Apoptosis efficiency of the transfected elephant and human P35 vectors in P53 varying leiomyosarcoma cells

Comparative Oncology is an approach that integrates and connects commonly occurring cancers seen in animals, into studies focused on cancer biology, prevention, and treatment in humans. Studies show that natural mechanisms can suppress cancer 1,000 times more adequately in certain animals than in humans and that incidence of cancer does not correlate with the number of cells in an organism. Understanding how evolution has allowed for effective cancer suppression in larger animals like elephants, will lead to improved cancer prevention methods to be used for humans, an effort of this study. The low rate of cancer within elephants is seemed to be caused by the increased number of Tp53 genes, a tumor suppressor gene, in the elephant genome and the presence of numerous retrogenes. Using Leiomyosarcoma, also known as LMS or soft tissue sarcoma, as a model we hoped to determine the apoptosis efficiency of the elephant and human p53 vectors in p53 varying LMS cells. We hoped to see more apoptosis occurring in LMS cells treated and transfected with combinations of elephant p53/R9 and Doxorubicin vectors. To test and answer the above research question, four leiomyosarcoma cell lines were transfected and treated with combinations of elephant p53 (Ep53), R9, human p53 (Tp53), and doxorubicin vectors. Incucyte analysis was done for the different cell lines to track the change in caspase activity and apoptosis in the leiomyosarcoma cell lines depending upon the vectors they were transfected with. Results from these analyses showed higher levels of apoptosis in the leiomyosarcoma cells treated with the elephant p53, R9, and Ep53-R9- Dox vectors in comparison to the human p53 vector suggesting that elephant p53, R9, and Doxorubicin may work together in increasing the apoptosis of human LMS cancer cells and therefore suppress cancer more efficiently than human p53 and Doxorubicin alone.

ABSTRACT Comparative Oncology is an approach that integrates and connects commonly occurring cancers seen in animals, into studies focused on cancer biology, prevention, and treatment in humans. Studies show that natural mechanisms can suppress cancer 1,000 times more adequately in certain animals than in humans and that incidence of cancer does not correlate with the number of cells in an organism. Understanding how evolution has allowed for effective cancer suppression in larger animals like elephants, will lead to improved cancer prevention methods to be used for humans, an effort of this study. The low rate of cancer within elephants is seemed to be caused by the increased number of Tp53 genes, a tumor suppressor gene, in the elephant genome and the presence of numerous retrogenes. Using Leiomyosarcoma, also known as LMS or soft tissue sarcoma, as a model we hoped to determine the apoptosis efficiency of the elephant and human p53 vectors in p53 varying LMS cells. We hoped to see more apoptosis occurring in LMS cells treated and transfected with combinations of elephant p53/R9 and Doxorubicin vectors. To test and answer the above research question, four leiomyosarcoma cell lines were transfected and treated with combinations of elephant p53 (Ep53), R9, human p53 (Tp53), and doxorubicin vectors. Incucyte analysis was done for the different cell lines to track the change in caspase activity and apoptosis in the leiomyosarcoma cell lines depending upon the vectors they were transfected with. Results from these analyses showed higher levels of apoptosis in the leiomyosarcoma cells treated with the elephant p53, R9, and Ep53-R9Dox vectors in comparison to the human p53 vector suggesting that elephant p53, R9, ii and Doxorubicin may work together in increasing the apoptosis of human LMS cancer cells and therefore suppress cancer more efficiently than human p53 and Doxorubicin alone. iii TABLE OF CONTENTS ABSTRACT ii INTRODUCTION 1 METHODS 4 RESULTS 11 DISCUSSION 23 REFERENCES 27 iv 1 INTRODUCTION Introduction to Comparative Oncology Comparative Oncology is a research approach that integrates and connects commonly occurring cancers seen in veterinary patients and large mammals with low rates of cancers to broader studies focused on human cancer biology, prevention, and treatment. According to the Journal of the American Medical Association, only 5% of elephants die from cancer compared to 25% for humans. It was thought that this was due to a correlation between body size and the risk of cancer; the larger the body size, the more cells an individual has which leads to a higher risk of cellular mutations and cancer. However, according to Peto’s Paradox the incidence of cancer does not correlate with the number of cells in an organism. Animals with 1,000 times more cells than humans do not encompass an increased F gure 1. An ustrat on of Peto’s Paradox. The so d red ne shows a re at onsh p between cancer rate and body mass) * fespan), and the dashed red ne represents an approx mat on of the expected cancer rate. The so d b ue ne represents the observat on that there s no re at onsh p between cancer r sk and body mass) * fespan) To s, oddy and Ma ey et al 2017). risk in developing cancer (Figure 1). This observation implies that natural mechanisms can suppress cancer 1,000 times more adequately in certain animals than in human cells (Caulin et al., 2011). Understanding how evolution has allowed for effective cancer suppression in larger animals such as the elephant, will lead to improved cancer prevention methods to be used for humans. 2 Role of p53 in Cancer Prevention in Animals and Humans p53, also known as the “guardian of the genome”, is a protein encoded by the TP53 tumor suppressor gene. It plays a crucial role in the prevention of cancer. If p53 function is lost it can cause suppression of cell apoptosis and, an increase in cell proliferation, and genomic instability. These characteristics are hallmarks of cancer cells, which are cells that never die, grow indefinitely, and are brimming with mutations. Human genomes carry two functional TP53 alleles. Even when both TP53 alleles are fully functioning, 33% females and 50% males develop cancer during their lifetime (Khanna et al., 2007). Individuals with Li-Fraumeni Syndrome, also known as LFS have only one functional TP53 which translates into a 100% lifetime cancer risk. Furthermore, 50% of all tumors contain a mutation in the TP53 gene. These data together highlight the importance of p53 and its role in cancer prevention (Abegglen et al., 2015). Role of p53 and Retrogenes in Preventing Cancer in Elephants As mentioned earlier, elephants have much lower rates of cancer incidence compared to humans (Caulin et al., 2011). When scientists examined the elephant genome, they found that elephants have 20 copies of the Tp53 gene. Humans on the other hand have one copy of the Tp53 gene. Thus, Elephants have 20 times the number of Tp53 alleles compared to humans. The elephant genome also contains of 19 retrogenes. One of the Tp53 copies in the elephant genome can be compared to other mammals. However, the 19 other copies are different. Most genes consist of coding (exons) and non-coding (introns) protein sections. Usually, after a gene has been transcribed the introns are spliced as part of post-transcriptional editing. This occurs before the mRNA is translated 3 into a protein. In elephants all but one of the Tp53 genes lacked introns. This shows that the 19 other copies of Tp53 came from an edited RNA molecule that was converted back to DNA after its introns were spliced. Genes with these characteristics are known as retrogenes (Abegglen et al., 2015). Expression of elephant retrogenes increase with DNA damage. Tp53 retrogenes functionally increase elephant cell response to DNA damage by triggering p53 dependent apoptosis rather than increasing DNA repair (Abegglen et al., 2015). The low rate of cancer within elephants is thus hypothesized to be caused by the increased number of TP53 genes and the presence of numerous retrogenes in the elephant genome. p53 Mediated Programmed Cell Death There are two specific p53 mediated apoptosis pathways in mammalian cells. The first pathway, the BCL-2-regulated pathway, is activated when cells are put under stressful conditions such as DNA damage and cytokine deprivation. The second pathway, the extrinsic death receptor pathway, is activated when two protein domains of the tumor necrosis factor receptor (TNFR) join. In both pathways, p53 initiates apoptosis by signaling the pro-apoptotic BH3 members of the BCL-2 protein family. The BH3 proteins bind and inhibit the pro-survival BCL-2 proteins which unleashes cell death effectors BAK and BAX. The activation of BAK and BAX causes the cell’s outer mitochondrial membrane to break apart, aka MOMP, which then leads to apoptosis (Aubrey et al., 2018). 4 Research Question and Strategy In this study we attempted to understand the role of elephant p53 and retrogenes in cancer biology. For our analysis we used Leiomyosarcoma cells, an aggressive soft tissue sarcoma derived from smooth muscle cells. Four Leiomyosarcoma (LMS) cell lines were investigated; each cell line consists of either wild type P53, mutant P53, or is P53 null. To explore the effect of elephant P53 and R9, vectors were transfected into LMS cells in various combinations of human P53, elephant P53, R9, and treated with doxorubicin. The goal of this investigation was to understand the apoptosis efficiency of the transfected elephant and human p53 vectors in p53 varying LMS cells, with the hopes of finding a cancer prevention method for the future. METHODS Cell Lines Used The cell lines used in this study, their origin, and p53 status are described in Table 1. Table 1. Characteristics of the Different LMS Cell Lines Used Cell Line LMS 1 Cell Line Origin Uterus P53 Status Mutation P53 Description Loss of function; overexpression of mutant protein LMS 3 Lung and Thigh Splice Site Homozygous Mutation genomic; no expression 5 LMS 4 Uterus Deleted Homozygous genomic; no expression LMS 5 Thigh Wild Type Wild Type Thawing Cells to Prep for Growing The media, flasks, and cell tubes were sterilized with ethanol. 12-13 mL of media was pipetted into the flasks. Frozen cells were thawed and then pipetted into the flask. The cell sample and media (RPMI media) were slowly mixed to cover the bottom of the flask. The flask lid was screwed on halfway to avoid pressure from building up inside the flask. The flasks were placed at 37 degrees Celsius in the incubator to incubate, and the cells were monitored daily for growth. Cell Growth and Passaging All Leiomyosarcoma cell lines were grown and passaged before the transfections. The cells were passaged and split if overgrowth occurred, and the media was constantly changed to provide the cells with fresh nutrients to avoid toxic metabolite accumulation. To begin cell growth, the cell lines were thawed and incubated in flasks with RPMI media. When cells needed to be passaged the following procedure was followed. PBS was used to wash the cells to remove the old media. PBS was aspirated from the cell flask and trypsin was added to the flask to help the cells detach from the flask. The cells were incubated for 3-7 minutes. RPMI media was added into the flask and the cells were squirted with media to fully detach them from the bottom of the flask. All the media and 6 cells from the flask were pipetted into a 50 mL conical tube and centrifuged at 1400 RPM for 3-5 minutes. Excess media was aspirated from the conical tube and the cell pellet was left at the bottom of the flask. Depending upon the cell to media ratio, media was added to the conical tube and the cell pellet and media were mixed. Depending upon the cell to media ratio, the cell pellet/media mixture was pipetted into a new flask and placed into the incubator for continued cell growth. Cell Line Growth Characteristics The type of cell growth and images of each LMS cell line used are shown in Table 2. Table 2. Type of Cell Growth for the Different LMS Cell Lines Used Cell Type LMS 1 Cell Growth The cells grow very quickly and are elongated in shape. Image 7 LMS 3 The cells grow very slowly and are spaced out. They do not grow to be very confluent like the LMS 1 cell lines. The cells are elongated and circularly shaped. LMS 4 The cells grow moderately. They grow in clumps and space out. The cells are more circularly shaped. LMS 5 The cells grow moderately and are more circular in shape. They are more clumped together rather than being spaced out. 8 Counting Cells 20 ul of the cell pellet/media mixture and 20 ul of trypan blue was pipetted into a well of a 96 well plate. The cells and trypan blue were mixed. Cell counts were obtained by pipetting 20 ul of the mixture from the 96 well plate and injecting the mixture into one of the holes in the hemacytometer plate. The plate was then placed in the automated cell counter to count cells. Freezing Cells When the passage number was high or when too many cells had grown, the cells were frozen down for future use. A cell pellet was obtained from passaging and was resuspended in media. In each cryovial, 950 uL of media + cells and 50 ul of 5% DMSO were pipetted. Labels were made for each cryovial with the cell type, date, media + freezing media, initials, and cell amount. The vials were stored in the -80 freezer. Media Preparation 10% FBS (50 mL) and 1 mL of Normocin were thawed in the water bath. FBS and Normocin were pipetted into a fresh bottle of 500 mL media (RPMI – turquoise colored bottle). The bottle was shaken to mix the components. 5 mL of PenStrep, ITS, Na Pyruvate, Hepes, and Glutamax were pipetted into the media bottle. Prepared media was stored under refrigeration. 9 Vector Transfection Once the cells were confluent, they were counted on the cell counter to at least one million cells. 20 microliters of the cell solution and 20 microliters of trypan blue were pipetted onto the hemacytometer plate and placed in the cell counter. The cells were then prepped for vector transfection by passaging. DPBS was added into the mix of cells and media. The tubes in which the transfection was to be done were labeled and an equal amount of the DPBS mixture was added to each tube. The tubes were centrifuged at 4 g for 1 min. The DPBS was aspirated. The transfection device was set up with its set values: 1250 as the voltage, 40 for the gap, and 1 for the pulse. The cells were resuspended in 110-115 ul of Buffer R. Transfection DNA, quantified using a nano drop was added into the tubes containing Buffer R and the cell pellet. Cells were kept on ice while 900 uL of OptiMem was pipetted into new tubes. Original cell pellet/DNA/Buffer R mixture was extracted from the original tubes using the neon pipette. The neon pipette was used to transfect the different vectors (R9, EP53, TP53) into the Leiomyosarcoma cells. It was important to ensure that there were no bubbles in the pipette. The neon pipette administered a small voltage to insert the vector into the cell. The pipette was removed from the transfection device and the transfected cell solution was placed into a new Eppendorf tube containing Optimem. 5 mL of media was pipetted into T25 flasks. Pictures were continually taken to analyze cell activity. The transfected cells were incubated for 3-4 hours in T75 flasks for the vectors to fuse with the cell’s DNA. The cells were prepped for sorting by pipetting the cells and media in the flasks into conical tubes. Sorting the cells separated the transfected cells from the cells that were not 10 transfected for the experiment to have a higher yield of transfected cells. The prepped cells were placed into the cell sorter and sorted. Plating Cells Sorted transfected cells were plated in 96 well plates. The cells + media in the sorting tubes were counted and centrifuged and the amount of media that was needed to have 6000 cells/100 ul or 60,000 cells/mL was added. A 100 ul of the cells + media mixture was added to a 96 well plate depending upon the number of wells needed for the experiment. Each well consisted of RPMI media and the sorted cell solution (6000 cells in each well). PBS was added around the plate in the extra wells to provide the cells moisture. The well plate was then incubated for two hours before being placed into the incucyte for analysis. Incucyte Analysis for Apoptosis Efficiency The 96 well plate was placed into the incucyte for scanning. The incucyte took pictures of the cells in the plate over a set time to graph cell apoptosis activity. To acquire scans for analysis, the cells were viewed for red vs green staining. mCherry shows which cells were expressing genes from the plasmids after transfection and caspase green stains the dead cells. The yellow cells are alive cells transitioning to dead cells. Within the incucyte software, confluence graphs were created to analyze the LMS cells apoptosis efficiency data. 11 RESULTS LMS 1 (p53 mutated) Vector Dependent Apoptosis Efficiency Using Confluence To analyze the apoptosis efficiency of the elephant and human p53 vectors in p53 mutated LMS 1 cells, the cells were transfected with various vector combinations and treated with 0.25 uM of Doxorubicin to measure their confluence (cell growth) over time. As seen in Figure 2, LMS 1 control cells (no plasmid but zapped with a voltage) and LMS 1 no plasmid cells (no plasmid or voltage applied) have the highest amount of confluence (grey colored lines with circle figure) compared to the other vectors. This result falls in line with the expectation since the control cells were shocked with the neon pipette but were not transfected with any vectors and the no plasmid cells were not shocked and received no new plasmid. The combined Tp53 and R9 vector had the highest percent of confluence (35%) (circular green line). The Tp53-R9 + Doxorubicin, Ep53-R9 + Doxorubicin, and Ep53 + dox vectors had the lowest percent of confluence. The combination of Ep53 and the R9 gene had a lower confluence percent when compared to Ep53 alone (circular red line) and R9 alone (circular yellow line). The Tp53 vector (circular blue line) had the lowest confluence percent compared to all the vectors that had no Doxorubicin added. The Tp53 and Ep53-R9 vectors were close in confluence percentage. 12 Figure 2. LMS 1 (p53 mutated) Vector Dependent Apoptosis Over Time LMS 1 (p53 mutated) Vector Dependent Apoptosis Efficiency Using Apoptosis To further analyze and compare the apoptosis efficiency of the elephant and human p53 vectors in p53 mutated LMS 1 cells, a green integrated intensity graph was created. This graph takes LMS 1 cell apoptosis directly into account to then compare it to the LMS 1 confluence graph. As seen in figure 3, the Ep53-R9 vector (circular orange line) had the highest amount of apoptosis. The Tp53-R9 + Doxorubicin vector had the highest amount of apoptosis when compared to the other vectors + Doxorubicin. R9 alone seemed to have caused more apoptosis than Tp53 alone, Tp53-R9, and Ep53 13 alone. This result does not line up with what was expected. Similarly, the untreated Doxorubicin vectors seemed to have caused more apoptosis than the treated Doxorubicin vectors. This should not be the case as doxorubicin is a chemotherapy drug that kills all cells. The control, no plasmid control, and control + Doxorubicin transfected LMS 1 cells had the lowest amount of apoptosis. Figure 3. LMS 1 (p53 mutated) Vector Dependent Apoptosis Over Time 14 LMS 1 (p53 mutated) Isolated Ep53, Tp53, & R9 Confluence To measure the apoptotic effect of Ep53, R9, and Tp53 in p53 mutated LMS 1 cells, confluence vector graphs were isolated to see a more direct correlation between confluence and vector type. As seen in figure 4, Tp53 alone had the least amount of cell growth, then came Ep53, and then R9. All vectors when treated with Doxorubicin caused complete cell death. The expectation was to see Ep53 alone and Ep53 + Doxorubicin cause more cell death when compared to Tp53 and R9 alone and with Doxorubicin. Figure 4. LMS 1 (p53 mutated) Isolated Ep53, Tp53, & R9 Confluence Over Time 15 LMS 1 (p53 mutated) Isolated Tp53 Apoptosis vs. Time To measure the apoptotic effect of Ep53 in p53 mutated LMS 1 cells, Tp53 vector graphs were isolated to compare Tp53 apoptosis with and without Doxorubicin treatment. As seen in figure 5, the Tp53 + Doxorubicin causes the highest amount of apoptosis at 20 hours, but the Tp53 vector alone causes the highest amount of apoptosis overall. The 16 control values were as expected and caused the least amount of apoptosis. It was expected that the Tp53 + Doxorubicin vector would cause the most amount of apoptosis, but according to figure 5 Tp53 alone had the highest amount of apoptosis overall. Figure 5. LMS 1 (p53 mutated) Isolated Tp53 Confluence Over Time 17 LMS 1 (p53 mutated) Isolated Ep53 and R9 Apoptosis vs. Time To measure the apoptotic effect of Ep53 and R9 in p53 mutated LMS 1 cells, Ep53 and R9 vector graphs were isolated to compare apoptosis with and without Doxorubicin treatment. As seen in figure 6, the R9 + Doxorubicin vector causes the highest amount of apoptosis at 23 hours but then seemed to fall after the 23-hour mark. The R9 vector alone causes the highest amount of apoptosis at 27 hours and then seemed to cause more apoptosis overall. The Ep53 + Doxorubicin vector caused the highest amount of apoptosis overall. All the control vectors caused the least amount of apoptosis overall. Figure 6. LMS 1 (p53 mutated) Isolated Ep53 and R9 Apoptosis Over Time 18 LMS 1 (p53 mutated) Isolated Ep53 and R9 Apoptosis vs. Time To measure the apoptotic effect of Doxorubicin and the Ep53 vector in p53 mutated LMS 1 cells, Doxorubicin and Ep53 isolated vector graphs were compared. Doxorubicin causes apoptosis within the LMS 1 cells (green colored graphs). In the apoptosis graph (top left side) the Dox treated p53 mutant LMS 1 cells have the most apoptosis and in the confluence graph (top right side) the untreated Dox p53 mutant LMS 1 cells have the highest amount of confluence. The Ep53 + Doxorubicin vector caused more apoptosis when compared to the Ep53 vector alone (bottom left side). The Ep53 vector alone cause more confluence when compared to the LMS 1 cells treated with Ep53 and Dox (bottom right side). The no plasmid p53 mutated LMS 1 cells had more confluence when compared to the Ep53 alone vector. The Ep53 + Dox vector had less apoptosis than the no plasmid Dox treated LMS 1 cells. The no plasmid LMS 1 cells had more cell growth due to mutant p53 which caused more apoptosis when compared to functional Ep53 vectors in the LMS 1 cells. Figure 7. LMS 1 (p53 mutated) Isolated No Plasmid and Ep53 Control and Apoptosis 19 LMS 4 (p53 deleted) Vector Dependent Apoptosis Efficiency Using Confluence To analyze the apoptosis efficiency of the elephant and human p53 vectors in p53 deleted LMS 4 cells, the cells were transfected with various vector combinations and treated with 0.25 uM of Doxorubicin to measure their confluence (cell growth) over time. As seen in figure 8, the control LMS 4 cells (no plasmid or voltage applied) have the highest amount of confluence (grey colored line with circle figure) compared to the other vectors. Compared to all the treated Doxorubicin vectors, all the untreated Doxorubicin LMS 4 cells caused more confluence. Tp53 (Hp53) alone caused less confluence when compared to R9 and Ep53 alone or Ep53-R9 combined. Ep53-R9 + Dox caused the least amount of confluence compared to Tp53 + Dox and R9 + Dox. However, DMSO + Ep53-R9 caused the least amount of confluence which was not expected. DMSO was used as another control value. 20 Figure 8. LMS 4 (p53 deleted) Vector Dependent Confluence Over Time LMS 4 (p53 deleted) Vector Dependent Apoptosis Efficiency Using Apoptosis To further analyze and compare the apoptosis efficiency of the elephant and human p53 vectors in p53 deleted LMS 4 cells, a green integrated intensity graph was created. This graph takes LMS 4 cell apoptosis directly into account to then compare it to the LMS 4 confluence graph. As seen in figure 9, the control LMS 4 cells seemed to have caused the leas amount of apoptosis. However, the DMSO control with Ep53-R9 vectors seemed to have caused the highest amount of apoptosis which was not expected. The dox treated vectors had higher amounts of apoptosis when compared to the untreated vectors. Ep53-R9 + Dox caused more apoptosis than the Hp53, Ep53, and R9 vectors alone. 21 Figure 9. LMS 4 (p53 deleted) Vector Dependent Apoptosis Over Time LMS 4 (p53 deleted) +/- Doxorubicin Cell Confluence To measure the apoptotic effect of Doxorubicin and the various vector combinations in p53 deleted LMS 4 cells, Doxorubicin treated and untreated vector graphs were isolated and compared. The control +/- Doxorubicin does not show a large difference in cell confluence. In both graphs the control cells caused the highest amount of confluence which is expected. The R9 + doxorubicin had less cell confluence than the R9 without Doxorubicin. Hp53 + Dox had less cell confluence than Hp53 without Dox. Ep53 without Dox had less cell confluence than Ep53 with Dox. Ep53-R9 + Dox caused the least amount of cell confluence when compared to all vectors treated with and without Doxorubicin. 22 Figure 10. LMS 4 (p53 deleted) +/- Doxorubicin Isolated Confluence 23 DISCUSSION The low rate of cancer within elephants is seemed to be caused by the increased number of p53 genes and the presence of numerous retrogenes. Using Leiomyosarcoma as a model we hoped to determine the apoptosis efficiency of the elephant and human p53 vectors in p53 varying LMS cells. We hypothesized to see more apoptosis occurring in LMS cells treated and transfected with Ep53-R9 + Dox vectors and the least amount of apoptosis occurring in the control, R9 alone, and Tp53 alone vectors. To test these predictions, incucyte analysis was done for the p53 varying LMS cell lines to track the change in caspase activity and apoptosis depending upon the vectors the cells were transfected with. LMS 1 (p53 mutated) Vector Dependent Apoptosis Efficiency As seen in figures 2-7, the LMS 1 control cells caused the least amount of apoptosis and caused the highest percent of cell confluence which suggests that when the LMS 1 cells lacked p53 they grew considerably because no cancer cell suppression was occurring. The Tp53 vector alone caused less cell confluence when compared to the Hp53 and R9 vectors alone which suggests that human p53 is quite effective in cancer cell suppression. A combination of Ep53-R9 vectors caused more cell apoptosis when compared to the Ep53 and R9 vectors alone suggesting that in elephants Ep53 and R9 retrogene work together in suppressing cancer cell growth. As seen in figure 2, the combination of vectors when treated with 0.25 uM Doxorubicin had the least percent of cell confluence overall which indicate that when the natural guardian of the genome (p53) was combined with a curated chemotherapy drug (doxorubicin) cancer cell 24 suppression was much more efficient. As seen in figure 3, R9 alone seemed to have caused more apoptosis than the other vectors (Tp53, Ep53, Tp53-R9) alone. This result does not line up with what was expected suggesting a limitation that could have taken place while treating, sorting, or transfecting the cells. Similarly (in figure 3), the untreated Doxorubicin vectors seemed to have caused more apoptosis than the treated Doxorubicin vectors indicating that the chemotherapy agent was not as efficient in cancer cell apoptosis. Figure 4 once again strengthens the result that Tp53 is quite efficient in cancer cell apoptosis and that Ep53 and R9 combined are more efficient in cancer suppression than when they are alone. LMS 4 (p53 deleted) Vector Dependent Apoptosis Efficiency As seen in figures 8-10, the LMS 4 control cells caused the least amount of apoptosis and caused the highest percent of cell confluence which suggests that the p53 deleted LMS 4 cells grew considerably because no cancer cell suppression was occurring. Like the LMS 1 cells, the Tp53 vector alone in LMS 4 caused less cell confluence when compared to the Hp53 and R9 vectors alone which suggests that human p53 is quite effective in cancer cell suppression. The Ep53-R9 + Doxorubicin treated LMS 4 cells had the least amount of confluence indicating that when the elephant p53 and the R9 retrogene were combined and treated with Doxorubicin, it caused the most amount of cancer cell suppression. All vectors caused less confluence when treated with Doxorubicin suggesting that when the natural guardian of the genome (p53) was combined with a curated chemotherapy drug (doxorubicin) cancer cell suppression was 25 much more efficient. DMSO was used as another control value, but it seemed to have caused an increased effect of cancer cell suppression. Other Case Studies The results in this investigation are close in consistency with previously published p53 investigations. For example, we discovered a higher p53 apoptosis efficiency in LMS 1 and LMS 4 cells when the Hp53-R9 combined vector was used. According to Michael Sulak in eLife, elephant cells up-regulate p53 and induce apoptosis in response to lower levels of DNA damage (caused by drugs and radiation) which suggests that elephant p53 is more sensitive to DNA damage and more prone to apoptosis. Sulak was also able to show that elephant cells needed retrogenes for their enhanced apoptosis response. Adding the same retrogenes to mouse cells made the mouse cells more sensitive to DNA damage as well. This suggests that Ep53 and the R9 retrogene worked together to cause a higher amount of apoptosis within the p53 mutated LMS 1 and p53 deleted LMS 4 cells. Study Limitations The LMS cells were placed under extensive amounts of stress when they were sorted, transfected, and treated with Doxorubicin. The stress induced by the different vectors and Dox treatment caused many cells to die before incucyte analysis was done. This may have caused the unexpected discrepancies that occurred within the DMSO control LMS 4 cells and may have also caused the difference in apoptosis between the treated and untreated LMS 1 cells as seen in figure 3. 26 Due to COVID-19 restrictions, incucyte analysis was not done for the LMS 3 and LMS 5 cells. However, further research can be done for the other cell lines to see how transfected vectors affect cells with different p53 mutations. New questions have arisen from this investigation that can be answered with further research. For example, in this investigation only one retrogene was experimented with. It is not clear whether other Ep53 retrogenes have similar effects, and if other elephant retrogenes also work with Ep53 to increase cancer cell apoptosis. Further research can include incucyte analysis for combinations of Ep53 and other elephant retrogenes along with +/- Doxorubicin. Conclusion Results from the incucyte analyses showed higher levels of apoptosis in the both lines of p53 deleted and p53 mutated leiomyosarcoma cells treated with the elephant p53, R9, and Ep53-R9-Dox vectors in comparison to the human p53 vector. This suggested that elephant p53, R9, and Doxorubicin may work together in increasing the apoptosis of human LMS cancer cells and therefore suppressing cancer more efficiently than human p53 and Doxorubicin alone. 27 References Mayo Clinic Staff. (2011). Soft Tissue Sarcoma. Mayo Foundation for Medical Education and Research (MFMER). https://www.mayoclinic.org/diseasesconditions/leiomyosarcoma/cdc-20387733?p=1 Paoloni, C. Melissa., Khanna, Chand. (2007). Comparative Oncology Today. National Institutes of Health. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2174910/ Caulin, F. Aleah., Maley, C. Carlo. (2011). Peto’s Paradox: Evolution’s Prescription for Cancer Prevention. National Institutes of Health. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3060950/ Schiffman Lab. P53 Studies. Huntsman Cancer Institute. https://uofuhealth.utah.edu/huntsman/labs/schiffman/research/ Tollis, Boddy and Maley. (2017). Peto’s Paradox Arizona State University. Eurek Alert! https://www.eurekalert.org/multimedia/796418 Aubrey, J. Brandon., Kelly, L. Gemma., Janic, Ana., Herold, J. Marco., Strasser, Andreas. (2017). How does p53 induce apoptosis and how does this relate to p53mediated tumor suppression?. Cell Death and Differentiation. https://www.nature.com/articles/cdd2017169 Abegglen, M. Lisa., Caulin, F. Aleah., Chan, Ashley., Lee, Kristy., Robinson, Rosann., Campbell, S. Michael., Kiso, K. Wendy., Schmitt, L. Dennis., Waddell, J. Peter., Bhaskara, Srividya., Jensen, T. Shane., Maley, C. Carlo., Schiffman, D. Joshua. (2015). Potential Mechanisms for Cancer Resistance in Elephants and Comparative Cellular Response to DNA Damage in Humans. Preliminary 28 Communication. https://faculty.wharton.upenn.edu/wpcontent/uploads/2016/08/shanejensen.elephant15.pdf Gaughran, J. Stephen., Pless, Evlyn., Stearns, C. Stephen. (2016). How Elephants Beat Cancer. US National Library of Medicine Nation Institutes of Health. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5089389/ Abegglen, M. Lisa., Caulin, F. Aleah., Chan, Ashley., Lee, Kristy., Robinson, Rosann., Campbell, S. Michael., Kiso, K. Wendy., Schmitt, L. Dennis., Waddell, J. Peter., Bhaskara, Srividya., Jensen, T. Shane., Maley, C. Carlo., Schiffman, D. Joshua. (2015). How Elephant Defend Against Cancer. National Institutes of Health. https://www.nih.gov/news-events/nih-research-matters/how-elephants-defendagainst-cancer