Via Research Recognition Day 2024 VCOM-Carolinas

Via Research Recognition Day VCOM-Carolinas • February 9, 2024

Contents

Welcome ........................................................................................................................... 3 Speaker ............................................................................................................................. 4 Agenda ............................................................................................................................... 5 Abstracts

Biomedical Studies..................................................................................................................6

Clinical Case-Based Reports................................................................................................18

Educational Reports..............................................................................................................82

2

2024 Research Recognition Day

Welcome

Welcome to the tenth annual Edward Via College of Osteopathic Medicine Via Research Recognition Day on the VCOM-Carolinas Campus. Each year, the Via Research Recognition day is a significant event for VCOM that supports the mission of the College to provide medical education and research that prepares globally minded, community-focused physicians and improves the health of those most in need. The Via Research Recognition Day offers a forum for health professionals and scientists in academic institutions, teaching hospitals and practice sites to present and benefit from new research innovations and programs intended to improve the health of all humans. By attending the sessions with the speakers, participants have the opportunity to learn cutting edge information in the physiological bases of osteopathic manipulative therapy efficacy, new trends in physician-based research networks, and how to develop innovative research projects with high impact for human health. Poster sessions allow participants to learn about the biomedical, clinical and education-simulation research activities at VCOM-Carolinas and its partner institutions.

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2024 Research Recognition Day

Speaker

Dr. Matthew (Matt) Gevaert is a Co-Founder and a Board Member of Kiyatec, Inc. Under Matt’s leadership Kiyatec is disrupting cancer therapy selection with patient-specific prediction of response to drug therapies, prior to treatment. Kiyatec achieves this by measuring the response of individual patient live cancer cells with its innovative 3D cell culture technology platform, with success demonstrated in peer-reviewed publications for multiple tumor types that score >95th percentile in on-line impact. Through a dedicated focus on direct relevance to cancer patients, Kiyatec has successfully attracted multiple rounds of private sector investment, developed its 3D Predict™ and KIYA Predict™ ex vivo 3D cell culture platforms, and published the first functional precision oncology assay with clinically-correlated prospective predictive therapeutic response evidence in multiple tumor types. To-date, Kiyatec has been awarded more than $5M of competitively awarded federal funding including contracts from the National Cancer Institute, cultivated clinical collaborations at leading national cancer institutions and built productive relationships with premier biopharmaceutical companies developing the cancer therapies of the future. Matt is a graduate of the University of Waterloo (B.Sc., Chemistry) and of Clemson University (M.S. and Ph.D., Bioengineering). He serves on a number of professional and community boards and occasionally teaches an MBA graduate course in technology entrepreneurship for professional business students.

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2024 Research Recognition Day

Agenda

7:30 - 9:00am

First Floor

Registration and Continental Breakfast.............................

8:30 - 10:30am

Third Floor

Poster Presentations..........................................................

10:30am - 11:00pm

Lecture Hall 2

Opening Remarks..............................................................

11:00 – 12:00pm

Keynote Address: Matt Gevaert, PhD, Kiyatec Co- Founder and Board Member..............................................

Lecture Hall 2

12:00 - 12:15pm

Lecture Hall 2

Student Researcher of the Year: Max Muir........................

12:15 – 1:15pm

First Floor Main Hall

Lunch.................................................................................

1:15 - 1:30pm

Reconvene in the Lecture Halls

1:30 - 3:45pm

Lecture Hall 2

Oral Presentations - Clinical Education............................. Nicholas Minner Vineet Madishetty & Mark Dawod Creighton Kellogg & Andrew Hospodor Oral Presentations - Biomedical Sciences......................... Rebecca Corallo An Nguyen Oral Presentations- Cased-Based Reports....................... Rayhan Karimi Quang-Minh Dang Break

Lecture Hall 2

Lecture Hall 2

3:45 - 4:00

4:00 - 4:30

Lecture Hall 2

Awards Ceremony..............................................................

4:30 - 4:45

Lecture Hall 2

Closing Remarks................................................................

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2024 Research Recognition Day

Biomedical Studies

Current Progress on the Influence Human Genetics Has on the Efficacy of Tyrosine Kinase Inhibitors Used to Treat Chronic Myeloid Leukemia Tara Prakash, OMS-III and Steven A. Enkemann, Ph.D. Edward Via College of Osteopathic Medicine, Dept. of Cell Biology and Physiology, Spartanburg, South Carolina

Results

Conclusions

Abstract Background

Conclusions

Liver Enzymes • Liver enzymes are a natural pharmacogenetic consideration when looking at how human variation can affect drug efficacy • Liver enzyme interactions are summarized on Table 1 • Human variation in liver metabolism may not be particularly relevant in imatinib, nilotinib, and bosutinib efficacy as these drugs are slowly metabolized and the majority of their administered dose is excreted unchanged • Dasatinib may be the exception to this rule due to its short half-life and the fact that only the minority of the administered dose is excreted unchanged. Thus, its interaction with liver enzymes should be further investigated. Transporters • Efflux transporters have been a major focus in the study of TKI metabolism • Known data regarding which transporters interact with which drugs is summarized in Table 2 • Existing studies do not account for the ubiquity of the expression of these transporters, ability of many tumors to overexpress proteins, or the fact that multiple transporters could be acting in concert • The above factors should be isolated in future research • Lesser-known transporters may be involved and are an important avenue for future research

• Chronic myeloid leukemia (CML) develops when hematopoietic stem cells acquire a reciprocal translocation between chromosomes 9 and 22 creating a fusion between the breakpoint cluster region (BCR) and the Abelson (ABL) gene [2]

Results Nilotinib Dasatinib Bosutinib ͵ Ͷ —„•–”ƒ–‡ȗ —„•–”ƒ–‡ȗ —„•–”ƒ–‡ȗ —„•–”ƒ–‡ȗ ͵ ͷ —‹„•Š– ‹”„ƒ‹––‡‘ȗ”ǡ ‹Š‹„‹–‘” ʹ ͸ ‹†— ‡” ʹ ͺ •—„•–”ƒ–‡ ‹ ‹ Š†‹ „— ‹ –‡‘””ǡ ʹ ͻ ‘ ‹–‡”ƒ –‹‘ ‹Š‹„‹–‘”ǡ ‹†— ‡” ʹ ʹ ͳ ͸ ͻ ‘ ‹–‡”ƒ –‹‘ ‘ ‹–‡”ƒ –‹‘ ‹Š‹„‹–‘” ʹ ͺ •—„•–”ƒ–‡ ‹Š‹„‹–‘” ͳ ͳ ‹Š‹„‹–‘” ‹Š‹„‹–‘” ƒŽˆǦŽ‹ˆ‡ ͳͺǦͶͲ Š”• ͳ͸ Š”• ͵Ǧͷ Š”• ͳͺǦͶͲ Š”• Transporter Imatinib Nilotinib Dasatinib Bosutinib ABCB1 —„•–”ƒ–‡ —„•–”ƒ–‡ —„•–”ƒ–‡ —„•–”ƒ–‡ ABCG2 Š‹„‹–‘” —„•–”ƒ–‡ —„•–”ƒ–‡ ‘ ‹–‡”ƒ –‹‘ ABCC4 —„•–”ƒ–‡ ABCC6 —„•–”ƒ–‡ OCT-1 —„•–”ƒ–‡ ‘ ‹–‡”ƒ –‹‘ ‘ ‹–‡”ƒ –‹‘ ‘ ‹–‡”ƒ –‹‘ OCT-2 ‘ ‹–‡”ƒ –‹‘ ‘ ‹–‡”ƒ –‹‘ SLCO1A2 —„•–”ƒ–‡ Table 1 . TKI interactions with liver enzymes. * = primary interaction. . Table 2 . TKI interactions with transporter proteins. Substrate indicates the TKI is a substrate for the protein, “no interaction” indicates that the TKI has been explicitly studied and research has not found there to be any interaction, an empty space signifies that the transporter has not been explicitly studied yet. . Liver Enzymes Imatinib

https://www.hybrigenics.com/contents/inecalcitol-2/chronic-myeloid-leukemia

• Tyrosine kinase inhibitors (TKIs) used in CML serve as distinguished examples of targeted therapy due to their high efficacy [2]

Introduction

• Imatinib, the first TKI to achieve FDA approval, has not been the panacea initially expected for CML [1,2] • Due to resistance mechanisms and other tumor characteristics, second generation TKIs are currently available to treat CML[14] • This review aims to discuss genetic factors that may influence the efficacy of four FDA approved TKIs used in the treatment of CML -- imatinib, nilotinib, dasatinib, and bosutinib • The objective is to establish criteria whereby one drug would be preferred over the others, with a focus on CYP enztmes and transporters since these factors have shown to affect the metabolism of several other drugs

.

References

Methods

A literature search was performed to review the history of TKIs in CML treatment, mechanisms of action of the drugs, and their metabolism. Any enzymes involved in TKI activity or metabolism were further investigated for human variation that might alter their activity and thus influence the efficacy of TKIs.

We would like to acknowledge the helpful efforts of the Library Staff at the Carolinas Campus who helped procure difficult to find manuscripts.

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2024 Research Recognition Day

Biomedical Studies

Barbiturate Metabolism and Their Known Genetic Associations: Present Understandings and Prospective Paths Cristin Grant, OMS-III, Steven Enkemann, PhD Edward Via College of Osteopathic Medicine, Spartanburg, South Carolina

Abstract

Results

Summary

Genetic Impact on Medication Metabolism v Barbiturate Induction of CYP3A4 12 :

Enzymes v Limited information is available about the metabolism of barbiturates. v Among the frequently utilized barbiturates, only amobarbital has identified enzymes associated with its metabolism. v Barbiturates induce numerous CYP enzymes, with phenobarbital being one of the few commonly used ones that also induces UGT enzymes.

Context : Barbiturates were developed well before the era of personalized medicine and are less commonly used today. For this reason, much of the standard analysis of drug metabolism has not been systematically investigated. Yet, genetic information about barbiturate metabolism could mitigate side effects, reduce cases of abuse, and optimize drug management. Objective : To ascertain the extent of knowledge concerning enzymes involved in barbiturate pharmacokinetics and the role genetic variation may play in barbiturate pharmacotherapy. Methods : A review of current barbiturate medications utilized in the United States was conducted and these medications were analyzed by indication, mechanism of action, and enzymes involved in metabolism. Additionally, a search was conducted to investigate genetic variations associated with these medications. Results : For most barbiturates there is little knowledge of the metabolism and how human variation might influence activity and adverse events. Amobarbital was the only barbiturate with extensive research regarding enzymes that are directly involved in its metabolism. Amobarbital is glucuronidated by UGT1A3, UGT1A1, and UGT1A4, as well as glucosidated by UGT2B4, UGT2B7, and UGT2B15. Research also suggests that all barbiturates currently in use are likely both hepatically metabolized by CYP3A4 and induce the enzyme as well. Secobarbital and phenobarbital were found to be inducers of other CYP enzymes as well. Secobarbital induces CYP1A2, CYP2C8, CYP2C9, CYP2C19, and CYP3A4 while phenobarbital induces CYP2A CYP2B, CYP2C, CYP3A, CYP2E1, UGT1A6, UGT1A10, UGT2B4, UGT2B15, and UGT2B17. Conclusion : Despite similar chemical structures, limited knowledge exists about the metabolism of barbiturates. Further work is required to understand which liver enzymes work on each barbiturate, and how they further influence liver metabolism. With this knowledge, the medical field can better understand how human variation affects both desirable and undesirable activities of barbiturates.

• Barbiturate enzymes induce CYP3A4, heightening metabolism. • Reduced effectiveness of medications processed by CYP3A4. • Essential to consider dosage adjustments when prescribing interacting medications. v Secobarbital's Broad CYP Enzyme Induction 5 : • Secobarbital induces 5 known CYP enzymes and may accelerate metabolism of medications via this pathway. v Phenobarbital's Diverse Enzyme Induction 14,15 : • Phenobarbital induces 5 known CYP and 6 known UGT enzymes and may accelerate metabolism of medications via this pathway. v Amobarbital Metabolism 13 : • Amobarbital is glucuronidated by UGT1A3, UGT1A1, and UGT1A4 and glucosidated by UGT2B4, UGT2B7, and UGT2B15. UGT2B15 Enzyme and Genetic Polymorphisms v UGT2B15 and Medication Metabolism 13,15,16 : • UGT2B15 interacts with both phenobarbital and amobarbital. • Phenobarbital induces this enzyme, while amobarbital is glucosidated by it. • Clinically significant UGT2B15*2 allele linked to reduced clearance of lorazepam and oxazepam. v UGT2B15 D85Y Polymorphism 17 : • UGT2B15 D85Y polymorphism decreases the risk of prostate cancer. • Metabolizes dihydrotestosterone, associated with prostate cancer progression. Implications for Clinical Practice and Future Research v Clinical Insights and Therapeutic Considerations: • Research on phenobarbital and amobarbital metabolism offers insights for prescribing lorazepam and oxazepam. 16 • Exploration of genetic polymorphisms may enhance their utility in oncology management. 17 v Limited Knowledge on Barbiturate Metabolism: • Barbiturates share a comparable chemical structure, but limited knowledge exists regarding their metabolic processes. 5 • Research regarding the specific CYP isoforms induced by barbiturates appears to be incomplete. Most notably, there is very limited evidence regarding the specific CYP isoforms induced by phenobarbital. • Understanding drug metabolism is pivotal for predicting variations in drug responses. • Further research is essential to comprehensively grasp the clinical implications of these enzymes and maximize their application in personalized medicine.

Table 2. Barbiturates and the enzymes they induce and the enzymes that are directly involved in their metabolism.

Background

Cytochrome P450 v The relative distribution of the CYP enzyme families induced by the barbiturates analyzed v All commonly used barbiturates serve as inducers of CYP enzymes, with CYP3A4 being the most frequently affected enzyme.

Current State of Barbiturate Use: • Use declining due to their high potential for abuse. 3 • Most used today: amobarbital, secobarbital, butalbital, methohexital, and phenobarbital. 4 • Currently barbiturates are not commonly used as first line treatment options due to their wide array of adverse effects. 4

History of Barbiturates: • Widely used for over 100 years. 1 • Synthesized by Adolf von Baeyer in 1864 and remodified by Edouard Grimaux in 1879. 2 • Mostly used for their depressive and sedative effects. 2

References

Methods: LitVar and DrugBank databases were used to identify enzymes induced by each barbiturate. LitVar was then utilized to identify any known genetic variants that influence enzymes involved.

1. Cozanitis DA. One Hundred Years of Barbiturates and Their Saint. J R Soc Med . 2004;97(12):594-598. doi:10.1177/014107680409701214 2. López-Muñoz F, Ucha-Udabe R, Alamo C. The history of barbiturates a century after their clinical introduction. 3. Sarrecchia C, Sordillo P, Conte G, Rocchi G. [Barbiturate withdrawal syndrome: a case associated with the abuse of a headache medication]. Ann Ital Med Interna Organo Uff Della Soc Ital Med Interna . 1998;13(4):237-239. 4. Skibiski J, Abdijadid S. Barbiturates. In: StatPearls . StatPearls Publishing; 2023. Accessed October 14, 2023. http://www.ncbi.nlm.nih.gov/books/NBK539731/

Figure 1. Illustrates the relative distribution of different CYP enzyme families so far demonstrated to be induced by the most commonly used barbiturates in the United States. 5,12,14 Only the CYP3A family seems to be induced by many barbiturates.

5. DrugBank Online. Drug bank. Accessed October 14, 2023. https://go.drugbank.com/unearth/q?query=barbiturates&button=&searcher=drugs 6. Syed Q, Kohli A. Methohexital. In: StatPearls . StatPearls Publishing; 2023. Accessed October 14, 2023. http://www.ncbi.nlm.nih.gov/books/NBK544291/ 7. DEA Diversion Control Division. Controlled Substances. Accessed October 14, 2023. https://www.deadiversion.usdoj.gov/

Barbiturate Overview v Most major barbiturates are similar in structure and should likely have overlap in their metabolism.

8. Kales A, Hauri P, Bixler EO, Silberfarb P. Effectiveness of intermediate-term use of secobarbital. Clin Pharmacol Ther . 1976;20(5):541-545. doi:10.1002/cpt1976205541 9. Silberstein SD, McCrory DC. Butalbital in the Treatment of Headache: History, Pharmacology, and Efficacy. Headache J Head Face Pain . 2001;41(10):953-967. doi:10.1046/j.1526- 4610.2001.01189.x 10. Alvarez N. Barbiturates in the treatment of epilepsy in people with intellectual disability. J Intellect Disabil Res JIDR . 1998;42 Suppl 1:16-23. 11. Lewis CB, Adams N. Phenobarbital. In: StatPearls . StatPearls Publishing; 2023. Accessed October 14, 2023. http://www.ncbi.nlm.nih.gov/books/NBK532277/ 12. Kim BH, Fulco AJ. Induction by barbiturates of a cytochrome P-450-dependent fatty acid monooxygenase in Bacillus Megaterium: Relationship between barbiturate structure and inducer activity. Biochem Biophys Res Commun . 1983;116(3):843-850. doi:10.1016/S0006-291X(83)80219-8 13. Kenji Toidea, Yoshiaki Terauchib, Toshihiko Fujiib, Hiroshi Yamazakia, Tetsuya Kamatakia. Uridine diphosphate sugar-selective conjugation of an aldose reductase inhibitor (AS-3201) by UDP glucuronosyltransferase 2B subfamily in human liver microsomes. Published online November 12, 2003. 14. Cantiello M, Carletti M, Giantin M, et al. Induction by Phenobarbital of Phase I and II Xenobiotic-Metabolizing Enzymes in Bovine Liver: An Overall Catalytic and Immunochemical Characterization. Int J Mol Sci . 2022;23(7):3564. doi:10.3390/ijms23073564 15. Plummer S, Beaumont B, Elcombe M, et al. Species differences in phenobarbital-mediated UGT gene induction in rat and human liver microtissues. Toxicol Rep . 2021;8:155-161. doi:10.1016/j.toxrep.2020.12.019 16. Guillemette, Chantal (Director) Ph.D. Pharmacogenomics Laboratory. https://www.pharmacogenomics.pha.ulaval.ca 17. Zhong X, Feng J, Xiao Y, et al. Uridine diphosphate-glucuronosyltransferase 2B15 D85Y gene polymorphism is associated with lower prostate cancer risk: a systematic review and meta-analysis. Oncotarget . 2017;8(32):52837-52845. doi:10.18632/oncotarget.17375 18. Sedative-hypnotic and anxiolytic drugs . Basicmedical Key. (2017, January 1). https://basicmedicalkey.com/sedative-hypnotic-and-anxiolytic-drugs-2/ We wish to acknowledge our appreciation for the valuable assistance provided by the Library Staff at the Carolinas Campus in obtaining difficult to find manuscripts. Acknowledgements

UGT2B15 v UGT2B15, has been identified to possess a corresponding allele and genetic polymorphism. v This enzyme plays a direct role in the metabolism of amobarbital and is induced by phenobarbital. v The found clinical implications of this enzyme are listed below.

Table 1. Most used barbiturates in the United States, mechanism of action, chemical comparison, and most common indications for use.

Table 3. The UGT2B15 enzyme and the role it plays in barbiturate metabolism and the known allele and polymorphism, along with their corresponding clinical implications.

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2024 Research Recognition Day

Biomedical Studies

Are Physicians Capturing Enough Data to Make Infant Growth Charts Useful Diagnostic Tools? Joshua Ranta, OMS-III; Andrew Walker, OMS-II; Steven Enkemann PhD, JuliSu DiMucci-Ward, PhD Edward Via College of Osteopathic Medicine- Carolinas Campus, Spartanburg, SC.

Student Poster Winner H-07

Abstract

Results

.

Figure 1 . Well-Check visits of participants where height and weight measurements were recorded.

Figure 3 . Various growth plots of height and weight with few measurements.

Introduction: • Growth Charts have been utilized for hundreds of years to identify aspects of normal human growth and development. They have been used as a clinical evaluation tool since 1977 1 • The American Academy of Pediatrics (AAP) recommends 10 well-child visits in the first two years of life; occurring at birth, 2 weeks, and 2 months of life and thereafter 4, 6, 9, 12, 15, 18, and 24 months of life 2 . Some studies indicate that growth trends in the 2 to 6 month age range can set metabolic expectations that follow a child throughout life. This period would therefore be critical for observing growth and possibly intervening when unfavorable conditions are observed 3 . • Growth charts can be the first thing to falter when health complications occur such as enzyme deficiencies, feeding difficulties, and genetic defects 4,5 . This would suggest that they should become more important in post-natal care. • With metabolic syndromes on the rise, it is more important than ever to properly plot growth curves for each patient. With enough measurements, it is easier than ever to make individual growth charts using modern technology. Getting those measures recorded accurately, however, seems to be the problem 5 . • Many studies looking at pediatric growth curves and relating them to metabolic complications have ignored the first two years of life due to the variability and volatility of these growth curves 8 . Accuracy in the recorded measurement seems to be only part of the problem. • Capturing enough measurements in the critical growth phases of life seems to also plague the desire to use growth charts for health monitoring. For this reason, we decided to document how far families in South Carolina fell behind the AAP recommendations for capturing infant measurements. Health Sciences South Carolina (HSSC) provided a dataset consisting of 186,849 patient visits from 9,513 patients that recorded growth measurements collected for infants from pediatric offices, outpatient clinics, hospitals, and emergency departments in the state. Inclusion criteria for the current analysis consisted of children at least 6 months old at the time the data was retrieved, for which 3 or more measures existed when considering only visits that occurred during the first 2 years of life. Exclusion criteria consisted of visits where both a height and weight measurement were not recorded and individuals with lengthy or frequent hospital stays (defined as over 30 visits). We conduct two independent analyses based on (1) all visits after considering inclusion and exclusion criteria, leaving 9449 patients with a combined 82,255 visits, and (2) well check visits, leaving 7806 patients with a combined 41,359 visits. Cohorts of children that had aged to at least 2,4,6,12, and 24 months by the end of the collection period were analyzed. At these timepoints, the expected number of well-check visits would be 3,4,5,7, and 10 respectively (including an initial visit within 1 week of birth). The cohorts were evaluated for the number of measurement events that had been recorded in the electronic health record during their life up to these time points and when these measurements occurred. Methods: Background: Growth monitoring is considered a fundamental component of routine pediatric care. Tracking the anthropometric measures of height, weight, and head circumference can be used to detect malnutrition, genetic and endocrine disorders, and even viral diseases. In the era of electronic health monitoring, what once was done with pencil and paper is now digital. The fundamental question is whether it is better. Objective: T his project was initiated to determine whether enough data was collected from infants to allow physicians to plot growth curves. Current pediatric recommendations indicate that a newborn infant should be measured at or shortly after birth, at 0.5, 2, 4, 6, and 9 months of life in the first year and 4 more times in the second year of life. We investigated the electronic health data of more than 9000 individuals to determine if this objective was being met for infants in South Carolina. Results: Infants in South Carolina were measured an average of 7 times in two years. Approximately half of all infants missed a measurement within the first two months of life and never record a later measurement. This trend continues with additional missed visits in the first two years. If only well visits were considered, more than half of all infants had missed 2 measurements within the first 6 months of life and averaged only 5 well check visits in two years. Conclusions: Anthropometric measurements are insufficient for detecting problems using growth curves. Introduction and Methods

With very few measurements it is not possible to develop a reliable pattern of growth. There are just too few data points with which to define a trend relative to growth standards. On the left are height and weight for an individual with just 5 measures in one year. These are plotted onto traditional growth charts. With just two measures in roughly 300 days, it is hard to trust that the final measures are establishing a trend towards overweight. On the right are height and weight measures plotted as the percentile in which the actual measurement fell relative to standard growth curves. This makes it easier to see how growth changes relative to expected normal growth. It is difficult to see anything in the first year of life for this individual.

The timing of well- check visits indicates that parents are trying to meet with their infant’s physician on the recommended schedule. However, the birth date measurements and the 2-week visits seem to overlap making it difficult to determine whether individuals have missed one of these scheduled measurements. After the first month of age the visit totals decrease as the child gets older. One can already see from this that parents are less diligent about obtaining measures at later ages.

Table 2 . Visit stats for all visits. ‘Š‘”–  —š ’‡„ ‡– ‡” † ‘ ˆ ˜‹•‹–• ‘ˆ‡ ˜†‹•‹ƒ‹–• —„‡” ƒ‘‰•– ‘Š‘”– ’ ‡—”‡„‡–”ƒ ƒ‰‡ †‘ ˆ ‹  ˆ ƒ  – • „ ‡— Ž‘ ™„ ‡ ‡”š ‘’ˆ‡ ˜ ‹–•‡‹ †– •

Table 1 . Visit stats for well-check visits only.

‘Š‘”–  —š ’‡„ ‡– ‡” † ‘ ˆ ˜‹•‹–• ʹȋ  α‘ ͹ͺ– ŠͲ•͸ Ȍ ͵ Ͷȋ  α‘ ͹ͺ– ŠͲ•͸ Ȍ Ͷ ͸ȋ  α‘ ͹ͺ– ŠͲ•͸ Ȍ ͷ ͳȋ ʹ Ꮰ͹‘Ͷ͵– Š͹•Ȍ ͹ ʹȋ Ͷ ᏠͶ‘ͷ͹– Š͵•Ȍ ͳͲ

’ ‡—”‡„‡–”ƒ ƒ‰‡ †‘ ˆ ‹  ˆ ƒ  – • –™Š‹ƒ– Š– ™‹ ‡ ʹ” ‡™ ‡ ‡‡ƒ•• —‘”ˆ ‡ –†Š ‹ • ”‡ ‘‡†‡† ˜‹•‹–

‘ˆ‡ ˜†‹•‹ƒ‹–• —„‡” ƒ‘‰•– ‘Š‘”–

’ ‡—”‡„‡–”ƒ ƒ‰‡ †‘ ˆ ‹  ˆ ƒ  – • „ ‡— Ž‘ ™„ ‡ ‡”š ‘’ˆ‡ ˜ ‹–•‡‹ †– •

’ ‡—”‡„‡–”ƒ ƒ‰‡ †‘ ˆ ‹  ˆ ƒ  – • –™Š‹ƒ– Š– ™‹ ‡ ʹ” ‡™ ‡ ‡‡ƒ•• —‘”ˆ ‡ –†Š ‹ • ”‡ ‘‡†‡† ˜‹•‹– 5519 (58.4%)

Discussion and Conclusions

ʹ ʹ ͵ Ͷ ͷ

ͷͶͶͷ ȋ͸ͻǤͺΨȌ ͶͲͶͷ ȋͷͳǤͺΨȌ ͷͺ͹Ͳ ȋ͹ͷǤͳΨȌ ͵ͷͷͺ ȋͶͷǤ͸ΨȌ ͸ʹͳͲ ȋ͹ͻǤ͸ΨȌ ʹͻ͹ͺ ȋ͵ͺǤʹΨȌ ͸͵͸ͳ ȋͺͷǤͷΨȌ ʹͷͲ͵ ȋ͵͵Ǥ͹ΨȌ Ͷ͵ͺͷ ȋͻͷǤͻΨȌ ͻͳͻ ȋʹͲǤͳΨȌ

3

2

4760 (50.3%)

2 months ( N = 9449) 4 months ( N = 9449) 6 months ( N = 9449) 12 months ( N = 9072) 24 months ( N = 5959)

3

4873 (51.6%)

5049 (53.4%)

4

A major health concern for infants in America is the early trend towards obesity. Research has suggested that unhealthy growth trends can be set in the first 1000 days of life, setting a child on a path of lifetime obesity. For this reason, many have suggested that infants, as early as two months of age, should be assessed for unhealthy growth trajectories to allow time to put a child on a healthier path. The data presented here shows that even though electronic record keeping should be conveniently capturing and storing anthropometric data, most infants are missing key data. Approximately three quarters of infants had fewer than the recommended number of recorded growth measurements and a majority of infants were missing at least one measurement during the key ages of 2 to 6 months. It is not possible to visually observe growth trajectories with the low number of measurements actually occurring in clinics at this time. Current guidelines suggest 10 well-check visits in the first 2 years of life and a recent publication proposed reasons for missing visits, including various socio-economic health concerns such as a lack of time, barriers to transportation, inability to afford care, and vaccination discrepancies 11 . Our data suggests that sick visits are often an excuse to miss the next well-visit and still the number of total visits does not meet recommendations. The critical growth phase from birth until 6 months of life is perhaps a period when even more growth measurements should be taken than currently recommended. Solutions to missing growth data could include at-home monitoring or programs reaching out to day care or rural homes. Fundamental growth monitoring is not currently occurring, so little opportunity exists for catching and reversing trends towards obesity.

4

5082 (53.8%)

4304 (45.5%)

5

6

5144 (56.7%)

3144 (34.7%)

7

7

4279 (71.8%)

1171 (19.7%)

10

Figure 2 . Distribution of measurement visits accrued for infants at 2, 4, 6, 12 and 24 months after birth. A) Well-check visits only B) All visits with measurements. The missing measurements begin early. These tables show that most infants have missed 1 measurement opportunity by the 2-month well-check visit and that 40% of infants do not have a recorded measurement within two weeks of the recommended 2-month well-check visit. Infants fall further behind on well-check visits as they age although measurements are taken out of the recommended window, possible during illness visits. The expected number of measurement visits in the first two years is 10 when also counting the measures taken at birth.

References

1. Kuczmarski R, Ogden C. 2000 CDC Growth Charts for the United States: Methods and Development. Center for Disease Control and Prevention. 2002. Accessed June 6, 2023. https://www.cdc.gov/growthcharts/2000GrowthChart-US.pdf. 2. Cole TJ, Singhal A, Fewtrell MS, Wells JC. Weight centile crossing in infancy: correlations between successive months show evidence of growth feedback and an infant-child growth transition. The American Journal of Clinical Nutrition . 2016;104(4):1101-1109. doi:https://doi.org/10.3945/ajcn.116.139774 3. Roy SM, Spivack, JG, et al. Infant BMI or Weight-for-Length and Obesity Risk in Early Childhood. Pediatrics . 2016;137(5):e20153492. https://pubmed.ncbi.nlm.nih.gov/27244803/ 4. Scherdel P, Dunkel L, van Dommelen P, et al. Growth monitoring as an early detection tool: a systematic review. The Lancet Diabetes & Endocrinology . 2016;4(5):447-456. doi:https://doi.org/10.1016/s2213-8587(15)00392-7 5. Marchand V. The toddler who is falling off the growth chart. Paediatrics & Child Health . 2012;17(8):447-450. doi:https://doi.org/10.1093/pch/17.8.447 6. Gittner LS, Ludington-Hoe SM, Haller HS. Utilising infant growth to predict obesity status at 5 years. Journal of Paediatrics and Child Health . 2013;49(7):564-574. doi:https://doi.org/10.1111/jpc.12283 7. Berkey CS, Reed RA, I Valadian. Longitudinal growth standards for preschool children. 1983;10(1):57-67. doi:https://doi.org/10.1080/03014468300006181 8. Lioret S, Harrar F, Boccia D, et al. The effectiveness of interventions during the first 1,000 days to improve energy balance ‐ related behaviors or prevent overweight/obesity in children from socio ‐ economically disadvantaged families of high ‐ income countries: a systematic review. Obesity Reviews . 2022;24(1). doi:https://doi.org/10.1111/obr.13524 9. Amirabdollahian F, Haghighatdoost F. Anthropometric Indicators of Adiposity Related to Body Weight and Body Shape as Cardiometabolic Risk Predictors in British Young Adults: Superiority of Waist-to-Height Ratio. Journal of Obesity . 2018;2018:1-15. doi:https://doi.org/10.1155/2018/8370304 10. Schwarzenberg SJ, Georgieff MK. Advocacy for Improving Nutrition in the First 1000 Days to Support Childhood Development and Adult Health. Pediatrics . 2018;141(2):e20173716. doi:https://doi.org/10.1542/peds.2017-3716 11. Wolf ER, O’Neil J. Caregiver and Clinician Perspectives on Missed Well -Child Visits. Annals of Family Medicine. 2020;18(1). doi:10.1370/afm.2466

The authors of this poster would like to acknowledge and thank the Edward Via College of Osteopathic Medicine Carolinas Library staff, several students who worked on early aspects of this project, and the Health Sciences South Carolina organization for their roles in this project.

The actual number of visits to a doctor where measurements are taken is far below the expected number for the majority of infants. By age two nearly half of the infants have 5 or fewer measurements recorded.

8

2024 Research Recognition Day

Biomedical Studies

The Role of Personalized Medicine in Platinum-Based Chemotherapy: Current Knowledge and Future Directions Laura Grace Goldsmith, OMS-III, Steven A. Enkemann, PhD. Edward Via College of Osteopathic Medicine, Spartanburg, South Carolina. Abstract Results

DNA Repair Mechanisms and Resistance • Resistance: associated with the ability of DNA repair mechanisms to recognize and fix platinum-DNA adducts 37,38 • Genetic variations in the repair mechanisms can lead to platinum resistance or increased efficacy of the platinum-cross link induced apoptosis 31,33,36

Transporters • Influx pumps: compounds into the cell à raising the intracellular concentration • Efflux pumps: remove compounds from the cell à lowering the intracellular concentration • Genetic variations in transporters have been found to affect the intracellular concentration of platinum-compounds • Many transporters are involved in the platinum-pathway 7 Metabolism of platinum-chemotherapy • Plasma protein binding affects the amount of drug reaching the cells and the cytotoxic effect; implicated in dosing of the drug • Alter pharmacokinetics by binding the platinum-compound in the blood 11 • Cisplatin & oxaliplatin bind irreversibly to the proteins, carboplatin binds reversibly 12 • Free agents (not bound to plasma proteins) are taken up by the cells to cross-link DNA • Glutathione S-transferases (GST): role in detoxification of platinum agents from cells • GSTP1 polymorphism: varying alleles associated with differences in the onset of peripheral neuropathies when treated with oxaliplatin 14 • rs1695 AG polymorphism : significant increase in onset of peripheral neuropathy, compared to AA 15

Context: Platinum-based chemotherapy has been a mainstay of solid tumor treatment for several decades with the continuing battle of balancing side effect profiles, maintaining cytotoxicity, and preventing pharmacologic resistance. The original compound, cisplatin, has been joined by carboplatin, oxaliplatin, and others in an attempt to improve efficacy and toxicity profiles. The medical field has shifted the battle to the human genome as it investigates how variation in human genes related to the platinum processing pathway could alter the effects of these medications. Methods: Literature searches were conducted to determine the current platinum-based agents utilized in cancer treatment. PubMed and Google Scholar were examined significantly for articles regarding the mechanism of action, side effects, and development of resistance for each agent. Additionally, a search was conducted addressing studies focused on genetic variation and polymorphisms associated with the medication transport and resistance. Results: The metabolism, mechanism of action, and elimination of cisplatin is well understood, but not the other platinum-based chemotherapeutics, such as carboplatin and oxaliplatin. The platinum component of these agents, once inside the cell, is handled by enzymes and transporters that normally regulate copper containing compounds. This ‘platinum-pathway’ contributes to efficacy when in tumor cells, and toxicity, when operating in the ear, kidneys, and elsewhere. A few studies reported variants in expression of CTR1 and OCT2 influx transporters to be associated with ototoxicity, nephrotoxicity, and oxaliplatin-induced peripheral neuropathy, while upregulation of efflux pumps, CTR2 and ATP7A/7B, is associated with decreased cytotoxicity of these agents. A secondary area of interest is the impact DNA repair mechanisms have on altering the cytotoxic effects of platinum. Components of non-homologous end joining, homologous recombination, and nucleotide excision repair contribute to tumor sensitivity or resistance to platinum compounds. Given the complexity of DNA repair mechanisms it is likely that more genes and therefore more human variants will be identified with more research. Conclusion: Genetics play a role in platinum-agent chemotherapy efficacy but further analysis of each compound in conjunction with specific variants of the platinum-pathway and DNA repair pathways is necessary for personalized treatment to be helpful. Nonetheless, a framework for where to look seems to exist and physicians can begin to tailor the treatment of some patients. Platinum-chemotherapy • Alkylating agents – alter helical structure of DNA via cross-links 1-3 • Each compound has similar mechanisms of actions but different leaving groups in the aquation process and different affinities for receptors in the body 4,10 • Constant battle between maintaining tumor cytotoxicity and minimizing adverse reactions • Chemotherapeutic agents lack specificity – lead to adverse effect profiles 5 Introduction

Repair Gene

Study Findings

ERCC1

Upregulation à platinum resistance

ERCC2

Upregulation likely associated with resistance

NER

ERCC5

Upregulation likely associated with resistance

RAD50

Downregulation/deficiency à increased cisplatin toxicity & efficacy

Downregulation/deficiency à decreased recognition of platinum adducts, increased efficacy

BRCA1

BRCA2

Downregulation/deficiency à increase cisplatin efficacy

NHEJ & HR

Table 2. DNA repair proteins associated with treatment success for platinum compounds 31-36 .

Conclusions There are three main areas emerging that influence the likely success of platinum compounds: plasma and cytosolic components influencing the amount of free platinum in the tumor, intratumoral concentration of the compounds affected by transporters, and the DNA repair mechanisms that fix the damage produced by platinum compounds. Further research should be done to investigate what specific genetic polymorphisms lead to the overexpression or under expression of each platinum pathway gene product, along with algorithms that can place a patient into a treat or don’t treat category based on the sensitivity of the tumor or the toxicity profile of the patient. These studies should include cisplatin, carboplatin, and oxaliplatin, given that all three compounds are used world-wide.

Pt cross links guanine bp ao ss eDi tsNi oaAnt No n7 bin b p a d l r H o d o p c d M t l k e u t G i i - c n nD t g s N A tranfascctroiprstion

Aquation P c o po r s l e ma a c t p h c in t o a i uu r o g mn n e d d [activated]

I nshy inbtiht eDs Ni sA Apo c p e to ll s s is of

Active transport pvui ami npfsl ux or Passive diffusion

References

Figure 1. Generalized cytotoxic mechanism of action for platinum-chemotherapy agents 1,7-9 .

We would like to acknowledge the helpful efforts of the Library Staff at the Carolinas Campus who helped in obtaining difficult to find manuscripts. Acknowledgements

Table 1. Approved cancer types targeted with platinum-based chemotherapeutics 1,4-6 .

This work examines the current knowledge regarding platinum-based chemotherapeutics; allowing for better selection of agents based on patient genetic profile, specifically the tumor receptors, mechanisms of resistance, and variations in metabolic pathways.

Figure 2. Cellular transporters with a known role in the intracellular concentration of platinum compounds 7,10,15-30 .

9

2024 Research Recognition Day

Biomedical Studies

^dZh dhZ > zE D/ ^ K& KE K' E WZKDKd ZͲWZKy/D > E ͗ 'ͲYh ZhW> y ^ E /ͲDKd/&^ /E E Z Z 'h> d/KE Rachel Daley, OMS-II 1 , Rebecca Corallo, OMS-II, Krishna Patel, OMS-II, Sundeep Bhanot, OMS-I, Shane Donahue, OMS-I, Lauren Hiers, OMS-I, Daniel Ross, Olivia Lewis, Bidyut K. Mohanty, PhD. Edward Via College of Osteopathic Medicine, Spartanburg, SC. . Abstract Results Results Continued

Background: The aberrant expression of oncogenes is a well-recognized hallmark in cancer pathogenesis. A pivotal area of focus in cancer research lies in understanding the role of oncogenes, particularly within their promoter-proximal regions (Figure 1A). These regions contain polyguanine/polycytosine-rich DNA sequences, which conventionally form Watson-Crick double-stranded base pairs. Yet, they also exhibit the potential to adopt noncanonical structures, such as G-quadruplexes (G4s) and intercalating motifs (i-Motifs) 1 (Figure 1C). These unconventional configurations have the potential to disrupt genome stability and exert influence on gene regulation, thereby permitting uncontrolled cell growth – the basis of numerous cancers.

Figure 5. 12% Non-denaturing polyacrylamide gel electrophoresis analysis of various DNA samples.

Conclusion: In conclusion, our study demonstrates that polycytosine polyguanine-rich DNA sequences exhibit a pH-dependent preference for i-motif and G-quadruplex structures, respectively. Also, the presence of polyguanine-rich DNA at pH 5.5 has a significant impact on i-motif formation. These findings suggest a preference for the environment-dependent formation of i-Motifs and G-quadruplexes, offering valuable insights into the structural dynamics of the promoter proximal regions of these various oncogenes. Future directions: Understanding the behavior of these regions will be imperative to determine their potential implication in regulating oncogenes. The findings of this study will lay the groundwork for further investigations into the structural dynamics of these DNA sequences. These data will assist in identifying specific targets for therapeutic intervention in oncology. rich DNA at pH 5.5 led to a reduction in i-motif formation by the polycytosine-rich DNA, indicating a dynamic interplay between these structures. Conclusions Results: CD analysis of polycytosine-rich DNA sequences of BCL-2, EGFR, HIF1- α, PDGF, and VEGF unveil distinct i-Motif patterns (Figure 3). Additionally, the polyguanine-rich sequence of BCL-2 displayed a unique G-quadruplex pattern (Figure 4) and its effect on i motif. At pH 5.5, all cytosine-rich DNA sequences formed i-motifs, while guanine-rich DNA showed minimal G-quadruplex formation. However, at pH 7.5, the guanine-rich DNA sequences formed distinct G4 structures. Interestingly, increasing the concentrations of guanine

Figure 1. G4- and iM-forming sequences at oncogene promoters. A. Promoter Proximal Region of oncogenes, B. Proximal Promoter Sequences of Bcl-2, EGFR, HIC, PDGF, and VEGF, C. G-quadruplex and i-Motif Structures

Figure 3. CD analysis of polycytosine-rich DNAs of PDGF, EFGR, VEGF, and HIF1- α reveals distinct i-motif patterns͘

Objective and Methods

Objective: This study aims to investigate the dynamics of G4s and i-Motifs in the promoter-proximal regions of various oncogenes, including Bcl-2, EGFR, HIF1- α, PDGF, and VEGF. We employ Circular Dichroism (CD) spectroscopy (Figure 2A) along with other biochemical and biophysical techniques to explore how environmental conditions influence the formation of G4s and i-Motifs. The CD spectroscopy performed on the promoter proximal regions of these oncogenes reveals distinct patterns of G4s and i Motifs. Our study also explores the effects of various factors, including pH conditions, the complementary DNA strand, and protein factors on the formation of these structures. 2A . 2B .

Figure 2. Circular dichroism spectroscopy. A. Jasco J-1500 CD Spectroscope, B. CD Spectroscope Principle Methods: We used CD spectroscopy to monitor DNA secondary structures in the promoter-proximal region of various oncogenes under differing environmental conditions. G4s and i-motifs are noncanonical DNA structures that display characteristic CD spectra that differ from DNA duplex structures, and from one another. 2

References and Acknowledgements

Figure 4. CD analysis showing the effects of increasing concentrations of polyguanine-rich BCL2 DNA on i-motif formed by polycytosine-rich BCL2 DNA.

BKM is funded by VCOM’s 2024 REAP grant (#1032453).

10

2024 Research Recognition Day

Biomedical Studies

Dynamics of noncanonical DNA structures of c-Myc Oncogene Levi Diggins OMS-III, Rebecca Corallo OMS-II, Daniel Ross, Olivia Lewis, Krishna Patel OMS-II, Sundeep Bhanot OMS-I, Rachel Daley OMS-II and Bidyut K Mohanty. Edward via College of Osteopathic Medicine, 350 Howard St. Spartanburg SC

research design, study implementation, and data analysis and interpretation.

Abstract

Results

Conclusions

DLJĐ ĂŶĚ DLJĐ '

Introduction and Methods Oncogenes are characterized as proto-oncogenes which have undergone a gain-of-function mutation which alters cellular processes and ultimately promotes cancer formation and growth. The mechanism and cellular environment for which oncogenes become mutated and subsequently add to cancer growth is dependent on a variety of physical, structural, chemical, and biological factors. Our goal is to elucidate the dynamics of noncanonical DNA structures formed at promoter-proximal region of c-Myc gene. Specifically, the guanine-rich DNA sequence, at this region of c-Myc promoter, which can form normal Watson Crick base pairing and double-stranded DNA with complementary sequence, can also form a noncanonical secondary structure called G-quadruplex (G4); across the sequence, its complementary cytosine-rich DNA also forms a noncanonical intercalating motif (i-motif or iM). These structures play important roles in cancer by affecting gene expression, DNA replication and other processes. Understanding the regulation of the dynamics between G4 and iM at Myc promoter, their stabilization or destabilization by proteins and ligands will help design drugs that can target these structures in cancer cells. The current work aims to understand the dynamics of G4 and iM by in vitro biophysical and biochemical techniques. C-Myc is aberrantly expressed in over 70% of human cancers. 1 Most notably, 70-80% of cases of Burkitt Lymphoma are associated with a t(8;14)(q24;q32) translocation causing a sequence of the Myc oncogene and one of its 2 promoters to be connected with a highly active IgG heavy chain enhancer . There are a variety of epistatic, epigenetics and post-translational mechanisms that can lead to Myc overexpression. 2 In-vivo formation of iM and G4 can play an important role as a genetic switch by facilitating or reducing cMyc expression. 3 Due to this, c Myc gene targeting therapy is promising for treating cancer. Methods: For this work, iM and G4 were formed in-vitro using buffers with varying pH which preferentially promoted the corresponding structure formation. The presence of these structures were then studied using Circular Dichroism (CD), a spectroscopic method that measures the absorption of right and left circularly polarized light in chiral molecules including DNA and DNA-protein complexes. Once the absorption peaks of G4 and iM were standardized the formation of these structures in various conditions could be analyzed. The formation of G4 and iM was studied in the presence of competing guanine and cytosine rich sequences as well as E. coli derived single strand binding protein (SSB) and hnRNP E1 protein. To further mimic in-vivo conditions, the capability of G4 formation in samples containing flanking sequences was studies. Electromobility shift assay was used to ensure protein-DNA complex formation and compare competition of the protein and complimentary sequences.

C-Myc encodes a transcription factor involved in cell differentiation, apoptosis, and metabolism. Due to its role in cellular regulation c-Myc is one of the most common oncogenes seen in cancer formation. Upstream to the c-Myc gene there lies a complementary cytosine and guanine rich sequence (Figure 1A). Within this sequence noncanonical iM and G4 structures can form altering gene expression. In G4 structures, four guanines form a Hoogsteen hydrogen bonding among them to put all four guanines in the same plane to generate the G-quartet or G-tetrad. In contrast, iM contains four strands of DNA in which two ‘Cs’ join to each other forming a C:C + pairing (Figure 1B). CD analysis showed the absorption peaks for G4 at ~260-263 nm and for iM at ~280-285 nm which is consistent with previous studies (Figure 2) 4. • Within the cell the formation of iM and G4 occurs in the presence of various DNA binding proteins such as SSB. SSB helps protect genome stability by coating single stranded DNA thus preventing DNA damage. • In the presence of SSB the formation of both iM and G4 decreased, this occurred to a greater degree with iM than G4 (Figure 3A and 3B). This is perhaps due to the increased binding strength of the guanine quartet of G4 compared to cytosine binding in iM. • The formation G4 was inhibited in the presence flanking sequences (Figure 3B). Thus, the flanking sequences may play a role in regulation of G4 formation. • When iM and G4 structures were treated with increasing concentrations of their complimentary strands there was a paradoxical increase in absorption peaks (Figure 4B and 4A). This was perhaps due to limitations of CD sensitivity as the absorption peaks of iM, G4 and double stranded are too close together to differentiate. • hnRNP E1 preferentially bind to cytosine rich sequences similar to those found in iM and c-MYC. In the presence of E1 there was a decrease in the formation of iM due to protein binding (Figure 5A). Increasing concentrations of the guanine rich sequence was able to displace E1 binding with the cytosine rich sequence in favor of double stranded DNA (Figure 5B).

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ƵĨĨĞƌ DLJĐ ' Figure 2 : CD analysis of i-motif and G quadruplex in sodium cacodylate (SCB, pH 5.5). Control (blue) contains 250 µL of SCB buffer. G4 (grey) Absorption peak ~260-263 nm. iM (orange) absorption peak ~280-285 nm. B. A. DLJĐ

Figure 3B: CD analysis showing the effect of SSB on the G4 structure in Guanine rich strand of C-Myc. Control (light blue): 250 µL of buffer. Sample 1 (orange): cMyc G strand DNA. Sample 3 (green): cMyc G strand DNA + SSB. Sample 4 (dark blue)L cMyc G strand DNA with its flanking sequence.

Figure 3A: CD analysis showing the effect of SSB on iM structure in cytosine rich strand of C-Myc. Control (blue): 250 µL of buffer. Sample 1 (orange): only cMyc C strand DNA. Sample 3 (green): cMyc DNA + SSB.

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Wavelength (nm)

Figure 4A: CD analysis of Guanine rich strand competition on cMyc C strand. Control (light green): 250 µL of SCB buffer. Sample 1 (dark green): cMyc C strand. Sample 2 (blue): cMyc C strand ½ concentration of G-rich strand. Sample 3 (Red): 1:1 ratio of cMyc C strand and G strand.

Figure 4B: CD analysis of Cytosine rich strand competition on G4. Control (blue): 250 µL of TrisKCL buffer. Sample 1 (grey): cMyc G4 strand. Sample 2 (orange): cMyc G strand + ½ concentration of C-rich strand. Sample 3 (green): 1:1 ratio of cMyc G strand and C strand.

1. Madden, Sarah K., et al. “Taking the Myc out of Cancer: Toward Therapeutic Strategies to Directly Inhibit C -Myc - Molecular Cancer.” BioMed Central , BioMed Central, 4 Jan. 2021, molecular cancer.biomedcentral.com/articles/10.1186/s12943-020-01291-6#:~:text=c Myc%20is%20a%20transcription%20factor%20that%20is%20constitutively,suggesting%20this%20to%20be%20a%20 viable%20therapeutic%20strategy. 2. Graham, Brittany, and David Lynch. Burkitt Lymphoma - Statpearls - NCBI Bookshelf , National Library of Medicine, www.ncbi.nlm.nih.gov/books/NBK538148/. Accessed 22 Jan. 2024. 3. Brown, Susie L., and Samantha Kendrick. “The I -Motif as a Molecular Target: More than a Complementary DNA Secondary Structure.” Pharmaceuticals , vol. 14, no. 2, Jan. 2021, p. 96, https://doi.org/10.3390/ph14020096. Accessed 3 Apr. 2021. 4. Wright, Elisé P., et al. “Identification of Multiple Genomic DNA Sequences Which Form I -Motif Structures at Neutral PH.” Nucleic Acids Research , vol. 45, no. 6, Feb. 2017, pp. 2951 – 59, https://doi.org/10.1093/nar/gkx090. Accessed 1 May 2022.

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Figure 5A: CD analysis showing the effect of E1 on the formation of iM. Control (blue): 250 µL of SCB buffer. Sample 1 (yellow) E1. Sample 2 (orange): cMyc C strand; Sample 3 (grey):cMyc C strand + E1.

Figure 5B: CD analysis of G-rich strand competition on iM+E1 complex. From left to right. Lane 1 (control) 2µL of cMyc C strand. Lane 2 cMyc C strand + E1. Lane 3 [cMyc C strand + E1] + 1µL G strand. Lane 4 [cMyc C strand + E1] + 2µL G strand. Lane 5 [cMyc C strand + E1] + 3µL G strand.

BKM was supported by VCOM’s REAP grant 1032453.

Figure 1A: C-MYC gene with rich cytosine and guanine strands

Figure 1B: G-quadruplex and i-Motif structure

11

2024 Research Recognition Day