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

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.

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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

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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

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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

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.

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

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

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,

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

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

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

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

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

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

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).

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

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

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

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.

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.

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

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%

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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).

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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.

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.