The Relationship Between MITF and Cell Cycle in Melanoma

The Relationship Between MITF and Cell Cycle In Melanoma

Lára Stefansson

Ritgerð til meistaragráðu Háskóli Íslands Læknadeild Námsbraut í Líf- og læknavísindi Heilbrigðisvísindasvið

Samspil MITF og frumhringsins í sortuæxli

Lára Stefansson

Ritgerð til meistaragráðu í Líf- og læknavísindum

Umsjónarkennarar: Eiríkur Steingrímsson

Meistaranámsnefnd: Eiríkur Steingrímsson, Margrét Helga Ögmundsdóttir, Valerie Fock

Læknadeild

Námsbraut í Líf- og læknavísindum

Heilbrigðisvísindasvið Háskóla Íslands

Maí 2019

The Relationship Between MITF and Cell Cycle in Melanoma

Lára Stefansson

Thesis for the degree of Master of Science

Supervisors: Eiríkur Steingrímsson

Masters committee: Eiríkur Steingrímsson, Margrét Helga Ögmundsdóttir, Valerie Fock

Faculty of Medicine

Department of Biomedical Sciences

School of Health Sciences

May 2019

Ritgerð þessi er til meistaragráðu í Líf- og læknavísindum og er óheimilt að afrita ritgerðina á nokkurn hátt nema með leyfi rétthafa.

Prentun: Háskólaprent

Reykjavík, Ísland, 2019

Ágrip

Umritunarþátturinn MITF er lykilprótein í þroskun og starfsemi litfruma. Sýnt hefur verið að MITF stjórnar tjáningu gena sem stjórna frumuhringnum svo sem p21 og CDK2. Í þessari ritgerð voru tengsl MITF og frumuhringsins í sortuæxlisfrumum rannsökuð auk þess sem áhrif MITF á tjáningu tiltekinna gena voru skoðuð. Sortuæxlisfrumur voru samstilltar, sýni tekin á reglulegum fresti og staða frumanna og tjáning MITF og annarra gena í frumuhringnum greind með frumuflæðisjá, Western blot greiningu og qPCR. Niðurstöðurnar sýndu að styrkur MITF próteinsins jókst þegar frumurnar voru flestar í G2/M fasa, en mRNA gildin voru óbreytt sem bendir til aukinnar próteinþýðingar MITF eða aukins stöðugleika þess á þessu stigi frumuhringsins. Nýlegar rannsóknir á rannsóknastofunni benda til að MITF stjórni tjáningu ýmissa áhugaverðra gena sem tengjast frumuhringnum. Tjáning nokkurra þessara gena var skoðuð í 501mel og A375P sortuæxlisfrumum eftir að MITF hafði verið yfirtjáð eða slegið niður í . Það að slá niður MITF hafði marktæk áhrif á tjáningu CABLES1 gensins auk þess sem tjáning CDK2 minnkaði. Yfirtjáning MITF hafði áhrif á tjáningu þeirra gena sem tekin voru fyrir, en reyndist hún ekki vera tölfræðilega marktæk. Áhrif þess að CDK2 og LZTS1 á tjáningu bæði MITF mRNA og próteins voru skoðuð með því að slá niður genin tvö með siRNA. . Í ljós kom að þegar CDK2 var slegið niður varð marktæk lækkun á bæði MITF mRNA og próteini. Þegar LZTS1 var slegið niður minnkaði hins vegar tjáningin á MITF próteininu en ekki mRNA sameindinni. Þetta bendir til þess að CDK2 hafi áhrif á tjáningu MITF í afturkasti (feedback loop) en að LZTS1 hafi áhrif á þýðingu eða stöðugleika MITF próteinsins.

Abstract

Microphthalmia-associated transcription factor (MITF) is the master regulator of melanocytes and is known to directly bind to the promoter regions of the genes encoding cell cycle regulators, such as p21 and CDK2. This thesis focuses on the relationship between MITF and the cell cycle in melanoma and tests the role of novel MITF target genes. A melanoma cell line, 501mel, was synchronized, samples were taken and analyzed via flow cytometry, Western blot and qPCR. The results showed that MITF protein levels increased when the cells were mainly in G2/M phase whereas mRNA levels were unaffected, suggesting a change in protein synthesis or stability during this stage. The relationship between MITF and several novel targets was determined using MITF knockdown and overexpression in 501mel or A375P cell lines. Knockdown of MITF showed a significant decrease on CABLES1 expression, with a trend of decreasing CDK2 expression. Overexpression of MITF did not significantly change expression of any of the cell cycle regulating genes checked, although there was a general increase of each gene checked. To determine if the cell cycle regulators CDK2 and LZTS1 affected MITF expression, these genes were knocked down with siRNA before collecting samples for Western blot and qPCR. Knockdown of CDK2 caused a significant decrease in MITF mRNA and protein levels, whereas knockdown of LZTS1 only caused a significant decrease in MITF protein levels. This suggests that CDK2 might regulate MITF transcription in a feedback loop and LZTS1 plays a role either in translation or stability of MITF.

Acknowledgements

First and foremost, I would like to thank Eiríkur Steingrímsson for giving me the opportunity to work in his lab. It has been a great experience and I have learned a lot in my time. I could not have asked for a better supervisor. I would also like to thank Margrét Helga Ögmundsdóttir for always being positive and encouraging. Through the ups and downs, she is always supportive and it really helped when things were looking less than positive. I would like to thank the past and present members of the Steingrímsson lab, as they have all helped me in navigating my project. Thanks to Valerie Fock, Remina Dilixiati, Berglind Einarsdóttir, Sara Sigurbjörnsdóttir, Josué Ballesteros, Hilmar Gunlaugsson, Romain Lasseur, Melanie Allram, Anna Köck and Philipp Cerny. It is quite a list of people, but every single one of them helped me along the way and I would have been lost without them. I would also like to thank Marta Sól Alexdóttir and Kristrún Ýr Holm, who have supported me greatly throughout my project. The last people I would like to acknowledge are the ones that are completely responsible for my scientific career. Not many of my peers can say that their parents understand what they do; I am truly priviledged to have parents who not only understand, but can give constructive criticism. Thanks to my mom and dad for being my first lab supervisors and supporting me throughout my project. I hope you two like the thesis.

Table of Contents

Ágrip...... 5 Abstract...... 6 Acknowledgements...... 7 Table of Contents...... 8 Figure Legend...... 10 Table Legend...... 11 List of Abbreviations...... 12 1 Introduction...... 13 1.1 Melanocytes...... 13 1.2 Melanoma...... 14 1.3 MITF...... 15 1.4 Cell Cycle...... 18 1.5 MITF and Cell Cycle Regulation...... 21 2 Aims...... 23 3 Materials and Methods...... 24 3.1 Cell Culture...... 24 3.1.1 General Maintenance...... 24 3.1.2 DOX Induction of PiggyBac Cell Lines for qPCR...... 24 3.1.3 siRNA Knockdown in 501mel...... 24 3.2 Cell Cycle Synchronization...... 25 3.2.1 Double Thymidine Block...... 25 3.2.2 Double Thymidine Block with DOX Induction...... 26 3.3 Flow Cytometry...... 26 3.3.1 Lysing Cells using Vindelöv Solution...... 26 3.3.2 Flow Cytometry Analysis...... 26 3.4 Western Blotting...... 28 3.4.1 Sample Collecting...... 28 3.4.2 Gel Electrophoresis...... 28 3.4.3 Transfer...... 28 3.4.4 Blocking...... 29 3.4.5 Staining...... 29 3.4.6 Developing and Quantification...... 29 3.5 RNA Expression Analysis using Quantitative Real-Time PCR...... 29

3.5.1 RNA Isolation...... 29 3.5.2 cDNA Synthesis...... 30 3.5.3 qPCR...... 30 4 Results...... 32 4.1 Cell Cycle Synchronization and MITF...... 32 4.1.1 Elevated Levels of MITF Correlated with G2/M phase...... 32 4.1.2 Knockdown of MITF in 501mel has No Effect on Cell Cycle Progression...... 35 4.1.3 Overexpression of GFP-MITF in 501mel has Little Effect on Cell Cycle Progression...... 37 4.2 MITF Affects Expression of Cell Cycle Regulating Genes...... 38 4.3 Knockdown of CDK2 or LZTS1 Changes Levels of MITF...... 40 5 Discussion...... 43 5.1 Higher MITF Levels in G2/M...... 43 5.2 Potentially Novel Targets of MITF...... 44 5.3 CDK2 and LZTS1 Expression Affects MITF Levels ...... 45 5.4 Future Experiments ...... 46 6 Conclusions...... 49 Appendix – Supplemental Figures...... 50 References...... 51

Figure Legend

Figure 1. The migration of melanin from melanocytes to keratinocytes

Figure 2. The signalling pathways of MITF

Figure 3. Schemativ of the cell cycle, showing relative time in each stage

Figure 4. Schematic of double thymidine block protocol

Figure 5. Gating used for flow cytometry to determine cell cycle profile

Figure 6. Levels of MITF throughout the cell cycle

Figure 7. Unsynchronized cell cycle profile upon MITF miRNA knockdown

Figure 8. Cell cycle progression upon MITF miRNA knockdown

Figure 9. Unsynchronized cell cycle profile upon GFP-MITF overexpression

Figure 10. Cell cycle progression upon GFP-MITF overexpression

Figure 11. Effects of MITF miRNA knockdown or FLAG-MITF overexpression on cell cycle regulating genes

Figure 12. Effect of CDK2 or LZTS1 knockdown on MITF

Table Legend

Table 1. Some cell cycle genes affected by MITF CRISPR mutation

Table 2. Recipe and protocol for siRNA knockdown of CDK2 and LZTS1

Table 3. Recipe for two 8% polyacrylamide resolving gels

Table 4. Recipe for two stacking gels

Table 5. Concentration of antibodies used in Western Blot

Table 6. Amount of components from the high capacity cDNA reverse transcription kit

Table 7. Temperature stages for the synthesis of cDNA

Table 8. Primers used for qPCR

List of Abbreviations

APS Ammonium persulfate bHLH-Zip Basic helix-loop-helix leucine zipper CABLES1 CDK5 and ABL 1 Enzyme Substrate 1 CDC16 Cell Division Cycle 16 CDK2 Cyclin-dependent kinase 2 ChIP-seq Chromatin Immunoprecipitation sequencing Dia1 diaphanous-related formin DNA Deoxyribonucleic acid DOX Doxycycline ERK extracellular signal-related kinase G1 Gap 1 G2/M Gap 2 and mitosis GSK Glycogen Synthase Kinase LEF-1 Lymphoid Enhancer Binding Factor 1 LZTS1 Leucine Zipper Tumor Suppressor 1 MAPK Mitogen-activated protein kinase MCM2 Minichromosome Maintenance Complex Component 2 miRNA micro RNA MITF Microphthalmia-associated transcription factor mRNA messenger ribonucleic acid qPCR Quantitative polymerase chain reaction RNA Ribonucleic acid Rsk-1 ribosomal S6 kinase 1 S Synthesis SDS Sodium dodecyl sulfate siRNA Short interfering RNA TEMED N,N,N′,N′-Tetramethylethylenediamine UV Ultraviolet

1 Introduction 1.1 Melanocytes

Skin is the largest organ of the human body, accounting for approximately 15% of total adult body weight. As such, the skin is a heterogeneous mix of cells that form various layers of the skin. The outermost layer is called the epidermis and mainly consists of cells called keratinocytes (Kolarsick et al., 2011). They serve as a protective layer, with constant renewal of the cells. At the basement membrane of the epidermis are melanocytes, which are dendritic cells that produce melanin, otherwise known as natural pigment of the skin (Colombo et al., 2011). Melanocytes are derived from neural crest cells, which then are specified into progenitor cells called melanoblasts. Melanoblasts are the precursor cells for melanocytes, as they can terminally differentiate into the melanin-producing cells (Mull et al., 2015).

Figure 1. The transport of melanin from melanocytes to keratinocytes. Melanocytes are located at the bottom of the epidermis and transport melanin via vesicles to the neighboring keratinocytes. The relative coloration of the skin is dependent on how much melanin is produced by the melanocytes. (Reprinted with permission from OpenStax (Anatomy and Physiology, 2013))

Melanocytes produce melanin in melanosomes and transport melanin to neighboring keratinocytes in vesicles as shown in Figure 1, which illustrates the process of transporting melanin to the upper layers of the skin. There are two different forms of melanin produced in melanocytes: eumelanin and pheomelanin. Eumelanin is brown/black in color and is the melanin that protects

DNA against ultra-violet (UV) rays, while pheomelanin is yellow/red in color and does not have a role in DNA protection. The eumelanin is made and stored in melanosomes; upon being transported into the keratinocytes, it surrounds the nucleus, which then protects the DNA inside from damage (Liu & Fisher, 2010).

1.2 Melanoma

Melanoma is the malignancy that develops from melanocytes. Metastatic melanoma is one of the most highly mutated, heterogeneous and lethal types of cancer (Kozar et al., 2019). Since the mid- 1960s, melanoma incidence has risen by 3-8% per year, mostly in populations of European background (Thompson et al., 2005). There is an increasing number of examples that show correlation between genes that regulate neural crest/melanocyte development and those that contribute to melanoma (Mort et al., 2015). While one of the most significant causes of melanoma is exposure to ultra-violet (UV) light, there are familial cases of melanoma. In a twin study done in Australia, it was estimated that 55% of the variation in liability to melanoma is due to genetic influences (Shekar et al., 2009). There are many genes that have been implicated in a higher risk of melanoma, including mutations in CDKN2A, CDK4, BAP1 and MITF as well as many others (reviewed in (Aoude et al., 2015)). Although these predispositions are valid, only about 1-13% of melanoma cases reported occurrence of melanoma in at least one first-degree relative, which lead to a belief that melanoma predisposition is hereditary in only about 10% of all cases (Ford et al., 1995). While understanding the genetic predispositions is important, the mechanisms of somatic mutations in the melanoma tumors is essential. To explore the different mutations within melanoma tumors, the Cancer Genome Atlas analyzed the genome of 333 primary tumor samples. After analysis, they were able to categorize four different subtypes of mutation within the tumors. Of the four subtypes, the most common was a somatic mutation in BRAF, which was found in 52% of the samples ("Genomic Classification of Cutaneous Melanoma," 2015). BRAF is a serine-threonine protein kinase that is responsible for signal transduction to direct cell growth, proliferation, differentiation and cell survival (Chan et al., 2017). The mutation that is most frequently found replaces a glutamate for valine at position 600, so this version of BRAF is known as BRAFV600E. This mutation causes a conformational change in the protein which results in over-activity of the protein (Ascierto et al., 2012). BRAF inhibitors, one of the more common being Vemurafenib (which specifically targets the mutated BRAFV600E), have now been developed for melanoma treatment and have shown an objective response rate of 48%; however, after approximately 6 months, the melanomas return and continue progressing at the same rate as before due to resistance to the drug (Griffin et al., 2017). Melanoma can be treated with mixed

14 therapies consisting of BRAF and MEK inhibitors, attacking the same pathway from two different points. This shows more promise with an overall response rate of 67% (Long et al., 2014). However, resistance is still an issue. More recently, immune checkpoint therapy has been used in melanoma cases and appears to be more effective in the long-term than the BRAF and MEK inhibitors (Callahan, 2016). Moreover, it is still a topic of research as to how patients develop resistance to BRAF and MEK inhibition. One approach would be to look at targets downstream of BRAF in the MAPK pathway. One of the most important genes in the differentiation of neural crest cells to melanocytes is microphthalmia-associated transcription factor (MITF) (Vandamme & Berx, 2019). Interestingly, the MAPK pathway regulates MITF expression and activity (Wellbrock & Arozarena, 2015), which will be further explained in the next chapter. Since BRAF is a component of the MAPK pathway and the MAPK pathway regulates MITF, it is believed that the mutated BRAFV600E protein has an impact on MITF activity and expression. As the master regulator of melanocytes, MITF is known to play an important role in melanoma (Bertolotto et al., 2011; Levy et al., 2006; Yokoyama et al., 2011). In the next chapter, the role of MITF in melanocytes and melanoma will be explored.

1.3 MITF

Microphthalmia-associated transcription factor (MITF) is a basic helix-loop-helix leucine zipper (bHLH-ZIP) transcription factor that regulates gene expression by directly binding to DNA (Pogenberg et al., 2012). MITF is a master regulator of melanocytes and is important for their differentiation from neural crest progenitor cells (reviewed in (Vandamme & Berx, 2019)). There are different isoforms of MITF, but the one most prevalent in melanocytes and most important in melanoma is the MITF-M isoform (Kawakami & Fisher, 2017). MITF is responsible for regulation of pigmentation. As such, it has been shown to regulate several pigment cell-specific genes, including tyrosinase (Tyr) and tyrosinase-related genes Tyrp1 and Dct/Tyrp2 (reviewed in (Wan et al., 2011)). As MITF is a master regulator of melanocytes, it also regulates genes important for other pathways. One example is direct regulation of BCL2, which plays a role in cell survival (McGill et al., 2002). It is also believed that MITF plays a role in melanocyte stem cells. It was found that the expression of MITF in Medaka embryo-derived stem cells induces differentiation into melanocytes (Bejar et al., 2003). Likewise, MITF expression in NIH3T3 cells can activate melanocyte markers while TFE3, a transcription factor of the same MiT family, did not (Tachibana et al., 1996). MITF is also known to play a role in autophagy, as it has been shown to regulate starvation-induced autophagy in melanoma cells and directly bind to many to the promoters of lysosomal and autophagosomal genes (Moller et al., 2019).

In humans, mutations in MITF can cause Waardenburg syndrome type 2, which is characterized by a combination of sensorineural hearing loss and patchy abnormal pigmentation of the eyes, hair and skin (Tassabehji et al., 1994). Mice that have homozygous MITF mutations have a white coat and small red eyes; heterozygotes have irregular pigmentation of the coat color. There are no melanocytes in MITF null mice. Mutations in MITF can also impact retinal pigment epithelial (RPE) cells, resulting in unpigmented or hypopigmented eyes that are smaller than normal—the finding that lead to the name microphthalmia (Steingrimsson et al., 2004). MITF has also been suggested to play a direct role in melanoma. MITF has been proposed to act as a lineage survival oncogene in melanomas with an overactive BRAF (Garraway et al., 2005). In that study, it was also found that reduction of MITF activity sensitized melanoma cells to chemotherapeutic agents, therefore indicating that MITF could potentially be an interesting target for melanoma treatment. In two different studies, a germline mutation in MITF, E318K, was correlated with an elevated risk of melanoma. Humans with the MITFE318K mutation have a larger risk of melanoma than the general population (Yokoyama et al., 2011). MITFE318K was found to decrease

SUMOylation of MITF, which then enhances transcriptional activity and increases migration and invasion in both 501mel melanoma cells as well as RCC4 renal cell carcinoma cells (Bertolotto et al., 2011). As MITF regulates many different pathways in melanocytes, it has been reported that the level of MITF can determine and change the phenotype of a melanoma population. It was found that mouse melanoma cell lines in which MITF was knocked down, stem cell markers such as Oct4 and Nanog increased (Cheli et al., 2011). This would indicate that a low amount of MITF would be correlated to a phenotype more similar to a stem-like neural crest cell than a differentiated melanocyte. It was also found that MITF regulates Dia1, which promotes actin polymerization; low levels of MITF decreased expression of Dia1 and high levels of MITF decreased invasiveness (Carreira et al., 2006). These discoveries gave way for a model which explains how MITF levels can cause differences or changes in melanoma phenotype. The rheostat model implies that melanoma cells with low amount of MITF are more stem-like, while melanoma cells with a high amount of MITF act more differentiated and have higher proliferation and changing levels of MITF can change the phenotype of the melanoma population (Goding, 2011).

Figure 2. The signalling pathways of MITF. Many pathways regulate MITF, which in turn regulates various genes important for cell-cycle progression, apoptosis and differentiation. (Reprinted from Trends in Molecular Medicine, volume 12, Levy et. al, MITF: master regulator of melanocyte development and melanoma oncogene, Pages 406-414, Copyright (2006), with permission from Elsevier)

There are multiple pathways that can regulate MITF expression and activity. One of the most studied pathways that impacts MITF activity is the MAPK/ERK pathway, shown in Figure 2. It has been implicated in regulating MITF transcription, activity and degradation. MITF is phosphorylated by Rsk-1 at the serine at position 409 and by ERK at the serine at position 73, which when mutated to an alanine, causes a loss of transcriptional activity in MITF (Wu et al., 2000). Post-translational modification of MITF on serine 73 is important, as the phosphorylation is known to contribute to transcriptional activity of MITF (Hemesath et al., 1998).There is a debate on exactly if and how the

MAPK/ERK pathway regulates MITF transcriptionally, but the proposed mechanism is through BRN2, which is upregulated upon activation of the MAPK/ERK pathway. BRN2 binds to the MITF promoter, which then activates transcription (Wellbrock & Arozarena, 2015). It was also shown that MEK plays a role in degradation of MITF. In an experiment using MEK inhibitors and cycloheximide—a substance that inhibits translation—it was found that when MEK is inhibited and translation is stopped, there is a larger amount of MITF protein. This would mean that MEK plays a role in MITF degradation (Wu et al., 2000). Another pathway that is shown in Figure 2 is Wnt signaling, which has been shown to activate expression of MITF mRNA through the recruitment of LEF-1 and β-catenin to the LEF-1- binding site (Takeda et al., 2000). It has also been implicated that Wnt signaling and MITF regulate each other post-translationally in a feedback loop; Wnt signaling stabilizes the MITF protein by inhibiting GSK and decreasing the C-terminal phosphorylation of MITF and then MITF increases expression of lysosomal genes (Ploper et al., 2015). There is a large interplay between pathways regulating MITF and MITF regulating other pathways. In this thesis, the attention will be directed towards how the cell cycle regulates MITF and how MITF then regulates cell cycle.

1.4 Cell Cycle

Cell cycle is the process by which cells grow and divide. There are two basic parts to the cell cycle: interphase and mitosis. Both parts are further broken up into stages; interphase can be divided into G1 (Gap 1), S (Synthesis) and G2 (Gap 2) while mitosis can be split into prophase, prometaphase, metaphase, anaphase, telophase and cytokinesis (Alberts et al., 2014). Mitosis is not a long process in most cells and will not be discussed further in this thesis. However, the different stages of interphase will be analyzed further. The most prominent event to happen during interphase is the synthesis of DNA. As the name might indicate, this takes place during synthesis (S) phase. During this stage, DNA helicase binds to DNA, opens up the double helical structure, and recruits the DNA polymerase complex to replicate the DNA (Alberts et al., 2014). The rest of interphase is characterized as either before or after S phase. Gap 1 (G1) phase comes before S phase. During G1, the cell is metabolically active and continuously grows but is not replicating its DNA. Essentially, the cell is growing and preparing for DNA synthesis. There are certain situations in which the cells do not intend to divide and in those cases they enter a quiescent stage of the cell cycle called G0. These cells are metabolically active but no longer proliferate (Cooper, 2000). The phase that occurs after S phase is called Gap 2 (G2). In this

18 stage, the cell continues to grow and proteins are synthesized in preparation for mitosis (Cooper, 2000). While it might appear that G1 and G2 are simply “breaks” between DNA synthesis and mitosis, these stages are essential for normal cell growth. During G1, there is a mechanism in the cell that stops progression through cell cycle if DNA is damaged. This allows the cell to repair any DNA damage before being replicated, or if there is too much damage, to undergo apoptosis. During G2, there is a similar mechanism that will stop the cell from continuing through mitosis if mistakes were made during DNA synthesis (Alberts et al., 2014). While there are many regulators of cell cycle, the most studied are cyclins and cyclin- dependent kinases (CDKs). Cyclins are proteins that are synthesized and destroyed at specific times during the cell cycle; they bind to CDKs to initiate their activity, thus regulating when CDKs are active and therefore regulating cell cycle (Malumbres & Barbacid, 2009). CDKs are serine/threonine kinases and mainly act to progress cell cycle upon activation by the cyclins (Harashima et al., 2013). While CDKs mostly bind to cyclins, they can also have other binding partners that can either activate or inhibit their ability to progress through the cell cycle (Wood & Endicott, 2018). Some cyclin/CDK complexes are shown in Figure 3, which also shows a schematic of the cell cycle. Also shown are some cell cycle inhibitors: p16, p21 and p27, which are proteins that bind to cyclin/CDK complexes to slow down progression of cell cycle (Bedir et al., 2016). As shown in Figure 3, CDK 4/6 binds to Cyclin D in order to progress through G1 phase. Then, the Cyclin E/CDK2 complex is necessary for transition between G1 and S phase. To continue through S phase requires Cyclin A/CDK2 complex. During G2, Cyclin A/CDK1 complex is necessary in order to continue to mitosis. An active Cyclin B/CDK1 complex is important for a regulated progression through mitosis. Figure 3 is also drawn to show the relative amount of time in each cycle, as most time is spent in G1 and S phase, while relatively little time is spent in G2 and mitosis.

Figure 3. Schematic of the cell cycle, showing relative time in each stage. Cells undergo a cycle of growing, synthesizing DNA, expanding organelles and dividing. Different genes regulate transition from one cell cycle stage to the next, as shown by p21, p16 and p27 all acting as cell cycle inhibitors. Cyclin-dependent kinases form complexes with cyclins in order to progress through the transitions, as shown by the pink ovals next to the respective cell cycle stage. (Reprinted by permission from Cancer Biology & Medicine, Copyright (2017) by Cancer Biology & Medicine)

In order to investigate the mechanisms of cell cycle in detail, it is a common practice to synchronize cells in culture so they are all at the same stage of the cell cycle and then follow them throughout the cycle. The most common methods of synchronizing cells are based on either physical fractionation or a chemical approach. Physical fractionation can vary from centrifugal elutriation, which is based on cell size, to flow cytometry and cell sorting, which is based on amount of DNA within the cells. Another method that utilizes the physical properties of cells to synchronize them is a method called mitotic shake-off in which a batch of adherent cells are physically shaken so smaller mitotic cells unstick from the plastic and float into solution. The chemical approaches that have been used include methods that arrest cells at mitosis by mitotic spindle poisons or stop them in S-phase by affecting DNA synthesis. One common treatment used for cell cycle synchronization in S-phase is thymidine, which inhibits the synthesis of DNA when in excess (Banfalvi, 2011). In this thesis, a double thymidine block was used for batch synchronization of cells.

1.5 MITF and Cell Cycle Regulation

MITF plays a role in many different pathways in the cell and one important pathway is regulation of cell cycle. Target genes of MITF that are involved in the cell cycle include p21 (Carreira et al., 2005), p16 (Loercher et al., 2005), p27 (Carreira et al., 2006), which are all known Cyclin/CDK inhibitors. Also a target gene is CDK2 (Du et al., 2004) which codes for a serine/threonine kinase that is essential for G1/S transition. By controlling both the inhibitors and activators of cell cycle, the role of MITF in cell cycle regulation may not be straight-forward. While MITF depletion causes a decrease in p21, which in theory would increase proliferation, there is actually an increase in p27-regulated cell cycle arrest in 501mel melanoma cells (Carreira et al., 2006). Using CRISPR/Cas9 technology, Remina Dilixiati, a member of the Steingrimsson lab, characterized a melanoma cell line completely lacking in MITF and characterized gene expression using RNAseq (Dilixiati, 2019). Knocking out MITF affects the expression of genes known to be involved in cell cycle regulation (Table 1). Interestingly, the expression of p15 (CDKN2B) and p21 (CDKN1A) was increased in the knockout cells whereas CDK2 was reduced in expression. This analysis did not

detect differences in the expression of Table 1. Some of the cell cycle genes affected by the MITF CRISPR mutation. Red indicates genes bound by either p16 or p27. MITF. Minus in front of fold value indicates reduced expression. (Dilixiati et al, in preparation) Available ChIP-seq data has shown MITF-binding peaks in some cell cycle-related genes including CDK2 and CDC16 (genes marked with red in Table 1), which are both reduced in expression upon MITF knockout (ChIP-seq data from (Laurette et al., 2015; Strub et al., 2011)). This suggests that MITF is directly involved in regulating expression of these genes. CDK2 is a well-studied cell cycle regulator that is known to be essential in the G1/S checkpoint, although there are some studies that imply that its role might not be nearly as important as once thought. CDK2 null mice develop almost normally, with the only difference being their fertility, suggesting that CDK2 plays a larger role in meiosis than in mitosis (Berthet et al., 2003; Ortega et al., 2003). CDK2 has previously been shown to be regulated by MITF (Du et al., 2004). In

21 the study, it was shown that CDK2 is a direct target of MITF and that MITF activates the expression of this cell cycle regulator. A couple of other genes suggested to be regulated by MITF are CABLES1 and LZTS1. Expression of both genes is severely reduced upon MITF knockout and ChIP-seq analysis shows MITF-peaks in both genes. CABLES1 interacts with cyclin-dependent kinases and plays a role during cell cycle regulation (Huang et al., 2017). LZTS1 is a leucine zipper that binds to DNA and interacts with the Cyclin B1/CDK1 complex, indicating a role in mitosis (Ishii et al., 2001). In about 60% of cancers, a loss or a reduction of LZTS1 expression has been reported (Vecchione et al., 2007). It was also shown that LZTS1 expression is reduced in metastatic uveal carcinomas, as compared to the primary tumor (Onken et al., 2008). MITF plays an important role in cell cycle progression in melanoma and it is important to explore every possibility to understand exactly how MITF regulates cell cycle.

2. Aims

MITF is the master regulator of melanocytes and is known to play a role in cell cycle progression. There are known MITF targets that are essential to cell cycle, but there is not much known about how MITF in turn is regulated throughout the cell cycle. Many pathways regulate expression or activity of MITF, like MAPK and Wnt signalling. We hypothesized that as MITF regulates expression of cell cycle regulaters, cell cycle regulaters also regulate MITF. In the effort to help characterize the relationship between MITF and cell cycle, this project has three aims:

1. Determine effects of the cell cycle on MITF levels and phosphorylation in melanoma after cell cycle synchronization. Investigate if MITF levels then affect the cell‘s ability to progress through the cell cycle. 2. Determine effects of gene expression CABLES1, CDK2, CDC16, LZTS1 and MCM2 upon MITF knockdown and overexpression. 3. Explore the effects of knockdown of cell cycle-related genes on MITF RNA and protein expression.

3. Materials and Methods 3.1 Cell Culture 3.1.1 General Maintenance All experiments were done using 501mel or A375P melanoma cell lines. Cells were cultured in DMEM 1x +GlutaMax (Gibco) supplemented with 10% FBS (Gibco). The cells were incubated at 37°C,

5% CO2, 98% humidity and maintained by splitting to fresh media when approximately 90% confluent. 3.1.2 DOX Induction of PiggyBac Cell Lines Another member of the Steingrimsson laboratory, Remina Dilixiati, generated a 501mel cell line using a Piggybac transposon system to carry a pPBhCMV-1-miR(BsgI)-pA-3 vector construct where a miRNA targeting MITF can be induced from a Doxycyclin-inducible promoter in 501mel cell lines. The cell line was made stable so upon addition of DOX to the media, the miRNA would transiently become expressed (Dilixiati, 2019). For this 501mel miRNA MITF knockdown cell line, DOX was added at a concentration of 1 µg/mL and incubated at 37°C for at least 48 hours before being harvested. Another cell line used was a FLAG-MITF overexpression A375P cell line made by Valerie Fock, a postdoc in the Steingrimsson laboratory. These cells were also created using the PiggyBac system to overexpress wild-type FLAG-tagged MITF upon DOX induction. She introduced the DOX-inducible vector pBAc-pFLAG-HA-MITF into A375P cells to create a stable cell line (unpublished). In order to overexpress MITF, the A374P FLAG-MITF overexpression cell line was treated with DOX at a concentration of 0.5 µg/mL and incubated at 37°C for at least 24 hours before being harvested. To be able to overexpress MITF in 501mel cells, another member of the Steingrimsson laboratory, Josue Ballesteros, made a 501mel cell line carrying the vector pBac-pEGFP-N1-MITF in the same PiggyBac system mentioned above, to create a 501mel cell line that overexpressed a GFP- MITF fusion protein (Álvarez, 2019). To induce the overexpression of GFP-MITF, 1 µg/mL of DOX was added to the media and incubated at 37°C for at least 48 hours before being harvested.

3.1.3 siRNA Knockdown in 501mel cells For transfection with siRNA, the cells were seeded to 60-80% confluency. The Lipofectamine RNAiMAX Transfection Reagent (ThermoFisher) was diluted in 1x Opti-MEMTM Media I Reduced Serum Medium (ThermoFisher) using the dilutions as shown in Table 2. siRNAs included siSCR (Thermo Scientific), siCDK2 (Thermo Scientific) and siLZTS1 (Thermo Scientific).

Table 2. Recipe and protocol for siRNA knockdown of CDK2 and LZTS1. Component 24-well plate 6-well plate Opti-MEM Medium 50 µL 150 µL Lipofectamine RNAiMAX 3 µL 9 µL Reagent The siRNA was then diluted Opti-MEM Media 50 µL 150 µL siRNA (10uM) 1 µL 3 µL The diluted LipofectamineTM RNAiMAX reagent was combined with the diluted siRNA in a 1.5mL Eppendorf tube in a 1:1 dilution as follows Diluted siRNA 50 µL 150 µL Diluted Lipofectamine 50 µL 150 µL RNAiMAX Reagent The Eppendorf tube with the mixture was incubated at room temperature for 5 minutes and then added to the cell culture. siRNA-lipid complex to cells 50 µL 250 µL Final siRNA used per well 5 pmol 25 pmol Final Lipofectamine 1.5 µL 7.5 µL RNAiMAX used per well Incubate cells for 48 hours at 37°C and collect for analysis

3.2 Cell Cycle Synchronization 3.2.1 Double Thymidine Block Cells were seeded to 40-50% confluency. Thymidine was added to the media for a final concentration of 2mM. Cells were incubated at 37°C for 16 hours before replacing with fresh media and incubated further at 37°C for 8 hours. Then, 2mM thymidine was added to the cells and incubated at 37°C for 16 hours. After the thymidine treatment, media was replaced and cells harvested at at 0 hours, 4 hours, 8 hours and 12 hours after treatment. Controls were also harvested 8 hours after release from thymidine treatment. A schematic of the block is shown in Figure 4, where each colored chevron represents the duration and treatment of the cells.

Release and Thymidine treatment Release Thymidine treatment harvest (every (16hr) (8hr) (16hr) 4 hours for 12 hours)

Figure 4. Schematic of double thymidine block protocol. Thymidine was placed onto cells overnight for two nights with a release time of 8 hours in between. Cells were harvested immediately following release of the second overnight treatment. The black arrows indicate when DOX was added during experiments that used the 501mel miR-MITF knockdown cells or 501mel GFP-MITF overexpressing cells.

3.2.2 Double Thymidine Block with DOX Induction In order to determine the effects of MITF knockdown or MITF overexpression on cell cycle progression, a protocol of DOX-induction of MITF knockdown or MITF overexpression cells alongside double thymidine block was used. Cells were plated to 50% confluency. A concentration of 1 µg/mL of DOX was added to the media at the same time as the first thymidine treatment. The DOX was replaced every time the thymidine treatment was released, meaning that it was replenished two times: after the first overnight treatment and after the second overnight treatment (when time points started). The times where DOX was added to the media are represented in Figure 4 with black arrows. The double thymidine block with DOX induction was done on 501mel MITF miRNA knockdown cells and 501mel GFP-MITF overexpressing cells and their respective control cell lines.

3.3 Flow Cytometry 3.3.1 Lysing Cells using Vindelöv Solution Vindelöv solution was used to lyse the cell membrane without affecting the nuclear membrane. The resulting solution consists of cellular debris and nuclei with stained DNA that can then be used for FACS analysis. Cells were spun at 300xg at 4°C for 5 minutes. Pellets were resuspended in Vindelöv solution (20mM Tris; pH=7.6, 100mM NaCl, 0.1% Nanicote-40, RNAse, 7AAD) for an approximate final volume of 100 cells/µL. The solution was then incubated in the dark on a heat block at 37°C for 30 minutes. Samples were placed at 4°C until further analysis. Time spent between the end of incubation and flow analysis ranged between 14 and 26 hours. 3.3.2 Flow Cytometry Analysis The nuclei in the Vindelöv solution were pushed through a filter and into a glass tube. These samples were run in an Attune NxT acoustic focusing cytometer (Life Technologies) for a final cell count of approximately 50,000 cells. The data was then exported and transferred into FlowJo for analysis. Figure 5A shows the gate used to exclude small particles that are unlikely to be cells. The x- axis is forward scatter, which is directly correlated with size of the cell, and the y-axis is side scatter, which is correlated to the complexity of the cell. The small and simple particles are found in the bottom left and were excluded as they are unlikely to be cells. Figure 5B shows a cell cycle profile curve. The x-axis is the intensity of the 7AAD and the y-axis is the number of cells with that intensity. As 7AAD stains DNA, it is assumed that the first peak is the peak for G1, in which cells only have one set of DNA and the second peak is for cells in G2/M, in which they have two sets of DNA. By setting a gate strictly around these peaks, any cells in less than G1 or more than G2/M were excluded from the calculation.

Figure 5C shows the estimated cell cycle peaks using the “Cell Cycle” program on FlowJo. The Watson (Pragmatic) model was used to determine percentages in each cell cycle phase. This model was first published by James Watson in 1987 and works under the assumption that each peak is distributed normally and one of the two peaks is identifiable (Watson et al., 1987). By identifying one peak and assuming both peaks are normally distributed, the algorithm calculates the percentage of cells in each cell cycle stage based on the area under the curve. G1 and G2/M peaks were constrained based on the control sample, with the G2/M constraint further being defined as =G1*V. This allows the algorithm to discern the G2/M peak and decreases the amount of variation. In Figure 5D, there is an example of how the data is shown in the thesis. The percentages generated from the Watson model were used as ratios between the cell cycle stages. The percentages were manipulated so the ratio of cell in G1: S: G2/M were the same, but the sum of all three stages added up to 100%. This allowed for a standard for normalization, as each repeat was assumed to have 100% of the cells either in G1, S or G2/M.

A) B)

C) D)

Unsynchronized Cell Cycle Profile

37.65% G1 33.41% S 28.94% G2

Figure 5. Gating used for flow cytometry to determine cell cycle profile. A) The first gating was used to exclude cellular debris. B) The second gating was used to exclude polyploid cells and unstained nuclei not filtered through the first gating. C) Constraints for G1 and G2 during cell cycle analysis. D) Donut graph representing percentages of cells in each cell cycle stage.

3.4 Western Blotting 3.4.1 Sample Collecting

For Western blotting, the samples were lysed with 1x Sample Buffer (pH=6.8; 2% SDS, 5% B-ME, 10% Glycerol, 62.5mM Tris, Bromphenol blue). The samples were then boiled at 95°C for 10 minutes and stored at -20°C until usage. 3.4.2 Gel Electrophoresis

The polyacrylamide gels were prepared by combining the chemicals as shown in Tables 3 and 4. The resolving gel was poured and then topped with 100% isopropanol. After the gel had completely polymerized, the isopropanol was removed and the top of the gel rinsed with water. The stacking gel was then poured and combs were inserted.

Table 3. Recipe for two 8% polyacrylamide resolving gels Component Volume Water 10.4 mL 40% Acrylamide/Bis solution (BioRad) 4.2 mL LBT (pH=8.8; 1.5M Tris, 0.4% SDS) 5.2 mL 10% APS 200 µL TEMED 20 µL

Table 4. Recipe for two stacking gels. Component Volume Water 3.6 mL 40% Acrylamide/Bis solution (BioRad) 625 µL UBT (pH=6.8; 500mM Tris, 0.4% SDS) 680 µL 10% APS 50 µL TEMED 5µL

After the stacking gel finished polymerizing, the gels were placed into the tank which was then filled with 1x running buffer (25mM Tris, 200mM Glycine, 0.001% SDS). The samples were loaded on the gel. Pre-stained Protein Ladder (Thermo Scientific) was used to determine protein band size. The gel was run at 90V at room temperature for approximately 90 minutes. Then, the voltage was increased to 115V and the gel run further or until the sample had reached the bottom of the gel. 3.4.3 Transfer

The PVDF Transfer Membranes (Thermo Scientific) were first activated with methanol for about 30 seconds before being placed into 1x transfer buffer (25mM Tris, 200mM Glycine, 0.0001% SDS). Then, the Western blot cassettes were assembled so the gel and membrane were sandwiched between small sheets of paper and sponges. The cassettes were closed and then placed into the transfer tank. The tank was filled with 1x transfer buffer, placed in 4°C and run at 20V overnight.

3.4.4 Blocking The membranes were blocked with 5% BSA in TBS-T (pH=7.6; 20mM Tris, 150mM NaCl, 0.01% Tween) for between 40 minutes to an hour at room temperature on a shaker. 3.4.5 Staining Membranes were placed in a primary antibody solution diluted in 5% BSA in TBS-T at the concentrations specified on Table 5 and then placed on a shaker at -4°C overnight. Following primary antibody staining, membranes were washed with TBS-T three times, ten minutes per wash. Secondary antibody was then added and stained between one and two hours in the dark at room temperature on a shaker.

Table 5. Concentrations of antibodies used in Western blot Antibody Primary/Secondary Dilution Producer Category # Mouse anti- Primary 1:1,000 Abcam ab12039 MITF (c5) Rabbit anti-β- Primary 1:1,000 Cell Signaling 2128S tubulin Rabbit anti- Primary 1:1,000 Cell Signaling 12231S Cyclin B1 Anti-mouse Secondary 1:15,000 Invitrogen A-11003 Anti-rabbit Secondary 1:15,000 Invitrogen A-11070

3.4.6 Developing and Quantification

The membranes were scanned with an NIR fluorescence scanner (LI-COR Biosciences, Odessey CLx). The images were analyzed using LI-COR’s Image Studio Software (Version 5.2). Quantifications of the images were done using ImageJ 1.51k software.

3.5 RNA Expression Analysis using Quantitative Real-Time PCR 3.5.1 RNA Isolation Cells were seeded onto a 6-well plate for RNA isolation. In order to isolate RNA, 1mL Tri Reagent (Ambion) was added and incubated at room temperature for 5 minutes. The Tri Reagent was then transferred to an RNase-free 1.5mL tube before 0.3mL of chloroform was added to the tube. The tube was then thoroughly shaken for 15-20 seconds and then incubated at room temperature for 3- 5 minutes. Samples were then spun in a centrifuge at 12,000xg for 15 minutes at 4oC. The aqueous phase on top was transferred to a different tube, the organic phase was discarded and 0.5mL isopropanol (100%) was added to the aqueous phase and incubated at room temperature for 10 minutes. The tube was then spun at 12,000xg for 10 minutes at 4oC. The supernatant was removed and the pellet was resuspended in 1mL of ethanol (75%). The tube was spun at 7,500xg for 5

29 minutes at 4oC. The supernatant was removed and the pellet was resuspended again in 1mL of ethanol (75%) before they were spun down at 7,500xg for 5 minutes at 4oC. The supernatant was removed and the pellet was left to dry for 5 minutes before resuspending it in 20 µL of nuclease-free

H2O. 3.5.2 cDNA Synthesis RNA samples were diluted to contain 500 ng in 2.5 µL. In a PCR tube, the RNA samples were mixed with a Master Mix using the high capacity cDNA reverse transcription kit (Applied Biosystems) in the proportions shown in Table 6. The samples were then loaded into a thermal cycler and the program shown in Table 7 used for the reaction. Table 6. Amount of components from the high capacity cDNA reverse transcription kit. Component Volume/Reaction (µL) 10XRT Buffer 0.5 25X dNTP Mix (100mM) 0.2 10X RT Random Primers 0.5 MultiScribeTM Reverse Transcriptase 0.25 RNase Inhibitor 0.25

Nuclease-free H2O 0.8 Total per Reaction 2.5

Table 7. Temperature stages for the synthesis of cDNA. Step 1 Step 2 Step 3 Step 4 Temperature (oC) 25 37 85 4 Time 10 min 120 min 5 min ∞ 3.5.3 qPCR cDNA was diluted to a concentration of 1ng/µL in Nuclease-Free H2O. The qPCR reactions were set up in 384-well plates by pipetting 2.5 µL of SYBR green (Sigma), 0.5 µL of the forward primer, 0.5 µL of the reverse primer, and 2 µL of cDNA into each well used. The plate was sealed and the liquid spun to the bottom of each well. A BIO-RAD C1000 Touch Thermal Cycler was programmed to run the following program:

1. 95°C for 2 minutes 2. 95°C for 5 seconds 3. 60°C for 10 seconds 4. Plate read

5. 72°C for 10 seconds 6. Repeat steps 2-5 for 39x 7. 65°C for 31 seconds 8. 65°C for 5 seconds 9. Plate read 10. Go to Step 8 for 60x The Cq values were then downloaded into Excel for analysis. ΔΔCT was used for relative expression to actin and the control sample for each experiment. The primers used are shown in Table 8. Table 8. Primers used for qPCR. All primers were designed to have a Tm = 60°C ± 2°C Target Forward/Reverse Sequence MITF Forward ATGGAAACCAAGGTCTGCCC Reverse GGGAAAAATACACGCTGTGAGC Actin Forward AGGCACCAGGGCGTGAT Reverse GCCCACATAGGAATCCTTCTGAC CDK2 Forward CCAGGAGTTACTTCTATGCCTGA Reverse TTCATCCAGGGGAGGTACAAC LZTS1 Forward AGCGTCAGTAGCCTCATCTC Reverse AGTCTTCGCTCTTGCCCATTT CABLES1 Forward ATGCGGCAACACGATACCAG Reverse AGTCCCCGACTTGGGTACTG Cdc16 Forward TCAAAGTGCTCTATTTTGGGCA Reverse TTGTCCAGTTTTCGTGACCGA MCM2 Forward ATGGCGGAATCATCGGAATCC Reverse GGTGAGGGCATCAGTACGC

4 Results 4.1 Cell Cycle Synchronization and MITF 4.1.1 Elevated Levels of MITF Correlated with G2/M phase

In order to determine the differences in MITF expression levels and phosphorylation status throughout the cell cycle, Western blot was conducted on 501mel melanoma cells that had undergone double thymidine treatment. The results are shown in Figure 6A, where there is the typical double-band pattern of MITF at around 50-65 kD. As shown in the blot, the double bands of MITF are relatively faint at 0 hours after double thymidine treatment. The band intensity increases and is the most intense at 8 hours after double thymidine treatment. At 12 hours after treatment, the band intensity decreases again. Both bands of MITF were quantified and shown in Figure 6B. The upper to lower band ratio of the MITF bands was quantified and is shown in Figure 6C. The upper to lower band ratio shows the phosphorylation of MITF, as the top band is the phosphorylated version of MITF and the bottom is the dephosphorylated version. This is known since an antibody that binds to the phosphorylated version of MITF only recognizes the upper band (data not shown). A high upper to lower band ratio would therefore indicate a larger portion of the phosphorylated MITF. A low upper to lower band ratio would indicate more of the dephosphorylated version of MITF. Also shown in Figure 6A is a single band for Cyclin B1 at around 50 kD. Cyclin B1 was used as a cell cycle stage marker, as it is known to be correlated with G2/M. Higher levels of Cyclin B1 should indicate a larger portion of cells in G2/M phase. While this blot only shows one band for Cyclin B1, other repeats showed two distinct bands for Cyclin B1 (shown in Appendix – Supplemental Figures). A potential reason for this might be unspecific binding of the primary antibody to Cyclin B2, which is 45 kD, and with every reused dilution of the primary antibody there was less antibody to bind and create the second band. Upon re-using the primary antibody, the bottom band disappeared as seen in Figure 6A. When there were two bands for Cyclin B1, only the top band had changed. Because of these reasons, all quantifications of Cyclin B1 were taken only from the top band. Figure 6D shows the quantification for Cyclin B1 protein levels throughout the cell cycle. β-tubulin was used as a loading control and appears as a single band at around 55 kD. To ensure that the cells were actually synchronized, cells that had undergone a double thymidine block were collected at different time points for flow cytometry analysis. The results of this analysis is shown in Figure 6E. Right after the double thymidine protocol, there is a large portion of cells in G1. At 4 hours, it appears that most of the cells are in S phase or G2/M. At 8 hours after treatment, the cells are mainly in G2/M phase and by 12 hours afterdouble thymidine block, the majority of the cells are back to G1 phase. This shows that the double thymidine block had

32 successfully synchronized the cells, where majority of the cells at each time point are in the same cell cycle stage. The expression of the MITF protein increased significantly at the 8-hour time point (Figure 6E), correlating with the majority of cells being in G2/M. There was a decrease in the ratio of the upper MITF band to the lower MITF band observed at the 0-hour time point—as shown by a more intense lower band and a less intense upper band. As the upper MITF band represents MITF that is phosphorylated at serine-73, this decrease might indicate a decrease in phosphorylation of MITF in G1. However, this could also be an off-target effect of the thymidine treatment. If it is G1 that had that effect on MITF, there would be a similar pattern at the 12-hour time point, at which most cells have undergone mitosis and have returned to G1. Since the upper to lower band ratio appears to not be significantly changed at the 12-hour time point, the explanation for the change in phosphorylation in MITF is most likely due to the thymidine treatment, although further analysis is required to be certain. In order to determine if MITF RNA expression changed throughout the cell cycle, the double thymidine block was used and RNA samples were isolated at different time points. As shown in Figure 6F, there was no significant change in transcription of MITF throughout the duration of the time points following double thymidine treatment. As each time point roughly corresponds to a cell cycle stage, this would indicate that the increase observed in MITF protein levels as shown in Figure 6A,B is not due to increased transcription of MITF. Rather it suggests changes in translation or stability of MITF during the cell cycle.

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Cell Cycle Progression of 501mel

100 G2/M

S l

e G1 m

1 50

0 unsync 0 4 8 12 Time (hours)

Figure 6. Expression of MITF protein and mRNA throughout the cell cycle. A) Western blot of 501mel cells at several different time points after double thymidine treatment. N=3. B) Quantification of MITF, normalized to tubulin and unsynchronized sample. * indicates p-value <0.05,N=3. C) Quantification of MITF upper to lower band ratio, normalized to unsynchronized sample. *** indicates p-value of 0.0004, * indicates p-value <0.05 N=3. D) Quantification of Cyclin B1 protein levels, normalized to tubulin and unsynchronized sample.**** indicates p-value <0.0001, * indicates p-value <0.05 N=3. E) Flow data showing percentage of 501mel cells in each cell cycle stage based on time after treatment. N=1. F) Relative MITF mRNA levels at each time point following double thymidine treatment, normalized to actin and the unsynchronized sample. N=3. Un-paired t-test was performed to determine significance.

4.1.2 Knockdown of MITF in 501mel has No Effect on Cell Cycle Progression To determine the effects of MITF knockdown on the ability of 501mel cells to progress through the cell cycle, a stable cell line was made using a PiggyBac transposon system. Another member of the lab, Remina Dilixiati, had generated a cell line carrying a pPBhCMV-1-miR(BsgI)-pA-3 vector construct where an miRNA targeting MITF was expressed in 501mel cell lines (Dilixiati, 2019). These cells were cultured in 1x DMEM + 10% FBS and expression of the MITF miRNA was induced using DOX. The knockdown was successful, as shown in Figure 8B, where there are distinct MITF bands for the time points of the miR-Control cells, but the bands for the time points of the miR-MITF cells are much less intense. The cells were synchronized using double thymidine block and the miRNA knockdown was induced using DOX. The cells were then harvested at different timepoints following treatment and analyzed for cell cycle status with flow cytometry. Figure 7 shows the cell cycle profile of the unsynchronized miR-Control cells compared to the miR-MITF knockdown cells. The cell cycle profiles from the three biological repeats were averaged to create the donut graphs. Upon knockdown of MITF, there was a tendency for more cells to be in G1 and less in S. This difference was not statistically significant, but was a consistent trend among the repeats. It has been reported that it is possible to estimate the growth rate of a tumor based on the S-phase fraction of a population of cells (Johnson, 1994). This would mean that the decrease of S-phase upon MITF miRNA knockdown could indicate a decrease in growth rate. Something else to note is that the cell cycle profile of the miR-Control cell line is different than the 501mel parental cells used for Figure 6E, as the miR-Control cell line appears to have more cells in G1. This could be attributed to the DOX treatment, as the 501mel parental cells from Figure 6 did not undergo a DOX treatment.

Unsynchronized miR-Control Unsynchronized miR-MITF Cell Cycle Profile Cell Cycle Profile

49.19% G1 55.62% G1 31.95% S 28.52% S 18.86% G2/M 15.86% G2/M

Figure 7. Unsynchronized cell cycle profile upon MITF knockdown using miRNA. Donut graph representing the percentage of cells in different cell cycle stages in the unsynchronized samples of the miR-Control and miR-MITF 501mel cell lines, 48 hours after DOX-induction. N=3

In order to determine if the MITF knockdown cells had stopped at a particular checkpoint of the cell cycle, the cells were synchronized and collected at different time points and underwent flow cytometry analysis. As shown in Figure 8, a large portion of both the control and the MITF miRNA

knockdown cells are in G1 immediately after double thymidine treatment. After 4 hours, these cells are mainly in S phase, which transition into mostly G2/M phase by 8 hours after treatment. For both groups, 12 hours after treatment, the cells are almost back to the conditions of the 0-hour time point. This would indicate that the synchronization worked for both the control and the MITF miRNA knockdown. It also shows that the two cell lines are essentially identical after treatment. There are no visible differences between the cell cycle progression of the control and of the MITF knockdown cell lines. This indicates that the trend of less S phase in the unsynchronized miR-MITF knockdown cells, as shown in Figure 7, might not be due to a difficulty transitioning from G1 to S phase.

A) miR-Control Cell Cycle Progression miR-MITF Cell Cycle Progression

100 100 G2/M G2/M

S S ) G1 )

% G1

(

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0 0 unsync 0 4 8 12 unsync 0 4 8 12 Time (hours) Time (hours)

Figure 8. Cell cycle progression upon MITF miRNA knockdown. A) Stacked column graph showing the percentage of cells in each cell cycle stage for each time point following double thymidine block and DOX induction. N=3. B) Representative Western blot stained with c5 MITF and tubulin that shows the efficiency of the knockdown throughout the time course, N=3 While there appeared to be a slight G1 arrest in the unsynchronized miR-MITF cell line, the knockdown cells progressed the same as the control cell line from G1 to S, as shown at 0 and 4 hours after double thymidine treatment. If these cells had difficulty transitioning, there would be less S phase 4 hours following release of the double thymidine block, but as shown in Figure 8, this is not the case. At 4 hours following the release of double thymidine treatment, both the control and the knockdown cells have a majority of S phase and there is no apparent difference between the groups. At 8 hours after treatment, there is slightly more S phase in the control cells, but by 12 hours after treatment, the two cell lines appear to have an identical cell cycle profile again.

4.1.3 Overexpression of GFP-MITF in 501mel has Little Effect on Cell Cycle Progression In order to determine the effects of MITF overexpression on cell cycle, a stable 501mel cell line overexpressing MITF was made. Josue Ballesteros made a 501mel cell line carrying the vector pBac- pEGFP-N1-MITF in the same PiggyBac system as was used to create the MITF knockdown stable cells lines (Álvarez, 2019). These cells were cultured in 1x DMEM + 10% FBS and expression of the GFP- tagged MITF was induced using DOX. As shown in the Western blot in Figure 10, the cells were able to overexpress MITF-GFP, with the overexpression becoming most prominent at 8 to 12 hours past the double thymidine block and the replenishment of DOX in the media.

Unsynchronized MITF-GFP Unsynchronized EV-GFP Cell Cycle Profile Cell Cycle Profile 48.69% G1 39.55% S 50.62% G1 11.76% G2/M 31.88% S 17.51% G2/M

Figure 9. Unsynchronized cell cycle profile upon GFP-MITF overexpression. Donut graph representing the percentage of cells in different cell cycle stages in the unsynchronized samples of the empty vector (left) and MITF-GFP (right) overexpression 501mel cell lines, 48 hours after DOX-induction. N=3.

After DOX was added to the cells to induce MITF overexpression, the cells seemingly proliferated faster, as shown by a larger percentage in S phase (Figure 9). However, unlike in the MITF miRNA knockdown 501mel cells, the percentage difference was not mainly due to a change in G1. There was not a large difference between the empty vector cell line and the MITF overexpression cell line when it comes to percentage of cells in G1. The empty vector cell line had around 51% of cells in G1 while the MITF overexpression cell line had around 49% in G1. Instead, the difference comes in G2/M, where the MITF overexpression cells have a smaller percentage of cells in G2/M. The empty vector cell line had around 18% of cells in G2/M whereas the MITF-GFP overexpression cell line had only around 12% in G2/M. This would indicate potentially a faster progression either through G2 or mitosis compared to the empty vector cell line. Another observation made was that the empty vector line cell cycle profile resembles the cell cycle profile for the 501mel miR-Control cell line more than the 501mel parental cells used for Figure 6E. This further indicates that DOX treatment impacts cell cycle, causing a larger percentage of cells to arrest in G1.

A) EV-GFP Cell Cycle Progression MITF-GFP Cell Cycle Progression

100 100 G2/M G2/M

S S )

G1 ) %

% G1

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0 0 unsync 0 4 8 12 unsync 0 4 8 12 Time (hours) Time (hours)

Figure 10. Cell cycle progression upon GFP-MITF overexpression. A) Stacked column graph showing the percentage of cells in each cell cycle stage for each time point following double thymidine block and DOX induction, N=3. B) Representative Western blot stained with GFP and tubulin that shows the efficiency of the overexpression throughout the time course, N=3

As shown in Figure 10, at the 0-hour time point, both the empty vector and MITF overexpression cell lines showed a majority of cells in G1 that go to a majority in S phase by 4 hours following the double thymidine treatment. At 8 hours after release of the treatment, there is a slight difference between the two groups. The empty vector cell line has a slightly larger percentage of cells still in S phase after 8 hours. However, the largest difference between the groups appeared at 12 hours, when the MITF overexpressing cells have almost completely gone back to a majority of cells in G1 and the empty vector has not.

4.2 MITF Affects Expression of Cell Cycle Regulating Genes Two different cell lines were used to explore the effects of MITF on expression of cell cycle regulating genes. The first cell line used was the MITF miRNA knockdown 501mel cells that was previously used to determine effects of MITF knockdown on cell cycle progression. These cells were made by Remina Dilixiati using a PiggyBac transposon system and were induced using DOX to express miRNAs that blocked MITF expression. DOX was placed in the media of the cells and incubated for 48 hours before RNA was isolated for qPCR. As shown in Figure 11A, the knockdown of MITF on an RNA level was significant (p-value <0.05).

The other cell line used was a FLAG-MITF overexpression A375P cell line made by Valerie Fock. These cells were also created using the PiggyBac system to overexpress FLAG-tagged wild-type MITF upon DOX induction. She used the vector pBAc-pFLAG-HA-MITF in A375P to create a stable cell line. These cells had the MITF overexpression induced with DOX and were incubated for 24 hours before RNA was isolated for qPCR. As shown in Figure 11B, this resulted in a small increase in MITF mRNA expression. Although it is not significant, the smallest fold increase of MITF mRNA was 2 (data not shown), so there is likely an adequate increase to determine effects of MITF overexpression in A375P to explore those effects on cell cycle regulating genes. In order to determine if MITF affected the expression of cell cycle regulating genes, we analyzed the expression of cell cycle genes identified in cells lacking MITF which also had ChIP-seq peaks in regulatory regions (Table 1). Among the genes were CABLES1, CDK2, CDC16 and LZTS1, which are cell cycle regulating genes. The expression of these genes was checked upon either MITF knockdown or MITF overexpression. The values are normalized to actin and the control cell line for each condition and the results are shown in Figure 11.

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Figure 11. Effects of MITF miRNA knockdown or FLAG-MITF overexpression on the expression of cell cycle genes. A) qPCR showing relative mRNA levels of MITF, CABLES1, CDK2, CDC16, LZTS1 and MCM2 upon miRNA knockdown of MITF in 501mel stable cell line. Values normalized to actin and the miR-CTRL cell line. * indicates a p-value of <0.05, N=2. B) qPCR showing relative mRNA levels of CABLES1, CDK2, CDC16, LZTS1 and MCM2 upon overexpression of FLAG- MITF in A375P stable cell line. Values normalized to actin and EV-FLAG cell line. N=3. Un-paired t-test was used to determine significance.

As shown in Figure 11A, the knockdown of MITF mRNA in 501mel cells was significant (p-value <0.05), with almost an average of 50% knockdown. Of the genes checked, only CABLES1 had a significant decrease in expression (p-value <0.05); CDK2 also decreases, but with a p-value of only 0.07, this is not significant. As CDK2 is a known target of MITF, this indicates that the knockdown of

MITF is potentially not strong enough to determine the true effects of knockdown of MITF in 501mel cells. In the FLAG-MITF overexpression A375P cell line, there were no significant changes among all of the genes checked. This is shown on Figure 11B, where there was an apparent, but not significant, increase in MITF mRNA levels. The lowest fold induction upon DOX treatment was 2 (data not shown), suggesting that the treatment worked and that the effects seen are likely due to the overexpression of MITF. There were slight increases among all of the genes checked upon MITF overexpression in the A375P cells, but none were significant.

4.3 Knockdown of CDK2 or LZTS1 Changes Levels of MITF To further explore the role between cell cycle and MITF, CDK2 and LZTS1 were knocked down using siRNA; levels of MITF protein and RNA expression were then determined. As a target of MITF, it is possible that CDK2 potentially regulates MITF through a feedback loop. While there have not been any published papers linking LZTS1 and MITF, the RNA-seq data from Remina Dilixiati and analysis of ChIP-seq data suggests that it might be a target of MITF, possibly also involved in a feedback loop. Furthermore, it is known that LZTS1 interacts with the Cyclin B1/CDK1 complex which would indicate a role in mitosis (Ishii et al., 2001). Since there appears to be a significant increase in MITF protein levels 8 hours after the double thymidine block (Figure 6B) when most of the cells were in G2/M (Figure 6E), there could also potentially be a role for MITF during that time. The decrease of G2/M phase upon MITF overexpression in 501mel cells (Figure 9) also might indicate a role for MITF during this phase. Because of the potential role of MITF during G2/M and the role of LZTS1 during mitosis, LZTS1 was chosen to explore if it had an impact on MITF expression. For both CDK2 and LZTS1, an siRNA knockdown was conducted. The cells were incubated with siRNA for 48 hours before either harvesting for flow cytometry, isolating RNA samples or lysing for Western blot protein analysis. The efficiency of the siCDK2 knockdown was good with an average efficiency of 69% mRNA knockdown (data not shown). The LZTS1 knockdown was not as efficient with an average mRNA knockdown of 52% (data not shown). Figures 12A, B and C show cell cycle profiles 48 hours after treatment with siRNA. In Figure 12A, there is the cell cycle profile of the control condition. The highest percentage of cells are in G1, with around 48% of cells in G1. Around 38% of cells are in S and almost 15% are in G2/M. Upon siCDK2 knockdown, there is a larger portion of cells in G1, as shown in Figure 12B. About 56% of cells are in G1, 34% in S and 9% in G2/M. This would indicate that there is some amount of G1 arrest in these cells. These cells also generally taking longer to split, as shown in a smaller degree of cells in S phase. This is in accordance with what one would expect from CDK2 knockdown cells.

The cell cycle profile of the siLZTS1 cells is shown in Figure 12C. There is a decrease in cells in G2/M, as there is only about 11% of cells in G2/M in comparison to the almost 15% in the control. There are also more cells in S phase, with about 41% in S. There is no large difference of cells in G1 compared to the control, as the control has about 48% in G1 and siLZTS1 has about 49% in G1. This is also in accordance with what would be expected, as cells with a low amount of LZTS1 show less time in G2/M, since the cell spends less time in prometaphase (Vecchione et al., 2007).

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Figure 12. Effects of CDK2 or LZTS1 knockdown on MITF. A) Cell cycle profile of siCTRL cells. N=2. B) Cell cycle profile of siCDK2 cells. N=2. C) Cell cycle profile of siLZTS1 cells. N=2. D) Western blot of protein lysis taken after siRNA knockdown of either a scramble siRNA, siCDK2, or siLZTS1, N=3. E) Quantification of the Western blot, relative protein expression to the scramble control. *** indicates p value of 0.0004, **** indicates p value of <0.0001, N=3. F) Graph showing relative fold induction of MITF RNA levels upon siRNA treatment. *** indicates p value of 0.0009, N=4. Un- paired t-test was used to determine significance.

In Figure 12D, there is a Western blot showing the typical double band pattern of MITF at 50-65kD and a single band for the β-tubulin loading control at 55 kD. Upon CDK2 or LZTS1 siRNA knockdown, the levels of MITF protein decrease. The quantification of the Western blot is shown in Figure 12E, in which the change in MITF protein levels is shown to be significant both after CDK2 and LZTS1 knockdown. mRNA levels of MITF were also determined after CDK2 or LZTS1 siRNA knockdown and the qPCR data is shown in Figure 12F. There was a significant decrease in MITF upon CDK2 siRNA knockdown, but not for LZTS1 siRNA knockdown.

5 Discussion Melanoma is a highly mutated and dangerous form of cancer, with incidences on the rise among people with European heritage. In order to combat this disease, understanding the mechanisms by which melanoma cells survive and proliferate is important. One of the most important and highly researched protein within melanoma is MITF. This thesis aimed to explore the relationship between MITF and cell cycle—both how MITF impacts cell cycle and how it is impacted by cell cycle. The results presented here show that expression of the MITF protein is regulated throughout the cell cycle, as shown by changing protein levels of MITF during the cell cycle. This suggests that the synthesis or stability of the protein is affected. The expression of cell cycle regulating genes CABLES1, CDK2, CDC16, LZTS1 and MCM2 were tested upon MITF knockdown in 501mel cells or MITF overexpression in A375P cells. It was found that CABLES1 significantly decreased in expression upon MITF knockdown. There were no significant changes in expression of the genes checked upon MITF overexpression in A375P cells. Changes of RNA and protein levels of MITF upon knockdown of CDK2 or LZTS1 indicate further regulation of MITF through cell cycle regulating genes.

5.1 Higher MITF Levels in G2/M The use of cell cycle synchronization has been around for a while and is an important method for exploring cell cycle specific phenomena. In this thesis, a double thymidine treatment was used to synchronize cells in G1 phase. Upon release of the treatment, MITF protein levels and phosphorylation status were tracked by taking samples at different time points after release of the treatment and running them on a Western blot. Samples were also taken for flow cytometry analysis to determine if the synchronization was successful. As shown in Figure 6E, the synchronization worked—the majority of the cells start in G1 immediately after release of double thymidine block, then continue to S phase after 4 hours upon release of treatment, going to mainly G2/M 8 hours past treatment, and then getting back to a majority of cells in G1 at 12 hours past treatment. There was a significant increase in MITF protein levels 8 hours after release of thymidine treatment (Figure 6 A,B). This indicates a correlation between G2/M phase of the cell cycle and increased amounts of MITF. This is in agreement with conclusions made by Colin Goding’s group that did a similar experiment but using the technique of mitotic shake-off instead of a double thymidine block (Ngeow, 2015). Since there was a significant increase in MITF protein levels 8 hours after release of the double thymidine block, it was important then to determine if this is due to an increase in transcription of MITF or increased translation or stability of the MITF protein. qPCR showed that

43 there was no difference between the samples, indicating that there is no difference in transcription of MITF for these time points (Figure 6F). This suggests that the differences in MITF protein levels as seen in Figures 6A,B is due to an increase in MITF protein synthesis or stability, not an increase of MITF transcription. While it would be interesting to use cycloheximide to determine if the changes were due to stability, there is evidence that cycloheximide treatment would cause cell cycle arrest (Polymenis & Aramayo, 2015). This would make it difficult to determine if the increase in MITF protein is due to stability, as the method for determining stability might impact the cell cycle stage. Of course, as the cell cycle synchronization was due to a chemical treatment and not a mechanical technique, like mitotic shake, there are likely artifacts of the thymidine treatment. An observation made in this data is the significant decrease in the upper to lower band ratio of MITF at 0 hours after the double thymidine block as shown on Figure 6B. At first, this would appear that there is an association between G1 and MITF de-phosphorylation, but if that were the case, then the same band pattern should be found at 12 hours following the double thymidine treatment, when the majority of cells are in G1 again. Since this is not the case, it is highly likely that the increase in the dephosphorylated version of MITF in the first time point is not due to the cell cycle stage and has more to do with the thymidine treatment. Although this may suggest that any finding may be due to the thymidine treatment, the cells were able to continue through the cell cycle without difficulty. As seen in Figure 6E, the cells were able to continue to S phase in 4 hours, which would likely have not been the case if thymidine was still taking effect. While it is possible that the effects seen at 8 hours after treatment could be due to the double thymidine block, it is more likely that the effects are because of the cell cycle stage.

5.2 Potentially Novel Targets of MITF In exploring the relationship between MITF and cell cycle, it is important to explore the impact MITF has on cell cycle regulating genes. There are already a number of known cell cycle related genes regulated by MITF, such as p21, p27, p16 and CDK2. In preliminary data from Remina Dilixiati, more cell cycle regulating genes changed expression upon MITF knockout, indicating possible MITF regulation. In this thesis, the RNA expression of some of these genes was determined upon knockdown of MITF in 501mel cells or overexpression of MITF in A375P cells. Several of these genes were selected for further analysis. Figure 11A shows the relative RNA expression levels of CABLES1, CDK2, CDC16, LZTS1 and MCM2 upon MITF knockdown in 501mel cells. It was shown that the knockdown of MITF was statistically significant (p-value <0.05), so it is likely that the differences seen in the potential target genes is due to a lack of MITF. The only statistically significant decrease was in RNA expression of CABLES1, although RNA expression of CDK2 also decreased upon MITF

44 knockdown. With a p-value of 0.07, the decrease in CDK2 could be significant upon more repeats and would therefore be in accordance with the literature that states CDK2 is directly bound by and activated by MITF. CDC16, LZTS1 and MCM2 did not change RNA expression upon MITF knockdown. Upon overexpression of MITF in A375P cells, there was no significant increase in CABLES1, CDK2, CDC16, LZTS1 nor MCM2. However, there was a slight trend for these genes to increase in expression upon MITF overexpression. While this does not convincingly show that MITF levels impact the expression of these cell cycle regulating genes, it does not refute the possibility. Something to note is that a Western blot was not conducted on these cells, as Figure 11B shows only increase in levels of MITF mRNA upon overexpression. As the MITF protein does the regulation, not the mRNA, a Western blot might be a better method to determine the efficiency of the overexpression. One thing to consider is that the knockdown and overexpression were done in different cell lines. 501mel is a cell line with a relatively high amount of endogenous MITF, while A375P has a low amount of endogenous MITF. The impact that MITF has on these two different cell lines is most likely very different. For example, the rheostat model proposes that increasing MITF activity leads to increased proliferation. However, Valerie Fock has preliminary data showing that when wild-type MITF is overexpressed in A375P cells, the proliferation rate actually decreases (personal communication). This is in stark contrast to 501mel cells, where overexpression of MITF-GFP led to faster progression through the G2/M phase. Both of these cell lines have the overactive BRAFV600E mutation, but the role of MITF appears to be different between the two. In this case, using the same cell line would make it easier to compare the results between the knockdown and overexpression studies. However, there must be studies to determine the roles of MITF in both MITF-high cells and MITF-low cells in order to fully understand the role MITF plays in these cells. With more information on the role of MITF in various types of melanomas, there is more knowledge there is for potential targeted therapies.

5.3 CDK2 and LZTS1 Expression Affects MITF Levels In experiments done by our group as well as others, CDK2 inhibitors were added to melanoma cells. This resulted in changes in the phosphorylation pattern of MITF—in one situation, the upper to lower band ratio was increased significantly, while in another, the upper to lower band ratio decreased (unpublished data). This lead to the belief that CDK2 might regulate MITF activity through post-translational modifications. However, as seen in Figure 12A, when CDK2 was knocked down in 501mel cells, the biggest change to MITF was not the phosphorylation pattern, but the overall reduced expression of MITF. The quantification of the Western blot showed that the decrease in

45 amount of MITF protein levels is significant (Figure 12B). To determine if this is due to a decrease in transcription of MITF, a qPCR was conducted. Upon CDK2 knockdown, MITF mRNA levels significantly decreased (Figure 12C). This would indicate that this change in MITF upon CDK2 knockdown is due to a reduction in transcription of MITF. This could either be because upon CDK2 knockdown, there is an arrest in G1 and that causes a lower level of transcription or because CDK2 regulates MITF in a feedback loop. As seen in Figure 6F, there is no change of MITF transcription at any point in the cell cycle. This would indicate that the cell cycle stage does not impact MITF transcription. Because of this, it appears that cell cycle arrest is unlikely to be the reason behind lower MITF mRNA levels and more likely to do with CDK2 regulating MITF in a feedback loop. The relationship between LZTS1 and MITF is a novel topic of interest. It was shown that LZTS1 expression is reduced in uveal melanoma (Onken et al., 2008). However, the significance of LZTS1 in melanoma has not been extensively explored. The experiments done in this thesis were to determine if there is a connection between LZTS1 and MITF. During the experiments to determine effects of MITF on certain cell cycle regulating genes, RNA expression of LZTS1 was checked upon MITF knockdown or overexpression. Upon MITF miRNA knockdown in 501mel cells, there was no difference in mRNA expression. Likewise, upon MITF overexpression in A375P cells, the increase in mRNA expression was not significant. However, because LZTS1 plays a role in mitosis and there was an association with MITF and G2/M, LZTS1 became a gene of interest to see if it has a different association with MITF. LZTS1 was knocked down and the effects on MITF were analyzed. There is a significant decrease in MITF protein levels upon LZTS1 knockdown (Figure 12A, B). However, as shown in Figure 12C, there was no significant difference in relative MITF RNA after LZTS1 knockdown. This would suggest that LZTS1 does not play a role in regulating the transcription of MITF. Instead, LZTS1 more likely plays a role to either decrease translation or stability of the MITF protein. The evidence that CDK2 and LZTS1 impact the expression levels of MITF suggests that the relationship between MITF and cell cycle is not one-sided. While MITF directly regulates cell cycle related genes, it is in turn impacted by expression of cell cycle related genes. Rapid proliferation of melanoma cells is one of the phenotypes that make the cancer so deadly and understanding how the cell cycle impacts MITF is crucial.

5.4 Future Experiments In this thesis, the relationship between MITF and cell cycle was explored. However, there are questions that have been left unanswered and other experiments that can be done.

While there was an increase in MITF protein levels in G2/M, the reasoning as to why is not fully known. It is difficult to know if there is an increase in stability during G2/M, a decrease in stability in G1 and S or a blocking of translation in G1 and S. It would be interesting to test stability through the traditional mechanisms of cycloheximide or inhibition of degradation pathways. However, these methods can have effects on cell cycle. It is known that cycloheximide can cause cell cycle arrest (Polymenis & Aramayo, 2015). This would make it difficult to understand the effects of cell cycle on protein stability, as the method to test stability impacts cell cycle. It could also be of interest to inhibit degradation pathways, although there is also interplay between metabolism and cell cycle in which proteins are degraded to produce metabolites and if there is a lack of raw materials, there could be cell cycle arrest (reviewed in (Salazar-Roa & Malumbres, 2017)). This creates a large problem in which any method testing stability would impact cell cycle and therefore it would be difficult to understand what is really happening to MITF during the duration of the cell cycle. The expression of cell cycle related genes upon MITF knockdown or overexpression was tested in this thesis. Additional testing could be interesting. Knocking MITF down in other cells lines and testing the expression of cell cycle related genes is possible. Additional ChIP-seq can be used to determine binding on the different cell cycle related genes. RNA-seq and more qPCRs can be used to test expression of genes upon MITF knockdown or overexpression. The usage of other proliferation techniques could also be of interest, as this thesis only used flow cytometry to determine cell cycle progression. The regulation of MITF through CDK2 and LZTS1 was tested in this thesis by knocking down CDK2 and LZTS1. It was found that upon CDK2 knockdown, there is a decrease in MITF RNA and protien level. However, it is not completely clear whether this is due to only the knockdown of the genes or by the other effects caused by the knockdown. For example, upon CDK2 knockdown, there is a cell cycle arrest in G1. While it could be that the decrease in MITF mRNA is due to CDK2 regulating MITF in a feedback loop, it could also be due to the G1 arrest. However, as shown in Figure 6F, there is no difference in transcription of MITF during different cell cycle stages. This would make it an interesting topic of experimentation to further characterize the effects of CDK2 on MITF. It would be interesting to determine the downstream effects of CDK2 and how that impacts MITF transcription. It would also be of interest to understand the effects of LZTS1 on MITF. There was no significant decrease in MITF RNA upon LZTS1 knockdown, but there was a significant decrease in MITF protein upon LZTS1 knockdown. It would be interesting to know whether or not this is due to a decrease of stability or a decrease of translation. This could be tested by inhibiting degradation

47 pathways. If there is a difference between the LZTS1 knockdown only and LZTS1 knockdown with degradation inhibition, then LZTS1 likely has a stabilizing effect on MITF. Then it would also be interesting to test the relationship between MITF and LZTS1 or the Cyclin B1/CDK1 complex. While the content of this thesis has explored the relationship between MITF and cell cycle, there is still much more to be discovered.

6 Conclusions MITF is the master regulator of melanocytes and it plays an important role in regulation of cell cycle. The experiments described in this thesis attempted to reveal more about the role of MITF in cell cycle and the impact cell cycle has on MITF. The main findings were that MITF plays an important role in cell cycle. There are slight changes in cell cycle profile upon MITF knockdown. Overexpression of MITF resulted in faster cell cycle progression. This is consistent with the rheostat model for MITF which states that high MITF levels are associated with faster cell cycle progression. Higher levels of MITF were also associated with G2/M, as shown by synchronizing melanoma cells. It is possible that there are more cell cycle regulated genes regulated by MITF than are currently known, and it is important to continue to researching each possibility. Based on the experiments done in this thesis, CABLES1 can potentially be regulated by MITF, but more experiments are necessary to determine its status for certain. While other cell cycle regulators such as CDK2, CDC16, LZTS1 and MCM2 were not significantly changed upon MITF knockdown or overexpression, MITF likely has a far-reaching impact on cell cycle regulators and in turn, these cell cycle regulators also have an impact on MITF. Upon the knockdown of CDK2, there is a decrease in MITF mRNA and protein levels, indicating that CDK2 might regulate MITF transcription, most likely through a feedback loop. LZTS1 might also play a role in regulating MITF protein levels, either translationally or through stability. More experiments are necessary to determine the exact mechanisms of regulation but this is a promising start.

Appendix – Supplemental Figures

Supplemental Figure 1. Remaining Western blots for synchronization of 501mel experiments. Shown are two bands for MITF around 50-65 kD. Two distinct bands are shown for Cyclin B1 around 50 kD. Β-tubulin was used as a loading control, shown as one band around 55 kD.

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