Analysing the Effects of Knocking Out the GOT1 in Pancreatic Tumour Cells

Amelie Carlström [email protected]

under the direction of Prof. Anna Karlsson Department of Clinical Microbiology Karolinska Institutet

Research Academy for Young Scientists July 8, 2015 Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a form of incurable cancer and it has been displayed that it is dependent on a metabolic pathway differing from healthy cells. The aim of this study is therefore to examine how certain are affected when an in this non-canonical pathway, glutamic oxaloacetic 1 (GOT1), is knocked out. To do this, RNA was extracted from cancer cells with and without GOT1 and real-time quantitative polymerase chain reaction (qPCR) was performed. Four genes, lactate dehydrogenase B (LDHB), glutathione reductase (GSR), growth arrest and DNA- damage-inducible protein (GADD34) and DNA-damage-inducible transcript 3 (CHOP), showed a difference after knockout of GOT1 and this is reason to further investigate the effect. The results are on genetic levels, however, when definitely deciding what effect the knockout has, the corresponding proteins must also be examined due to regulation post-transcription. Mapping of the metabolic pathways in may lead to potential treatment due to the possibility of inhibiting parts of the pathway it depends on. Contents

Abbreviations 1

1 Introduction 2

1.1 Metabolic Pathways in KRAS-transformed Tumour Cells and Oxidative Stress ...... 2 1.2 Unfolded Protein Response ...... 3 1.3 Gene Analysis Using RNA Extraction and Real-time qPCR ...... 3 1.4 Investigating the Effects of Knocking Out GOT1 ...... 4

2 Method 5

2.1 Extraction of RNA ...... 5 2.2 Synthesis of cDNA from RNA ...... 6 2.3 Preparation of Primers ...... 7 2.4 Performing qPCR on Synthesised cDNA ...... 7

3 Results 8

4 Discussion 11

5 Acknowledgements 13

6 Appendix 17

A Primers used for qPCR 17

B Measurements of RNA Concentration with NanoDrop Spectrophotome- ter, ND-1000 18 Abbreviations

BIP Binding Immunoglobulin Protein cDNA Complementary DNA CHOP DNA-damage-inducible Transcript 3 GADD34 Growth Arrest and DNA-damage-inducible Protein GLUD1 Glutamate Dehydrogenase 1 GOT1 Glutamic-oxaloacetic Transaminase 1 GOT2 Glutamic-oxaloacetic Transaminase 2 GSR Glutathione Reductase GSS Glutathione Synthetase KRAS Kirsten Rat Sarcoma Viral Oncogene Homolog LDHA Lactate Dehydrogenase A LDHB Lactate Dehydrogenase B PDAC Pancreatic Ductal Adenocarcinoma PKM2 Pyruvate Kinase, Muscle 2 qPCR Real-Time Quantitative Polymerase Chain Reaction ROS Reactive Oxygen Species S18 Ribosomal Protein S18 SOD1 Superoxide Dismutase 1 TCA-cycle UPR Unfolded Protein Response

1 1 Introduction

Pancreatic ductal adenocarcinoma (PDAC) is a cancer form of which 95-97% of the patients die within five years. It is the fourth most common cancer form leading to death in the United States and the reason is the fast dissemination of it to other parts of the body. Due to its resistance to today’s conventional forms of cancer treatments it is diagnosed as incurable [1]. It has been shown that the PDAC cells exploit a different metabolic pathway compared to normal cells [2].

1.1 Metabolic Pathways in KRAS-transformed Tumour Cells and

Oxidative Stress

An oncogene is a gene that has the potential of causing cancer when mutated [3]. Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) is a gene of aforementioned and in PDAC it has been shown that mutated KRAS reprograms the cell metabolism and that the reprogrammed metabolism is dependent on the amino acid , see Figure 1.

Figure 1: Metabolic pathways in healthy cells (A) and KRAS-transformed tumor cells (B) [4].

In the mitochondria of healthy cells, glutamate derived from glutamine is converted to α-ketoglutarate by the enzyme glutamate dehydrogenase 1 (GLUD1). Subsequently, the α-ketoglutarate plays an active role in the citric acid cycle (TCA-cycle). In PDAC cells

2 the KRAS mutation reprograms this part of the metabolism by repressing GLUD1 and activating glutamic-oxaloacetic transaminase 1 (GOT1) instead. When GLUD1 is re- pressed the conversion of glutamate to aspartate by glutamic oxaloacetic transaminase 2 (GOT2) is promoted. GOT1 converts aspartate to oxaloacetate in the and this pathway generates an increase of NADPH from reduction of NADP+. NADPH is an antioxidant that helps maintain the cellular homeostasis, protecting the cells from reactive oxygen species (ROS). Redox balance is necessary for the cell to be able to proliferate and function properly [4]. When the glutamine pathway of the metabolism does not work, the PDAC cells are more vulnerable to ROS. Inhibition of any enzyme in the metabolic pathway PDAC cells depend on, such as GOT1, results in decreased cell proliferation and increased levels of ROS [2]. The increase leads to oxidative stress generated by an imbalance between ROS and antioxidants. Oxidative stress can damage the nucleic acids, proteins and amino acids amongst other things in the cell and even cause apoptosis [5].

1.2 Unfolded Protein Response

Unfolded protein response (UPR) is a form of cellular stress occurring in the endoplasmic reticulum (ER). The ER has the task of controlling the quality, the modification and the folding of translated proteins. The stress arises when the proteins are not folded correctly and the response diminishes the protein translation as a way of regaining control [6]. It is possible that UPR is generated when changes are made in the genome.

1.3 Gene Analysis Using RNA Extraction and Real-time qPCR

The expression profile of a gene can be measured by quantifying the RNA of that partic- ular gene. When extracting RNA, the quality and concentration of it can be determined with a NanoDrop spectrophotometer. The absorption maximum for RNA is at 260 nm. To decide the quality of the RNA, the absorption is measured at 260 and 280 nm. For

3 good quality the ratio 260/280 should be around 2 since the absorption maximum should not be spread over a larger area than right at 260 nm. A 260/230 ratio is also measured because a peak at 230 indicates a contamination in the RNA such as proteins, chaotropic salts and phenol. This ratio should also be around 2, however some protocols do not mention this ratio and for some subsequent procedures RNA with a 260/230 ratio below 2 can be used without problems [7]. In order to quantify the RNA it has to be converted to complementary DNA (cDNA) because it is double stranded and consequently more stabile than RNA [8]. With real-time quantitative PCR (qPCR) it is possible to detect and measure each cycle of amplification of DNA during the process. The number of copies doubles every cycle and the Ct value measured shows in which cycle of the process the amplification is first detected [9]. In this study Sybr Green is used to detect the cycles. Sybr Green is a fluorescent molecule which binds to the double stranded DNA during the amplification. In the course of the PCR process and when exposed to light of the right wavelength, the Sybr Green will be detected in each cycle by fluorescing. [10].

1.4 Investigating the Effects of Knocking Out GOT1

To take further steps towards potential treatment for the resistant PDAC tumors, the aim of this study is to investigate whether knocking out the gene coding for GOT1 affects the expression of other genes in the cell. The expression can be upregulated or downregulated. The goal is to find a potential way of inhibiting parts of the metabolic pathways in cancer cells once the mechanisms are completely understood and by that also inhibit the malignancy and growth of tumor cells. In this study of some genes will be analysed with the help of qPCR. Ribosomal Protein S18 (S18) will be used as a reference gene since this gene is a part of the ribosome and is consequently expressed in every cell [11]. An osteosarcoma 143B cell line with known KRAS mutation is used in this study. Cells having GOT1 knocked out will be compared to wild type cells and the cell line is from

4 human bones [12]. Three genes protecting the cell from oxidative stress will be analysed in this study; glutathione reductase (GSR), glutathione synthetase (GSS) and superoxide dismutase 1 (SOD1) [13, 14, 15]. Moreover, three genes involved in ER stress will be analysed. These are growth arrest and DNA damage-inducible protein (GADD34), bind- ing immunoglobulin protein (BIP) and DNA-damage-inducible transcript 3 or DDIT3, referred to as CHOP [16, 17, 18]. In addition, lactate dehydrogenase A (LDHA), lactate dehydrogenase B (LDHB) and pyruvate kinase muscle 2 (PKM2), which all play different roles in the cell metabolism, will be analysed [19, 20, 21].

2 Method

The expression in the GOT1 knockout cells was analysed using RNA extraction, cDNA synthesis and qPCR. All solutions were kept on ice during preparation.

2.1 Extraction of RNA

The RNA extraction was performed with the RNeasy Mini Kit (Qiagen) following the instructions for Purification of Total RNA from Animal Cells using Spin Technology [22]. Three different cell cultures of osteosarcoma cell lines grown in monolayer were used, each with one GOT1 cell line and one cell line without GOT1. They were grown in a cell culture of Dulbecco’s Modified Eagle Medium with 10% Fetal Bovine Serum, 1% penicillin and streptomycin at 37 °C in 5% CO2. A total of 18 samples were extracted; 9 wild types and 9 GOT1 knockout. The grown osteosarcoma cell lines had been lysed and stored in buffer RLT before the experiment. 350 µL of 70% ethanol was added to the cells and mixed gently. 700 µL of the solution was transferred to an RNeasy spin column placed in a 2 ml collection tube. The columns were centrifuged for 15 s at 10 000 rpm. 700 µL of Buffer RW1 was added to the spin column and the solution was centrifuged for 15 s at 10 000 rpm. 500 µL of Buffer RPE was added and centrifuged for 15 s at 10 000 rpm and then another 500 µL was added

5 and centrifuged for 2 min at 10 000 rpm. The flow-through was discarded after each centrifugation. The spin column was placed in a new 2 ml collection tube and centrifuged at full speed for 1 min. The spin column was then placed in a 1.5 ml collection tube and 40 µL of RNase-free water was added directly onto the filter of the column. The solution was centrifuged for 1 min at 10 000 rpm. The quality and concentration of the RNA was measured using NanoDrop Spectrophotometer, ND-1000.

2.2 Synthesis of cDNA from RNA

The High Capacity cDNA Reverse Transcription Kit (Applied Biosystems) was used to perform synthesis of cDNA from the extracted RNA [23]. One master mix without RNase inhibitor was prepared and divided into 20 tubes, see Table 1. The different RNA samples were added separately and 2 tubes were used as controls with distilled water. 2.5 µL of RNA sample was added in each well, calculated on 1 µg RNA and a general concentration of 400 ng/µL in the extracted RNA. The 2720 Thermal Cycler (Applied Biosystems) was used for reverse transcription. The conditions were 25 °C for 10 min, 37 °C for 120 min and 85 °C for 5 min.

Table 1: Master mix components for cDNA synthesis

Components 1 reaction [µL] Master mix x20 [µL] 10X RT Buffer 2 40 25X dNTP mix 0.8 16 10X RT Random Primers 2 40 MultiScribe™ Reverse Transcriptase 1 20

Nuclease-free H2O 11.7 234 RNA 2.5 - Total 20 350

6 2.3 Preparation of Primers

Primers ordered from Sigma-Aldrich were diluted to 10 µM and placed in the freezer until further use. The sequences used for each primer can be found in Appendix A.

2.4 Performing qPCR on Synthesised cDNA

Different master mixes with the different primers were prepared using the KAPA SYBR FAST qPCR Kit Universal (KAPA BIOSYSTEMS) [24]. The master mixes were prepared for 20 reactions each for each gene analysed, see Table 2. The master mix and 1 µL of the cDNA templates were pipetted in 96-well plates with one negative control for each gene. The plate was then centrifuged at 1000 rpm for 1 minute. Standard deviation and T-tests were calculated on the results of the qPCR..

Table 2: Master mix components for qPCR

Components 1 reaction [µL] 20 reactions [µL]

KAPA SYBR Fast qPCR Master Mix 5 100 Universal Forward Primer 0.2 4

Reverse Primer 0.2 4

ROX LOW 0.2 4

Distilled Water 3.4 68

Template (cDNA) 1 -

Total 10 180

7 3 Results

The concentration and quality measurement of the RNA can be seen in Appendix B. The results of the qPCR are calculated relative to the reference gene S18 and relative to wild type 1. The amount of amplifications were then calculated based on the Ct value and the figures show the mean value of the wild types and GOT1 knockout samples. Figure 2 shows the result of GOT1 expression. The expression of genes involved in metabolism can be seen in Figure 3, genes responding to oxidative stress in Figure 4 and genes responding to ER stress in Figure 5. The results from the NanoDrop indicated some contamination according to the 260/230 ratio in some samples and the results after omission of the contaminated samples can be seen in Figure 6, 7 and 8. Three samples in each group (wild type and GOT1 knockout) that had good quality RNA were selected for analysis and the gene expression was quantified relative to S18 and compared to the wild type. GOT1, LDHB, GADD34 and CHOP showed difference when all samples were included and GSR showed a difference after omission. LDHB also showed a tendency of a difference after omission.

8 Figure 2: GOT1 expression in wild type and GOT1 knockout cells.

(a) LDHA (b) LDHB (c) PKM2

Figure 3: Expression of metabolism genes in wild type and GOT1 knockout cells.

(a) GSR (b) GSS (c) SOD1

Figure 4: Expression of genes responding to oxidative stress in wild type and GOT1 knockout cells.

9 (a) BIP (b) CHOP (c) GADD34

Figure 5: Expression of genes responding to ER stress in wild type and GOT1 knockout cells.

(a) LDHA (b) LDHB (c) PKM2

Figure 6: Expression of genes involved in metabolism in wild type and GOT1 knockout cells with non-contaminated RNA.

(a) GSR (b) GSS (c) SOD1

Figure 7: Expression of genes responding to oxidative stress in wild type and GOT1 knockout cells with non-contaminated RNA.

10 (a) BIP (b) CHOP (c) GADD34

Figure 8: Expression of genes responding to ER stress in wild type and GOT1 knockout cells with non-contaminated RNA.

4 Discussion

The knockout of GOT1 was successful considering there is no GOT1 expression in the knockout cells. This is essential to be able to draw any conclusion of the change in expression of the other genes. The amplification of the reference gene S18 looked good and was similar in the different cells, indicating that it is a good reference gene for relative quantification. The negative controls had no amplification or a little amplification starting late which could be due to unspecific binding of the Sybr Green. When choosing which RNA samples should be omitted the three samples in wild type and the three samples in GOT1 knockout with highest 260/230 ratio were selected to remain, resulting in a minimum ratio of 1.87, which is acceptable. GOT1 plays an important role in PDAC cells, therefore the change in expression of the other genes in GOT1 knockout cells is of great interest. LDHB, an enzyme that catalyses the conversion of pyruvate to lactate [20], shows a reduced expression in GOT1 knockout cells in comparison to wild type cells, both in all the samples and in the ones where the contaminated RNA samples are omitted. GSR shows an increased expression in GOT1 knockout cells compared to wild type cells in the samples with only non-contaminated RNA but not when all samples are included. This could indicate that GOT1 knockout potentially affect genes responding to oxidative stress. CHOP and GADD34 show an increased expression in GOT1 knockout

11 cells compared to wild type cells when all samples are included, which indicates a possible increase of UPR but not after omission considering the overlapping standard deviation. This could be affected by the contaminated RNA and has to be further investigated. The rest of the genes (LDHA, PKM2, GSS, SOD1 and BIP) do not show a difference in expression comparing wild type and GOT1 knockout cells. They also have an overlapping standard deviation between the wild type and GOT1 knockout cells which is why it cannot be assuredly assumed that there is a difference. There is a large standard deviation in the GSS and GSR wild types when all samples are included but since it is significantly smaller after omission it is possible that the contamination have affected the PCR results. However, to be sure of this more samples with non-contaminated RNA would be needed. When all samples are included the result could be affected by the contamination but when the contaminated samples are omitted there is a small population of samples. Both ways has disadvantages in the reliability of the result. Another reason to why there is a variation between the samples is that they come from different cells and it is possible that the gene in question is expressed slightly different in different cells - even with the same genome. Due to a small sample size used for analysis and small differences observed which could be due to small variations between the cells, one cannot conclude that there is a significant difference in any of the genes except in GOT1 where the difference is very high. However, there is a tendency of a change in some of the genes in the GOT1 knockout cells compared to the wild type cells, which can be further analysed with a larger sample size. Further experiments should also be performed with duplicates or triplicates of each wild type and each GOT1 knockout to get a larger certainty of the result. The results in this study show the gene expression on a RNA level, however, one must not forget that the gene expression could also be regulated on translational an post-translational levels and a difference in RNA-levels do not have to correspond to the protein expression or activity, although it is the case most of the times [25]. Therefore, to be completely sure what effect the knockout of GOT1 has, the genes has to be studied

12 both as RNA and as the proteins they code for. Before reaching the goal of finding a way to treat PDAC there are many studies of the metabolism to be performed but eventually it might lead to saving lives and decrease the percentage of humans passing away because of PDAC.

5 Acknowledgements

First of all I would like to thank my mentor Shuba Krishnan for guidance throughout the project and for giving me knowledge of the field I have been working with. Secondly, thanks to Prof. Anna Karlsson at Karolinska Institutet for inviting me to her laboratory and to Dr. Sophie Curbo who has been very helpful during the project. I would like to direct a special thanks to my project partners, Erica Andersdotter and Karolina Hanthe, for a great cooperation and for making the experience even more enjoyable. Also, thanks to the director Philip Frick and the counsellors Sofia Svensson, Anna Broms and Serhat Aktay at RAYS for all help and support. Lastly, thanks to Research Academy of Young Scientists for giving me this opportunity and to Europaskolan for a lovely residence.

13 References

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16 6 Appendix

A Primers used for qPCR

Full gene sequenses can be found in [26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36] GOT1 Forward: AGTCTTTGCCGAGGTTCCG GOT1 Reverse: GTGCGATATGCTCCCACTC

LDHA Forward: ATGGAGATTCCAGTGTGCCT LDHA Reverse: TGGGTGCAGAGTCTTCAGAG

LDHB Forward: GAGGCAACAGTTCCAAACAA LDHB Reverse: AGCCAGAGACTTTCCCAGAA

PKM2 Forward: GGCATCTGATGTCCATGAAG PKM2 Reverse: TTTCATCAAACCTCCGAACC

CHOP1 Forward: TCCTGGAAATGAAGAGGAAGA CHOP1 Reverse: CAGGGAGCTCTGACTGGAAT

BIP1 Forward: CTCAACATGGATCTGTTCCG BIP1 Reverse: CCAGTTGCTGAATCTTTGGA

GADD34 Forward: TCCGAGTGGCCATCTATGTA GADD34 Reverse: AGGGTCCGGATCATGAGTAG

GSS Forward: AAGCCTATGCTGTGCAGATG GSS Reverse: TTTGATGGTGCTGGAAAGAG

17 GSR Forward: ACTTGCGTGAATGTTGGATG GSR Reverse: ACCCTCACAACTTGGAAAGC

SOD1 Forward: TGAAGAGAGGCATGTTGGAG SOD1 Reverse: ATGATGCAATGGTCTCCTGA

S18 Forward: TCACTGAGGATGAGGTGGAA S18 Reverse: GCTTGTTGTCCAGACCATTG

B Measurements of RNA Concentration with NanoDrop

Spectrophotometer, ND-1000

Table 3: RNA concentration and quality

Sample Concentration/[ng/µL] A260 A280 260/280 260/230 wt1 461,76 11,544 5,583 2,07 1,29 wt2 454,44 11,361 5,592 2,03 2,08 wt3 470,89 11,772 5,708 2,06 1,98 wt4 678,16 16,954 7,993 2,12 1,44 wt5 775,70 19,392 9,156 2,12 1,31 wt6 549,37 13,734 6,381 2,15 1,69 wt7 515,20 12,880 6,044 2,13 1,87 wt8 499,00 12,475 5,878 2,12 1,64 wt9 516,17 12,904 6,041 2,14 0,99 blank 0,26 0,007 0,019 0,34 0,57 431,22 10,781 5,190 2,08 1,88 393,29 9,832 4,686 2,10 2,09 got3 395,56 9,889 4,740 2,09 1,21 got4 426,20 10,655 5,179 2,06 1,86 got5 580,13 14,503 6,512 2,23 1,93 got6 512,38 12,809 5,827 2,20 0,98 got7 456,61 11,415 5,526 2,07 1,93 got8 507,38 12,685 5,843 2,17 1,41 got9 450,71 11,268 5,392 2,09 1,46

18