Featuring The Exley Legacy Updates A Machine Learning Approach to Comparing Human and Mouse Molecular Pain Pathways — by Andrew Torck Settling In — by Robert Powers Development and Analysis of a Mechanical Gait Training System for Lower-Limb Amputees — by Catherine Davis Chimera — by Richard Wu Estrogen Receptor Promotes Breast Cancer Using Tumor Microenvironment — by Rachel Couch
Are you interested in publishing your research or creative work?
Visit oue.utdallas.edu/research/the-exley-submission-guidelines for more information. Submit your proposal for Volume 6 starting April 1, 2016.
About The Exley
Dear Readers, As you know, many UT Dallas undergraduate students participate in research activities. Our students interact with the University’s faculty, graduate students, and postdoctoral professionals. Some undergraduates enroll in courses or participate in programs that provide an opportunity to demonstrate their creative talents. The Exley, UT Dallas’ undergraduate research journal, supports this process by encouraging students to publish their work where it can be appreciated by a larger community. The Exley provides undergraduate students from every discipline an opportunity to publish substantive work that illustrates their creative abilities or research skills. The Office of Undergraduate Education manages the journal and publishes each issue in collaboration with the O ffice of R esearch, the O ffice of Communications, administrators from our schools, and the University’s faculty and students. The work published in The Exley recognizes the dedication of both the authors and their faculty research mentors. I hope these contributions inspire other students to engage in research and share their creative work.
Andrew Blanchard Dean of Undergraduate Education
I would like to personally thank Elizabeth Exley Hodge. Ms. Hodge dedicated 19 years to UT Dallas as a valued employee and has continued her contributions by graciously supporting this forum for undergraduates. The Office of Undergraduate Education is very grateful for Ms. Hodge’s generosity and commitment to the University’s continued excellence in undergraduate education and research.
In the spring of 2011, Ms. Elizabeth Exley Hodge made a generous donation to support the publication of UT Dallas’ first interdisciplinary undergraduate research journal. Hodge’s maiden name of Exley represents the rich history of her family. The surname Exley, originally Ecclesley, dates to 1245 and means “church fields.” Her great-great-grandfather’s birthplace is now known as Exley Hall in Yorkshire, England. This journal was named The Exley to show the University’s appreciation of Hodge’s support for undergraduate research.
The Exley Name
Elizabeth Exley Hodge Biography Elizabeth Exley Hodge was born in 1920, in a small farming community in Worcester County, Maryland. She is one of 11 children of Lola Marie Watson and John O. Exley, gold medalist in rowing at the 1900 and 1904 Olympic Games. She was a Depressionera child, who worked on the family’s strawberry and tomato farm with her father and brothers. Entertainment for the children was playing marbles, drawing and playing hopscotch in the sand, flying homemade kites, and swimming in the millpond. Her first five years of education were at a one-room school near her home, and she later graduated from Snow Hill High School, one of 43 members of the class of 1936. It was during her senior year that she became “Libby” to her classmates. After high school, Hodge worked for an insurance company in Philadelphia. During World War II, she volunteered with the U.S. Air Corps, where she met her future husband, Noble H. Hodge, who was from Fannin County, Texas. They married in 1942, and, in 1945, after the completion of Noble’s military service in England, they settled in Dallas, where Hodge still resides. In 1967, Hodge joined the administrative offices of the Southwest Center for Advanced Studies. When the center became UT Dallas in 1969, she transferred to the Department of Biology in the School of Natural Sciences and Mathematics, where she assisted faculty members in preparing research grant applications. After a number of years in grants management in the School of Natural Sciences and Mathematics, and later in the Office of Sponsored Projects, she retired in 1986. Hodge has been an avid gardener for many years and has a personal arboretum and an orchid hybrid bearing her name. A member of St. John’s Episcopal Church, she also enjoys cooking and sharing her time with others and has volunteered weekly for the last 23 years at Baylor Medical Center in Garland.
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The Effectiveness of 5-Layered Pencil Beam Rescanning Proton Therapy for Mitigating Interplay Effects — by Pramukh Atluri Development and Analysis of a Mechanical Gait Training System for Lower-Limb Amputees — by Catherine Davis Electron Relaxation Behavior of MRI Signal-Enhancing Free Radicals — by Armin Khamoshi
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A Machine Learning Approach to Comparing Human and Mouse Molecular Pain Pathways — by Andrew Torck
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Unearthing Novel Antibiotics from Streptomyces — by Karthik Hullahalli
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Estrogen Receptor Promotes Breast Cancer Using Tumor Microenvironment — by Rachel Couch
Advisory Board Andrew Blanchard, PhD Dean of Undergraduate Education Morganne Blaylock Student, Erik Jonsson School of Engineering and Computer Science Courtney Brecheen Associate Dean, Office of Undergraduate Education Bruce Gnade, PhD Vice President for Research
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Ali Kara Student, School of Behavioral and Brain Sciences Beth Keithly Associate Director of Research Development Elizabeth Ashley Kennon Student, School of Natural Sciences and Mathematics Kim Knight, PhD Assistant Professor, School of Arts, Technology, and Emerging Communication Bruce Novak, PhD Dean, School of Natural Sciences and Mathematics
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Creating Magic — by Richard Wu Chimera — by Richard Wu Flowers — by Khadijah Mazhar
Nicole Leeper Piquero, PhD Associate Provost and Professor of Criminology, School of Economic, Political and Policy Sciences Managing Editors Hillary Beauchamp Campbell Jennifer LaPrade Magazine Layout and Design Pete Pagliaccio Push Pin Studios Masthead Design LeeDon Moore oue.utdallas.edu/the-exley Opinions expressed in The Exley are those of the authors and managing editors and do not necessarily represent the view or opinion of the UT Dallas administration or The University of Texas System Board
Settling In — by Robert Powers Cake It On — by Korina Guerra
of Regents. The Exley does not claim copyright interest for any material published herein. Copyright ownership remains with the authors or other copyright holders.
Exley Legacy Updates These outstanding UT Dallas alumni contributed their undergraduate research submissions for publication in prior issues of The Exley. It is a privilege for the UT Dallas Office of Undergraduate Education to provide the opportunity for students engaged in research to experience publication of their early research endeavors. Congratulations to these past contributors for their outstanding accomplishments since graduating from UT Dallas.
Since her last publication in The Exley, Carrie Crossley graduated from UT Dallas with a bachelor’s degree in arts and technology. She is now attending the University of Central Florida, where she is studying to earn her PhD in modeling and simulation with funding from a prestigious fellowship. Carrie’s research focuses on using games and interactive media for health, education, and social change. In addition to her studies, Carrie works as a designer and researcher at the Institute for Simulation and Training, where she helps create serious games for a variety of purposes, including health education, firefighter training, and financial literacy. Carrie would like to give her sincere thanks to Courtney Brecheen, Hillary Campbell, and all of the other hardworking individuals involved with The Exley; these wonderful opportunities for early publication experience have been invaluable to her career.
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Anandini Rao graduated from UT Dallas in May 2014 with Collegium V Honors, major honors in biology, and cum laude Latin honors. Postgraduation, she took a gap year to focus on gaining healthcare experience through her job as a scribe and numerous hours of community service in healthcare clinics and in a homeless shelter. In the summer of 2015, she began working towards her master of science in medical sciences, a competitive graduate program at the University of North Texas Health Science Center, For t Wor th, geared for training future healthcare professionals. She anticipates graduating in May 2016. Anandini is pleased to state that, with persistent hard work, invaluable support of mentors and professors at UT Dallas, and the consistent support of her family, she has been accepted as a member of the Texas A&M Health Science Center College of Medicine for the M.D. program, Class of 2020, achieving her dream of a career in medicine.
Shrinath Kadamangudi published his work on neural mechanisms that underlie drug addiction and its effect on cognition in the third issue of The Exley. After graduating with his bachelor’s degree in neuroscience, Shrinath worked as a behavioral therapist at Brain Balance of Greater Houston, where he helped children overcome autism using tailored sensory-motor exercises and cognitive therapy. Shrinath recently accepted a fully-funded international scholarship from the Queensland Brain Institute (QBI) in Brisbane, Australia. As a member of the first MPhil Neuroscience R e s e a rc h S c h o l a r s c o h o r t , h e collaborates with world-renowned neuroscientists at The Clem Jones Center for Ageing and Dementia Research and is currently working in one of only two laboratories in the world which are pioneering the use of non-invasive ultrasound technology to treat Alzheimer’s disease. Shrinath would like to thank UT Dallas faculty, staff, and students for providing him with ample academic, career development, and research opportunities during his undergraduate years as an AES Scholar.
The Exley Team In 2015, The Exley broadened its student involvement efforts to include undergraduate students in various phases of the publishing process. For the first time, managing editors utilized student researchers in reviewing and editing Exley proposals and submissions. Also, a Student Advisory Board, which worked closely with the existing Faculty Advisory Board in reviewing and selecting content for The Exley’s fourth issue, returned to service for the second year. This partnership between students and faculty serves to provide even more robust content to the journal, and provides a valuable opportunity for undergraduate students interested in pursuing research careers.
Morganne Blaylock is a freshman M c D e r m o t t S c h o l a r f ro m S a n Antonio, Texas, studying mechanical engineering. She began her journey in research as a freshman in high school at The University of Texas Health Science Center at San Antonio, where she studied transformation techniques for C. glabrata. She currently researches at UT Dallas in Dr. Robert Gregg’s Locomotion Lab, studying prosthetics and the intersection between aesthetics and functionality. In her free time, Morganne participates on the UT Dallas combat robotics team and in competitive hackathons. Exley Team Roles – Student Submission Editor and Student Advisory Board
Faraha Hasan is a junior psychology and child development double major and Collegium V honors student from Dallas. After spending her high school years pursuing a plethora of fields, including journalism, psychiatry, and history, she fell in love with psychology during her first semester at UT Dallas. She is currently working in Dr. Rosanna Guadagno’s Online Social Influence Lab at UT Dallas, where social interaction and social influence processes are studied in online contexts. She plans to pursue a graduate degree in counseling or clinical psychology after graduation. Outside of school, she enjoys traveling, reading, and watching YouTube videos and her favorite TV shows. Exley Team Role – Student Submission Reviewer
Nick Bell is a senior mathematics major from Plano and is a graduate of the Texas Academy of Mathematics and Science. A Collegium V Honors student, an Eagle Scout, and a member of the National Society of Leadership and Success, he started his UT Dallas career in the Clark Summer Research Program, working with Dr. Anton Malko in the Physics Department. Nick’s current research with Dr. Viswanath Ramakrishna in the Applied Mathematics Department earned him a UT Dallas Undergraduate Research Grant. He is currently a fast-track student and plans to enter the graduate applied mathematics program at UT Dallas. Exley Team Role – Student Submission Reviewer
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The Exley Team Elizabeth Ashley Kennon is a junior biology major and physics minor from Houston, and a graduate of the Texas Academy of Mathematics and Science. Ashley’s earlier research endeavors include investigating the downstream targets of the Tuberous Sclerosis Complex under Dr. Yogi Wairkar at the University of Texas Medical Branch (UTMB) and, later, researching the relationship between the lysosomal degradation of Tau and Alzheimer’s disease under Dr. Gail Breen at UT Dallas. During the summer of 2015, Ashley worked as a counselor for a STEM-based program at the University of Texas Medical Branch, where she was responsible for teaching and guiding high school students through basic re s e a r c h t e c h n i q u e s, s u c h a s ELISA, electrophoresis, and bacterial transformation. In addition to her research involvement, she has also collectively volunteered for and shadowed more than 500 hours at the Michael E. DeBakey Veterans Affairs Medical Center in Houston and Presbyterian Hospital in Dallas. Ashley is pursuing a future in the medical field, where she hopes that the problem-solving sk ills she developed through researching, and the insights and experiences she gained through volunteering and shadowing, will help prepare her to best use her mind and resources to serve and heal others. Exley Team Roles – Student Editor and Student Advisory Board 3
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Megan Zerez is a UT Dallas undergraduate molecular biology student from Honolulu. She has engaged in research since high school, an interest she plans to sustain well into the future, focusing specifically on systems and synthetic biology. She joined Dr. Leonidas Bleris’ bioengineering lab at UT Dallas, where she worked on an independent resea rch projec t on r iboz yme engineering and CRISPR/Cas9. She has contributed to a successful National Science Foundation grant and is the recipient of a UT Dallas Undergraduate Research grant and a Research Experiences for Undergraduates fellowship. She is presently researching full time in the Ranganathan Lab at UT Southwestern as a Green Fellow, focusing on the cyanobacterial circadian rhythm clock. Exley Team Role – Student Submission Reviewer
Justin Raman is a freshman biochemistry major and McDermott Scholar from Orlando, Florida. He became initially interested in research while in high school, after working in a university lab studying Parkinson’s disease. He currently researches in Dr. Walter Voit ’s Advanced Polymer Research Lab to i m p ro ve a n d d e ve l o p f l e x i b l e implantable neural electronics. Raman aims to become a physicianscientist in order to develop novel medical technologies and to test such innovations on patients. Exley Team Role – Student Submission Editor
Keri Denson is a senior doublemajor in chemistry and molecular biology, with a minor in literature. She is currently researching in a polymer chemistry lab under the direction of Drs. Ben Batchelor, D.J. Yang and Walter Voit. Keri currently holds her bachelor’s in nursing from UT Arlington and works on the Rapid Assessment Team/Critical Care for Parkland Health and Hospital System. Her clinical research at Parkland focuses on attempting to decrease mortality rates among high-risk populations. Keri enjoys spending her free time with her boyfriend, cat and dog. Exley Team Role – Student Submission Reviewer
Aaminah Farooq is a junior pre-med biology major, minoring in healthcare studies, from Dallas. She was born and raised in California, and moved to Texas at age 12. Aside from academics, Aaminah has been involved in a wide array of activities, including three years volunteering as a medical assistant at a nonprofit community clinic, serving as an Urdu-English translator for refugees, being a part of the SERV summer program at Texas Health Dallas, and serving as a mentor for the Undergraduate Success Scholars program at UT Dallas. She also served as a research assistant for Dr. Shayla Holub’s research lab, the Healthy Development Project. She is currently a member of the UT Dallas/UT Southwestern Emergency Medicine Research Associates Program. Aaminah aspires to be a practicing physician and use her passion for medicine to aid her in helping others. In her spare time, Aaminah enjoys reading, photography, cooking, and spending time with loved ones. Exley Team Roles – Student Proposal Reviewer and Student Submission Editor
A l i M a n s o o r K a ra i s a s e n i o r neuroscience and child learning & development major who desires to become a practicing physician and make meaningful contributions to the field of neuroscience. While wor k ing in Dr. Sven K roener ’s neuroscience laborator y at UT Dallas, he received the Buhrmester Undergraduate Research Award, given for originality and excellence in undergraduate research work. Ali is a Terry Scholar and member of Delta Epsilon Iota Academic Honors Society. He is currently working on two neuroscience research projects that study the synaptic transmission in the prefrontal cortex circuitry in schizophrenia and alcohol addiction. Ali has expertise in rodent stereotactic intracranial surgeries, virus infusions in the ventral hippocampus and midbrain regions, bilateral cannulation implants in nucleus accumbens, prefusion intracardiac surgeries, tissue preparation, and confocal microscopy. After graduating in spring 2016, he will work in Dr. Susan Amara’s laboratory at the National Institute of Mental Health, studying the role of excitatory transporters in the striatum, a part of the brain critical to reward circuitry. Exley Team Roles – Student Proposal and Submission R e v i e we r, S t u d e n t E d i t o r a n d Student Advisory Board
Brendan Schmidt is a sophomore biology major who grew up in central Florida before moving to Austin, Texas, in 2006. Both his innate love of biology and the premature passing of an immediate family member from disease fuel his interest for biomedical research. He is currently a Green Fellow at UT Southwestern Medical Center, where he works under immunobiologist Dr. Nan Yan to determine the effects that genes implicated in autoimmune diseases (i.e., lupus) have on tumorigenesis, as well as characterizing a pathway of T cells in the innate immune system. Prior to his work at UT Southwestern, he worked under Dr. Kelli Palmer in clinical microbiology at UT Dallas to characterize two very similar strains of bacteria that showed varying susceptibility to antibiotics, culminating in an oral presentation at the fall 2015 meeting of the Texas Branch American Society for Microbiology. Outside of academics, Brendan enjoys composing music and spending time with his friends. After graduation, he plans to pursue an MD or MD/PhD program. Exley Team Role – Student Proposal Reviewer
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Undergraduate Research Programs at UT Dallas Student Research Resources UT Dallas offers a variety of research opportunities and resources. Engaging in research as an undergraduate has academic, personal, and professional benefits. Research activities enhance analytical and critical thinking skills, which prepare undergraduates for the rigors of their profession or graduate studies. Learning how to practically apply academic concepts, discovering how to work independently, and experiencing how to function successfully as a team member are other benefits many students attribute to participating in undergraduate research activities. Exploring a discipline through research also allows students to make informed decisions about the career they wish to pursue. The Office of Undergraduate Education and the Office of Research provide a variety of research event opportunities and other resources to assist both prospective and current undergraduate researchers. For more information about undergraduate research opportunities, visit: oue.utdallas.edu/research/student-research-resources.
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Patti Henry Pinch Scholarship for Undergraduate Research The Patti Henry Pinch Scholarship is designed to assist undergraduates at UT Dallas with research and travel expenses by offering supplemental financial support of up to $1,000 (half of the award will be provided by the student’s school, and half will be provided by Patti Henry Pinch Scholarship funds) on a competitive basis. Students should consult their research advisor in order to complete the application. For more information, visit: oue.utdallas.edu/research/patti-henry-pinch-scholarship.
Undergraduate Research Scholar Awards Undergraduate Research Scholar Awards are made for a single semester and are awarded by the vice president for research. Funds are limited, and awards are made on a competitive basis. The award consists of a cash stipend of $500 paid to the student, as well as an award of $300 transferred to a University account controlled by the faculty sponsor to support the research project (e.g., laboratory equipment, project travel, etc.) or related activities. Cash stipends paid to students are subject to deductions for required taxes. There are no restrictions on the nature of the research—it can be in any field. However, the research must be on a serious, credible topic of inquiry, and there must be a faculty supervisor for the project. At the end of the spring semester, recipients of this grant have the opportunity to join in a poster competition to receive additional funds.
2015 Undergraduate Research Scholar Awards – Winners and Finalists Winners: Justin Miller (First Place) Synthesis and Self-Assembly of Core-Shell Nanocrystals for Solar Energy Harvesting Applications Advisor: Yves Chabal Vasu Jindal (Second Place) Improving the Lower Bound of TopSwops Problem and Gene Reversals Advisor: Sergey Bereg Taylor Sells (Third Place) Isolation and Characterization of ySod1 and yCcs1 Advisor: Duane Winkler Finalists: Hans Chiwuike Ajieren Devising a Robotic Arm for Insertion of a Cochlear Implant Electrode Array Advisor: Walter Voit Mark Ditsworth 500 Watt Wireless Charging System for Electric Vehicles Advisor: Babak Fahimi
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Matthew J. Gillings Caprolactone Containing Pendant Cholesterol for Drug Delivery Advisor: Mihaela C Stefan Dennis Gonzaga Outsourcing: Call Centers within U.S. and Beyond Advisor: Shawn Carraher An Huang Differentiation of Carbon Nanotube and Particulate Matter Contamination on Workplace Surfaces Using microProbe Raman Spectroscopy Advisor: Paul Pantano Lance King Proximity-Based Art Education and Exploration Platform: Using Cutting Edge Technology to Deliver Educational Media Dynamically Advisor: Cassini Nazir Ali Mahmoud Minority Fraternities and the American Identity: America’s First Muslim Fraternity Advisor: James Harrington Melanie Maurer Macrophage Proliferation as a Function of Substrate Stiffness Advisor: Heather Hayenga Thong Nguyen Exclusive Productions of Charmed Baryon Pairs Plus 0-6 Pion Mesons in High Energy Electron-Positron Annihilations Advisor: Joseph Izen Pryanka Sharma Pairing Speech Sounds with Vagus Nerve Stimulation Drives Stimulus Specific Cortical Plasticity Advisor: Michael Kilgard Danyal Siddiqui Ionic Liquids as Novel Coatings for Metallic Surfaces Advisor: Danieli BC Rodrigues Rebecca Tjahja How Powerful is Apple Inc.? Advisor: Greg Durham Andrew F. Torck Identifying Novel Drug Targets for Pain Using Machine Learning Approaches Advisor: Theodore Price
Clark Summer Research Program The objective of the Clark Summer Research Program is to enrich the academic experience of UT Dallas students, by providing an opportunity for them to conduct hands-on research with some of UT Dallas’ nationally recognized faculty and talented undergraduate, graduate and postdoctoral students. This program seeks to instill the value of research in undergraduate students so that they remain actively engaged in research activities that contribute to their fields of interest and the University throughout their academic and professional careers. The research and lab experiences afforded to students in this program are rarely offered at the undergraduate level. Students are actively engaged in serious research activities that give them a realistic view of the work conducted in their academic disciplines. Each student is assigned a faculty research mentor for the duration of the program.
2015 Clark Summer Research Scholars Sivesh Balaji Deletion of Genome Defense Systems in Enterococcus faecalis Strains Leads to Utilization of Different Carbon Sources Mentor: Kelli Palmer, Biology Daniel Barron Developing a System to Remotely Manage an Army of Raspberry Pi Computers Deployed in the Wild Mentor: Ravi Prakash, Computer Science Ruchika Darapeneni The Effect of Acamprosate and Calcium Chloride on Alcohol Consumption and Attentional Set-Shifting in a Mouse Model of Alcohol Addiction Mentor: Sven Kroener, Behavioral and Brain Sciences Brisa Diaz The Effect of Elevating Thermal Treating on Morphology and Gas Separation Performance of Colloidal ZIF-7/6FDA-Durene: Polybenzimidazole Blend Membranes Mentor: John Ferraris, Chemistry Yadiel Graciano Internet of Things Mentor: S. Venkatesan, Computer Science Joan Jacob Visualization of Glut1 Specific Upregulation in Squamous Cell Lung Carcinoma Mentor: Jung-Whan (Jay) Kim, Biology Emily Jensen Pairing Speech Sounds with Vagus Nerve Stimulation Drives Stimulus Specific Cortical Plasticity Mentor: Michael Kilgard, Behavioral and Brain Sciences Justin John Interferon-beta Expression Upon Serial Infection in B Cell GM12878K and Normal Fibroblast IMR90 Mentor: Tae Kim, Biology Corinne Kelly Vagus Nerve Stimulation Directs More Cortical Plasticity at Longer Interstimulus Intervals Mentor: Michael Kilgard, Behavioral and Brain Sciences
Andrew Le Multi Domain Routing Using Dijkstra’s Shortest Path Algorithm Mentor: Andrea Fumagalli, Electrical Engineering Russell Martin Internet of Things Mentor: S. Venkatesan, Computer Science Neil Mascarenhas Effect of BDNF on Acid-Sensing Ion Channel Expression in Trigeminal Nerves Mentor: Gregory Dussor, Behavioral and Brain Sciences Tushar Rahi Shortest Path Algorithm for Use in an Optical Network Simulator Mentor: Andrea Fumagalli, Electrical Engineering Maisha Razzaque Ketamine Administration during Second Postnatal Week Induces Enduring Schizophrenia-like Behavioral Symptoms and Reduces Parvalbumin Expression in the Medial Prefrontal Cortex Mentor: Sven Kroener, Behavioral and Brain Sciences Vipul Reddy Novel Carbon Nanotubes/Titanium Nitride Nanorods Composite Electrode for Supercapacitors Mentor: Ken Balkus, Chemistry Emily Risinger Development of the VOTE Algorithm for Improved Selection in VR Mentor: Ryan McMahan, Computer Science Asim Siddiqui Enhancing Website Fingerprinting Attacks Utilizing Vector Space Mentor: Latifur Khan, Computer Science Calvin Stence Design and Implentation of Rehabilitative Powered LowerLimb Orthoses Mentor: Robert Gregg, Mechanical Engineering Frederick Wang Understanding the Surface Potential of Dental Implants in Vitro Mentor: Danieli BC Rodrigues, Bioengineering Joseph Welkener Mixed-Matrix Membranes Derived from Immiscible Polymer Blends Compatibilized with Colloidal ZIF-90 for Gas Separation at High Pressure and High Temperature Mentor: John Ferraris, Chemistry Yibo Yang Advancements in Voice Command Detection for Interactive Human-Machine Systems Mentor: John Hansen, Electrical Engineering Alexander Yu Sod1 Activation by Copper Chaperone CCS1 Mentor: Duane Winkler, Biology Aaleena Zaidi Analyzing the Effect of Metformin on P-body Formation in Dorsal Root Ganglia as a Marker of Translation Regulation Mentor: Theodore Price, Behavioral and Brain Sciences
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About the Creative Contributors
Korina Guerra is a junior art and performance major. She developed an interest in art at a young age, inspired by the many paintings in her grandparents’ home. Her favorite paintings as a child were “The Swing” by Jean-Honoré Fragonard and “Woman with a Parasol” by Claude Monet. With the encouragement of friends and teachers, she developed into a passionate aspiring artist. She interned at the Dallas Contemporary Art Museum during her senior year of high school and has experimented with studying drawing, painting, ceramics, sculpting, printmaking, and ar t histor y as a student at Eastfield College, UT Arlington, and UT Dallas. She is a mostly self-taught painter and often does custom paintings for friends and family. Her family is her greatest inspiration in life, and her goals include working in an art museum and owning her own art studio and gallery.
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Dallas native Robert Powers is a senior art and performance major with a minor in visual arts. In pursuing a life-long aspiration of becoming an ar tist, he expresses his ar t primarily through drawing and s c u l p t u re, a l t h o u g h h e d r a w s inspiration from various other forms of media in order to synthesize an effec tive piece. Rober t ’s ar t is the meditative response to his surroundings, and he recently discovered a new voice using oil paints. Producing electronic music, playing percussion, and designing 2-D and 3-D sculptural pieces — both digital and physical — are his main forms of recreation. Robert is most in his element when he remains passive in his pieces and allows the process to coalesce and materialize all on its own.
Richard Wu is a freshman McDermott Scholar and Collegium V Honors Program biochemistry student. Despite his STEM-heavy course load, Richard still finds time to integrate ar t into his life. I n addition to producing original artwork, he also enjoys creative writing and music composition. Richard finds inspiration for his creative pursuits in science, and has also been able to better understand scientific concepts through an artistic perspective. Ultimately, he believes that the sciences and arts do not have to separate; instead, the two fields can enrich one another.
Khadijah Mazhar is a junior biology student. She is enrolled in the Collegium V Honors Program, is a recipient of the Academic Excellence Scholarship, and has earned Dean’s List recognition. Khadijah is interested in pursuing medicine and research as a physician-scientist. She is a frequent volunteer at the Monday Clinic and was an undergraduate manager at the Union Gospel Mission clinics in 2015. Khadijah studied mechanisms involved in chronic pain development under the mentorship of Dr. Theodore Price and is currently a Green Fellow studying mechanisms that influence synaptic plasticity and memory formation under the mentorship of Dr. Kimberly Huber. Khadijah finds beauty in the struc tures of the physical world, in the patterns that relate them, and in the functions they ser ve both directly and symbolically. The avid painter and photographer saw her work published on the cover of the fourth issue of The Exley.
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About theResearch Contributors
Karthik Hullahalli is a sophomore pursuing a degree in biology. He completed his high school education in Frisco, Texas, and began researching with Dr. Kelli Palmer during the last semester of his senior year. As a Clark intern during his first summer at UT Dallas, he continued his research into the cellular immune system known as CRISPR-cas. He currently employs CRISPR-cas to find ways to prevent the dissemination of antibiotic resistance genes. Karthik also founded the UT Dallas Biological Sciences Association, where he encourages undergraduates to find research opportunities, in addition to hosting talks from scientists from other universities. He plans to continue research after his undergraduate education, with the goal of one day running his own lab.
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Andrew Torck is a senior pre-med neuroscience major from Dallas. He started his undergraduate career while working at The Medical Center of Plano in Texas, where he gained valuable experience in direct patient care and the treatment of complex neurological disorders. Currently, he is an undergraduate research assistant under Drs. Theodore Price and Gregory Dussor, where he is training in computational biology and studying the cellular makeup of chronic and neuropathic pain states. Outside of school, Andrew enjoys working on his Olympic lifts, investigating new places to scuba dive, and spending time with family and friends. He will be graduating with honors in December 2016, and he plans to pursue a career as a physician-scientist.
Catherine Davis is a senior biomedical engineering student from Austin, Texas. She joined the Green Fellowship in spring 2015, an opportunity that enabled her to effect change for others in a hightech, yet personal way. Although her studies in biomedical engineering have been challenging, she plans to pursue them further in graduate school. Apart from her studies, Catherine led the UT Dallas women’s cross country team as team captain during the fall 2015 season. Besides running, she enjoys playing basketball, watching Jane Austen films, and visiting local coffee shops with friends.
Armin Khamoshi is pursuing a dual degree in physics and pure mathematics at UT Dallas. He is a former Green Fellow (2015) and two-time recipient of the Undergraduate Research Grant Award. His research interests span many areas of theoretical and experimental physics. Lately, he has become interested in quantum mechanical proper ties of thin materials as platforms for future quantum computers. Having brought many AP credits from high school, Armin started his freshman ye a r at UTD by tak ing upper- division physics classes. He finished all the major courses of the physics curriculum by his second year, and took the graduate-level quantum and classical mechanics in his junior year. Outside of academics, Armin is interested in analytic philosophy, philosophy of science, ethics, and epistemology. After graduating in spring 2016, he will pursue a PhD in theoretical physics.
Rachael Ann Couch is a junior biology major at UT Dallas working on lung cancer research. Af ter obtaining a bachelor’s degree, she plans to pursue a PhD in translational science and a career in clinical research. She expresses immense thanks to Dr. Helen Hathaway for support and guidance in her research and Sara Alcon, whose doctoral research this project was built upon. Additionally, she thanks the University Pipeline Network summer program at the University of New Mexico, which gave her the chance to work alongside an inspiring mentor and cultivated her passion for research.
Pramukh Sai Atluri is a junior McDermott Scholar who is doublemajoring in psychology and biology as a pre-med student. Pramukh’s prior research experiences include studying vagus nerve stimulation and tinnitus in Dr. Michael Kilgard’s neuroscience lab and smart polymers in Dr. Walter Voit’s materials science lab. His current research assignment in Dr. Christa McIntyre’s neurobehavior lab involves studying the effects of vagus nerve stimulation and fear conditioning. In addition to conducting research, Pramukh is a founding father of and current director of academic affairs for the Iota Omega chapter of the Delta Tau Delta fraternity. He enjoys tutoring and teaching and has volunteered with Teach for India to help educate elementary students in his family’s community. He hopes to study abroad this summer to increase access to basic medical care in rural communities. His research with proton therapy for cancer treatment was conducted at the SCCA Proton Therapy Center in Seattle with medical physicist Dr. Tony Wong.
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The Effectiveness of 5-Layered Pencil Beam Rescanning Proton Therapy for Mitigating Interplay Effects Cancer is a significant cause of death in people worldwide. Although radiation therapy is currently one of the most effective treatment modalities, it faces challenges. When given in sufficient doses, conventional X-ray photon radiation techniques can effectively kill cancer cells, but physicians have to balance delivering a sufficient dose of radiation to the target tissue with limiting the dose to the surrounding tissues, to mitigate unwanted side effects. Current photon therapies use fixed-field intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) with image guidance for improved treatment precision and accuracy. IMRT and VMAT demonstrate that while more accurate photon radiation techniques can be used to treat riskier cancerous growths, these conventional radiation therapies are still incapable of protecting surrounding healthy cells, especially in critical areas such as the head and neck and areas undergoing significant movement.1
— by Pramukh Atluri
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Introduction Because most of the residual radiation energy is deposited far beyond the tumor site, physicians must use insufficient dosing to control the cancer in these critical areas, resulting in a much higher chance of cancer recurrence. Unlike photon beams, proton beams do not deliver an exit dose, due to the property known as the Bragg peak. Research and clinical studies have shown that proton therapy is able to deliver the target dose while keeping surrounding healthy tissue relatively unharmed. 2 In proton therapy, protons are characterized by a constant low dosage from the entry point to near the planned depth of the tumor but undergo an exponential increase in dose as they reach the target site. This characteristic is commonly known as the Bragg peak. This physical characteristic of protons offers the advantage of less unwanted radiation dosage to patients compared to conventional photon therapy. As seen in Figure 1, the sharp, distal Bragg peak drop is caused by the reduction in velocity of the protons as they interact with the target tissue.
Additional Dose Outside the Target Delivered with Photons
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Relative Dose (%)
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Protons Spread Out Bragg Peak (SOBP)
10 Me V X-Rays (Photons)
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Figure 1: Percentage depth dose characteristic of proton and X-ray beams shown in dark blue and black, respectively. The tumor (shown in brown) is located at a certain depth. The light blue line represents the spread out Bragg peak (SOBP), which is created by superimposed proton beams of different energies. The SOBP is used to treat the tumor. Image courtesy of Dr. Tony Wong at SCCA Proton Therapy Center. Pencil beam scanning (PBS) is the most advanced treatment delivery technique in proton therapy. As shown in Figure 2, a target is divided into many layers and each layer into many spots. The dose to each spot is accurately calculated by a treatment planning system, and the dose is then delivered spot by spot. Previous studies have shown how pencil beam scanning allows for increases in therapeutic dosage to
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the target site while delivering less radiation to the surrounding tissues.3,4 Significantly less tissue is irradiated along the beam’s entry and exit pathways because a proton’s interaction with tissue is inversely related to its velocity; the protons stop their movement within a specified range at the target, unlike photon radiotherapy, in which high radiation doses are necessary in order for the photons to reach target tissues that lie at deeper depths beneath the skin. The advantage of using PBS is the increased ability to deliver a highly conformal dose to the tumor while minimizing the dose to the surrounding tissues. Along with dose conformity to complex shapes, PBS also offers the advantages of intensity modulation and not requiring the use of specially made compensators and apertures to adjust the shape of the beam delivery as in passive scattering techniques.5 However, in PBS proton dosing, precision can deteriorate when irradiating a moving target. The dynamic relationship between the dose delivered spot by spot and the moving tumor is called the interplay effect. The interplay effect can cause overdosing and underdosing of the target and of various areas around the target site.6 The greatest interplay effects arise from the smallest tumor sizes undergoing the largest movement ranges in their patients.7 This result mainly occurs because the beam’s movement to subsequent layers is delivered in a relatively similar time frame as the breathing cycle, and the irregularity and inconsistency of breathing often produces deviations in the planned dose distribution. Two known methods to mitigate the interplay effect are volumetric rescanning and layered rescanning. Volumetric rescanning involves treating the entire 3-D target with a partial dose once before repeating the process multiple times. In layered rescanning, each isoenergy layer perpendicular to the beam is irradiated multiple times in partial doses before proceeding to the proximal layer. Because the dose delivery system using PBS takes the most amount of time in switching depth between successive layers — approximately four seconds in our IBA (Ion Beam Applications S.A.) system — the layered rescanning technique is faster than the volumetric rescanning technique. Figure 2 depicts the layered rescanning method using PBS.
Methods and Materials To carry out the testing, we utilized the MatriXX PT with 8cm water equivalent phantom blocks stacked in front of it. The MatriXX PT is a 2-D ion chamber array. We created and planned three virtual tumor sizes of 100cc, 200cc and 400cc, with a uniform dose of 2Gy to cover the tumors using the Raystation treatment planning system (version 4.5.1) and a fixed PBS beam. For treatment delivery, we placed the MatriXX PT and the phantom on top of the Quasar moving platform.
Figure 2: Depiction of the PBS method and the division of the target into layers and spots. Image courtesy of MD Anderson. Layered rescanning has also been noted to reduce the distorted dose distributions resulting from the interplay effect.8 This outcome is especially vital in patients with cancerous tissue in the lung that moves due to breathing. By specifically quantifying the interplay effect in pencil beam proton scanning of a moving target with and without 5-layered rescanning, we will be able to examine the magnitude of effect of the motion on various tumor sizes in different movement ranges.
The movement of the platform represented different tumor motion with different sinusoidal wave forms. The motion amplitudes were 20mm, 15mm, 10mm, and 5mm. These oscillations, along with different tumor sizes, were used to test the interplay effect as compared with the stationary setting. Every tumor size underwent a 5-layered rescanning scan and a single-scan regular dosing for each movement setting. The beam delivery time was approximately 2 minutes. After collecting the data, we analyzed the measured 2-D dose distribution and compared it to the calculated 2-D dose distribution by using the gamma index of 3 percent/3mm, with 90 percent pass rate as the threshold of acceptance.
The dose delivery without any rescanning vs. rescanning analysis based on planned vs. measured dose in 2-D plans will serve as a justification of rescanning as a motion mitigation strategy. The 90 percent pass rate based on a 3 percent/3mm gamma index analysis is used as a surrogate to analyze the effectiveness of dose rescanning for various tumor sizes and motion magnitudes. Before implementing PBS to treat a moving target, it is necessary to quantify the interplay effect and to investigate if a 5-layered rescanning technique is effective in mitigating the interplay effect. The results will help to understand whether or not PBS, as opposed to other radiation therapies, is a viable treatment option for specific tumor sizes undergoing significant ranges of motion and whether or not 5-layered rescanning is an effective method for negating interplay effects.
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Results
The trendlines on Figures 4b and 5b show that the 5-layered rescanning yielded more consistent linear results for gamma pass rate percentages for the medium and large tumors, but the small tumor (Figure 3b) received better dosing from the single-scan method. The gamma pass rate decreases as the motion amplitude increases across all tumor sizes. Sources of error include an imperfect sinusoidal motion, possibly due to the weight of the phantom blocks impeding the movement of the platform and the potential small movement of the 2-D ion chamber array on the moving platform during measurements.
Small Tumor Size with Single-Scan Gamma Pass Rate
Small Tumor Size with 5-Layered Rescanning Gamma Pass Rate
Difference in Gamma Pass Rates
Stationary (0mm)
96.51%
97.49%
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84.94%
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82.84%
70.79%
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75.92%
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Figure 3a: Gamma pass rate percentage scores for small tumor sizes undergoing different sinusoidal motion amplitudes with and without 5-layered rescanning. The differences between the pass rates are shown as percentages in the last column and were calculated with the equation: % difference = (Small Tumor Size with 5-Layered Rescanning Gamma Pass Rate Percentage) – (Small Tumor Size with Single-Scan Gamma Pass Rate Percentage) Gamma Pass Rate Scores of Small Tumor Size with Single-Scan and 5-Layered Rescanning 100.00%
Gamma Pass Rate %
The results, shown in the following figures, indicate that the 5-layered rescanning method did not consistently contribute a significant increase to the gamma pass rate percentage over the single-scan method, for which the 90 percent pass rate is the threshold of acceptance for patient-specific quality assurance. A significant difference is calculated as >3 percent difference between the 5-layered rescanning technique and single-scan method. Significant observed decreases in percentages for tumors undergoing 5-layered rescanning occur when the small tumor underwent sinusoidal motion of 5mm and 15mm amplitude (-4.01 percent and -12.05 percent, respectively) as seen in Figure 3a, when the medium tumor underwent sinusoidal motion of 10mm amplitude (-3.88 percent) as seen in Figure 4a, and when the large tumor underwent sinusoidal motion of 20mm amplitude (-4.65 percent) as seen in Figure 5a. The observed significant increases were obtained from the medium tumor (Figure 4a) oscillating with 5mm and 20mm amplitudes, an increase of 4.75 percent and 6.89 percent, respectively, and from the 15mm oscillation of the large tumor size (Figure 5a), an increase of 12.16 percent. Nevertheless, the 5-layered rescanning technique does not improve the gamma analysis pass rate up to or exceeding 90 percent.
Motion Amplitude
75.00%
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Motion Amplitude (mm) Small Tumor Size with Single-Scan Gamma Pass Rate y = -0.0466x + 0.9969 R2 = 0.954 y = -0.0523x + 0.9924 R2 = 0.6848
Small Tumor Size with 5-Layered Rescanning Gamma Pass Rate Linear (Small Tumor Size with Single-Scan Gamma Pass Rate) Linear (Small Tumor Size with 5-Layered Rescanning Gamma Pass Rate)
Figure 3b: Line graph comparing the single-scan and 5-layered rescanning effectiveness for the small-sized tumor. The trendline equations and R2 values are next to the respective trendlines. The 5-layered rescanning does significantly worse than the single-scanning for two of the five ranges of motion. The higher R2 value shows better consistency of the singlescanning method.
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Motion Amplitude
Medium Tumor Size with SingleScan Gamma Pass Rate
Medium Tumor Size with 5-Layered Rescanning Gamma Pass Rate
Difference in Gamma Pass Rates
Motion Amplitude
Large Tumor Size with Single-Scan Gamma Pass Rate
Large Tumor Size with 5-Layered Rescanning Gamma Pass Rate
Difference in Gamma Pass Rates
Stationary (0mm)
99.51%
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Figure 4a: Gamma pass rate percentage scores for medium tumor sizes undergoing different sinusoidal motion amplitudes with and without 5-layered rescanning. The differences between the pass rates are shown as percentages in the last column and were calculated with the equation:
Figure 5a: Gamma pass rate percentage scores for large tumor sizes undergoing different sinusoidal motion amplitudes with and without 5-layered rescanning. The differences between the pass rates are shown as percentages in the last column and were calculated with the equation:
% difference = (Medium Tumor Size with 5-Layered Rescanning Gamma Pass Rate Percentage) – (Medium Tumor Size with Single-Scan Gamma Pass Rate Percentage)
% difference = (Large Tumor Size with 5-Layered Rescanning Gamma Pass Rate Percentage) – (Large Tumor Size with Single-Scan Gamma Pass Rate Percentage) Gamma Pass Rate Scores of Large Tumor Size with Single-Scan and 5-Layered Rescanning
Gamma Pass Rate Scores of Medium Tumor Size with Single-Scan and 5-Layered Rescanning
100.00%
75.00%
50.00%
25.00%
y = -0.0615x + 1.0279 R2 = 0.8452 y = -0.0537x + 1.0169 R2 = 0.8933
Medium Tumor Size with Single-Scan Gamma Pass Rate Medium Tumor Size with 5-Layered Rescanning Gamma Pass Rate 0.00% Stationary (0mm)
5mm
10mm
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Gamma Pass Rate %
Gamma Pass Rate %
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Motion Amplitude (mm) y = -0.0615x = 1.0279 R2 = 0.8452 y = -0.0537x + 1.0169 R2 = 0.8933
Medium Tumor Size with Single-Scan Gamma Pass Rate Medium Tumor Size with 5-Layered Rescanning Gamma Pass Rate Linear (Medium Tumor Size with Single-Scan Gamma Pass Rate) Linear (Medium Tumor Size with 5-Layered Rescanning Gamma Pass Rate)
Figure 4b: Line graph comparing the single-scan and 5-layered rescanning effectiveness for the medium-sized tumor. The trendline equations and R2 values are next to the respective trendlines. The 5-layered rescanning does not perform significantly better than the single-scan method. The higher R 2 value shows slightly better consistency of the rescanning method.
5mm
10mm
15mm
20mm
Motion Amplitude (mm) y = -0.0644x = 0.9995 R2 = 0.7982 y = -0.0625x + 1.0167 R2 = 0.8826
Large Tumor Size with Single-Scan Gamma Pass Rate Large Tumor Size with 5-Layered Rescanning Gamma Pass Rate Linear (Large Tumor Size with Single-Scan Gamma Pass Rate) Linear (Large Tumor Size with 5-Layered Rescanning Gamma Pass Rate)
Figure 5b: Line graph comparing the single-scan and 5-layered rescanning effectiveness for the large-sized tumor. The trendline equations and R2 values are next to the respective trendlines. The 5-layered rescanning performs as well as or better than the single-scan method for four of the five motion ranges. The higher R2 value shows better consistency of the rescanning method.
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Discussion
Conclusion
Most of the 5-layered rescanning results are close to the single-scan regular dosing data and do not show a significant increase in the gamma analysis pass rate when motion is involved. The figures show a general increase in effectiveness of the 5-layered rescanning over the single-scanning technique as the size of the tumor increased. The R2 values of the medium and large tumors undergoing 5-layered rescanning show that the layered rescanning method yields more linearly precise and consistent gamma pass rate scores but is not necessarily better and more accurate.
A potential way to mitigate the interplay effect is to increase the number of layered rescans to 10 or 20, but the increase in treatment time may lead to further inaccuracies in dosing. The scores did not meet the threshold of a gamma index of 3 percent/3mm passed at 90 percent with rescanning, the standard for clinical use and patient treatment; therefore, this research showed that further methods must be tested before PBS 5-layered rescanning is implemented as the best option to treat tumors undergoing a significant range of motion.
The large tumor size and medium tumor size with rescanning showed the least amount of gamma index score decay as the motion amplitude increased. A noticeable consistency is the gradual improvement of gamma pass rate percentages as the tumor size increased. Our data was consistent with previous findings stating that the smallest tumors were the most difficult to dose sufficiently and effectively, especially when they moved through a larger amplitude of sinusoidal motion.9-13
Our results show that the use of the 5-layered rescanning technique with pencil beam scanning does not significantly mitigate the interplay effect. More research with delivery techniques other than pencil beam scanning and research using 10- or 20-layered rescanning may provide insight into ways to reduce the unpredictable variability in proton dose distribution caused by interplay effects.
We attempted to show that pencil beam scanning proton therapy with 5-layered rescanning may mitigate the interplay effects of tumor motion caused by movement similar to that of the lungs during respiration. Surprisingly, our results contrast with Kardar’s findings, as one can clearly see that increasing the number of layered rescannings did not significantly affect the gamma score. Therefore, there was not a significant reduction in the interplay effects while maintaining a high degree of accuracy via layered rescanning. A potential cause of these results could be the differences in delivery systems between our IBA dose delivery system and the delivery system used in prior studies. This discrepancy could also be due to the beam rescanning the target too quickly. These results do not support our hypothesis that 5-layered rescanning can effectively mitigate the interplay effect when utilizing pencil beam scanning to treat a moving target.
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Acknowledgments The laboratory research conducted for this study occurred at the Seattle Cancer Care Alliance Proton Therapy Center. I would like to thank Dr. Tony Wong and his medical physics team for assisting in collecting and analyzing the data. Dr. Wong has been a wonderful mentor, providing me with guidance and advice throughout this project and its publication.Â
References 1. Min Rao, et al., “Dosimetric Impact of Breathing Motion in Lung Stereotactic Body Radiotherapy Treatment Using Intensity Modulated Radiotherapy and Volumetric Modulated Arc Therapy,” International Journal of Radiation Oncology 83, no. 2 (2012): e251–256. 2. Steven Petit, Joao Seco, and Hanne Kooy, “Increasing Maximum Tumor Dose to Manage Range Uncertainties in IMPT Treatment Planning,” Physics in Medicine and Biology 58, no. 20 (2013): 7329–7341. 3. We Liu, et al., “Dosimetric Benefits of Robust Treatment Planning for Intensity Modulated Proton Therapy for Baseof-Skull Cancers,” Practical Radiation Oncology 4, no. 6 (2014): 384–391.
11. J. Lambert, et al., “Intrafractional Motion During Proton Beam Scanning,” Physics in Medicine and Biology 50, no. 20 (2005): 4853–4862. 12. Kim Kraus, Emily Heath, and Uwe Oelfke, “Dosimetric Consequences of Tumour Motion Due to Respiration for a Scanned Proton Beam,” Physics in Medicine and Biology 56, no. 20 (2011): 6563–6581. 13. Clemens Grassberger, et al., “Motion Interplay as a Function of Patient Parameters and Spot Size in Spot Scanning Proton Therapy for Lung Cancer,” International Journal of Radiation Oncology, Biology, Physics 86, no. 2 (2013): 380–386.
4. Laleh Karder, et al., “Evaluation and Mitigation of the Interplay Effects of Intensity Modulated Proton Therapy for Lung Cancer in a Clinical Setting,” Practical Radiation Oncology 4, no. 6 (2014): e259-e268. 5. Xiaodong Zhang, et al., “Intensity-Modulated Proton Therapy Reduces Normal Tissue Doses Compared with Intensity-Modulated Radiation Therapy or Passive Scattering Proton Therapy and Enables Individualized Radical Radiotherapy for Extensive Stage IIIB Non-Small Cell Lung Cancer: A Virtual Clinical Study,” International Journal of Radiation Oncology, Biology and Physics 77, no. 2 (2010): 357–366. 6. Karder, et al., “Evaluation and Mitigation,” e259-e268. 7. Ibid. 8. Ibid. 9. Timothy Bortfeld, “Effects of Intra-fraction Motion on IMRT Dose Delivery: Statistical Analysis and Simulation,” Physics in Medicine and Biology 47, no. 13 (2002): 2203–2220. 10. Mark H. Phillips, et al., “Effects of Respiratory Motion on Dose Uniformity with a Charged Particle Scanning Method,” Physics in Medicine and Biology 37, no. 1 (1992): 223–234.
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Magic I’ve always felt that artists are capable of creating and capturing magic. To me, an artist’s paintbrush or pencil is a kind of magic wand, one that casts spells of color. With little more than this wand and a canvas or paper, an artist can conjure anything — people and objects, or events and places, or even the impossible — from the depths of the human imagination. This piece began as a pile of cardboard scraps and some old oil pastel nubs. Instead of throwing away these leftover supplies, I decided to experiment with them to create a new artwork. For me, part of the magic of being an artist is the ability to express myself and create beauty using potentially anything, even unwanted rubbish and/or scraps. The resulting artwork depicts an artist using a paintbrush to channel some creative magic. Though the artist stands before a monotonously monochromatic background, that artist is able to envision an entirely different world. The spells of color cast by the paintbrush open a window into the artist’s inner world for others to see as well. I created this artwork to showcase the potential and power of creative art. I hope that through this piece, viewers will be able to see another world — a world not constrained by time or money or the laws of physics, but only by one’s imagination.
— by Richard Wu
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Development and Analysis of a Mechanical Gait Training System for Lower-Limb Amputees Amputees properly fitted with prosthetic limbs must learn to function just as they did with their biological limb. Transfemoral amputees (those with amputation above the knee) often demonstrate altered gait as a result of sideways movement of the torso toward the prosthesis. A new gait training system proposes to assist, correct and even strengthen the gait of transfemoral amputees through assistive and/or resistive forces applied in the frontal plane.
— by Catherine Davis
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Background
Methods
Learning to walk is one of the earliest and most critical developmental skills. For most individuals, this process comes naturally at a young age; however, amputees must relearn to walk all over again with a prosthetic leg. In many cases, an amputee never learns how to ambulate properly with the prosthetic limb. As a result, many transfemoral amputees experience sideways torso movement toward their prosthesis as well as resultant muscle strain in all lower body regions. Over time, this aberrant movement can lead to lower back pain and may also cause degenerative joint disease.1
Trials were conducted using the Accelerated Rehabilitation Technologies (ART) gait training system, primarily with healthy patients to obtain trial data. This system consists of a base metal frame secured to a treadmill and software for user control. The programming of the system was completed using LabVIEW to control several devices (footswitches, load cells, accelerometer, etc.). Using several variations of the system, we conducted trials by applying assistive/ resistive forces to the subject during walking on the flat surface of the treadmill. By analyzing both the tension in the cable during walking and the timing of the foot sensors, we were able to evaluate the motor function. In addition, each trial was captured by video to calculate the angle of sideways torso movement.
Previous research has been conducted in the field of mechanical and robotic gait training, in which devices are used to assist patients affected by brain or spinal cord injuries, with the goal of helping them regain or improve the ability to walk.2 These devices provide support for the entire lower body, and the patient exerts little to no effort when using them. However, these systems are often extremely cumbersome and difficult for patients to operate and understand. Therefore, use of these systems would be beneficial not to amputees, but rather to patients who have suffered brain trauma, as they need much more assistance in learning to walk in a completely different capacity. Medical professionals turn to old and basic methods when training amputees to walk with a new limb. Therapists must physically assist the patient, which requires a great deal of strength and can be extremely tiring. Other methods involve a passive assistive device, such as parallel bars or a walker, to aid the amputee in becoming accustomed to the new prosthesis. These methods require a great deal of initial confidence from the amputee and also exact a physical toll on the therapist.3 Other developments in the area of gait training include the microprocessor-controlled knee, which attempts to use computer control to simulate muscle activity in ambulation. This activity allows amputees to expend less energy when walking. Thus far, this type of prosthesis has been successful. However, it is still quite costly when compared with the passive knee prosthesis.4 Despite the advanced technologies of such a knee, the amputee still requires extensive assistance with learning to ambulate properly with the new limb. Consequently, this study focuses on the retraining of muscles in lower-limb amputees to promote the use of a prosthetic limb. The design and analysis of this study proposes to improve the gait of amputees by offsetting gait imbalances typically incurred through self-taught ambulation, thereby preventing postural and muscular imbalances that may result in pain or injury, and leading toward complete independence.
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Participants Preliminary trials were conducted with healthy subjects to provide baseline measurements for future testing and to ensure the proper functioning of the system and software. These trials consisted of three healthy subjects: one male, age 20, and two females, ages 20 and 19. The average height for these subjects was 68”±2”. The average weight was 159.33±26.86 lbs. All of the subjects were healthy, with no lower-limb injuries or limitations. Because healthy subjects were used for all preliminary tests of this device, we were able to obtain an enormous amount of data using only three subjects.
Device Structure Although the system’s software allows for the prosthetic limb to be on either side of the patient’s body, we ran pilot tests simulating the right leg as the one with the prosthetic. The treadmill settings for these initial tests measured a 0.0 degree incline and speed between 0.5–1.0 mph. Because all trials simulated the right leg as the prosthetic leg, the software was used only to control this scenario, resulting in the user utilizing only half of the software system. The length of time to run each test and record data was approximately 10–60 seconds.
Subject Setup After we attached traction shoe covers to the subject’s feet, he or she stood in the center of the treadmill. The subject then donned the major harness, and two cables were attached at the waist and either the shoulder or the hip. The electromyography (EMG) leads were attached to the gluteus medius of each leg. Finally, a video camera behind the subject recorded all of the subject’s movements.
Subject Safety
Measurement and Analysis
Significant measures were in place to ensure subject safety during each trial. The treadmill and the interior of the mechanical frame’s power box each featured power shutdown buttons. In addition, the shutdown button for the treadmill was wired directly into the software, allowing for the entire system (mechanical and software) to shut down simultaneously. A limit was placed within the software on the amount of force that the cables could apply to the patient. To remove the risk of falling, we familiarized the subject with the mechanical setup (shutdown buttons) before he or she donned the harness and clipped into the cables.
Outcome Variables
Device Setup
During each trial, we took measurements using video footage later edited using Tracker software. Along with the video data, we were able to record the tension in each cable and the timing of the footswitches. This information allowed for gait timing and analysis. Lastly, the footage from the video camera directly behind the subject gave a visual representation of the rotation in the torso of each subject. Using the Tracker software in each iteration, we were able to set coordinate axes (Figure 2: purple axes) to mark the center of the subject’s back. The protractor (Figure 2: green protractor) placed along the axis displayed the degree of rotation in comparison to the subject’s movement.
We began testing after powering on the entire system and opening the LabVIEW program (ART State Machine) for user control. After performing pre-tension on each cable (Figure 1: A and B), we set the treadmill to 0.5 mph, and instructed the subject to walk normally. As the subject walked, he or she clicked the “Begin” button to start the walking program (Figure 1: C). For all initial tests, we set the wind tension (the amount that the cable tightens when the subject steps with the prosthetic leg) to 40 N. We set the unwind tension (the amount the cables loosen when the subject steps with the healthy leg) to 10 N. Additionally, our team set the LabVIEW program to store readings from the tension in each cable, the footswitches and the EMG signal. We saved each data file from the LabVIEW program in an Excel file for future processing and analysis.
Figure 2: Angle calculation and analysis using Tracker software.
Figure 1: ART system user control. A) Front page of user control within the State Machine program; B) Controls for winding/ unwinding the left and right cables; C) Controls for left/right bad leg walking program.
This information allowed for the calculation of the degree of flexion in the subject. Placing this data alongside the tension and footswitch measurements produced comprehensive data for the entire system. Lastly, we used an EMG signal from the skeletal muscles in the gluteus medius to display the activity in the stance of each subject. Spring 2016
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Data Acquisition and Analysis
Left Leg EMG 150 Motor Unit Firing Rate
In order to observe all of the data in one console, we wrote another LabVIEW program. With this new program, we were able to view the signals from the load cells, footswitches and EMG channels simultaneously. The first data processed were the gait cycle (footswitch pattern) and tension readings. As the subject stepped with the right foot and engaged the right heel footswitch, the program increased the tension in both cables (Figure 3). After comparing the tension with the gait cycle, we observed the EMG signals when graphed with the gait cycle. There are notable differences between the skeletal muscle activity of the right and left legs and how they align with the gait pattern of the subject (Figure 4).
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Left Tension Right Tension Right Heel
Figure 3: Gait cycle of a healthy female subject. Left cable tension is in light blue, right cable tension is in red, and right heel footswitch is in black. In addition to the tension readings and EMG signals, we looked at the degree of sideways torso movement experienced by each subject. The comparison of sideways torso movement in one healthy female subject shows more flexion when using the gait training system than when ambulating independently (Figure 5).
Sample
Figure 4: EMG signals of both the left and the right leg for comparison. Sideways Movement (Baseline) Lateral Flexion (Degrees)
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We then created a comparison of the trial run for each subject, focusing primarily on the degree of sideways torso movement experienced by the subject during the trials. Each subject experienced slightly varying degrees of sideways torso movement across the three trials. However, all subjects experienced a much greater amount of sideways torso movement toward their healthy leg than toward their prosthetic leg (Figure 6).
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Figure 5: Lateral flexion (sideways torso movement) of healthy female subject, baseline measurement, and using the system. 27
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Acknowledgements
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I would like to thank Dr. Dean Sherry, Dr. Nancy Street, and Vanessa Powell for giving me the opportunity to participate in the Green Fellowship program, and also Deborah McGill for her support and encouragement through this challenge. I would also like to thank Dr. Fan Gao for his willingness to let me work in his lab and to answer my unending questions. Last of all, I would like to thank Dr. Benna Hill for her unwavering energy in helping me to edit my work.
References
Figure 6: Comparison of three healthy subjects using the gait training system. Each bar represents the degree of torso rotation toward each side of the body.
1. Robert Gailey, “Secondary Conditions Related to Prosthetic Users and Ten Steps to Reduce the Risk of Injury,” InMotion 18, no. 5 (2008): 15.
Results and Interpretation
2. Lars Lünenburger, Gery Colombo, and Robert Riener, “Biofeedback for Robotic Gait Rehabilitation,” Journal of NeuroEngineering and Rehabilitation 4, no. 1 (2007): 1.
When healthy subjects used this system, they were forced to walk with sideways torso movement in the direction applied by the couple force created by the two cables (Figure 6). The results show that the three healthy subjects each exhibited a greater degree of sideways torso movement toward the simulated left leg (without the prosthetic), which confirmed the proper functioning of the system. From this data, we hypothesized that, when an amputee uses the system, any side-to-side movement toward the leg with the prosthesis should be prevented. In addition, the results indicated that the EMG signal had strong activation (spikes) in the left leg during ambulation using the system. The system, when applying force to the healthy subjects, forced them to walk unnaturally, with a lean toward one side. As a result, the muscle activity on that particular side increased to compensate for the added weight. However, in amputees, the force applied by the system would be in contrast to abnormal ambulation. Therefore, the system can actually alleviate added weight on the prosthetic leg due to sideways torso movement and will straighten the trunk. Future testing conducted on lower-limb amputees who have been fitted with a prosthetic limb should involve analysis of the system using varying cable configurations.
3. Mary Kay Zane, PT, OCS, “Physical Therapist’s Guide to Above-the-Knee Amputation,” Move Forward (2014), http:// www.moveforwardpt.com/symptomsconditionsdetail. aspx?cid=7e9549ef-0bff-4b50-88f1-8a8bf4f1e496. 4. Kenton R. K aufman, et al., “Gait and Balance of Transfemoral Amputees Using Passive Mechanical and Microprocessor-Controlled Prosthetic Knees,” Gait and Posture 26, no. 4 (2007): 489–493, http://www.ncbi.nlm.nih. gov/pubmed/17869114.
Using this mechanical gait training system can greatly reduce the pain amputees experience and the energy they expend when walking. These changes have significant potential to improve the quality of life for lower-limb amputees. Amputees not only will experience less pain but also will experience a freedom and independence previously unobtainable. In turn, this system will also greatly improve the therapy and training methods of therapists working in the field. Spring 2016
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The word “chimera” is defined as an imaginary creature made from disparate parts. Alternatively, the word “chimera” can also refer to a fantasy that cannot be realized. My artwork is about both definitions of “chimera.” The chimeras in these pieces — which I drew with pen — are conglomerations with features of various animals, plants, machinery, architecture and more. In both artworks, the overall result of all of these eclectic elements is the appearance of an intricately exquisite, yet realistically impossible creature. As an artist, I often encounter barriers between imagined fantasy and reality. The world does not always appear as picture-perfect, or sound as harmonious, or read as poetically as I want it to. The chimeras I have drawn — products and symbols of my imagination — are depicted as splintering and unraveling to show the tension between the real and the ideal. Sometimes, though, I find ways to break the barriers that separate imagined fantasy from reality. Through creating artwork, I can straddle that fine line between what exists and what I wish existed. Art allows me to capture those fleeting moments of beauty in the world, when fantasy becomes reality.
— by Richard Wu
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Electron Relaxation Behavior of MRI Signal-Enhancing Free Radicals Nuclear magnetic resonance (NMR) is a widely used analytical tool for structural elucidation and materials characterization in chemistry, biology, materials science, medicine, and industry. What makes NMR particularly outstanding is that it allows for nondestructive analytic study of materials ranging from very small molecules to large organic polymers. This very useful property of NMR has made it a prominent technique in medical settings when magnetic field gradients are used — by means of magnetic resonance imaging (MRI) — as it can provide exquisite images of soft tissues in the body without the need to perform surgery. In spite of their numerous applications, NMR and MRI suffer from inherently low signal sensitivity due to the relatively weak magnetism of nuclear spins, especially for nuclei other than protons such as carbon-13. 1 To circumvent this problem, a recently developed technology called dissolution dynamic nuclear polarization (DNP) can be employed to increase the NMR or MRI signal by at least 10,000-fold. First predicted by Albert Overhauser in 1953, the DNP technology takes advantage of the high magnetism of electrons, which is at least three orders of magnitude larger than that of nuclear spins, thus leading to a high number of surplus electron spins aligning in a particular direction under a magnetic field.2
— by Armin Khamoshi
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Optimized sample preparation and conditions are crucial to attaining the highest sensitivity enhancements in DNP. Central to the NMR or MRI signal-amplification process is the source of free electrons, which are mainly provided by stable organic free radicals. Thus, an elucidation of the physical properties of the polarizing agents is a key to understanding their role in the MRI signal-amplification process. In this article, we report electron spin resonance (ESR) spectroscopy of two free radicals used in dissolution DNP, BDPA, and DPPH. The dynamics of the free electrons, embedded in the parameter known as spin-lattice relaxation time T1, were studied from room temperature down to 4 K. These electron relaxation behaviors were compared and analyzed using existing models, and the implications of these results to DNP are discussed.
Physics of Dissolution DNP The intensity of NMR or MRI signal is directly proportional to the number of spins and to the net magnetization, which itself is dependent on the population difference in Zeeman energy states.4 This spin population difference is known as nuclear polarization (P), which is a measure of the NMR signal strength. This parameter is dictated by StefanBoltzmann law, so one can increase nuclear polarization by lowering the temperature and increasing magnetic field strength.5 DNP offers an applicable solution for increasing nuclear polarization. In theory, DNP exploits the high alignment of electron spins under a magnetic field to accomplish such a task. By creating a thermal contact between the electrons’ spin-spin interaction system and the
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Before Microwave Irradiation
After Microwave Irradiation
Nuclear Spin Electron Spin
Electron Spin
Flip
Flop
Nuclear Spin Flip
Figure 1: Schematic representation of the DNP process in thermal mixing regime: Nuclear spins are flipped as microwave radiation flips some electrons and starts a “flip-flop” reaction throughout the sample. This is made possible by thermal contact between the electron’s spin-spin interaction and the nuclei’s Zeeman states.7
energy
By using microwaves, one is able to transfer the high electron spin alignment to the nuclear spins under high magnetic field and low temperature.2 Once hyperpolarized at cryogenic temperature, the frozen sample, which consists of an important metabolic tracer or biomolecule such as 13C glucose, is then rapidly dissolved with superheated water. This dissolution method allows us to harness the superenhanced NMR signal at cryogenic temperature and bring it to a physiologically tolerable temperature for biomedical studies. The hyperpolarized liquid containing high-enhanced 13C NMR or MRI signals of biomolecules can then be administered in vitro to living cells, or in vivo in mice, and eventually in humans. This is the only technique so far that can provide real-time, noninvasive monitoring of metabolism in living cells with sub-second time resolution. Furthermore, this technique has both the requirements of high specificity and superb sensitivity for a true metabolic imaging of diseased tissues such as cancer.3
nuclei’s Zeeman system, DNP transfers the electrons’ spin polarization to nuclei via microwave radiation.6 Figure 1 provides a schematic explanation of this process. The left side of the image depicts a pair of electrons being thermally coupled to the nucleus before microwave irradiation. After receiving microwave irradiation, depicted on the right, the electrons’ spins are mutually flipped, causing a “flip-flop” effect throughout the sample. This will eventually lead to reversing the nuclei’s spin in the sample, which results in a population inversion between the Zeeman energy states, and hence a higher nuclearpolarization, as shown in Figure 2.
energy
Introduction
Thermal Equilibrium
Hyperpolarized State
Figure 2: Population of Zeeman states at thermal equilibrium (before DNP) and hyperpolarized states (after DNP).8 For biomedical applications, DNP can be implemented via the dissolution method. In dissolution DNP, a frozen sample, typically consisting of stable-isotope enriched biomolecules dissolved in a glassing matrix, is doped with trace amounts of free radicals. Free radicals are the polarizing agents that provide free electrons in the solution. A dissolution device is then used to rapidly dissolve the frozen sample and, consequently, produce an injectable hyperpolarized liquid at a physiologically tolerable temperature. This means that
the NMR/MRI signals of reporter molecules (molecular probes that can detect important biological activities) can be enhanced by at least 10,000-fold.9 As described above, because the mediation by which polarization is transferred to target nuclei in DNP is contingent upon the allocation of free electrons, studying physical properties and spin dynamics of electrons provided by free radicals is of special interest to us. These properties include spectral diffusion, spin-spin interactions (ISS) process, nuclear spin-lattice relaxation, and electron spin-lattice relaxation, among which temperature dependence of electron spin-lattice relaxation T1 is the subject of inquiry here.10 The electron spin-lattice relaxation process entails the interaction between the electron system and the lattice; therefore its characteristics affect the total polarization of DNP. A quantitative description of this correspondence at maximum polarization is given by the following equations.11 Pmax = tanh (
µB ) kB TS, min
Ts, min =
2DTL
(1+ f ) e
mOn BDPA and DPPH BDPA and DPPH, whose chemical structures are depicted in Figure 3, are the two hydrophobic, stable free radicals of interest in this study. BDPA is one of the most stable carbon-centered free radicals. It was first produced by Koelsch in 1957, and was used in polarized target experiments and solid-state DNP. 15 The efficiency of BDPA for the fast dissolution DNP was studied by Lumata in 2011; large signal enhancements of 13C, 15N, 31P, 6Li, 29Si and 89Y nuclei were achieved with BDPA using sulfolane-based glassing matrices. DPPH was synthesized by Goldschmidt and Renn in 1922 and is widely used to test the antioxidant properties of a variety of natural and synthetic products. Like BDPA, DPPH was shown to be a reasonable candidate for fast dissolution DNP, with several thousand-fold NMR signal enhancements achieved when used to polarize 13C, 15N, 89Y and 109Ag compounds in the liquid state.
A
B
Where Pmax and Ts,min are nuclear polarization and spin temperature respectively, kb is the Boltzmann constant, µ is nuclear magnetic moment, B is the magnetic field, f is the nuclear relaxation leakage, n is the relaxation time, and D is the ESR linewidth. It is evident from these equations that the minimum spin temperature (Ts) is a crucial factor in achieving high spin polarization; however, because Ts itself is dependent on relaxation time (n) and ESR linewidth (D), free radicals featuring slower relaxation rates with narrower linewidth are preferable in obtaining a high level of polarization.12 The most frequently used polarizing agents in 13C and 15N DNP, due to having a very narrow linewidth (D = 60–70 MHz), are water-soluble trityl-based stable free radicals. However, the production of these radicals is expensive. A viable alternative to trityl radicals is carbon-center 1 , 3 - b i s d i p h e n y l e n e - 2 - p h e n y l a l l y l ( B D PA ) a n d 2,2-diphenyl-1-pycrylhydrazyl (DPPH).13,14 The efficiency and feasibility of using BDPA and DPPA were extensively studied by Lumata et al.; a several thousand-fold NMR signal enhancement was observed when these radicals were utilized. While the information on efficiency and overall EPR lineshapes of BDPA and DPPH is known, in this study we aim to investigate the spin-lattice relaxation rates of these radicals as one of the factors affecting the polarization of DNP samples. The behavior of electron relaxation rates was studied as a function of temperature, the results of which may shed light on the electronic structure and dynamics of these paramagnetic species in their environments.
2,2-diphenyl-1-pycrylhydrazyl (DPPH)
1,3-bisdiphenylene-2-phenylallyl (BDPA)
Figure 3: Chemical structures of the free radicals BDPA and DPPH. DPPH (A) is surrounded by spin-1 14N nuclei, resulting in hyperfine coupling between the unpaired electrons and nitrogen nuclei. BDPA (B) is in contact with majorly 12C spin-less nuclei, causing minimal occurrence of hyperfine interactions. Looking at the ESR properties of these free radicals, it can be observed that the spectrum of BDPA, as shown in Figure 4, is very narrow and symmetric with a linewidth comparable to that of trityl radicals. This is because the free electron is surrounded by mostly spin-less nuclei (carbon-12); thus, there is minimal hyperfine coupling. On the other hand, the free electron in DPPH is coupled with magnetically active 14N nuclei (spin-1, 99.6 percent natural abundance), resulting in a broad and asymmetric ESR spectrum. The linewidth of DPPH is between that of carbon-centered BDPA and nitroxide-based TEMPO free radicals.
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relaxation rates (1/T1) with respect to temperature is then acquired, from which inferences about the electronic nature of the radicals and their environment can be made.
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Figure 4: W-band (95GHz) spectra of 20mM BDPA (A) and DPPH (B) in 1:1 vol/vol sulfolane: dimethyl sulfoxide (DMSO). BDPA has an ESR linewidth comparable to that of trityl; the linewidth of DPPH is intermediate of BDPA and TEMPO.
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Results and Discussions The framework, wherein the electron spin-lattice relaxation rates are studied, requires measuring the time span during which the spin system relaxes to an equilibrium value with different thermal baths; the data acquisition needs to be conducted at several different temperatures and the resulting data fitted to exponential recovery equations to extract their respective relaxation time (T1). The behavior of Spring 2016
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Aliquots (100 µL) of 20 mM and 40 mM BDPA solutions were prepared in 1:1 vol/vol sulfolane: dimethyl sulfoxide (DMSO). The same sets of solutions were prepared for 20 mM and 40 mM DPPH. The ESR experiments were performed at the National High Magnetic Field Laboratory in Tallahassee, Florida. W-band (95 GHz) measurements were done on a Bruker E680 ESR spectrometer with a cylindrical cavity for W-band (Bruker E-600-1021 HE). The ESR spectra were recorded by an echo-detected field sweep method. The electron spin-lattice relaxation time T1 data were measured using an echo-detected inversion recovery technique. Samples doped with BDPA and DPPH were hyperpolarized at 3.35 T and 1.4 K using a commercial DNP hyperpolarizer HyperSense (Oxford Molecular Biotools, U.K.) with 100 mW microwave source at the University of Texas Southwestern Medical Center.
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Figure 5: The magnetization recovery curve fits — by doubleexponential buildup equation Mz(t) = M0 [1- exp(-t / T1,SL ) —exp(-t / T1,CR )] — displayed at 5 K, 25 K and 150 K. Plot (A) corresponds to 20mM BDPA; (B) to 40mM BDPA; (C) and (D) are 20mM and 40mM DPPH, respectively.
Electronic Magnetization Recovery Fitting Curves In this study, T1 relaxation data were fitted by the doubleexponential buildup equation MZ (t) = M0 [1 - exp(-t/ T1 , S L ) - exp(-t/ T1 , CR )] where T1 , SL and T1 , CR are known to be the contributions from relaxation effects via spin-lattice and cross-relaxation, respectively. The longer relaxation time component T1 , SL is attributed to the interaction of electrons in a higher energy spin state and nearby electrons, and the shorter T1 , CR component is attributed to cross-relaxation effects, which imply the interactions of the unpaired electrons with the lattice.16 As depicted in Figure 5, the double-exponential recovery equation yielded good fits for both BDPA and DPPH.
Temperature Dependence of Spin-lattice Relaxation Rates The theoretical formulation of temperature-dependent spin-lattice relaxation rates accounts for the modulation of ligand field — produced by the vibrations in the lattice — and its effects on the orbital motions of electrons through spin-orbit coupling. Along with the contributions from coordination geometry and electronic energies of the paramagnetic centers, experimental evidence has also confirmed the influence of spin-orbit coupling as the dominant effect for the relaxation rates of spin 1/2 species. The electron relaxation rate (1/T1), therefore, can be written as a sum of contributions predominantly from the direct -1 -1 -1 (T 1,dir ), Raman (T 1,ram ), and Orbach (T 1,orb ) processes.17
be said of the low temperature 1/T1 data of BDPA, where 1/T1 becomes almost temperature independent for T < 40 K . At T > 40 K , the electron relaxation rate of BDPA exhibits 2 a behavior close to the T Raman process prediction.
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In the direct process (T -11,dir coth ( 2k BeT) ) , an electron spin is flipped with the emission or absorption of a phonon with energy equal to the electron Zeeman energy . In the high-temperature limit , the relaxation rate has a linear dependence with temperature, and in the lowtemperature limit , the relaxation rate becomes independent of temperature. In the case of Raman processes , two phonons are involved where the difference (less than the Debye temperature 0 D) of the absorbed and emitted energies for an excited state is transferred to the lattice. In the high-temperature limit for Raman processes, the relaxation rate follows a T2 dependence. The Orbach process also involves two phonons where ∆ orb is the energy separation between the ground and excited states.18 Inspection of the power law fittings in Figure 6 suggests that the temperature dependence of the electron relaxation rate of DPPH (20 mM/40 mM) exhibits a behavior close to the direct process prediction — meaning one phonon is involved with the excitation of an electron spin. The same can
2
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Figure 6: Log-log plot of the electron spin-lattice relaxation rate 1/T1 vs. temperature. These ESR data were taken at W-band (3.35T). In terms of DNP efficiency, the narrow ESR linewidth BDPA is more effective in enhancing low-gamma nuclei such as 13 C spins, as shown in Figure 7, than the relatively large ESR linewidth DPPH. This may be attributed to the fact that the ESR linewidth of BDPA is a closer match for the nuclear Zeeman energies of 13C spins. It should also be noted that while both free radicals show direct process behavior in the low-temperature regime, the relaxation times of DPPH are relatively higher than that of BDPA. The physical attributes of BDPA, such as narrow ESR linewidth and longer T1 , appear to be beneficial to achieving higher NMR or MRI signal enhancements.
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References
9.4 T, 298 K
1. Anatole Abragam and Maurice Goldman, “Principles of Dynamic Nuclear Polarisation,” Reports on Progress in Physics 41, no. 3 (1978): 395.
Hyperpolarized
2. Albert Overhauser, “Polarization of Nuclei in Metals,” Physical Review 92, no. 2 (1953): 411–415.
Thermal
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Figure 7: An example of thermal liquid-state 13C NMR signal that is enhanced 9,000-fold via hyperpolarization using the free radical BDPA. A signal enhancement of about 4,000-fold is produced when the free radical DPPH is used as the polarizing agent.
Conclusion The DPPH electron relaxation rate at W-band shows a behavior close to the high-temperature limit of the one-phonon direct process prediction. In the case of BDPA at low temperature (T < 40 K), the electron relaxation is best described by the low-temperature limit of the direct process where the rate is almost temperature independent. In the high-temperature limit (T > 40 K), the BDPA electron relaxation is attributed to a behavior close to the twophonon, T 2 Raman relaxation process. These results point out that electron relaxation slows down at the cryogenic temperature where DNP is operating. While both free radicals show direct process behavior at low temperatures, the narrow ESR linewidth BDPA has generally longer relaxation times than DPPH. The smaller ESR linewidth and longer T1 of BDPA appear to be more beneficial attributes in producing higher NMR/MRI signal enhancements. Therefore, for magnetic fields at which DNP is currently operating, narrow ESR linewidth free radicals such as BDPA are the preferred polarizing agents for carbon-13 DNP because the linewidths are better matched with the nuclear Zeeman energies of carbon-13 spins. The attainment of the highest MRI signal enhancements using these narrow linewidth free radicals is an important step toward translating this new MRI technology to clinical applications.
Acknowledgment The author acknowledges the support of the U.S. Department of Defense grant No. W81XWH-14-1-0048, the Robert A. Welch Foundation grant No. AT-1877, and the UT Dallas Undergraduate Research Scholar Award.
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3. John Kurhanewicz, et al., “Analysis of Cancer Metabolism by Imaging Hyperpolarized Nuclei: Prospects for Translation to Clinical Research,” Neoplasia 13, no. 2 (2011): 81–97. 4. Malcolm H. Levitt, Spin Dynamics: Basics of Nuclear Magnetic Resonance (Chichester: John Wiley & Sons Ltd., 2008). 5. Ibid. 6. Abragam and Goldman, “Principles of Dynamic Nuclear Polarisation,” 395. 7. Lloyd Lumata, “Hyperpolarized Magnetic Resonance Lab at UT Dallas,” n.d., accessed November 3, 2015, http:// dnpnmr.weebly.com/research.html. 8. Ibid. 9. Jan H. Ardenkjær-Larsen, et al., “Increase in Signal-toNoise Ratio of >10,000 Times in Liquid-State NMR,” Proceedings of the National Academy of Sciences 100, no. 18 (2003): 10158. 10. Sonia Colombo Serra, Alberto Rosso, and Fabio Tedoldi, “Electron and Nuclear Spin Dynamics in the Thermal Mixing Model of Dynamic Nuclear Polarization,” Physical Chemistry Chemical Physics 14 (2012): 13299–13308. 11. Ibid. 12. Lloyd Lumata, et al., “DNP by Thermal Mixing under Optimized Conditions Yields >60,000-fold Enhancement of 89Y NMR Signal,” Journal of the American Chemical Society 133, no. 22 (2011): 8673–8680. 13. Lloyd Lumata, et al., “BDPA: An Efficient Polarizing Agent for Fast Dissolution Dynamic Nuclear Polarization NMR Spectroscopy,” Chemistry – A European Journal 17, no. 39 (2011): 10825–10827. 14. Lloyd Lumata, et al., “The Efficiency of DPPH as a Polarising Agent for DNP-NMR Spectroscopy,” RSC Advances 2, no. 33 (2012): 12812–12817. 15. Thomas R. Carver and Charles P. Slichter, “Polarization of Nuclear Spins in Metals,” Physics Review 92, no. 1 (1953): 212. 16. C.T. Farrar, et al., “Mechanism of Dynamic Nuclear Polarization in High Magnetic Fields,” Journal of Chemical Physics 114, no. 11 (2001): 4922. 17. Anatole Abragam and Brebis Bleaney, Electron Paramagnetic Resonance of Transition Ions (London: Oxford University Press, 1970). 18. Ibid.
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Flowers
This oil painting was inspired by a view of flowers blooming near an old bench. The hues of the roses and the background were manipulated in order to build contrast, as the yellow and green colors of the flowers are complementary to the red and purple colors given to the surroundings. The value of the hues was also adjusted in order to emphasize the roses, which are the lightest in value. The rose leaves and stems, while carrying dark tones, were also given bright highlights so that they would glisten. This, along with a soft texture, distinguishes them from the background. Meanwhile, the bench was painted monochromatically with relatively dark values. In effect, a distinction is created between the man-made bench and the natural beauty. The actions of our past remain in the background; however, by nature, we are given a chance to improve ourselves and our surroundings with every dawn.
â&#x20AC;&#x201D; by Khadijah Mazhar
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A Machine Learning Approach to Comparing Human and Mouse Molecular Pain Pathways The sensation of pain originates from the activation of specialized peripheral neurons known as nociceptors. While pain is a complex emotional experience, in almost all cases we cannot feel pain without the activation of these damage-sensing neurons. The purpose of this experiment is to understand how nociceptors in the dorsal root ganglion (DRG) â&#x20AC;&#x201D; a part of the peripheral nervous system that serves as a fundamental epicenter for the sensation of pain1 â&#x20AC;&#x201D; are ac t ivate d, an d w h e t h e r t h e i r ac t i vat i on mechanisms are likely to be similar or different between mammals. Studying these mechanisms in mice is especially crucial because a considerable amount of neurological pain research utilizes mouse models to draw conclusions about mechanisms of human pain. By evaluating these similarities and differences, we will gain new insight into how pain occurs and how we can treat it more efficiently.
â&#x20AC;&#x201D; by Andrew Torck
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Background In order to help quantify these similarities more precisely, we have taken a high-throughput approach to studying the genes present in human and mouse DRG neurons, by using a computational method known as RNA-sequencing. In general, RNA becomes quickly translated into proteins to be used by the cell, so studying RNA expression levels makes it possible for us to draw conclusions about the cellular proteins that are important to our tissues of interest. We have used RNA-sequencing to identify the relative abundance of the RNA molecules that are expressed by each tissue sample at a certain point in time.2 By contrasting these RNA profiles of DRG samples with profiles of other tissues, we are able to identify genes that are characteristic to the DRG. We then compared these gene expression profiles between species in order to shed light on their similarities and differences.
In order to determine the expression levels of each of the genes in our tissue samples, we analyzed our data with the TopHat-Cufflinks pipeline from the Tuxedo Tools Suite (Figure 1).6 Our first step was to use TopHat to match the RNA sequences back to their original positions in their respective genomes, like pieces of a puzzle, using the Gencodev14 human genome and GencodevM4 mouse genome as references.7 We then ran our data through Cuffdiff, a variant of the Cufflinks program, in order to quantify the expression levels of all the genes present in each of our tissue samples. If a gene is highly expressed, greater amounts of its RNA match back to its location in the genome. The TopHat-Cufflinks pipeline uses this feature to ultimately report relative gene expression levels in transcripts per million (TPMs).
Methods
1. TopHat
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We obtained our post-mortem human DRG tissue samples from a tissue-sourcing company named Anabios. Anabios har vested the tissue samples and stored them in a preservative solution to prevent the RNA from degrading over time. A third-party laboratory then extracted the RNA molecules from the tissue samples and determined each nucleotide sequence using an Illumina sequencer. We used a text file containing each individual RNA sequence from the human DRG samples for our subsequent research. We chose 12 specific tissue samples for comparison against DRG in order to find genes that are primarily present only in the DRG. Of general importance to the development of new pain drugs is eliminating genes that are present in one of these other excitable tissues (e.g., muscle) or organs (e.g., lung), in order to lower the risk of possible side-effects. Hence, we selected six of the 12 tissues from various other critical locations in the nervous system and six more from different visceral organs. The chosen tissues were: spinal cord, olfactory epithelium (only for the mouse analysis), nucleus accumbens, caudate nucleus, hippocampus, frontal cortex, whole brain, heart, skeletal muscle, liver, lung, small intestine, and whole blood. In addition to mouse DRG, we downloaded text files for all of the tissue samples from the GTEx database (human), the Encode database (mouse) and the SRA database (mouse).3-5
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Quantification of the RNA Present in the Tissue Samples of Humans and Mice
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Figure 1: TopHat-Cufflinks pipeline. First, RNA sequences are aligned to their original locations on the reference genome. Then, using Cuffdiff, the relative abundance of each gene is determined. This process is repeated for all tissue samples.
Statistical Analyses We performed all computational analyses for human and mouse separately using MATLAB. Ideally, finding genes that are specific to the DRG in humans and mice would allow us to study them as drug targets for treating neuropathic pain. We calculated tissue specificity — the degree to which a gene is exclusively expressed in a certain tissue — for each gene in the human and mouse genomes by using Shannon’s Entropy Equation.8,9 From a statistical
When looking at the entire genome for genes specific to the DRG, we identified 72 genes as exclusively expressed in human DRG and 127 genes as specific to mouse DRG. We then examined the different families of genes involved in cellular signaling and pain. Clustering of human and mouse kinase genes did not reveal many that were exclusive to a particular tissue but indicated a slight divide between those kinases expressed in neural tissues vs. those expressed in non-neural excitable tissues. Many of the kinases that were highly expressed in neural tissues were lowly expressed in non-neural tissues. This suggests that there are cellular mechanisms involving kinases specific to the nervous system. Clustering of the human ion channel genes did not uncover any previously unknown genes. However, many ion channels known to be specific to DRG did arise. Clustering of the mouse ion channels revealed a very similar pattern with the same DRG-specific genes being expressed, along with a few more, such as Trpv1 and Trpm8 (Figure 2).
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Kcnj1 Hvcn1 Chrna9 Cnga1 Gjb2 Gjb1 Chrne Aqp9 Gja10 Aqp8 Trpv6 Kcnk16 Cftr Aqp12 Trpm5 Mcoln3 Aqp3 Mcoln2 P2rx1 Kcne3 Catsper4 Chrnd Ano7 Gjb3 Ano9 Gjb4 Scnn1a Clic3 Gja4 Ano1 Kcnq1 Aqp7 Gja5 Kcnj15 Clcnka Scnn1g Aqp5 Gjb5 Gabrp Scnn1b Kcng2 Scn7a Scn5a Clcnkb Best3 Scn4a Kcna7 Chrna10 Cacna1s Kcnj5 Gja3 Kcnv2 Cngb3 Kcne1 Cnga3 Clcn1 Ano5 Chrna1 Ryr1 Cnga2 Kcne2 Glra1 Kcnk15 Ryr2 Kcnv1 Kcna4 Pkd2l1 Kcns1 Scn9a Htr3a Chrnb3 Chrna6 Gabra6 Trpa1 Kcnd1 P2rx3 Trpv1 Asic3 Trpm8 Kcnk18 Scn11a Kcna10 Htr3b Scn10a Aqp6 Kcnn4
2.5
Log(TPM)
Results
GJB7 ITPR2 GJB2 TRPM5 SCNN1A KCNK16 P2RX2 MCOLN2 HTR3C CLIC3 GRIN3B SCNN1G CATSPER1 AQP7 P2RX1 ANO9 KCNQ1 AQP10 AQP9 KCNJ15 KCNJ8 CNGA1 SCN5A CLIC5 RYR2 GJA3 KCNF1 GABRD KCNJ4 KCNH4 GLRA4 KCNJ12 KCNA10 P2RX3 SCN7A SCN10A SCN9A SCN11A CHRNA6 KCNK18 KCNMB2 KCNS2 CHRNA5 KCNJ13 CHRNA3 GABRR1 TRPC5 KCNH7 TRPM8 KCNK12 KCNS1 KCNH5 CHRNB3 KCNG4 CHRNA9 KCNH6 HTR3A CNGA3 GABRQ GRIN2B KCNJ16 SCN3A TRPM3 KCNK2 GRID2 TRPC7 GLRA1 ANO3 KCNA1 GABRG3 SCN2A SCN1A GLRA3 AQP4 KCND2 GLRA2
Log(TPM)
point of view, larger entropy values for a gene signal expression levels that are more equally expressed among all of the tissue samples. This means that genes with larger values of entropy are less specific to a tissue, and genes with smaller values of entropy are the most tissue-specific. We ranked all genes by their entropy values and selected those from each species’ genome that had the lowest entropy, and therefore the highest tissue specificity. We continued by taking these genes and clustering them based on Pearson’s correlation formula.10,11 We calculated correlation values between all possible pairs of genes by looking at their expression levels in each of the 13 tissues. This allowed us to identify gene clusters, or sets, which contained genes highly correlated to each other, helping us to understand their possible biological roles. We performed specific analyses for families of genes known to be involved in cellular signaling and pain, including G-protein coupled receptors (GPCRs), kinases, and ion channels. This allowed us to focus directly on genes involved in cellular mechanisms of pain in order to determine if the same expression patterns arise in humans and in mice.
Figure 2: Human and mouse tissue-specific ion channel heatmaps. Entropy < 2.2 and DRG TPM > 0.01. The human heatmap shows many known DRG-specific ion channels, such as NTRK1, SCN9A, SCN10A, SCN11A and HTR3A.12,13 As in mice, these genes have shown to cluster together, which suggests that they have similar biological functions.
Human Gene GRIA1 GRIA2 GRIA3 GRIA4 GRID1 GRID2 GRIK1 GRIK2 GRIK3 GRIK4 GRIK5 GRIN1 GRIN2A GRIN2B GRIN2C GRIN2D GRIN3A GRIN3B
Human TPM 0.128 1.864 3.719 7.469 3.863 0.124 5.361 4.952 10.373 1.651 11.447 34.566 0.433 0.041 0.563 0.256 0.303 0.436
Mouse Gene Gria1 Gria2 Gria3 Gria4 Grid1 Grid2 Grik1 Grik2 Grik3 Grik4 Grik5 Grin1 Grin2A Grin2B Grin2C Grin2D Grin3A Grin3B
Mouse TPM 9.398 10.991 13.855 23.402 10.251 2.41 21.555 7.097 6.756 19.354 24.409 78.235 0.014 0.181 2.066 1.0844 3.662 0.352
Table 1: GRIX gene expression in humans and mice. This expression table shows that glutamate receptor subunits have significantly lower (p = 0.046) expression in humans than in mice.
Looking specifically at the GRIX family — subunits of glutamate ligand-gated ion channels, which are some of the most important signaling channels in the nervous system — showed a significantly large difference in expression levels from humans to mice (p < 0.05) (Table 1).14
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With mice having a much higher expression level of these genes and, likely, proteins in their DRGs, many functional studies of these genes performed in mouse models may not harbor much validity in the context of human pain. When focusing our analyses on ion channels and G-protein coupled receptors, we found that their general expression levels were all much lower than in many other genes in the genome, consistent with previous studies.15 Because of this pattern, we decided to lower our expression threshold to accommodate these genes. Clustering of GPCR genes for humans and mice showed results very similar to the clustering of ion channels. As expected, many well-known DRG-specific GPCRs did appear in our results as validation, such as OPRM1 and the MRGPR family (Figure 3).16 Ranking the GPCR gene family by level of expression in the human DRG enabled us to identify a list of the 50 highestexpressed GPCRs, with human-to-mouse correlation values as high as 0.998. This indicates that, unlike the GRIX genes, there are many genes that could possibly have similar physiological roles and expression levels in mice and in humans. For these genes, using a mouse model may be an accurate method for studying their roles in pain.
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Figure 3: Human and mouse tissue-specific G-protein coupled receptor heatmaps. Entropy < 1.8 and DRG TPM > 0.01. The human heatmap shows important DRG-specific GPCRs, including OPRM1, MRGPRD, MRGPRX1, MRGPRX4 and MRGPRE. The mouse heatmap shows similar results to those seen in human, and also a tight clustering of many olfactory receptors present in the olfactory epithelium.
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The majority of genes in our genome-wide search for DRG specificity are either uncharacterized genes that have not yet been studied or microRNAs that could play important roles in regulating gene expression within the DRG. The presence of all of these unknown genes demonstrates how truly unbiased the RNA-sequencing approach is. This opens the door to the possibility that there are many different molecular mechanisms of pain that scientists have yet to discover. When focusing our search on certain families of genes, we uncovered many interesting and validating results. Even though many of these genes were already known to be DRG-specific, the fact that they were present in both human and mouse DRG, given the thousands of genes we looked at, gives good validation to both our RNA-sequencing experiment and many other functional studies of those genes done in mouse models. However, our results also indicate the possibility that a mouse model may be sufficiently applicable to humans only in the context of certain gene families. The significant drop in expression levels of the GRIX genes from mice to humans could easily translate into a difference in the involvement of these glutamate receptor subunits in the transmission of pain.17 However, in looking at the top 50 expressed GPCRs in the human DRG, there are many genes for which mouse models may be sufficiently applicable.
0
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uc
le
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cto ry E le S pit D us p h R A ina eliu G cc l m u Co H S mb rd ip Fr po tria ens on c tu ta a m W l mp ho Co us le rte Sk B x ele r ta H ain l M ea us rt Sm c Liv le a W ll In Lu er ho te n s le ti g B ne lo od
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lfa
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uc
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O
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S us pin D A al R cc C G um o r H C be d ip a n F r po ud s on ca a t ta m e W l C pu ho o s le rte Sk B x ra ele ta H in l M ea us r t c Sm Liv le all e W In Lunr ho t e g le stin B e lo od
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Mrgpra2a Mrgpra2b Mrgpra3 Olfr284 Mrgprx1 Mrgprd Olfr224 Ackr3 Olfr1396 Mc5r Mc2r Olfr1419 Cckar Nmur1 Glp1r Olfr1342 Ffar4 Gprc5a Lpar5 Ltb4r2 Gpr20 Tacr2 Ffar3 Olfr165 Olfr1033 Olfr1034 Avpr1a Mtnr1a P2ry4 Rgr Olfr20 Sucnr1 Gcgr Gpr15 Hrh4 Gpr174 Gpr18 Ccr6 Gpr171 Gpr141 Grm6 Gpr183 Olfr164 Olfr98 Tas2r135 Cxcr1 Tas2r143 Fpr1 Gpr65 Fpr2 Cxcr4 Ptafr C5ar1 Ccr2 Hcar2 Ccr1 Ccr4 Avpr2 Cxcr6 Gpr132 Cnr2 Ccr7 Cxcr5 P2ry10 Ccr3 Ffar1 Cxcr2 Olfr877 Olfr1140 Olfr881 Olfr316 Drd3 Calcr Prlhr Gpr88 Gpr6 Drd1a Agtr2 Olfr112 Olfr32 Olfr110 Olfr414 Olfr987 Olfr309 Olfr136 Olfr152 Olfr1270 Olfr1217 Olfr1428 Olfr868 Olfr65 Olfr123 Olfr727 Olfr180 Olfr1463
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OR2W3 CCR9 NMUR1 CCR3 OR52K3P PTGIR CCR4 GPR84 GPR25 LPAR2 S1PR4 GPR141 HCAR2 FFAR2 FFAR3 P2RY13 TAS2R40 PTAFR C5AR1 OR52B3P FPR1 CXCR2 FPR2 CXCR1 GLP2R OR52H1 GPR31 TAS2R46 GPRC5A OR7E12P TAAR3 GCGR GPR182 OR2B8P OPN5 AGTR1 CHRM2 OR51E1 NPY6R HTR4 GPR88 DRD3 GPR6 DRD2 OR7E22P TACR3 TAS2R50 TAS2R63P HCRTR2 TAS2R64P TAS2R43 PTGER1 OR2I1P PROKR1 OPRM1 CCKAR MRGPRE MRGPRD OR7E101P MRGPRX4 MRGPRX1
Conclusion
As expected, comparing human and mouse gene expression is not clear cut. We calculated many validating results and saw a number of interesting gene expression patterns arise, but the line between human and mouse is not definitive. For the gene families that are not well conserved between humans and mice, it would be helpful to determine an animal model that has a pattern of expression more similar to humans. However, for the genes that show very good expression conservation between humans and mice, studying their functions in a mouse model could provide valid, novel results that can be accurately extrapolated to humans. Alternatively, genes that show high correlation from human to mouse but low tissue specificity may be present in a tissue-specific manner as alternate gene forms — isoforms. Future studies will take into account how these isoforms display similarities between humans and mice in order to determine a more precise comparison model for the study and treatment of human pain.
Acknowledgments I would like to thank my principal investigator, Dr. Theodore Price, as well as Dr. Gregory Dussor from the School of Behavioral and Brain Sciences at UT Dallas for challenging and mentoring me throughout the course of this project. I would also like to thank Dr. Michael Zhang and Dr. Pradipta Ray from the School of Natural Sciences and Mathematics at UT Dallas for allowing access to the MZ computer cluster to perform our computational analyses and for teaching me the fundamental skills of bioinformatics. A special thank you goes to Dr. Ji-Young Kim from the University of Arizona Department of Pharmacology for starting this project and offering me initial guidance in my own research.
9. Claude Elwood Shannon, “A Mathematical Theory of Communication,” ACM SIGMOBILE Mobile Computing and Communications Review 5, no. 1 (2001): 3–55. 10. Ed S. Lein, et al., “Genome-wide Atlas of Gene Expression in the Adult Mouse Brain,” Nature 445, no. 7124 (2007): 168–176. 11. Karl Pearson, “Note on Regression and Inheritance in the Case of Two Parents,” Proceedings of the Royal Society of London 58 (1895): 240–242.
References
12. Douglas K. Rabert, et al., “A Tetrodotoxin-resistant Voltage-gated Sodium Channel from Human Dorsal Root Ganglia, hPN3/SCN10A,” Pain 78, no. 2 (1998): 107–114.
1. Patrick D. Wall and Marshall Devor, “Sensory Afferent Impulses Originate from Dorsal Root Ganglia as Well as from the Periphery Sensory Afferent Impulses in Normal and Nerve Injured Rats,” Pain 17, no. 4 (1983): 321–339.
13. Sulayman D. Dib-Hajj, et al., “Sodium Channels in Normal and Pathological Pain,” Annual Review of Neuroscience 33 (2010): 325–347.
2. Zhong Wang, Mark Gerstein, and Michael Snyder, “RNA-Seq: A Revolutionary Tool for Transcriptomics,” Nature Reviews Genetics 10, no. 1 (2009): 57–63. 3. John Lonsdale, et al., “The Genotype-Tissue Expression (GTEx) Project,” Nature Genetics 45, no. 6 (2013): 580–585. 4. ENCODE Project Consortium, “An Integrated Encyclopedia of DNA Elements in the Human Genome,” Nature 489, no. 7414 (2012): 57–74. 5. Rasko Leinonen, Hideaki Sugawara, and Mar tin Shumway, “The Sequence Read Archive,” Nucleic Acids Research: 39, Supplement 1 (2010): D19–21, https:// nar.oxfordjournals.org/content/39/suppl_1/D19.
14. Ohannes K. Melemedjian, et al., “Targeting Adenosine Monophosphate-activated Protein Kinase (AMPK) in Preclinical Models Reveals a Potential Mechanism for the Treatment of Neuropathic Pain,” Molecular Pain 7, no. 1 (2011): 70. 15. Buyong Ma and Ruth Nussinov, “Amplification of Signaling via Cellular Allosteric Relay and Protein Disorder,” Proceedings of the National Academy of Sciences 106, no. 17 (2009): 6887–6888. 16. Qin Liu, et al., “Sensory Neuron-specific GPCR Mrgprs are Itch Receptors Mediating Chloroquine-induced Pruritus,” Cell 139, no. 7 (2009): 1353–1365. 17. Ohannes K. Melemedjian, et al., 70.
6. Cole Trapnell, et al., “Differential Gene and Transcript Expression Analysis of RNA-seq Experiments with TopHat and Cufflinks,” Nature Protocols 7, no. 3 (2012): 562–578. 7. Jennifer Harrow, et al., “GENCODE: Producing a Reference Annotation for ENCODE,” Genome Biology 7, Supplement 1 (2006): S4. 8. Koji Kadota, et al., “ROKU: A Novel Method for Identification of Tissue-specific Genes,” BMC Bioinformatics 7, no. 1 (2006): 294.
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This form was a landscape assignment for the Painting Foundations class I am taking this semester at the University of Texas at Dallas with Trey Egan. I had a conceptual idea of what I wanted to achieve, but I didn’t want to force it. I find that art is more like a form of meditation for me, and by focusing on other things (perhaps capturing an emotion or expression) — anything other than what I am actually doing and just allowing the process to unfold — a great many opportunities arise in the experience. That has always been key in my execution. I find that being limber mentally and making adjustments when prompted will always allow room for improvement along the way. I knew I wanted to contextualize this piece in an abstract sense. I wanted to indicate an unnatural landscape by infusing it in odd pigments of greens and yellows. I also wanted to
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Settling In incorporate patterns, repetition and geometry. I have always been fascinated with combining organic and synthetic shapes, and in this piece I wanted to carry over some of my styles in other forms of media to see how they interacted in oil. I wanted to allow myself the freedom to just figure it out as I went along. I masked out all three boards and plotted a very loose design. I carved out portions to work on in stages. Before I knew it, most of the panels were covered. But I wanted to indicate areas of experimentation and processes in varying areas of each panel. I wanted a stamp or an indication of evolutionary progress in each section and to borrow back and forth between each panel.
â&#x20AC;&#x201D; by Robert Powers
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Unearthing Novel Antibiotics from Streptomyces Actinomycetes are a remarkably diverse group of bacteria, and the most notable actinomycetes belong to the genus Streptomyces. Streptomyces are commonly found in soil and resemble fungi in that they often form a filamentous mycelium.1 Upon depletion of carbon sources, aerial mycelia form and subsequently release spores that can germinate to continue the life cycle. The genus is composed of more than 500 species, and each species can vary significantly in nutrient utilization and metabolite production. Streptomyces encode many genes that synthesize unknown metabolites, including antibiotics.
â&#x20AC;&#x201D; by Karthik Hullahalli
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Introduction Streptomyces have been exploited for antibiotic production since 1943, with the discovery of streptomycin.2 Many more antibiotics have been discovered and produced since then, and in many cases, a single species can produce multiple antibiotics. However, many of these antibiotics are expressed conditionally, meaning that certain external conditions must be met before the compound is produced.3 Antibiotics are conditionally produced by microorganisms for a variety of reasons, including competition and cell-cell signaling.4 The aim of this study was to identify antibiotics produced by Streptomyces that were effective against antibiotic-resistant organisms. Coinciding with the discovery of new antibiotics is the development of antibiotic resistance, whereby a bacterial population that was previously susceptible to an antibiotic becomes resistant to it by adapting specific biochemical mechanisms of resistance. Because antibiotics are produced in nature, resistance to these compounds is common. However, healthcare burdens arise when antibiotics in clinical use become ineffective at treating infections. The first generation of antibiotics discovered shortly after penicillin are now mostly ineffective at treating many infections due to the increasing prevalence of antibiotic resistance.5 Newer antibiotics have succumbed to the rapid pace of bacterial evolution as well. Infections caused by organisms that are resistant to drugs of last resort â&#x20AC;&#x201D; those antibiotics used only after all the others have failed â&#x20AC;&#x201D; are often unmanageable due to extremely limited treatment options. In 2013, the Centers for Disease Control and Prevention reported that antibiotic-resistant bacteria had caused 2 million hospitalizations and 23,000 deaths in the United States.6 Due to the current overuse of antibiotics and the horizontal transfer of antibiotic resistance genes across national borders, antibiotic resistance is a rapidly growing threat. As more antibiotics are used, more antibiotic resistance arises, and this pattern will likely continue until a new mechanism for combating microorganisms is found. Therefore, the expedited discovery of new antibiotics is essential in keeping pace with the increasing rate of antimicrobial resistance. Enterococcus faecalis is a gram-positive bacterium that resides in the GI trac t but can cause infec tions in immunocompromised patients.7 E. faecalis infections in hospitals are increasingly caused by multidrug resistant (MDR) strains, which are incredibly difficult to treat. Of these MDR strains, vancomycin-resistant enterococci (VRE) are particularly concerning because vancomycin is used as a drug of last resort, and E. faecalis can disseminate the
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resistance genes to other bacteria.8,9 The relative inability to treat these infections has launched a global effort to combat MDR lineages, and drug discovery is a powerful tool in this fight. In this study, Streptomyces isolates from local soil were genetically confirmed and then examined for their antimicrobial activity against MDR E. faecalis. We discovered that many of the Streptomyces isolates cultured in the presence of E. faecalis produced antimicrobials, indicated by the lack of growth of E. faecalis. However, isolates cultured independently of E. faecalis seemed to lack antimicrobial activity, indicating that Streptomyces conditionally produce antibiotics in the presence of E. faecalis.
Materials and Methods Streptomyces Isolation and Genetic Verification The Streptomyces enrichment protocol was derived from El-Nakeeb and Lechevalier.10 Because Streptomyces are found in soil, soil samples were collected from various locations around the University of Texas at Dallas campus in Richardson, Texas. The process enriches sporulating organisms in the sample, including actinomycetes, if present. The final enrichment plating is done on arginine-glycerol-salt (AGS) media. This is advantageous because the only nitrogen source in AGS media is arginine, and Streptomyces are able to utilize arginine as the sole nitrogen source. However, because this process is an enrichment rather than a selection, other sporulating organisms that can utilize arginine as the sole nitrogen source will also grow on the plate (Figure 1); therefore, subsequent genetic confirmation of potential Streptomyces is necessary. Once potential Streptomyces were identified, they were streaked on ISP2 agar medium. This medium allows for routine growth of Streptomyces and can be used for further laboratory manipulations.11 Once pure streaks were obtained, liquid cultures were made by resuspending spores in Tryptic Soy Broth (TSB) and shaking at 220 rpm at 28°C, one of the optimal growth conditions for Streptomyces. The developed mycelium was lysed and genomic DNA (gDNA) was purified using the UltraClean Microbial DNA Isolation Kit. Subsequently, the 16S ribosomal RNA (rRNA) coding sequence was amplified using polymerase chain reaction (PCR). 16S rRNA is a conserved RNA that constitutes a part of ribosomes, which are responsible for protein synthesis in cells. Because the RNA folds in specific ways to allow activity of the ribosome, its structure is conserved, but sequence variations exist that are useful to differentiate organisms at the genus or species level.12 Once the 16S rRNA region was amplified, it was sequenced at the Massachusetts General
Hospital DNA Core Facility, and then BLAST DNA sequence analysis was used to confirm that the collected isolates were Streptomyces.
Streptomyces might produce a growth defect in V583, Streptomyces were grown in both TSB and ISP2 liquid cultures for 24 hours. Subsequently, each tube was split into two groups. The first group included Streptomyces with the addition of V583, to test whether Streptomyces were producing antibiotics in the presence of V583 in liquid culture. For the second group, the supernatant of media used to grow Streptomyces was filtered and then inoculated with V583, to test whether Streptomyces were producing antibiotics in the absence of V583 in liquid culture. Unlike the previous test, this experiment kept the growth conditions the same between V583 and Streptomyces, whereas in the previous experiment, V583 was grown on solid Brain-heart infusion (BHI), a common growth media for E. faecalis, and Streptomyces in liquid ISP2 or TSB. V583 was also confirmed to be able to grow in ISP2 and TSB at 28째C at 220 rpm after 24 hours.
Results Diversity and Morphology of Streptomyces
Figure 1. Streptomyces enrichment plate (AGS). Because the initial isolation process was an enrichment, many other organisms were collected, as shown above. Potential Streptomyces generally display concentric rings and are hard to the touch.
Antibiotic Screening Streptomyces spore suspensions were spotted onto ISP2 agar. Once spores developed into an embedded mycelium (usually in one to two days at 28째C), MDR Enterococcus faecalis V583 was swabbed across the plate. If the Streptomyces isolate in question produced antibiotics that are effective against V583, it would diffuse across the plate and a zone of inhibition would appear around the Streptomyces spot, indicating that V583 was not able to grow near the Streptomyces due to the high concentration of antibiotics being produced. Similarly, if Streptomyces produced these antibiotics in the absence of V583 in liquid culture, they would be present in the culture supernatant. That is, production and secretion of antibiotics by Streptomyces might allow for antibiotic activity to be detected in the liquid media. Therefore, the supernatant was filtered to remove the cells, while retaining the possible antibiotics. Spotting of the filtered supernatant on V583 swabs and the subsequent appearance of a zone of inhibition would indicate the E. faecalis-independent production of antibiotics in sufficient concentrations in liquid cultures. To determine if co-culturing E. faecalis and
Among 24 presumptive isolates, we sequenced the 16S rRNA region of three and confirmed them as Streptomyces. The remaining 21 isolates are likely Streptomyces, but their genetic confirmation will be an ongoing project. Nonetheless, the three confirmed isolates displayed a remarkable array of diversity (Figure 2), and the other unconfirmed isolates also exhibited unique properties. Particular isolates secreted droplets containing natural products, possibly antimicrobials (Figure 3), while others diffused colored liquids into the agar, thereby darkening the color of the plate. The variety of displays is indicative of the various metabolic processes occurring in each of these strains, thereby illustrating the possibility that previously uncharacterized compounds may be produced in each.
Figure 2. Morphological diversity of confirmed Streptomyces. The variety of color and morphologies demonstrates the diversity present in our collection. It is likely that each strain has distinct metabolic processes that may be unique and useful for natural products discovery.
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were grown in ISP2 (Figure 5).
A
Figure 3. Secretion of natural products. Streptomyces are known to produce a variety of compounds, which are occasionally visible to the naked eye and appear as slightly colored or clear droplets.
Antibiotic Production Among the confirmed isolates, the strain designated KH20 produced antimicrobials that were effective against MDR E. faecalis V583, indicated by a clear zone of inhibition around the Streptomyces spot (Figure 4). Furthermore, at least five of the other presumptive Streptomyces isolates exhibited zones of inhibition, indicating they also produced antibiotics active against MDR E. faecalis.
Figure 4. Streptomyces antibiotic production. Central spots of Streptomyces KH20 produce a zone of inhibition when swabbed with E. faecalis V583. This lack of growth around the central Streptomyces spot indicates the production of an antimicrobial that has activity against MDR E. faecalis. It is possible that Streptomyces KH20 was conditionally expressing antibiotic production in response to E. faecalis. This is of particular interest because it may uncover novel antibiotics through Streptomyces and E. faecalis interactions â&#x20AC;&#x201D; interactions that are currently poorly characterized. To test whether the production of antimicrobials was V583independent, liquid cultures of Streptomyces strain KH20 53
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B
Figure 5. Growth morphologies of Streptomyces and E. faecalis in liquid culture. A) represents Streptomyces and B) represents E. faecalis. Because Streptomyces mycelia clump together, they appear significantly less homogenous than E. faecalis. These differences allow for easily observable growth of both organisms in co-culture. Cultures were shaken at 220 rpm at 28°C overnight and the cell-free supernatant was spotted on BHI agar swabbed with V583. Surprisingly, no zone of inhibition appeared, indicating that the antibiotics were either not produced or not produced in sufficient concentration to establish a zone of inhibition on a lawn of V583. Antibiotic extractions are usually per for med on pure liquid cultures of Streptomyces, in which secondar y metabolites are extracted and subsequently purified. Because there was no observable antibiotic activity in pure liquid cultures by spotting on solid media, a new method for expressing or detecting the antibiotic prior to extraction will be required. To test whether antimicrobial activity is observable in broth, liquid cultures of Streptomyces KH20 were made and subsequently used as growth medium to inoculate V583. The Streptomyces-containing media was inoculated with V583 to determine if antibiotic production is E. faecalisdependent, while the filtered, and thus Streptomyces cellfree, media was inoculated with V583 to investigate E. faecalis-independent antibiotic production. The observable growth differences between E. faecalis and Streptomyces KH20 shown in Figure 5 allow for easy detection of E. faecalis growth, indicating a lack of antibiotic production by Streptomyces. When Streptomyces KH20 is grown in TSB, V583 is able to grow with and without Streptomyces. However, when Streptomyces KH20 is grown in ISP2, V583 only grows when Streptomyces KH20 is removed. This may indicate that the ISP2 media is more conducive to antibiotic production by our strain. As an alternative explanation, because ISP2 is not as rich as TSB, the growth of V583 in ISP2 may be slowed enough that antibiotic production is able to reach inhibitory concentrations. Taken together, our evidence supports the notion that Streptomyces KH20 is conditionally expressing antibiotics in sufficient concentrations in response to E. faecalis, and that it is most observable in liquid co-cultures (Figure 6).
References TSB
ISP2
Add Streptomyces
Streptomyces KH20 grows in TSB
Add Streptomyces
Streptomyces KH20 grows in ISP2
Add V583
V583 grows with Streptomyces in TSB
1. Klas Flardh and Mark J. Buttner, “Streptomyces Morphogenetics: Dissecting Differentiation in a Filamentous Bacterium,” Nature Reviews Microbiology 7, no. 1 (2009): 36–49.
Filter supernatant to remove Streptomyces then add V583
2. Rudi Emerson de Lima Procópio, et al., “Antibiotics Produced by Streptomyces,” The Brazilian Journal of Infectious Diseases 16, no. 5 (2012): 466–471. 3. Gang Liu, et al., “Molecular Regulation of Antibiotic Biosynthesis in Streptomyces,” Microbiology and Molecular Biology Reviews 77, no. 1 (2013): 112–143.
Add V583
No V583 grows with Streptomyces present
V583 grows in the absence of Streptomyces
Possible conclusions:
Possible conclusions:
1) Streptomyces KH20 is not producing antibiotic in TSB liquid cultures.
1) Streptomyces KH20 is producing antibiotics only in the presence of E. faecalis.
2) Because TSB is a rich media, V583 grows too quickly to allow sufficient antibiotic production.
2) Because V583 can grow when Streptomyces KH20 is removed, Streptomyces KH20 is not depleting substrate media of nutrients that would otherwise inhibit growth of E. faecalis.
Figure 6. Experimental procedure for antibiotic assay in liquid culture.
Discussion and Future Work In this study, we isolated putative Streptomyces from soil and found that many of them produced antibiotics effective against MDR E. faecalis. Further examination of one of the antibiotic-producing Streptomyces (isolate KH20) revealed that it did not produce antibiotics in sufficient quantities in pure liquid culture to subsequently inhibit the growth of E. faecalis on solid media. However, when co-cultured in ISP2 liquid medium, Streptomyces KH20 was able to inhibit growth of E. faecalis. In order to purify the antibiotic and determine its chemical structure, a method will need to be established to observe production of this antibiotic in sufficient quantities for chemical analysis. In particular, it will be interesting to determine the mechanism by which Streptomyces detect E. faecalis in its environment, and the specific features of E. faecalis that alert Streptomyces to its presence. Understanding the biochemical and metabolic complexities that govern the production of specific compounds will allow for a quicker and more thorough identification of natural products. Moreover, as we have found, some of these natural products may contain antimicrobial activity against MDR strains that are otherwise resistant to a broad range of antibiotics. These findings support the examination of interspecies interactions for detection of novel antimicrobials, which may help in the discover y of antibiotics that significantly improve outcomes for patients with untreatable infections.
4. Julian Davies, “Are Antibiotics Naturally Antibiotics?” Journal of Industrial Microbiology and Biotechnology 33, no. 7 (2006): 496–499. 5. Julian Davies and Dorothy Davies, “Origins and Evolution of Antibiotic Resistance,” Microbiology and Molecular Biology Reviews 74, no. 3 (2010): 417–433. 6. “Antibiotic Resistance Threats in the United States, 2013,” Centers for Disease Control and Prevention, http://www. cdc.gov/drugresistance/threat-report-2013. 7. Francois Lebreton, Rob J.L. Willems, and Michael S. Gilmore, “Enterococcus Diversity, Origins in Nature, and Gut Colonization,” in Enterococci: From Commensals to Leading Causes of Drug Resistant Infection, eds. Michael S. Gilmore, Don B. Clewell, Yasuyoshi Ike, and Nathan Shankar (Boston: Massachusetts Eye and Ear Infirmary, 2014). 8. Dawn M. Sievert, et al., “Antimicrobial-Resistant Pathogens Associated with Healthcare -Associated Infections: Summary of Data Reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2009-2010,” Infection Control & Hospital Epidemiology 34, no. 1 (2013): 1–14. 9. Kelli L. Palmer, Veronica N. Kos, and Michael S. Gilmore, “Horizontal Gene Transfer and the Genomics of Enterococcal Antibiotic Resistance,” Current Opinion in Microbiology 13, no. 5 (2010): 632–639. 10. Moustafa A. El-Nakeeb and Hubert A. Lechevalier, “Selective Isolation of Aerobic Actinomycetes,” Applied Microbiology 11, no. 2 (1963): 75–77. 11. Micah D. Shepherd, et al., “Unit 10E.1 Laboratory Maintenance of Streptomyces Species,” Current Protocols in Microbiology (2010). 12. J. Michael Janda and Sharon L. Abbott, “16S rRNA Gene Sequencing for Bacterial Identification in the Diagnostic Laboratory: Pluses, Perils, and Pitfalls,” Journal of Clinical Microbiology 45, no. 9 (2007): 2761–2764.
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CAKE IT ON
Women have always been influenced by beauty, whether they are putting on makeup or dropping weight to fit society’s expectations. Women have been used as fashion icons, and society has shaped the meaning of beauty through the ages. Putting on corsets to get a tiny waist, skipping meals to stay thin — women have been through it all. This idea of ideal beauty for women influenced me to paint this piece. I was looking through a magazine and found a photo shoot of Rihanna, posing for the Dior brand. I decided to paint her through a reduction method by painting her silhouette and wiping away the lighter areas of her to reveal her. What better way to top it off than with a tube of lipstick covering her mouth; this huge bright red lipstick is painted on thickly, representing the amount of layers of makeup women put on daily. Some women hide themselves with makeup to meet society’s expectations of beauty. Using oil paint helped me to achieve the thickness I wanted. I wanted to add a human touch by smearing the lipstick across her face. I also paired my left hand with Rihanna’s right hand and smeared her red nail polish down her dress. I wanted it to feel raw and to have a part of me because I experience difficulty in meeting society’s expectations of beauty. I used oil on a 36-by-48-inch canvas.
— by Korina Guerra
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Estrogen Receptor Promotes Breast Cancer Using Tumor Microenvironment Current breast cancer research and treatment has largely been directed at the hormone estrogen. Estrogen (E2) is required for normal proliferation during development of the female mammary glands, but it also promotes proliferation in breast cancer. E2 can act by binding to the G proteincoupled estrogen receptor (GPER), and GPER activation in breast tumors has been associated with increased tumor progression. Similarly, GPER is also expressed by breast cancer-associated fibroblasts (CAFs) in the tumor microenvironment, and GPER ac tivation in CAFs may also promote tumor progression and metastasis. Thus, it is unclear if GPER actions in tumor cells, CAFs, or both could be pro-tumorigenic.
â&#x20AC;&#x201D; by Rachael Couch
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In our study, we seek to define the interaction between E2 and the tumor microenvironment in tumor progression via in vivo mouse studies and complementary in vitro cultures. Our studies suggest that activation of GPER stimulates normal mammary fibroblasts to form CAFs that assist in tumor progression. The GPER’s CAF-dependent signaling mechanism provides an alternative therapeutic target for the treatment of breast cancer and encourages future research toward GPER actions in the tumor cell microenvironment.
Background Fundamental Concepts of Cancer This year, it is estimated that in the United States over 1.6 million new cases of cancer will be diagnosed and more than 500,000 people will die from the disease.1 Breast cancer is the most common and fatal cancer, with more than 40,000 women in the U.S. expected to die from it this year. 2 With the prevalence of cancer, it is common to wonder why it has yet to be cured. To understand the state of treatment, it is important to first understand the basics of cancer. Cancer is a cumulative name for multiple diseases that arise from the abnormal growth, or proliferation, of cells in the body. Most cells have innate mechanisms that allow them to stop growth at normal times and to die off as new cells are made. However, normal cells can mutate to lose these mechanisms, and then these mutated cells begin to reproduce uncontrollably. The loss of the control mechanisms arises from the accumulation of genetic mutations, each of which can be inherited from a parent or can arise spontaneously through mistakes in cell reproduction or exposure to carcinogenic substances. This gives rise to endless possibilities of carcinogenic mutation combinations; therefore, no two cancers are the same and each requires tailored treatment. Another difficulty in treatment arises with the spread of cancer. A tumor, once formed, can invade nearby tissues and cause local damage or migrate to other parts of the body in a process called metastasis. Once the tumor has metastasized, it can form secondary tumors at a new site, causing the cancer to spread throughout the body. Cancer cells present in multiple areas of the body create difficulties in targeted treatments and increase the chance of later recurrence.3 Currently there are cancer treatments that seek to inhibit hormonal signaling mechanisms in the body that have been associated with excessive cell division or metastasis. In breast cancer, one of the major targets of these hormonal therapies has been a natural form of estrogen called 17β-estradiol, or E2.4
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Estrogen in Breast Cancer E2 is naturally produced in both males and females, and it activates pathways that induce both normal and carcinogenic development in the breast’s mammary glands. E2 stimulates a response by binding to one of three receptors: estrogen receptor α (ERα), estrogen receptor β (ERβ), or the most recently discovered G protein-coupled estrogen receptor (GPER).5 Estrogen-related breast cancer treatments have sought to selectively regulate ERα, the receptor most commonly associated with cancer progression. However, Tamoxifen, the most common of these treatments, is only effective in cells that naturally express ERα, and many patients become resistant to Tamoxifen during treatment.6 Unlike ERα, GPER is an endogenous receptor, and thus offers a possible alternative target for breast cancer treatments. Several retrospective clinical studies have shown that cancer cells overexpress GPER, and that increased GPER expression is associated with increased tumor size and metastasis.7,8,9 One study, through in vivo modeling using knockout (KO) mice, specifically identified increased expression of GPER in both the tumor and the microenvironment as promoters of metastasis.10 While previous studies have described GPER in the cancer cells themselves as a major carcinogenic contributor, future studies are needed to define the role of GPER in the tumor microenvironment.
Tumor Microenvironment The tumor microenvironment involves the area surrounding the tumor including the extracellular matrix (ECM) and the fibroblast cells, or CAFs, that interact with the tumor during development.11 Although previously thought to only provide structural support to the cells, recent research has indicated that the microenvironment is responsive to cancer cell signaling, allowing it to make accommodations to the growing cancer cells. 12 Because the ECM and CAFs are signal-receptive components of the tumor microenvironment, distinct changes in them progress alongside the cancer. For example, the ECM stiffens to increase the proliferative and metastatic potential of the cancer cells, possibly resulting from an increase in the crosslinking of collagen fibers. 13,14 Similarly, CAFs can respond to E2 stimulation through a GPER-dependent mechanism. 15 M ammar y fibroblasts are nor mally quiescent, but when tissue becomes wounded or when a tumor develops, fibroblasts become activated. As a tumor progresses, these fibroblasts, now classified as CAFs, become pro-tumorigenic and are associated with increased tumor size and metastasis.16,17,18
Research Question
Results
The role of GPER in the tumor microenvironment has been largely unresearched, as the majority of previous studies have focused on the effect of GPER activity in the tumor cells. 19,20 The purpose of this study is to assess GPER interaction with fibroblasts and the production of ECM components, and thus characterize GPER in the signaling pathway that contributes to tumor development. Our hypothesis is that GPER activation leads to increased activation of fibroblasts to form CAFs, which promote tumor progression and an increase in ECM production.
Our analysis of in vivo cultures of WT fibroblasts showed that GPER activation increased fibroblast activation (Figure 1A). Unactivated
Sham
G-1
TBF-β
Methodology
To assay for the formation of CAFs, normal mouse (WT) mammary fibroblast cells were isolated and cultured with a GPER agonist (G-1), antagonist (G36), and positive control (TGF-β). These cultures were immunostained using α-smooth muscle actin (SMA) for fibroblast activation. In vitro cultures were designed to assess the interaction between GPER signaling, fibroblasts, and tumor cell proliferation. Untreated tumor cells were isolated and grown using recombinant basement membrane (MatrigelTM), which creates an environment that resembles in vivo formation. Some cultures were grown with supplemental unactivated or activated fibroblasts. The cultures were treated with a GPER antagonist, E2, or both for three days, and then were assayed using immunofluorescence techniques with phospho-Histone H3 (pH3) as a marker of proliferation and α-smooth muscle actin (SMA) as a marker of fibroblast activation. Proliferation was quantified as the average number of mitotic cells per spheroid. Fibroblast activation was quantified as the average percent of activated fibroblasts in each corner of the well. To assess changes in the ECM as a result of GPER modification, MMTV-PyMT mice were treated in vivo via pellet injection with a sham treatment, a GPER agonist, a GPER antagonist, E2, or both E2 and a GPER antagonist. Tumors from these mice were assessed for collagen I using immunofluorescence. Sections of all slides were imaged using fluorescent microscopy.
Figure 1A: WT fibroblasts were treated with DMSO vehicle, G-1 (100nM), TGF-β (10ng/mL), G36 (500nM), or G-1 (100nM) and G36 (500nM), immunostained for SMA (green), and counterstained with DAPI nuclear stain (blue). Photos courtesy of Sara Alcon. Positive staining analysis revealed a significant increase in fibroblast activation in GPER-activated cultures relative to sham-treated and GPER-inhibited cultures (Figure1B). Fibroblast Activation in Treated PyMT Tumor Cells 2.5 Arbitrary Units
All studies were performed using MMTV-PyMT mice, which express the polyoma middle T virus, causing them to naturally develop mammary tumors as early as 5 weeks of age.21
2 1.5 1 0.5 0
Sham
G-1
TGF-β
G36
G-1 + G36
Figure 1B: Positive staining was quantified using ImageJ threshold analysis.
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These results support our hypothesis, suggesting that GPER activates normal mammary fibroblasts in the PyMT mouse. Similarly, tumor cells on a recombinant basement membrane indicated overall that treating cultures with GPER inhibitor greatly reduced fibroblast activation (Figure 2A). Sham
Figure 2A: PyMT tumor cells were cultured on Matrigel with unactivated fibroblasts and treated with PBS sham, E2 (1 μM), G36 (5 μM), or E2 (1 μM) and G36 (5 μM). Cultures were immunostained for SMA. Photos shown are representative areas of sham- and G36-treated cultures. The difference in activated fibroblasts between the sham and the E2 treatments was significant and confirms prior findings as well as our hypothesis; however, the experiment should be repeated before drawing conclusions as time constraints did not allow multiple repetitions (Figure 2B).
Average % of Fibs Activated
Fibroblast Activation in Treated Tumor Cells 90 80 70 60 50 40 30 20 10 0
Sham
E-2
G36
E2+G36
Figure 2B: Positive staining was quantified by counting SMA-positive cells/total cells in corner areas of the wells, which contained the highest concentration of fibroblast cells. Because the results of this experiment reflect those from the in vivo analysis, in vitro modeling using Matrigel® in all experiments is confirmed as a useful research technique and likely resembles in vivo development.
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Figure 3A: Mice were treated in vivo with pellets of G-1 (3mg/ pellet), sham, E2 (0.36mg/pellet), G36 (2mg/pellet), or E2 and G36 (both pellets implanted). Tumor sections were stained for collagen I. This is a representative photo of positive collagen I staining in PyMT tissue. Collagen I Presence in Tumor Sections of Treated Mice % Positive Collagen I Staining
G36
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Contrary to our expectations, both GPER-inhibited and GPER-activated cultures showed decreased collagen I production (Figures 3A and 3B).
7 6 5 4 3 2 1 0
Sham
E2
E2 + G36
G-1
G36
Figure 3B: Positive staining was quantified using ImageJ threshold analysis. While our data showed large variation and should be repeated for statistical validity, it may still indicate that GPER signaling and ECM production are mechanistically related in unsuspected pathways. PyMT tumor cells were cultured in the presence of activated fibroblasts and were compared with cells cultured with unactivated fibroblasts or in the absence of fibroblasts. These cultures were treated with GPER antagonists and assayed for proliferation. As predicted, the tumor cells
showed increased proliferation when cultured with activated fibroblasts compared to those cultured with unactivated fibroblasts (Figures 4A and 4B).
Unexpectedly, tumor cells cultured in the absence of fibroblasts also showed increased proliferation (Figures 5A and 5B).
Sham
E2
None Added
G36
E2+G36
Activated
Figure 4A: Isolated tumor cells were cultured with unactivated fibroblasts and treated with PBS sham, E2 (1 μM), G36 (5 μM), or E2 (1 μM) and G36 (5 μM). These cultures were immunostained for mitotic cells using pH3. Photos shown are representative of treatment type.
Figure 5A: Isolated tumor cells were cultured with activated, unactivated, or no added fibroblasts and treated with G36 (5 μM). These cultures were immunostained for mitotic cells using pH3. Photos shown are representative of treatment type.
Proliferative Cells/Spheroid in G-36-Treated Tumor Cell Cultures Avg % Mitotic Cells/Spheroid
Avg % Mitotic Cells/Spheroid
Proliferative Cells/Spheroid in Treated Tumor Cell Cultures 16 14 12 10 8 6 4 2 0
Sham
E2
G36
E2 + G36
Figure 4B: Positive staining was quantified by choosing 10 midsize spheroids and comparing the number of stainpositive cells with the total number of cells in each spheroid.
Unactivated
20 15 10 5 0
None Added
Unactivated
Activated
Type of Fibroblasts Added
Figure 5B: Positive staining was quantified by choosing 10 midsize spheroids and counting the number of stain-positive cells per total number of cells in spheroid.
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Discussion
References
Our results support the primary part of our hypothesis by demonstrating that GPER activates normal mammary fibroblasts to create tumorigenic CAFs in the tumor microenvironment. This suggests that GPER exhibits protumorigenic effects beyond the scope of the tumor cell.
1. “U.S. Breast Cancer Statistics,” Breastcancer.org, accessed Oc tober 23, 2015, http://w w w.breastcancer.org/ symptoms/understand_bc/statistics.
The secondary part of our hypothesis — that the formation of CAFs increases proliferation — was partially confirmed as there was an increase in proliferation of tumor cells in the presence of active fibroblasts. The increase in the absence of fibroblasts compared to unactivated fibroblasts necessitates further study as it demonstrates that unactivated fibroblasts may have anti-tumorigenic effects. The tertiary part of our hypothesis — that the production of ECM components is increased in a GPER-related mechanism — was not clearly supported as the results showed that modification of GPER expression, whether activated or inhibited, decreased the production of tumor microenvironment components. These results indicate that there may be multiple unexpected interactions between GPER and ECM production. To form conclusions regarding GPER-mediated ECM production, the in vivo collagen I testing should be repeated to reduce variation for each treatment type. Additionally, the experiment should be repeated, assaying for other ECM components, including other types of collagen. Further studies investigating the effect of modifying GPER in tumor cells would provide further insight as to whether GPER could be a therapeutic target and promote the understanding of the interaction between tumor cell progression and changes in the tumor microenvironment. Further validation of our preliminary results may show GPER inhibitors to be a useful part of future breast cancer treatment.
2. “Cancer Statistics,” National Cancer Institute, accessed October 23, 2015, http://www.cancer.gov/about-cancer/ what-is-cancer/statistics. 3. “What is Cancer?” American Cancer Society, accessed October 23, 2015, http://www.cancer.org/cancer/ cancerbasics/what-is-cancer. 4. Atanas Ignatov, et al., “G-Protein–Coupled Estrogen Receptor GPR30 and Tamoxifen Resistance in Breast Cancer,” Breast Cancer Research and Treatment 128, no. 2 (2011): 457. 5. Allison L. Scaling, Eric R. Prossnitz, and Helen J. Hathaway, “GPER Mediates Estrogen-induced Signaling and Proliferation in Human Breast Epithelial Cells and Normal and Malignant Breast,” Hormones and Cancer 5, no. 3 (June 2014): 146–160. 6. Sandra A. Polin and Susan M. Ascher, “The Effect of Tamoxifen on the Genital Tract,” Cancer Imaging 8, no. 1 (2008): 135–145. 7. Edward J. Filardo, et al., “Distribution of GPR30, a Seven Membrane-Spanning Estrogen Receptor, in Primary Breast Cancer and Its Association with Clinicopathologic Determinants of Tumor Progression,” Clinical Cancer Research 12, no. 21 (2006): 6359–6366. 8. Qun Liu, et al., “Expression of CD133, PAX2, ESA, and GPR30 in Invasive Ductal Breast Carcinomas,” Chinese Medicine Journal 122, no. 22 (2009): 2763. 9. Xin Zhou, et al., “Estrogen Regulates Hippo Signaling via GPER in Breast Cancer,” Journal of Clinical Investigation 125, no. 5 (2015): 2123–2135. 10. Nicole A. Marjon, et al., “G Protein-coupled Estrogen Receptor Regulates Mammary Tumorigenesis and Metastasis,” Molecular Cancer Research 12, no. 11 (2014): 1644. 11. “Tumor Microenvironment,” NCI Dictionary of Cancer Terms, accessed October 23, 2015, http://www.cancer.gov/ publications/dictionaries/cancer-terms?cdrid=561725.
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12. Ori Maller, Holly Martinson, and Pepper Schedin, “Extracellular Matrix Composition Reveals Complex and Dynamic Stromal-epithelial Interactions in the Mammary Gland,” Mammary Gland Biology and Neoplasia 15, no. 3 (2010): 301. 13. Michael W. Pickup, Janna K Mouw, and Valerie M. Weaver, “The Extracellular Matrix Modulates the Hallmarks of Cancer,” EMBO Reports 15, no. 12 (2014): 1243–1253. http://www.breastcancer.org/symptoms/understand_bc/ statistics. 14. Kandice R. Levental, et al., “Matrix Crosslinking Forces Tumor Progression by Enhancing Integrin Signaling,” Cell 139, no. 5 (2009): 891. 15. Pengfei Lu, Valerie M. Weaver, and Zena Werb, “The Extracellular Matrix: A Dynamic Niche in Cancer Progression,” Journal of Cell Biology 196, no. 4 (2012): 395. 16. Sara Alcon, “G Protein-Coupled Estrogen Receptor Regulation of Migration and Metastasis in the Breast,” (Ph.D. diss., University of New Mexico, 2014). 17. Giulio Gabbiano, G.B. Ryan, and Guido Majne, “Presence of Modified Fibroblasts in Granulation Tissue and Their Possible Role in Wound Contraction,” Experientia 27, no. 5 (1971): 549–550. 18. Akira Orimo and Robert A. Weinberg, “Stromal Fibroblasts in Cancer: A Novel Tumor-Promoting Cell Type,” Cell Cycle 5, no. 15 (2006): 1597–1601. 19. Ignatov, et al., “G-Protein–Coupled Estrogen Receptor,” 457. 20. Marjon, et al., “G Protein-Coupled Estrogen Receptor,” 1644. 21. “FVB/N-tg(MMTV-PyVT)634Mul/J,” JAX Mice Database The Jackson Laborator y, http://jaxmice.jax.org/ strain/002374.html.
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Annual Exhibition of Excellence in Undergraduate Research Each year, the Office of Undergraduate Education and the Office of Research collaborate to host the Exhibition of Excellence in Undergraduate Research. This weeklong series of events allows undergraduates to learn more about research opportunities, to receive recognition for research related accomplishments, and to compete for valuable research awards. Research Resume Workshop Students receive valuable advice on how to prepare a scientific/ research-oriented resume or to strengthen an existing research resume. The workshop, sponsored by the Office of Undergraduate Education, the Office of Research and the UT Dallas Career Center, also covers best practices for approaching the research interview and guides students in preparing their best presentation for Undergraduate Research Match Day.
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The Exley Luncheon The Office of Undergraduate Education hosts an annual luncheon to celebrate the latest issue of The Exley. This event is open to all students, faculty and staff, and each attendee receives a copy of the most recent issue of The Exley. Students interested in learning more about publishing in The Exley are able to receive important best-practice information. Current contributors to The Exley, as well as the annual recipients of the Patti Henry Pinch Scholarship, are recognized during the luncheon. Undergraduate Research Match Day The Match Day event is open to all students and is designed to facilitate the process of pairing motivated undergraduates with faculty who are conducting research projects. Students who participate gain valuable research experience that contributes to future success professionally or in graduate school. Faculty who engage undergraduates in research enhance grant applications by demonstrating mentorship while receiving quality researchers.
Undergraduate Research Faculty/Student Panel Undergraduate students and faculty researchers share their research experiences, tips on competing for research programs and fellowships, and strategies for obtaining the ideal research position. Undergraduate Research Scholar Awards Poster Contest Current recipients of the Undergraduate Research Scholar Awards discuss their research with event attendees, utilizing a research poster and their in-person research presentation skills. Research faculty members select two semifinalists per academic discipline, all of whom advance to the final round of judging. The three final winners are selected by members of the external research community. The Three Minute Thesis (3MTÂŽ) This academic competition challenges PhD students to describe their research within three minutes to a general audience. 3MT celebrates the discoveries made by research students and encourages them to communicate the importance of their research to the broader community. The first UT Dallas 3MT competition will be held on April 15, 2016, and judges will include undergraduate students.
Provostâ&#x20AC;&#x2122;s Award for Faculty Excellence in Undergraduate Research Mentoring The Office of Undergraduate Education and the Office of the Provost are pleased to annually recognize an outstanding UT Dallas faculty member who excels in his or her undergraduate research mentoring efforts. The Provostâ&#x20AC;&#x2122;s Award for Faculty Excellence in Undergraduate Research Mentoring seeks to formally acknowledge a faculty mentor who demonstrates superior leadership, support and guidance toward the development of UT Dallas undergraduate students and their research endeavors. The awarded faculty member receives a one-time cash award and recognition during the Spring Honors Convocation Ceremony. Announcement of the winner takes place during the annual Exhibition of Excellence in Undergraduate Research. Past winners are Dr. Sven Kroener, Dr. Paul Pantano, and Dr. Mihaela Stefan.
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Provost’s Award for Faculty Excellence in Undergraduate Research Mentoring The University of Texas at Dallas is fully committed to providing a robust and dynamic research environment for its undergraduate students. In appreciation of the exceptionally dedicated efforts of the UT Dallas faculty, the Office of Undergraduate Education and the Office of the Provost are pleased to annually recognize an outstanding UT Dallas faculty member who excels in his/her undergraduate research mentoring efforts. The Provost’s Award for Faculty Excellence in Undergraduate Research Mentoring seeks to formally acknowledge a faculty mentor who demonstrates superior leadership, support, and guidance towards the development of UT Dallas undergraduate students and their research endeavors. If selected to receive the annual Provost’s Award for Faculty Excellence in Undergraduate Research Mentoring, the awarded faculty member receives the following: • One-time $5,000 cash award • Recognition during the Spring Honors Convocation ceremony • Commemorative plaque presented to faculty member during the Spring Honors Convocation ceremony
Provost’s Award for Faculty Excellence in Undergraduate Research Mentoring Selection Criteria The Provost’s Award for Faculty Excellence in Undergraduate Research Mentoring Selection Committee is responsible for selecting the annual recipient. Candidates should exemplify a commitment to personalizing the undergraduate student research experience through involving students in hands-on projects, encouraging scholarly publication and presentation, and mentoring researchers one-on-one. In addition, candidates must meet the following qualifications: • Taught at UT Dallas for a minimum of two years • Engaged in mentoring UT Dallas undergraduate students for a minimum of two years • Engaged in research at any UT Dallas campus for a minimum of three years
Winners
2013 Dr. Sven Kroener Assistant Professor of Neuroscience, School of Behavioral and Brain Sciences
2014 Dr. Paul Pantano Associate Professor of Chemistry, School of Natural Sciences & Mathematics
2015 Dr. Mihaela Stefan Associate Professor of Chemistry, School of Natural Sciences & Mathematics