment must involve
specialized therapy that can be manipulated based on individual patients’ genetic profiles
and other medical needs (Van Meir et al., 2010).
Treatment of GBM
In the past decade, there has been much deliberation about the most efficient
treatment protocol for patients with primary GBM. Until 2005, the standard treatment
regimen consisted of surgical resection of the tumor and adjuvant radiotherapy (RT)
(Stupp et al., 2005). However, since there have been no improvements made in inhibiting
tumor growth or extending patient survival time, researchers are required to look at other
potential therapies.
Chemotherapy research has been of the most interest and recently led to an
exciting new treatment protocol for GBM patients. One chemotherapy drug has proven
effective in glioblastoma patients: temozolomide (TMZ). In a randomized study of 573
primary GBM patients, Stupp and colleagues provided evidence for TMZ as a novel
therapeutic strategy to treat GBM (2005). This trial compared the treatment of RT alone
with a new approach consisting of RT with concomitant TMZ followed by adjuvant TMZ
chemotherapy for 6 months. At 28 months, the study showed a statistically significant
37% decrease in the risk of death in patients receiving the new approach as treatment.
Furthermore, the median survival of patients receiving this new therapy—RT plus
concomitant and adjuvant TMZ treatment—was 14.6 months, while that of patients
receiving RT alone was 12.1 months, indicating a small but significant 2.5-month

12. 6
increase in survival when using the combination of RT and chemotherapy (Stupp et al.,
2005; Helseth et al., 2010). Since this study, the “Stupp Regimen” has become the new
standard treatment for patients with newly diagnosed primary GBM (Lefranc et al.,
2009).
Molecular Targets for Potential Therapies
Given the lack of treatment advancements made recently with the current
radiotherapy and chemotherapy methods available, it remains essential to investigate
other possible treatment regimens. Accordingly, recent research has investigated other
chemotherapies through the targeted inhibition of molecular indicators previously
mentioned. As reviewed in an article by Harter et al., these experiments show
contradictory results when performed in the laboratory and in the clinical setting (2014).
In vitro studies of the drugs erlotinib, imatinib, bevacizumab, and cilengitide, along with
others, show successful inhibition of EGFR, PDGFR, VEGF, and integrin proteins,
respectively (Lefranc et al., 2009; Harter et al., 2014). Unfortunately, when taken to
clinical trials, none of these drugs has been proven beneficial to overall patient survival,
even when combined with RT and chemotherapy regimens (Harter et al., 2014).
It has become clear that the radiotherapy, drug, and other chemotherapy strategies
used today are not preventing tumor invasion or recurrence, improving patients’
standards of living, or prolonging survival. Therefore, it has become a high priority to
investigate the specific mechanisms by which these regulatory pathways function.
Additionally, it is crucial to determine which signaling pathways are responsible for cell
survival, growth, proliferation and invasion and how they are interconnected. A deeper

13. 7
knowledge of how these pathways adapt under the presence of different molecular
indicators could lead to a far more thorough and specialized treatment for GBM.
SPARC
One protein, SPARC (secreted protein acidic and rich in cysteine), has been of
great research interest since its initial characterization and association with various types
of cancer. Originally given the name osteonectin, this protein was described as a tissue-
specific protein that interacts with collagen, strongly suggesting its interaction with
cellular matrices (Termine, et al., 1981). Though, as the name implies, this protein was
originally associated with bone cells, its overexpression was also discovered in most
gliomas (Rempel et al., 1998). This discovery initiated the research of SPARC signaling
pathways and their roles in glioblastoma progression. Immunohistochemical analyses
revealed that SPARC is overexpressed in astrocytomas grades II-IV, but the level of
expression is not correlated with tumor grade (Rempel et al., 1998). Additionally,
SPARC overexpression was present at both the transcript and protein levels, which
supports the assumption that GBM tumorigenesis may originate from biologic changes at
the transcriptional level (Rempel et al., 1998).
SPARC is a 43-kDa matricellular protein that plays an important role in key
regulatory pathways involving cell proliferation, cell cycle, survival and anti-apoptosis,
adhesion, and angiogenesis (Sage et al., 1984; Sage, 2009). It is widely accepted that the
biologic function of SPARC depends on the cell type and may be influenced by SPARC
cleavage and the resulting peptide(s) (Tai and Tang, 2008). Accordingly, the complexity
of SPARC function is evident in the opposing roles it plays in different forms of cancer.
While SPARC is overexpressed in breast cancer, melanoma, and GBM, lower levels are

14. 8
found in ovarian, colorectal, and pancreatic cancers (Tai and Tang, 2008). High
expression of SPARC is thought to be an anti-proliferative, tumor-reducing agent in
neuroblastomas and colorectal cancers, whereas it promotes proliferation and metastasis
in prostate cancer and melanoma (Tai and Tang, 2008). Each of these effects of SPARC
has been shown in both ovarian and breast cancers, demonstrating that other factors
including the tumor microenvironment and the specific genetic changes present in a
particular tumor can regulate the function of SPARC, highlighting the complex regulation
that takes place (Tai and Tang, 2008).
SPARC in Gliomas
It has been shown that in gliomas, SPARC is expressed throughout the tumor and
is overexpressed in the cells at the periphery of tumors, suggesting its role in tumor cell
migration and invasion (Rempel et al., 1998). A study with transfected U87MG clones in
a spheroid confrontation assay showed that overexpression of SPARC does indeed
increase the migration of tumor cells in vitro and also promotes changes in the
morphology and adhesion of the cells (Golembieski et al., 1999). However, it was also
shown that inhibition of SPARC alone enhanced tumor cell survival in vitro, indicating it
may not be a great therapeutic target (Schultz et al., 2012). Another in vitro study
revealed that increased SPARC expression led to delayed, but not inhibited, glioma cell
proliferation due to a change in SPARC’s interaction with the extracellular matrix (ECM)
(Rempel et al., 2001). An in vivo study using the same clones revealed novel indications
toward SPARC-induced signaling pathways. Clones with both high and low expression
of SPARC showed the greatest amount of migration into surrounding brain and the
corpus collosum, growing predominantly in finger-like projections or appearing

15. 9
elsewhere as satellite tumors (Schultz et al., 2002). Contrastingly, the clone expressing an
intermediate amount of SPARC gave rise to a well-circumscribed tumor along the corpus
collosum (Schultz et al., 2002). Results also show that while SPARC increased invasion,
proliferation and tumor growth were decreased (Schultz et al., 2002). It is believed that
the complexity of these results stem from the fact that the downstream functions of
SPARC rely primarily on the delicate balance between its roles in adhesion, suspension
of the cell cycle, and inhibiting cell proliferation (Schultz et al., 2002).
The conclusion that SPARC regulates pathways involved in cell proliferation and
tumor growth holds novel indications for the treatment of GBM. Previous studies sought
to directly target SPARC to inhibit downstream pathways responsible for the tumor’s
invasive phenotype, in the hopes of promoting more circumscribed tumor growth that
would allow for a more complete removal of the tumor and lower the chance of
recurrence. However, the discovery that SPARC slows tumor growth and proliferation in
glioma cells thwarted this approach. Rather, it became necessary to define the signaling
pathways regulating these functions to target invasion without affecting the suppression
of tumor growth. Thus, characterization and a deeper understanding of the mechanisms of
these pathways have become of increasing research interest.
It is now widely accepted that SPARC is secreted into the extracellular space,
where it interacts with cell membrane proteins and receptors, including collagen proteins,
VEGF, and matrix metalloproteases (MMPs) (Chong et al., 2012). Of particular interest
to this paper, Weaver et al. proved that under stressful conditions, SPARC, located in the
extracellular space, forms a membrane complex by binding β1-integrin protein, which
then activates integrin-linked kinase (ILK) (2008). Activation of ILK in this way leads to

16. 10
various signaling cascades that induce cell adhesion, migration, differentiation and
survival (Weaver et al., 2008).
Key Pathways Downstream of SPARC in Gliomas
There are two key survival proteins, AKT (also called protein kinase B) and HSP27, a
heat-shock protein, whose fates are ultimately regulated by pathways downstream of the
SPARC—β1 integrin—ILK interaction in gliomas. Importantly, depending on the
manipulation of the pathways upstream, AKT and HSP27 are also believed to impede cell
death by inhibition of pathways that induce autophagy and apoptosis (Concannon et al.,
2001; Degtyarev et al., 2008). Recently, Zhang et al. discovered a novel role of AKT in
the tumorigenesis of gliomas (2010). More specifically, total AKT (tAKT) directly
correlated with the WHO grade of the tumor indicating an association with the aggressive
nature of GBM (Zhang et al., 2010). They showed that down-regulation of the isoform
AKT2 inhibited invasion, growth, and survival of glioma cells in vivo (Zhang et al.,
2010). It has been shown that such results are a consequence of autophagy induced by a
reduction in AKT activity (Degtyarev et al., 2008). As shown in the diagram below,
taken from Alam et al. (2013), phosphorylation of AKT is regulated by ILK, which then
leads to the previously noted functions of phosphorylated, and consequently activated,
AKT (pAKT) (McDonald et al., 2008; Alam et al., 2013; Figure 1). It was shown that the
SHC—RAF—MEK—ERK pathway might contribute to AKT activation as well
(Thomas et al., 2010; Figure 1).
HSP27, a protein known to regulate the actin organization of the ECM and, thus,
migratory capabilities of cells, is colocalized with SPARC in vivo (Golembieski et al.,
2008). A previous study showed that upregulation of SPARC increases the expression of

17. 11
Figure 1. Proposed signaling pathways for SPARC +/- and PTEN +/- cell lines. (A)
Proposed SPARC-induced pathways that affect glioma cell survival and migration as
proposed in Alam et al. (2013). (B) Proposed mechanisms for SPARC and PTEN
regulation of HSP27 expression and phosphorylation as proposed in Alam et al. (2013).
Red arrows = positive signaling or activation; black arrows = suppressed signaling; ECM,
extracellular matrix. (Figure was taken from Alam et al. (2013).

18. 12
total HSP27 (tHSP27) as well as phosphorylated HSP27 (pHSP27) (Golembieski et al.,
2008). Experiments involving HSP27 siRNA proved that knockout of HSP27 prevents
the SPARC-induced morphological alterations as well as cell migration in vitro
(Golembieski et al., 2008). The theorized pathway by which HSP27 is activated consists
of the following signaling cascade: ILK—P38 MAPK (P38 mitogen-activated protein
kinase)—MAPKAPK2 (MAPK-activated protein kinase 2)—HSP27 (Alam et al., 2013).
As indicated, ILK allows for activation of P38 MAPK (P38), which directly activates
MAPKAPK2 (MK2), resulting in the direct phosphorylation of HSP27 at one of three
highlighted sites—Ser78, Ser15, and Ser82, each thought to play a unique role in the
noted migratory effects on glioma cells (Butt et al., 2001).
While AKT and HSP27 are primarily known to induce survival and invasion,
respectively, they also both play a role in each other’s activation (Wu et al., 2007;
Schultz et al., 2012). It was shown that inhibition of tAKT suppresses proliferation and
migration, while inhibition of pAKT suppresses the survival of tumor cells (Schultz et al.,
2012). Additionally, direct inhibition of AKT alone in vitro significantly reduced glioma
cell proliferation and migration (Thomas et al., 2010). Furthermore, inhibition of pAKT
and HSP27 together produces a synergistic effect in reducing cell survival in vitro, a
result that was also more effective than treatment with TMZ alone; therefore, targeting
these two proteins together could hypothetically function as a novel treatment method for
GBM (Schultz et al., 2012). Unfortunately, the siRNA used in these experiments cannot
be used in vivo, so other methods of suppressing these pathways must be considered.
Schultz et al. also showed that inhibition of HSP27 alone in vitro suppresses cell survival
in all glioma cells tested; however, it is most effective in cells with overexpressed

19. 13
SPARC because it eliminates the pathway by which HSP27 inhibits SPARC-induced
apoptosis (2012). Clearly, the regulation of these pathways that induce glioma cell
survival and invasion are complex and sensitive to the condition of the other.
PTEN in Gliomas
Another critical component of this system is PTEN (phosphatase and tensin
homolog), a novel protein that is commonly lost on chromosome 10 of GBM patients and
acts as a mediator of both the AKT and HSP27 signaling pathways (Figure 1; Thomas et
al., 2010; Alam et al., 2013). PTEN is believed to inhibit two different pathways that lead
to the activation of AKT and ERK, another protein known to enhance glioma cell
proliferation and migration (Thomas et al., 2010). PTEN inhibits PI3K (PI3 kinase) from
activating PIP3, which directly phosphorylates AKT (Kitamura et al., 2014). On the other
hand, PTEN also prevents activation of ERK by inhibiting the SHC—RAF—ERK
pathway (Thomas et al., 2010). Furthermore, it was shown that the most effective method
of preventing glioma cell migration and proliferation was to express PTEN, promoting an
additive inhibitory effect of both of these pathways (Thomas et al., 2010). This held true
in vitro, with results showing that PTEN expression suppressed cell proliferation and
migration in control cells; however, it was most efficient in cells with SPARC
overexpression (Thomas et al., 2010). Thus, the use of PTEN expression may prove
beneficial in preventing SPARC-induced proliferation and migration while maintaining
SPARC reduction of tumor growth.
PTEN also plays a role in the SPARC-enhanced (p)P38 MAPK—pMK2
(phosphorylated MAPK-activated protein kinase 2) signaling pathway that
phosphorylates HSP27 at three potential sites (Ser78, Ser15, and Ser82). PTEN was

20. 14
shown to suppress phosphorylation of Ser78 HSP27 in vitro by a mechanism that acts
downstream of pP38 MAPK (Alam et al., 2013). The fact that the levels of pHSP27
relative to tHSP27 differ depending on both SPARC and PTEN status indicates a
sensitive balance between these two proteins. It was shown that although PTEN does not
eliminate the phosphorylation of HSP27, it does prevent the SPARC-induced up-
regulation of its activation (Alam et al., 2013). It was also hypothesized that PTEN
inhibits SPARC-induced migration in vitro by means of down-regulating the pP38
MAPK—pMK2—pHSP27 signaling pathway (Alam et al., 2013). Together, these studies
suggest that expression of PTEN, inhibition of pAKT, and inhibition of pHSP27 could
act as therapeutic methods in reducing the aggressive invasiveness and proliferation
characteristic of glioblastoma tumors.
The Present Study
The present study sought to investigate the effects of AKT inhibitor IV (AKT IV)
alone and in combination with three different P38 inhibitors—SB 293063, SB 202190,
and SB 203580—each also tested individually, on SPARC-expressing and/or PTEN-
expressing glioma cells. The four established U87 MG clones used were C2a2_EV
(control; SPARC-/PTEN-), C2a2_PTEN (SPARC-/PTEN+), A2b2_EV (SPARC+/
PTEN-), and A2b2_PTEN (SPARC+/PTEN+). Figure 1B shows the proposed pathways
acting in each of the four cell lines, which are under investigation in this study. Each
panel shows the proposed effects of the presence or absence of SPARC and/or PTEN on
each of the downstream pathways, and indicates the subsequent effects on HSP27 levels
seen in Alam et al. (2013). The four cell lines were analyzed by Western blot analysis for
the following proteins: SPARC, pAKT, tAKT, pMK2, tMK2, Ser15 pHSP27, Ser78

21. 15
pHSP27, Ser82 pHSP27, tHSP27, and actin as a control. It was hypothesized that the
combination of AKT inhibitor IV with a P38 inhibitor would be needed to suppress AKT,
MK2, and HSP27 activation in SPARC+/PTEN- cells; whereas the combination
treatment would suppress only pMK2 and HSP27 in SPARC+/PTEN+ cells. To this end,
the present study holds novel implications on potential treatments for GBM patients, in
terms of a deeper understanding of the signaling pathways that impact the survival,
migration, and invasion of such malignant tumors relative to the status of genetic
mutations present in the glioma cells.

22. 16
Materials and Methods
Cell lines
Established cell lines derived from U87MG (grade IV primary GBM) cells were
utilized in the following experiments. Empty vector (EV), SPARC and PTEN plasmids
were transfected into cells as indicated: C2a2_EV (control; SPARC-/PTEN-),
C2a2_PTEN (SPARC-/PTEN+), A2b2_EV (SPARC+/PTEN-), and A2b2_PTEN
(SPARC+/PTEN+). The following three non-cancerous cell lines were also used: Ast
11.9 (mouse astrocytes; gift of Oliver Bogler), MLF4 (mouse lung fibroblasts; gift of
Helene Sage), and SVARBEC (rat brain endothelial cells; gift of Henry Ford Hospital).
Cell culture
Cell lines were maintained in 5.5 mL of Dulbecco’s Modified Eagle Medium
(DMEM) with 10% fetal bovine serum (FBS) and 10 µg/mL gentamicin (Gibco Life
Technologies). Cells expressing PTEN and SPARC were selected for with the antibiotics
blasticidin (9 µg/mL) and puromycin (1 µg/mL; Sigma-Aldrich), respectively. 80-90%
confluent T25 flasks of cultured cells were washed with 2 mL of 1X Dulbecco’s
Phosphate Buffered Saline (DPBS) for 30 seconds. 1.2 mL of 0.05% 1X Trypsin-EDTA
was added to the flasks and cells were incubated at 37°C for 5 minutes. Cells were
visualized under a Nikon Eclipse TS100-F microscope to ensure detachment from the
flask surface. Cells were rinsed with growth media and extracted from the flask. Cell
suspensions were diluted as necessary in 5.5 mL of DMEM in T25 flasks and incubated
at 37°C for 7 days or until confluent. Cell culture reagents were obtained from Life
Technologies (Carlsbad, CA) unless otherwise specified.

23. 17
Treatment of cells
All media solutions were made in 12 mL aliquots. T25 flasks of C2a2_EV,
C2a2_PTEN, A2b2_EV, A2b2_PTEN, Ast 11.9, MLF, and SVARBEC cell lines were
washed with DPBS, trypsinized, and counted using a hemocytometer. On the same day,
equal numbers of cells were plated in fresh T25 flasks and incubated in DMEM. The
following day, DMEM was replaced with treated media in the following conditions: 1.
Control: DMSO only (Life Technologies), 2. AKT inhibitor IV (CalBiochem), 3. SB
239063 P38 inhibitor (Santa Cruz Biotechnology), 4. SB 239063 + AKT inhibitor IV, 5.
SB 202190 P38 inhibitor (Santa Cruz Biotechnology), 6. SB 202190 + AKT inhibitor IV,
7. SB 203580 P38 inhibitor (Santa Cruz Biotechnology), and 8. SB 203580 + AKT
inhibitor IV. All inhibitors were diluted appropriately in DMSO. 2.5 µM AKT inhibitor
IV and 0.25 µM of each P38 inhibitor were added appropriately to treat the cells. The
control samples were treated with a concentration of DMSO equal to that of the
combination (P38 inhibitor + AKT inhibitor IV) treatments. Cells were incubated for 48
hours at 37°C. All treated media was extracted and fresh DMEM + 10% FBS was added.
All treated cell lines were then lysed for protein isolation (see below).
Lysate preparation
T25 flasks of cells were aspirated of media and washed twice with 5 mL DPBS on
ice for 5 minutes each. Lysis buffer was prepared (10 mL 1M Tris pH 8.0, 1.75g NaCl,
0.040g Na Azide, 2 mL Triton-X-100, H2O to 200 mL), then protease and phosphatase
inhibitors were added (60 µL 0.1M PMSF, 100 µL 0.5M sodium ortho-vanadate, 100 µL
1M NaF). Each flask was incubated on ice in 300 µL of the lysis buffer solution for 30
minutes. Cell lysates were scraped from the flask bottom with a cell scraper and pipetted

24. 18
into sterile Eppendorf tubes. The lysate was worked with a 23 G needle attached to a 1
mL syringe 10 times to disassociate cellular components. Samples were spun in a
centrifuge at 13,300 RPM for 10 minutes at 4°C. Supernatant was removed and stored at
-80°C for later use. Cells and lysis buffer solution were kept on ice whenever possible.
To quantify the amount of protein, OD562 was calculated for each sample from a standard
curve obtained with a bicinchoninic acid (BCA) protein assay (Pierce Biotechnology;
Rockford, IL).
Western blot analysis
Polyacrylamide tris-glycine gels were made according to the Life Technologies
protocol. 50 mL of 12% acrylamide resolving gel solution (6.2 mL deionized H2O
(dH2O), 14 mL 1.5M Tris pH 8.8, 20 mL 30% 29:1 acrylamide/bis solution, 8 mL 50%
sucrose, 500 µL 10% sodium dodecyl sulfate, 500 µL 10% ammonium persulfate (APS),
15 µL TEMED) was mixed and 8.5 mL of the solution was cast into each of four 1.5mm
cassettes to a depth of 6cm. 300 µL of dH2O was added on top of the acrylamide solution
in each cassette and gels were allowed to polymerize at RT for 1 hour. dH2O was
decanted from cassettes. 15 mL of 5% acrylamide stacking gel solution (10.2 mL dH2O,
1.9 mL 1M Tris pH 6.8, 2.5 mL 30% 29:1 acrylamide/bis solution, 150 µL 10% SDS,
150 µL 10% APS, 15 µL TEMED) was mixed and 1.5 mL was cast on top of each
resolving gel to the top of the cassette. A 10-well 1.5mm comb was added to each
cassette. Gels were allowed to polymerize at RT for 1 hour.
Dilutions of equal amounts (µg) of protein (20-36 µL per gel) from each lysate
were prepared as determined from BCA protein assay and volumes were equalized in
dH2O. 35 µL of 4X loading buffer (2 mL 1.25M Tris pH 6.8, 4 mL glycerol, 4 mL 20%

25. 19
SDS, 0.5 mg bromophenol blue) solution (100 µL:12 µL ratio of 4X loading buffer: 2-
mercaptoethanol) was added to each sample before boiling samples in a heat block for 5
minutes. 50 µL/well of each mixture was loaded into gels. Two ladders were loaded into
each gel: Precision Standard (5 µL for pAKT detection; 10 µL for all other gels) (Bio-
Rad) and MagicMark [1:3:5 dilution of MagicMark (Life Technologies): loading buffer
solution: dH2O], prepared so that each gel obtains 1-2 µL of MagicMark.
Gels were run in XCell SureLock® Mini-Cell Blot Module (Novex®) (Life
Technologies) with a PowerPacTM
Adaptor, 4mm power supply attachment (Bio-Rad).
FisherBiotechTM
FB300 Power Supply (Fisher Scientific; Hanover Park, IL) was used to
run gels at 160 V for 90 minutes. After electrophoresis, the layer of stacking gel and
bottom 1cm of resolving gel was cut off and gels were incubated in 40 mL transfer buffer
(5.9g Tris, 2.9g glycine, 0.37g SDS, 200 mL methanol, 800 mL dH2O) for 20 minutes.
Polyvinylidine fluoride (PVDF) transfer membranes were cut in 8.5 cm x 6.0 cm
rectangles and put in 40 mL of methanol for 3 minutes before incubation in transfer
buffer for 15 minutes. Blotting pads were also cut in 8.5 cm x 6.0 cm rectangles (2 per
gel) and incubated in transfer buffer for 15 minutes. Gels were transferred to PVDF
membranes between 2 blotting pads at 25 V for 1 hour using a Bio-Rad Trans-Blot SD
Semi-Dry Transfer Cell. Membranes were dried on chromatography paper at RT for 1
hour.
Membranes were wet in 40 mL of 100% methanol at RT for 3 minutes.
Membranes were washed in TBST (12.1g UltraPureTM
Tris, 18g NaCl in 2L dH2O, pH
adjusted to 7.6-7.7 with HCl, then 2 mL Tween 20 added) three times for 10 minutes on a
rocker at RT (TBST wash protocol).

26. 20
Membranes were blocked in 40 mL of 5% blocking buffer (5g Blotting-grade
Blocker in 100 mL TBST) on rocker at RT. Ser78 pHSP27Ser78 detection membrane
was blocked overnight; all others were blocked for 1 hour. After blocking, blocking
buffer was replaced with the appropriate primary antibody solutions. The following
primary antibodies were used: pAKT, tAKT, Ser15 pHSP27 (Enzo Life Sciences), Ser78
pHSP27 (Enzo Life Sciences), Ser82 pHSP27, tHSP27 (Santa Cruz Biotechnology; Santa
Cruz, CA), Thr222 pMK2 (1:500), Thr334 pMK2 (1:500; Santa Cruz Biotechnology),
tMK2, pP38, tP38, SPARC (Haematologic Technologies, Inc.), PTEN (Santa Cruz
Biotechnology) and actin (Santa Cruz Biotechnology). Antibody solutions were prepared
in 1:1 000 dilutions (25 µL in 25 mL of 5% blocking buffer) unless otherwise specified.
Membranes were covered and incubated overnight in primary antibody solutions on a
rocker at 4°C. Antibodies were obtained from Cell Signaling Technologies (Danvers,
MA) unless otherwise specified.
The following day, membranes underwent TBST wash protocol. TBST was
replaced with the appropriate horseradish-peroxidase (HRP) conjugated secondary
antibody solutions. Rabbit, mouse, and goat antibodies were used as indicated on the
primary antibody protocols. Secondary antibodies were prepared in 1:2 000 dilutions
(12.5 µL in 25 mL of 5% blocking buffer) and rocked at RT for 1 hour. Membranes
underwent TBST wash protocol. Membranes were incubated in 4 mL of ClarityTM
Western ECL Substrate (Bio-Rad) at RT for 3 minutes for detection. All secondary
antibodies were obtained from Santa Cruz Biotechnology. Detection of pHSP27Ser78
membrane was incubated in the appropriate primary antibody solution overnight on a
rocker at 4°C and underwent the preceding procedures the following day.

27. 21
For detection of the blots, membranes were placed between plastic sheets for
chemiluminescense visualization on the ChemiDocTM
MP Imaging System (Bio-Rad).
Blots were captured using Image LabTM
Software Version 4.1 (Bio-Rad). Membranes
then underwent TBST wash protocol and were allowed to dry on chromatography paper
at RT for ≥ 90 minutes.
To strip off the antibody and probe with a different antibody, membranes were
wet in 40 mL of 100% methanol (Fisher Scientific) at RT for 3 minutes. Membranes
underwent TBST wash protocol. Membranes were stripped in 30 mL of RestoreTM
PLUS
Western Blot Stripping Buffer (Thermo Scientific) while rocking at RT for 1 hour.
Membranes underwent repetition of previous protocols beginning with blocking for up to
two other sets of antibodies before being allowed to dry completely on chromatography
paper at RT. All membranes underwent detection for actin, which was used as the loading
control. Membranes were stored on chromatography paper at RT.
Image analysis
Blots were detected using a ChemiDoc apparatus and captured with the ImageLab
software (Bio-Rad).
Statistical analysis
Data from each blot was normalized to its respective actin blot using ImageLab
software (Bio-Rad). Data were analyzed using Microsoft Excel to determine fold changes
in the amount of protein. Due to the lack of duplicate data, a ≥ 2-fold rise or decline will
be considered an increase or decrease, respectively, throughout this paper.