Rutgers Science Review -- Fall 2013

Rutgers Volume 3, Issue 1, Fall 2013 Volume 3 | Issue 1 | Fall 2013

The Human Brain Project

Science Review

The Belly of the BeasT Competitive Eating The Human Body

&

Features The Belly of the Beast: Competitive Eating & the Human Body

05

The Human Brain Project

08

NASA Kepler Mission

12

T able of

C ontents

Interview Dr. Carlton (Tad) Pryor

16

Research Aresty Abstracts

18

Search for Pair-Produced Stops in Dilepton Final State

28

Optogenetic Modulation of ParvalbuminExpressing Interneurons in the R6/2 Mouse Model of Huntington’s Disease

37

Front Cover: Cornelius De Witt, 1959 Back Cover: Š Frederick C. Skvara, MD who retains all rights

About The Rutgers Science Review (RSR) biannually publishes student-written scientific features, opinions, and research papers.

RSR is supported by RUSA Allocations. For more information, including submission guidelines, visit us at thersr.com

Staff Co-Editors-in-Chief Alexandra DeMaio Stephanie Marcus

Design Editors Riasat Zaman Bo Tang

Features Editor Lauren Fish

Business Manager Margaret Morris

Associate Editor Arvind Konkilmalla

Web Developer Lawrence Xie Faculty Advisor: Dr. Charles Keeton

Editorial Review Board Lynn Ma Jonathan Shao Parth Shukla

Articles

Features

The Belly of the Beast

Competitive Eating & the Human Body Matthew Mikolay, edited by Stephanie Marcus and Alex DeMaio This past Independence Day, thousands of cheering fans gathered at Coney Island to witness the 2013 Nathan’s Famous Fourth of July International Hot Dog-Eating Contest. Before a crowd of awestruck spectators, competitive eater Joey Jaws Chestnut of San Jose, CA, set a new world record, consuming an astonishing 69 hot dogs in just ten minutes [3].

F

or the average person, eating such an enormous number of frankfurters is an unimaginable feat, but downing vast quantities of food is commonplace in the world of competitive eating. Just this year, the sport witnessed a number of incredible achievements. In addition to breaking the hot dog world record, Joey Chestnut won the second annual Hooters World Wing-Eating Championship, finishing 179 wings in ten minutes. At the Day-Lee Foods World Gyoza Eating Championship, 21-year-old Matt Megatoad Stonie ate 268 potstickers in ten minutes, and Miki Sudo swallowed 8.5 pounds of kimchi in six minutes at the Chicago Korean Festival [10, 6, 21].

A Day in the Life of a “Gurgitator” After witnessing the gluttonous competitors stuffing their faces, one feels compelled to ask – how exactly does competitive eating affect the human body? Do the competitors face any health repercussions? How do their bodies compare to the average person’s?

At the moment, it is unclear whether the eating habits of professional “gurgitators” could result in permanent damage to the body. However, it is known that the excess consumption of food can lead to

tearing of the esophagus and perforations in the stomach. Immediately after a competition, participants are likely to experience increased perspiration, urination, and defecation [17].

Surprisingly, many professional eaters manage to keep their food down, as heaving leads to instant disqualification in most events.

Even after a competition, the most dedicated eaters push past nausea for the sake of sportsmanship.

Still, if competitors find themselves vomiting regularly, certain complications can develop. Prolonged vomiting can cause dehydration, Mallory-Weiss tears (tearing in the mucus membrane of the esophagus), and the erosion of tooth Joey enamel. In some casJaws es, vomited stomach Chestnut contents can enter the lungs, resulting in aspiration pneumonia [17, 19, 13, 20, 14, 11].

During both competitions and

Fall 2013 | Rutgers Science Review | 5

Features training sessions, competitive eaters choose to ingest astronomical amounts of calories, sodium, fat, and other nutrients. Eating just 20 hot dogs amounts to over 6000 calories. For the average person, this immense intake would result in substantial weight gain, but amazingly, many currently top-ranked eaters manage to remain slim and trim. Sonya Thomas, Major League Eatings top-ranked female eater, weighs in at only 105 pounds, despite her unnatural ability to consume 46 dozen oysters in just ten minutes. This overwhelming dedication to staying in shape can be attributed to Japanese competitive eater Takeru Kobayashi, who inspired the belt of fat theory that excess fat in the abdomen restricts expansion of the stomach. Fully aware of the possibility of weight gain, competitive eaters count calories carefully, maintain balanced diets, and exercise rigorously. Some competitive eaters even choose to fast before competitions [18, 15, 7].

As in other sports, professional competitive eaters push their bodies to the limit to improve their competitive edges. Many eaters expand their stomachs by intentionally eating past the feeling of satiety.

During training sessions, eaters like Joey Chestnut and Bob Shoudt drink large quantities of water for the sake of gastric stretching. Unfortunately, drinking excessive amounts of water can prove dangerous for the human body. Water intoxication results when the electrolyte levels in the blood shift out of balance due to excessive water content. Symptoms include fatigue, headache, vomiting, nausea, frequent urination, disorientation, seizures, and swelling of the brain. In some cases, even death can result.

In 2007, a 28-year-old woman died

from water intoxication after consuming approximately six liters of water during a radio contest to win a Nintendo Wii [9, 16, 5].

Although professional gurgitators run certain risks, stomach stretching has proven to be an effective method of improving competitive performance. Some eaters have increased their stomach volumes by up to 400 percent. As a result, competitive eaters often fail to develop sensations of fullness after

6 | Rutgers Science Review | Fall 2013

eating meals of average size [8, 17].

Medical Implications To investigate the gastrointestinal systems of competitive eaters, Dr. David C. Metz of the University of Pennsylvania performed a study on 29-year-old competitive eater Tim “Eater X� Janus. Using water load tests, solid-phase nuclear gastric emptying scans, and a speed-eating test, Metz determined

Features that stomach stretching techniques were indeed responsible for Janus’s observed ability to delay fullness [17, 12].

Metz noticed that gastric emptying took three times longer in Januss body than in the body of an average person. He speculated that Janus’s stomach failed to empty at the normal rate because his brain was unable to perceive “fullness.” Metz also warns that competitive eaters are more susceptible to `chronic binge eating’ because they lose the ability to feel sated.

Extreme stomach expansion is hypothesized to prevent solid food elimination, which could lead to intractable nausea and vomiting. This condition would warrant a gastrectomy to restore the stomach to a normal state of function [17, 12].

Some competitive eaters strengthen their jaws as well as their stomachs. Competitive eater Eric Badlands Booker has been known to chew on large amounts of gum - up to five sticks at a time to build up strength in his jaw. This technique has proven effective; the strength of competitive eaters’ masseter muscles has been measured at 280 pounds - stronger than the bite of a German Shepherd. Like the stomach, the jaw is not immune to damage. In 2009, Takeru Kobayashi harmed his jaw during a training session and was unable to open his mouth [12, 19]. Despite competitive eaters’ dedication to staying in shape, the increasing popularity of competitive eating over the past few years has caused concern among dietitians and nutritionists.

Fearful that a positive image of excess eating will encourage overconsumption at large, they urge the masses to turn their backs on the sport. Whether or not opponents of competitive eating are saving

the public from supporting a hazardous and self-destructive sport is still unclear. Yes, competitive eaters push their bodies to the limit during training and competitive eating events, but the question remains: What happens when Joey Chestnut eats one hot dog too many?

References 1. Abramovitch, S. (2013, July 5). Joey Chestnut Wins Again: 5 Things to Know About Competitive Eating.

The Hollywood Reporter. Retrieved October 30, 2013, from http://www.hollywoodreporter.com/

2. Alexander, B. (2005, September 29).

Eating champs to chow down at Everett wingding. The Seattle Times. Retrieved October 30, 2013, from http:// www.seattletimes.com/

3. Associated Press. (2013, July 4). San Jose’s Joey Chestnut wins 7th straight hot dog eating contest, breaks record.

San Jose Mercury News. Retrieved October 30, 2013, from http://www.mercurynews.com/

4. BBC. (2007, June 27). Japan speedeater’s jaw arthritis. BBC News. Retrieved October 30, 2013, from http://news.bbc. co.uk/

5. Ballantyne, C. (2007, June 21). Strange but True: Drinking Too Much Water Can Kill. Scientific American. Retrieved October 30, 2013, from http:// www.scientificamerican. com/

6. Barrabi, T. (2013, August 19). Joey Chestnut Loses PotSticker Eating Contest To Matt Stonie At World Gyoza Eating Championships. International Business Times. Retrieved October 30, 2013, from http://www.ibtimes.com/ 7. Chau, D. (2013, August 29). Can Matt Stonie Bring About a New Golden Age in Com- petitive Eating?. Grantland. Retrieved October 30, 2013, from http://www.grantland.com/ 8. ESPN. (2011, July 1). Sport Science: How Joey Chestnut destroys hot dogs. ESPN Sport Science. Retrieved October 30, 2013, from http://www.youtube.com/

9. Hayden, E. (2013, July 5). Hotdog Eating Champ Joey Chestnut: ‘My Body Is Just Pretty Much at Its Limit’. The Hollywood Reporter. Retrieved Oc-

tober 30, 2013, from

http://www.holly-

woodreporter.com/

10. Hooters. (2013, July 26). Joey Chestnut Claims Victory as Hooters World WingEating Champion. Hooters Newsroom. Retrieved October 30, 2013, from http://news.hooters. com/ 11. Jaret, P. (n.d.). Preventing Dehydration When You Have Diarrhea or Vomiting. WebMD. Retrieved October 30, 2013, from http://www.webmd.com/ 12. Kobayashi in Depth. (n.d.). The Score.

Retrieved October 30, 2013, from http://www.thescore.com/ Levine, M. S., Spencer, G., Alavi, A., & Metz, D. C. (2007). Competitive Speed Eating: Truth And Consequences. American Journal of Roentgenology, 189(3), 681686.

13. Mallory Weiss tear. (n.d.). MedlinePlus Medical Encyclopedia. Retrieved October 30, 2013, from http://www.nlm.nih. gov/

14. Norwood, D. (n.d.). Aspiration Pneumonia. NYU Langone Medical Center. Retrieved October 30, 2013, from http://www.med.nyu.edu/

15. O’Brien, L. (2011, July 3). The Lonesome Independence Day Of Kobayashi, Eater In Exile. Deadspin. Retrieved October 30, 2013, from http:// www.deadspin.com/

16. Rose, B. (2012, November 21). Tips From Professional Eaters on Maximizing Your Thanksgiving Meal.

Gizmodo. Retrieved October 30, 2013, from http://gizmodo.com/ 17. Science of Speed Eating. (n.d.). National Geographic Channel. Retrieved October 30, 2013, from http://channel.nationalgeographic.com/

18. Seminara, D. (2012, July). The Insatiable Black Widow. Northern Virginia Magazine. Retrieved October 30, 2013, from http://www.northernvirginiamag.com/ 19. Sine, R. (n.d.). Competitive Eating: How Safe Is It?. WebMD. Retrieved October 30, 2013, from http://www.webmd.com/

20. Tooth Enamel: Erosion and Restoration. (n.d.). WebMD. Retrieved October 30, 2013, from http://www.webmd. com/

21. Toxic, S. (2013, August 14). Miki sets kimchi record. The Rake & Herald.

Retrieved October 30, 2013, from http:// www.therakeandherald.tv/

Fall 2013 | Rutgers Science Review | 7

Features

THE HUMAN BRAIN PROJECT Aayush Visaria, edited by Alex DeMaio

I

n October 2013, a consortium of institutions gathered in Switzerland to discuss what has officially become an international, multibillion-dollar venture to reveal the mysteries of the human brain [8]. Named the Human Brain Project (HBP), this undertaking is an analog of the Human Genome Project, with an arguably more demanding goal: mapping and simulating the entire human brain.

The Neuroinformatics Platform

The Human Brain Project is a colossal amalgamation of neuroscience, computing, and medicine, whose overarching goal is to build a supercomputer that mimics human brain function exactly. To this end, HBP scientists have established a list of 13 milestones (called plat-

forms) to meet in the next decade.

The Neuroinformatics Platform addresses the need for models and ontologies that can explain ion channels, synapses, and neural connections. These tools will enhance predictive neuroinformatics - or, in other words, allow scientists to

8 | Rutgers Science Review | Fall 2013

extrapolate neurological data more accurately.

Diffusion magnetic resonance imaging (dMRI) is one of the most common methods of obtaining in vivo data [8]. In dMRI, the anisotropic diffusion of water is observed as a person is put through a large, rotating magnetic field. In a hypothetical empty box with water

Features Water molecules diffuse fastest when parallel to a semi-permeable membrane and slowest when perpendicular to the membrane. The rate at which water flows is measured in ellipsoid units. In every imaging voxel, it is assumed that there is only one ellipsoid unit and that all axonal fiber tracts are parallel to each other within the voxel. This is not always true because there may be tracts crossing over other axonal tracts or axons branching into multiple axons. Consequently, several improved types of dMRI have been developed, including High-Angular Diffusion Imaging (HARDI) [9]. Imaging techniques are expected to play a substantial role in gathering usable in vivo data for the Neuroinformatics Platform. The results will be used in simulations and high performance computational models.

The Brain Stimulation Platform

droplets concentrated in the middle, water molecules would diffuse in all directions equally. In the human brain, however, there are various obstructions in the form of semi-permeable membranes. This allows one to distinguish between different axonal fiber tracts and cells. The rate of diffusion is orientation-specific (anisotropic) [3].

The primary goal of the Brain Simulation Platform is to develop mathematical models that simulate brain behavior. This includes point neuron models, cellular models, molecular models, and dynamic multi-scale models.

The Hodgkin-Huxley (HH) model is a fundamental neurological tool often used in point neuron models to simulate neuronal circuitry and action potential propagation. Each part of the neuron is compared to a circuit element. The lipid bilayer acts like a capacitor, voltage-gated ion channels and leak channels act like conductors, electrochemical gradients are voltage sources, ion pumps generate current, and the membrane potential is the starting voltage [6]. (For further discussion and mathematical development of the model, see Hodgkin and Huxley’s original 1952 paper [6]).

Although the HH model is powerful, it does not account for (1) inherently stochastic processes in different neurons or (2) certain ion channels like calcium and chloride channels. The HBP Brain Simulation Platform will expand on this foundation and develop multineuronal models to explain behavior and cognition [9].

Figure 1 Left: Diffusion magnetic resonance imaging (dMRI) showing imaging voxels and ellipsoid units. If you look closely at the expanded image, each element is an ellispoid [10]. Right: A subsection of the Human Connectome delevoped by the Human Connectome Project [11].

Fall 2013 | Rutgers Science Review | 9

Similar Projects Most of the HPB’s data is open source, so anyone can access it at any time. In fact, a number of smaller, local initiatives have been created to complement HBP research. The United States is sponsoring an endeavor called the BRAIN initiative, but it is still unclear when the project will officially begin and how much funding it will receive [1]. Two smaller projects, the Human Connectome Project (HCP) and the Blue Brain Project, are currently active in the US. The data from these projects will serve both the BRAIN initiative and the HBP. HCP focuses on creating a human connectome - a map of the neural networks and connections in the brain. These connections include both structural connections (axonal tracts between brain regions) and functional connections (regions with similar functions but not necessarily located in the same area of the brain) [3].

The Blue Brain Project began in 2005 and has produced multiple successful results.

Researchers have developed a realistic model of the rat cortical column, which consists of over 10,000 neurons [4]. The ultimate goal of this research is to reverse-engineer the human brain (which consists of ~ 1015 neurons). The Blue Brain Project also established a brain simulation facility that allows researchers to perform in silico experiments to test their hypotheses.

Advances in Neuroscience Neuroscientists constantly discover new neurological mechanisms and develop new theories about brain function. The vast majority of the human brain’s mysteries is yet to be revealed. Only a few days after HBP began, scientists from the Julich Supercomputing Centre published a new theory of synapse formation, claiming that synapses form homeostatically. This means there are specific upper and lower thresholds between which synapses can form.

When a neuron has too many synapses, it can sense the increase in electrical activity and degrade unnecessary synaptic connections [5]. Similarly, if the neuron is synapse-deficient, it will form new synapses to improve function. If this theory withstands further experimentation, it could be pivotal in determining how to simulate the ~ 1015 neurons of the human brain simultaneously!

Computational neuroscience is a rapidly evolving field that professors, scientists, physicians, and students are rushing to learn about with the very minds that they seek to understand. In a world where mental illness diagnoses and incurable neurological conditions seem to be on the rise, the HBP and other brain projects are opportune catalysts for the development of new treatments and technologies.

References [1] Advisory Committee. (2013, September 16). BRAIN Interim Report. Retrieved October 21, 2013, from National Institutes of Health website:

http://acd.od.nih.gov/presen-

tations/BRAIN-Interim-Report.pdf

[2] Allen Institute for Brain Science. (2013). Retrieved October 21, 2013, from Allen Brain Atlas website: http://human. brain-map.org/mri_viewers/data

[3] Behrens TEJ, Sporns O. Human connectomics, Curr Opin Neurobiol (2011), doi:10.1016/j.conb.2011.08.005 [4] Blue Brain Project EPFL. (2013, September 6). Retrieved October 21, 2013, from Blue Brain Project website: http:// bluebrain.epfl.ch/page-56882-en.html

[5] Butz, M. and van Ooyen, A. (2013, October 10). A simple rule for dendritic spine and axonal bouton formation can account for cortical reorganization after focal retinal lesions. Retrieved October 21, 2013, from:

http://

www.fz-juelich.de/SharedDocs/Pressemitteilungen/UK/EN/2013/ 13-10-11synapsenbildung-gehirn. html

[6] Hodgkin, A. L. and Huxley, A. F. (1952, March 10). A Quantitative Description of Membrane Current and its Application to Conduction and Excitation in Nerve. Retrieved October 21, 2013, from National Center for Biotechnology Information website:

http://www.ncbi.nlm.nih.gov/pmc/

articles/PMC1392413/pdf/jphysiol01442-0106.pdf

[7] Oksana Sorokina, Anatoly Sorokin, J. Douglas Armstrong, and Vincent Danos A simulator for spatially extended kappa models Bioinformatics 2013 : btt523v2-btt523.

[8] SP5 - Neuroinformatics. (2013). Retrieved October 21, 2013, from Human Brain Project website: https://www.humanbrainproject.eu/neuroinformatics-platform

[9] SP6-Brain Simulation. (2013). Retrieved October 21, 2013, from Human Brain Project website:

https://www.human-

brainproject.eu/brain-simulation-platform

[10] Artifact: An Art-Science Collaboration (2013). Retrieved October 29, 2013 from: https://www.union.ic.ac.uk/ arts/artifact/news/ [11] National Institute on Drug Abuse (2013). Retrieved October 29, 2013 from: http://irp. drugabuse.gov/nrb.php

Features

NASA Kepler Mission George King, edited by Alex DeMaio

T

he study of extra-solar planets, or exoplanets, is one of the fastest growing pursuits in modern astrophysics. Since the first discovery in the early 1990s [1], improvements in technology have progressively enhanced our methods of exoplanet detection. At the time of its launch in March 2009, the NASA Kepler Space Telescope was at the cutting edge of exoplanet detection technology. Kepler contributed to numerous findings until its mission came to a close this August.

12 | Rutgers Science Review | Fall 2013

Features

Artist Rendition of Kepler Exo-Planet: James Dyson

Fall 2013 | Rutgers Science Review | 13

An Exoplanet Pioneer Kepler used the transit method, which involves analyzing the small, periodic dimming of a star’s light as planets move in front of its disk. In fact, in our own solar system, Mercury and Venus occasionally transit the Sun’s disk, most recently Venus in June 2012. Johannes Kepler, after whom NASA named the Kepler Telescope, is credited with the first pre- diction of a Venus transit in the 17th Century [2]. Accurate measurement of a star’s brightness is crucial, as the dip caused by a transiting planet is often much less than one percent of the total light emitted.

Using the values from light curves like the one shown in Figure 1, together with properties of the host star, astrophysicists can estimate the size of a planet and its distance from the star. Additional details are required to estimate the planet’s mass; to this end, it is common to analyze the “wobble” of a star caused by orbiting planets [3].

Figure 1: A light curve obtained during follow-up observations of the planet Kepler 33-b.

The vast majority of early exoplanet discoveries were of planets with sizes and masses comparable to those of Jupiter and Saturn, primarily because transit and stellar “wobble” are easier to detect when the planets involved are very massive. The NASA Kepler Mission, however, was specifically in- tended to locate Earth-sized planets. The telescope observed a patch of sky near the Cygnus and Lyra constellations.

About 150,000 stars in this region were selected for observation [4].

Kepler’s impact on exoplanet exploration has been profound. For the first time, a significant number of Earth-sized planets was discovered - some even falling in the Goldilocks zone, the region at just the right distance from a star to enable Earth-like planetary environments. These results raise hopes of finding habitable planets in our galaxy.

Kepler has made a number of remarkable discoveries, including the smallest confirmed planet (Kepler 37-b, which is smaller than Mercury [5]) and planetary systems orbiting two suns [6] (reminiscent of Luke Skywalker’s home planet Tatooine in the Star Wars saga).

Although only 16 percent of the 999 currently confirmed exoplanets are Kepler discoveries [7], 3588 additional candidates are currently being considered, and a significant portion of the data that was collected by Kepler is still being analyzed [8]. An ongoing Zooniverse project called Planet Hunters was established to search light curves manually; NASA made this research public, and anyone can contribute. (Go to their website for more information [9].)

The Future of Kepler Kepler’s termination in August was the result of mechanical malfunction. NASA confirmed that attempts to fix two of the craft’s four gyroscope-like wheels (for controlling the directional system) had proven unsuccessful. Kepler was functional with one wheel out of action, but the failure of a second wheel meant that the telescope could not be pointed as precisely as necessary. NASA has asked the scientific community to suggest new missions that the craft can undertake without using the two broken wheels [8]. This process is ongoing.

Whatever lies in store for Kepler, the telescope will remain one of the greatest feats of technological pioneering in modern astrophysics. One hopes that Kepler will pave the way for even greater successes, just as the Viking landers of the 1970s led to more advanced rovers like Curiosity (currently exploring Mars). Improvements in technology will allow us to discover ever more about systems of planets that lie far beyond our solar system.

With each new development, we can learn a little more about our place in the cosmos.

Kepler supernova remnant

References [1] B. Dum, ”physicsworld.com,” 2005. [Online]. Available: http:// physicsworld.com/cws/article/news/2005/feb/11/astronomers-find-smallest-exoplanet [Accessed 21 October 2013].

[2] R. H. van Gent, ”Transit of Venus Bibliography,” 12 June 2004.

[Online]. Available: http://www.staff.science.uu.nl/~gent0113/ venus/venustransitbib.htm [Accessed 18 October 2013].

[3] T. Yaqoob, Exoplanets and Alien Solar Systems, Baltimore: New Earth Labs, 2011.

[4] astrobites, ”Is Anyone Home in Cygnus?,” 26 February 2013.

[Online]. Available: http://astrobites.org/2013/02/26/is-anyone-home-in-cygnus/ [Accessed 21 October 2013].

[5] B. News, ”Exoplanet Kepler 37b is tiniest yet - smaller than Mercury,” 20 February 2013. [Online]. Available: http://www. bbc.co.uk/news/science-environment-21471908\ [Accessed 18 October 2013].

[6] NASA, ”NASA’s Kepler Mission Discovers a World Orbiting Two Stars,” 15 September 2011. [Online]. Available: http:// www.nasa.gov/mission$\_$pages/kepler/news/kepler-16b. html$\#$.UmX$\_$ORDFrH9 [Accessed 21 October 2013].

[7] J. Schneider, ”The Extrasolar Planet Encyclopaedia,” 31 January 2013. [Online]. Available: http://exoplanet. eu/ [Accessed 10 February 2013].

[8] NASA, ”Kepler: A search for habitable planets,” 21 October 2013. [Online]. Available: http:// kepler.nasa.gov/ [Accessed 21 October 2013].

[9] Zooniverse, ”Planet Hunters,” [Online]. Available: http://www.planethunters.org/ [Accessed 18 October 2013].

Interview Q: Can you give me a little information about your background? What kind of research do you do?

Well, I’m an astrophysicist - an observer primarily. That means I either go to telescopes, or these days I actually do a lot of observations with the Hubble Telescope. My general field is globular star clusters - the oldest, densest, most populous star clusters in our galaxy, and I tend to do dynamical measurements.

Measure the motions of things.

Interview with:

I use the motions of stars in globular clusters to measure the total amount of mass that’s there, and I also look for binary stars: two stars orbiting around a common center of mass. That feature is reflected in a doppler shift that varies with time. Binary stars are interesting because they’re... well our sun is actually unique, being a single star. How many stars are single and how many stars are found in binary or multiple systems is a fundamental feature of star formation, so it’s something people like to study in different populations of stars.

Dr. Carlton (Tad) Pryor

Conducted by Alex DeMaio

My other topic of study is satellite galaxies in the Milky Way. They’re interesting because they contain a large amount of dark matter. Most galaxies have more dark matter than luminous matter. Some of these galaxies - even at their very centers - have a higher density of dark matter than luminous matter, which is not common in bigger galaxies like our own.

I’m measuring the orbits - the motions - of the satellites of our own Milky Way Galaxy with the goal of constraining models for the formation of galaxies. These models predict a lot of substructure, a lot of small clumps, in the dark matter halo of our galaxy. We don’t see many dwarf galaxies, which you would expect to inhabit those small clumps, and trying to understand that conundrum is actually one of the interesting problems in astrophysics today.

Q: What question(s) in astrophysics are you particularly passionate about investigating?

Like most people, I’m interested in, well, what’s the dark matter? By studying these dwarf galaxies that contain a lot of dark matter, one of the things I hope to do is illuminate that question. One of the most important questions in astrophysics - and even in physics today - is “What is that dark matter?”.

But that’s a big question. You have to sort of pick out a piece of it to tackle, so the piece that I’m working on is dwarf satellite galaxies.

Q: How did you get started in physics? When did you know you wanted to study astrophysics?

I knew pretty early on. I grew up with the space program in the United States, so I remember Mercury, Gemini, Apollo - the first Apollo landing happened when I was in high school.

I was always interested in science, and I was an amateur astronomer way back; I had the little amateur telescopes - I would take pictures of the sky and things like that! It was a natural progression for me to study astrophysics. I was an undergrad at Caltech, where there was a long tradition of astronomy - at the Palomar Observatory. So I decided to major in - well, one of the things I was certainly thinking of as a possibility in freshman year - was astronomy. Actually, at that point it was relatively rare. Few undergraduate programs had majors in astronomy. Mostly you would major in physics and specialize in graduate school, and that’s still true today, but yeah basically, it was a physics degree with... well astronomy courses as well *laughs*.

16 | Rutgers Science Review | Fall 2013

Interview I worked in this building named Robinson. There was a heliostat in the building because Caltech had solar astronomy when they first built it. They could send light all the way down to the bottom. And there were only two stories, but there were four basements! So of course the graduate students and the undergrads were all down in the basements. Subterranean.

Q: It’s funny that they build basements at Caltech because of the earthquakes!

Well, I guess you can’t fall down *laughs*. Things can just fall on top of you!

Q: How has college changed since you were an undergrad?

Well, things have changed significantly in astrophysics and in physics - since I was an undergrad. Technology has significantly changed the way we do science. Made the world a more connected place. Before, to really know what was going on, you wanted to keep up with the pre-prints.

Before they appeared in journals, people would send out copies of papers to various institutions. Unless you had access to one of those institutions, you didn’t know what was going on until it came out in the journals. Now all that’s online. You can get it from anywhere in the world!

Back in the day, I mean, you were physically mailing paper copies of these things out *laughs*! So you only wanted to send them to so many places!

Q: Can you explain.... well.... people are wondering how you got your nickname.......

Right *laughs*! Carlton, in parentheses “Tad.” Well, that’s a good story - though goes back a long ways! When my parents took me home from the hospital and put me on their big double bed, my father said, “He’s a little tad, isn’t he?” *laughs*. And it stuck! From then on!

So yeah, it’s interesting - it causes... actually... significant confusion! It has throughout my entire life. When I’m talking to people, I’m always... Tad, basically. But professionally, I’m formally Carlton; when I write papers, I’m Carlton. I mean, I could have started signing them “Tad,” but I didn’t. And so people who know me personally know me as “Tad,” and people that know me professionally know me as Carlton, so when they actually meet me, there’s usually all this confusion!

Q: Do you have any advice for current undergrads looking to go to grad school?

The fairly obvious things! Get a solid ground in math and physics... if you’re interested in physics and/or math and/or astronomy! Nowadays, computer skills are particularly important as well. This is sort of... obvious, but being able to manipulate significant amounts of data - that’s clearly part of the future of most fields of science - in astronomy probably even more than in physics. In the next few decades we’ll be getting big data in astronomy. And having the skills to deal with that - looking at 100 terabytes of data, for example - you don’t just do that by looking at it bit by bit. As an undergrad, you may want to look around and ask yourself, “Well, what’s at the cutting edge of computer science?” and then learn some of those tools.

Q: Are there any expectations you’d like to set as the new director? Do you have any specific, overarching goals?

This year, my goal is to survive *laughs*. In subsequent years, I want to look at the undergraduate curriculum again - and that’s something we’ll do as a department - and see if it’s serving our majors in the modern world. Make sure we’re teaching the right courses and all that kind of stuff.

Fall 2013 | Rutgers Science Review | 17

Aresty Abstracts

Abstracts

Rebalancing cellular reductants

Overexpression of transhydrogenase in a model cyanobacterium

By Prathusha Maduri, Xiao Qian and G. Charles Dismukes

Waksman Institute of Microbiology, Dept. of Chemistry & Chemical Biology

Cyanobacteria are capable of acting as cell factories converting light, water, and carbon dioxide into carbohydrates through photosynthesis, followed by conversion to hydrogen and fuel precursors by auto-fermentation. This study aims to overexpress the enzyme transhydrogenase (coded for by the gene pnt) which mediates the reversible transfer of hydride:

NADH + NADP+ ↔ NAD+ + NADPH We hypothesize that by over-expressing pnt, transhydrogenase activity will increase and the respective pool sizes of NAD(H) and NADP(H) will adjust, thus altering the yields of desired fuel precursors and hydrogen. Manipulation of these pools is important since cyanobacterial hydrogenase utilizes NADH, while the photosynthetic light reaction produces NADPH. We tested this hypothesis using the model cyanobacterium, Synechococcus sp. PCC 7002; both wild type and the pnt knockout strains were made available courtesy of the Bryant Lab (PSU). The construction of the pnt over-expression mutant (pnt+) is currently in progress. Thus far, the procedure to construct the pnt+ mutant has been to isolate the gene and plasmid vector. From there, both fragments must be digested with restriction enzymes and ligated to form a circular plasmid vector containing the gene. This plasmid is then transformed into E. coli via heat shock to quickly create a high copy number. Then, the plasmid is again isolated and digested to isolate the linear portion of the plasmid containing an unregulated promoter, the gene and a Spectinomycin antibiotic cassette. This entire linear fragment is transformed into wild type Synechococcus sp. PCC 7002 via homologous recombination.

Upon construction of the mutant, metabolomic studies and assays testing for transhydrogenase levels will be studied.

Fall 2013 | Rutgers Science Review | 19

Abstracts

INTERACTIONS OF THE APP1-ENCODED PHOSPHATIDATE PHOSPHATASE WITH ENDOCYTIC PROTEINS IN SACCHAROMYCES CEREVISIAE Katie Fullerton Endocytosis is an important homeostatic mechanism in cells that allows large, polar molecules that cannot pass through the plasma membrane to be internalized by forming a vesicle. The yeast Saccharomyces cerevisiae protein App1p is known to associate with cortical actin patches, the site of endocytosis in yeast, and has many known interactions with various endocytic proteins. APP1 encodes for one of four phosphatidate phosphatase (PAP) enzymes that convert phosphatidate (PA) to diacylglycerol (DAG), a critical reaction with implications in lipid synthesis, lipid signaling, and cell homeostasis. Previous studies show that app1Δ single mutants show no abnormal phenotypes, making it difficult to determine the function of the protein. However due to its localization and protein interactions, it has been postulated that App1p may play a role in endocytosis. Nine genes encoding endocytic proteins with known interactions with App1p were studied; these genes are ABP1, BBC1, CRN1, LSB1, LSP1, PIN3, RVS161, RVS167, and YSC84.

Mutant strains of yeast were created by introducing deletiondisruption mutations in these genes into wild-type (W303-1A) and app1Δ mutant strains of yeast. Assays for growth and temperature sensitivity were preformed on these mutants to identify abnormal phenotypes. Of the tested mutants, app1D in combination with bbc1D, crn1D, rvs161D, rvs167D, and ysc84D showed a growth defect at 37 oC. In contrast, neither app1D alone or in combination with the other mutations showed a growth defect at 30 oC. The molecular basis for the synthetic growth defects caused by the app1D mutation will be examined in future studies.

Saccharomyces Cerevisiae

Taken from my balcony shortly before a “haboob” swallowed downtown Phoenix.

20 | Rutgers Science Review | Fall 2013

Abstracts

VALIDATION OF RIBOGREEN ASSAY TO ANALYZE TRANSCRIPTION INHIBITION FROM BACTERIAL EXTRACTS Daniel Ilg

Bacterial RNA polymerases are common targets for inhibitors, used clinically to treat bacterial infections. Rifamycin antibacterial agents are currently utilized to inhibit the function of RNAP in both gram-positive and gram-negative bacteria. This form of treatment, however, is threatened by certain strains of E. coli and Mycobacterium tuberculosis that are resistant to rifamycin treatment. A mutation allows for these strains of bacteria to resist the binding of the rifamycin antibacterial agent, rendering the treatment useless. Various microbial extracts contain natural inhibitors, some of which may inhibit transcription despite this mutation. A fluorescence analysis study has been developed using the compound RiboGreen to screen many extracts at once to find new methods of inhibition.

RiboGreen is a compound that works by binding to transcribed RNA or DNA, increasing a fluorescence reading. Carried out in microplate format, many extracts can be analyzed at the same time. Extracts that show inhibition can then be used to create new antibacterial agents for fighting E. coli and Mycobacterium tuberculosis infections.

Mycobacterium tuberculosis

THE EFFECTS OF CHARGES ON SANDSTORMS Lillian Wang, Will Pittman, Theodore Siu and Troy Shinbrot

“Haboobs” are spectacular storms that cover tens of square miles with dense sand or dust. These storms have been known for over a century to spontaneously generate lightning strikes.[1] Likewise dust devils – small desert tornados– have been reported to produce lightning.[2] The charged dust particles affect their neighboring particles, which in turn each affect one other, creating nonlinear feedback.

Recent experiments have revealed that complex structures form due to these nonlinear interactions, including an abrupt transition at which localized columns of particles become unstable in favor of a swirling storm of rapidly moving grains. No existing model adequately accounts for these behaviors.

In this study, we simulate the formation and break-up of these structures in a computational model. We evaluate the dependence of this transition on system parameters, and compare the simulation with experiments. We propose that improved modeling of this kind may in the future unveil both how particles collectively generate strong voltages and how voltages affect particle behaviors.

Kok, Jasper F., and Nilton O. Renno. “Electrostatics in Wind- Blown Sand.” Physical Review Letters. 11 Jan. 2008. Web. 28 July 2013.

[2] Baddeley, P. F. H. Whirlwinds and Dust-Storms of India. London: Bell & Daldy, 1860. University of California Library, 18 Mar. 2010. Web. 29 July 2013.

[1]

Fall 2013 | Rutgers Science Review | 21

Abstracts

STRANDEDNESS AFFECTING CODON USAGE BIAS IN RNA VIRUSES Christopher Lin, Dr. Siobain Duffy Department of Ecology, Evolution & Natural Resources RNA viruses aren’t all alike: some have positive and some have negative singlestranded genomes, and others have double-stranded genomes. Depending on their genome type, the virus interacts with the host in different ways. In order to determine which is better at host adaptation, codon usage bias (CUB) is used to see how well viruses are at adapting their protein in the host. CUB refers to the unequal use of synonymous codons – nucleotide triplets that encode the same amino acid – in genes.

Virus CUB is of particular interest because viruses use the translation machinery of their host cells, and should adapt to match the CUB of their hosts. However, viruses with different genome types may not be able to equally respond to that selection pressure. Since single-stranded RNA genomes form sequence-specific stem-loop secondary structures more readily than double-stranded genomes, we hypothesized that ssRNA viruses will not match the codon usage bias of their hosts as well as double-stranded RNA viruses. We tested this by examining the relative synonymous codon usage (RSCU) of +ssRNA, -ssRNA and dsRNA viruses that infect the same host. RSCU reveals which codons are under- and over-used, compared to equal usage of all synonymous codons (which would be RSCU=1). The correlations between virus and host RSCU were measured. Most viruses were poorly adapted to the CUB of their hosts, but a few +ssRNA viruses were the best adapted to the CUB of their hosts. While secondary structures may play a role in influencing viral CUB, it does not explain our results. Viruses in general appear to not be under strong selective pressure to match host CUB. 22 | Rutgers Science Review | Fall 2013

Abstracts

DEVELOPMENT OF A TOOL TO DRIVE HASPIN INDEPENDENT CPC LOCALIZATION IN MOUSE OOCYTES Jake Ohring, Vibha Shrivastava, and Karen Schindler Haspin, a protein kinase that phosphorylates histone 3 at threonine 3, plays an important role in driving centromeric localization of the chromosome passenger complex (CPC) by recruiting Survivin in mitosis. The CPC consists of Aurora kinase B (AURKB), inner centromere protein (INCENP), Survivin, and Borealin and it plays a major role in chromosome segregation, spindle assembly checkpoint (SAC) activation, and cytokinesis. In mitotic cells, loss of Haspin activity leads to CPC delocalization, chromosomal misalignment, and SAC inactivation with subsequent aneuploidy.

In mammalian germ cells, another Aurora kinase, AURKC, is expressed, and is also a member of the CPC. AURKC localizes to the centromere and inter-chromatid axes (ICA) during Metaphase I in mouse oocytes. Our unpublished data shows that during oocyte meiosis I, Haspin inhibition perturbs the ICA localization of the CPC and causes abnormal chromosome and spindle morphology. To determine which of these phenotypes are due to delocalized AURKC activity, we sought to design a tool to re-localize the CPC to the ICA independent of Haspin activity. In mitotic cells, retargeting AURKB to centromeres was accomplished by using CB-INCENP, a fusion protein combining the DNA binding domain (DBD) of Cenp-B with INCENP (Higgins, 2011). We subcloned CB-INCENP into an oocyte expression vector and generated cRNA. Microinjection of the subclone, CB(DBD)-INCENP-GFP, into mouse oocytes exhibits centromere localization of CB(DBD)-INCENP-GFP as well as higher AURKC expression. Therefore, we have successfully generated and validated a tool to drive the CPC to the ICA in mouse oocytes.

Fall 2013 | Rutgers Science Review | 23

Abstracts

BROADENING A NON-REDUNDANT DATASET FOR THE STATISTICAL ANALYSIS OF DNA-PROTEIN ENERGY INTERACTIONS Nicole Bassous A comprehensive understanding of the mechanical and physical properties of DNA, particularly of its conformational energy distributions, requires the statistical analysis of a sufficiently assorted and well-diversified

composite of structures. After accessing structural datasets of double-helical protein-bound DNA from the Nucleic Acid Database (NDB) and from the complementary Protein Data Bank (PDB), the Structural Classification of Proteins (SCOP) was employed to classify structures into domains using a structural homology cutoff of 90%. This step was facilitated by Excel parsing capabilities, and analogous clusters were maintained using ClustalW, which also yielded scores for the elimination of orphan structures. The applications of such developmental and visual aids as Blast and Jmol helped account for a greater than 40% reduction in the representation of homologous sequential information relative to unfiltered datasets.

This semi-automated process concluded with the manual selection of a few structural prototypes from each cluster, and a non-redundant database of a few hundred protein-nucleic-acid complexes was generated.

24 | Rutgers Science Review | Spring 2013

Abstracts

TUMOR SUPPRESSOR BRCA1 IS REQUIRED FOR REPAIR OF FORMALDEHYDE-INDUCED DNA DAMAGE Monika K. Masanam, Samuel F. Bunting Mutations in the tumor suppressor gene BRCA1 are associated with ~10% of hereditary breast cancer cases and are linked with ovarian cancer. BRCA1 is required for correct DNA repair through the homologous recombination pathway, specifically in the assembly of Rad51 nucleoprotein complexes at resected DNA breaks. In addition, BRCA1 seems to be needed for recruitment of a key mediator, FANCD2, to damage sites during repair of DNA interstrand crosslinks. Crosslinking agents like formaldehyde (FA) impede DNA replication by covalently linking DNA to surrounding proteins, causing a form of DNA damage called ‘DNA-protein crosslinks’ (DPCs). To study the role of BRCA1 in repair of DPCs, we set up colony formation assays to test the sensitivity of BRCA1-deficient and BRCA1/53BP1 double-deficient MEF cells to FA. We found a threefold decrease in colony growth in BRCA1-deficient cells treated with FA compared to WT. Western blotting revealed an increase in phosphorylated KAP1 in protein lysates from BRCA1-deficient cells in response to FA treatment, indicating an activation of DNA damage signaling pathways. Further measurements of post-translational modification of Replication Protein A (RPA) and PCNA-associated factor (PAF) revealed a decrease in the overall level of PAF and its ubiquitylated form in samples treated with FA. However, this outcome was not BRCA1-dependent. Our results indicate that the DNA double-strand break repair function of BRCA1 is required for normal cellular proliferation in the presence of formaldehyde. As formaldehyde is an abundant metabolic byproduct, this function of BRCA1 is likely to be important for maintenance of genomic stability.

Spring 2013 | Rutgers Science Review | 25

Abstracts

LIGAND EXCHANGE REACTIONS OF MN 4O 4L 6

(L = DIPHENYLPHOSPHINATE) WITH AMMONIUM HALIDE SALTS Donald Chawla, Paul F. Smith, and G. Charles Dismukes Nature’s solar activated water oxidation complex (WOC) is a CaMn4O5 cluster which, through photosynthesis, is responsible for producing all of earth’s atmospheric oxygen. Recently the Mn4O4L6 (L = diphenylphosphinate)”cubane” structural mimic has been shown to catalyze water oxidation under certain laboratory conditions, but not all. The O2 evolution reaction for the Mn4O4L6 via water oxidation has been experimentally demonstrated photolytically in the gas phase and electrolytically upon

suspension in a proton conducting membrane (nafion) [1]. These environments are thought to enable the dissociation of one ligand, which precludes spontaneous release of O2. On the other hand, catalysis is not seen in homogeneous solution. Hence, the goal of this project is to perform ligand substitution reactions in an effort to find a ligand set flexible enough to demonstrate solution-phase O2 evolution.

Fluoride was observed to exchange onto Mn, but in the process decompose the cubane core.

INFORMATION RETRIEVAL AND INTERACTION SYSTEM Kevin Albertson and Dr. Chirag Shah Information retrieval systems are designed to give users the ability to get relevant results from queries on a set of documents. Typical information retrieval systems like search engines analyze and extract information from documents using a variety of tried and tested algorithms combined in many different ways. For example, not only will Google rank pages by the number incoming links, it will also rank based on the number of outgoing links, the number of keywords, your location, and quality of content. The intention of the IRIS (Information Retrieval and Interaction System) project is to modularize various functionalities pertaining to information accessing and processing. Our method involved defining very simple tasks for each module and generalizing input and output so the output of one module could be easily used as input to another. Therefore, each module of the IRIS system performs a unique but very simple task (i.e. finding the most frequent words of a document, or removing words from a document). With the ability to combine these simple tasks, the aim is to build layers of abstraction to make algorithms used in information retrieval easier and more powerful. Some of our results include being able to perform tasks such as finding the pros and cons of a product from analyzing the reviews. We hope that eventually abstract concepts such as getting the topic of a list of documents or “making sense” of documents can be accomplished with IRIS.

26 | Rutgers Science Review | Fall 2013

Research Papers

Research Search for Pair-Produced Stops in Dilepton Final State David Kolchmeyer1, 2 1

Department of Physics and Astronomy, Rutgers University, New Brunswick, NJ 2 Department of Physics, University of Michigan, Ann Arbor, MI

Stop squarks are top quarks’ superpartners in the framework of supersymmetry. In this study, we search for events with two oppositely charged, high-pT leptons (electrons or muons), which characterize the dilepton decay channel of pair-produced stops. Further cuts are performed to reflect b-jet and missing transverse energy criteria. We outline the tight-to-loose method as a means of calculating the fake lepton background, and we present a preliminary estimate of this background.

I. A.

INTRODUCTION The Standard Model

The Standard Model (SM) of particle physics is a quantum-field theoretical framework that excellently describes the data measured from Large Hadron Collider experiments. Within the SM, there are three generations of spin-1/2 fermions, each consisting of an up-type quark, a down-type quark, a lepton, and a lepton neutrino.

There are also spin-1, force-carrying bosons, namely the W+ /W− , the Z, the gluon, the photon, and the Higgs boson. At experimental energies, gravity is so weak that its effects are negligible.

While the SM agrees with experimental data, it leaves open questions which may lead us to Beyond the Standard Model (BSM) physics. One question asks why the Higgs mass is not at the Planck scale. The Planck mass is defined as the mass where the Compton wave length ( mc ) equals the Schwarzschild radius (∼ Gm c2 ).

The Compton wavelength is the smallest distance in which one can localize the particle before producing a particle-antiparticle pair. Also, if the particle is localized within the Schwarzschild radius, it becomes a black hole and is no longer a fundamental particle (many gravitons will be produced). At energies higher than the Planck mass, the Compton wavelength will be smaller than the Schwarzschild radius, and a black hole will form. Hence, fundamental particles do not exist above the Planck scale, and new physics is needed. However, loop corrections to the Higgs mass can bring the Higgs mass to the Planck scale; one possible, but unnatural, solution is to finely tune the uncorrected Higgs mass [4].

B.

Supersymmetry

Supersymmetry (SUSY) is a BSM theory that posits an extra set of particles to mirror the Standard Model particles. Each boson is paired with a fermion, and each fermion is paired with a boson. More specifically, bosons and fermions are regarded as unified objects in a higher dimensional superspace; in our space, we see projections of these objects. SUSY offers a solution to the hierarchy problem, as its particles can cancel loop corrections to the Higgs mass. SUSY particles are also candidates for Dark

28 | Rutgers Science Review | Fall 2013

Matter in R-parity conserving models. Notably, the theory allows for unification of the strong and electroweak forces at high energies [5].

II. A.

METHODS

The Large Hadron Collider

The Large Hadron Collider (LHC) is a 27 km ring consisting of multiple collision points. In the latest run, the LHC accelerated√each proton beam to 4 TeV, producing pp collisions at s = 8 TeV. The proton beams collide with each other at various points along the ring; the CMS experiment is located at Point 5. The beams are designed to collide at an angle that so that the position of the interaction point can be controlled. Because of its circular shape, the LHC can collide the beams multiple times at very high frequencies before re-filling. This enables higher luminosities - and thus a greater likelihood of new discoveries - than achievable in linear e+ e− colliders. The LHC accelerates protons in groups separated by 25 ns. Each group, or bunch, contains billions of protons.

Due to the high collision rate, analyses at the LHC suffer from a phenomenon called pileup, wherein an event containing a hard interaction of interest also contains many additional interaction vertices that create lowenergy tracks and energy deposits in the calorimeter.

These extra tracks and vertices, which constitute the underlying event, complicate event reconstruction and introduce uncertainties into measured quantities. Events in linear e+ e− colliders are much cleaner because electrons are pointlike objects whereas protons contain many partons (quarks and gluons), which contribute to the interactions in a collision.

The LHC steers proton beams with magnetic dipoles that produce an 8.3 T magnetic field. The dipoles are constructed from superconducting Niobium-Titanium (Nb-Ti) wires cooled with liquid helium. Their current densities are 1500 A/mm2 (the normal current density of copper is 5 A/mm2 ). The proton beams are focused with magnetic quadrupoles, shown in Figure 1. Protons that deviate in the vertical direction will be focused while protons that deviate in the horizontal direction will be defocused. Hence, the LHC magnets employ a “FODO” configuration that consists of a focusing quadrupole mag-

Research2

FIG. 1: A magnetic quadrupole [10]

as inward force is proportional to velocity. Thus, protons with phases slightly greater than φ0 will be accelerated more and will have greater relative velocities; they will return sooner (and with smaller phases) toward φ0 .

Likewise, protons with initial phases slightly less than φ0 will not be accelerated as much, will have a lower relative velocities, and will therefore take more time to orbit around the LHC. Their phases will be delayed (increase) with each turn.

In the relativistic regime, φ0 lies on the downwardsloping region of the sinusoid (see Figure 2). Protons that enter with a phase slightly lower than φ0 will be accelerated more; in the relativistic regime, this acceleration negligibly increases the velocity because it’s so close to c. Instead, the effective mass of the proton increases, causing it to circulate at a larger radius and increasing its phase toward φ0 . Protons with slightly higher phases will be accelerated less and will orbit at smaller radii.

Their phases will decrease with each turn. As the protons transition from the non-relativistic regime to the relativistic regime, the phase of the RF system must be switched because φ0 with respect to the oscillating cavity must change [3, 6].

B.

FIG. 2: Principle of phase stability. Let P1 and P2 be fixed points. If velocity increases, M1 and N1 will approach P1 , and M2 and N2 will retreat from P2 [8].

net, a dipole magnet, a defocusing quadrupole magnet (at 90o to the first), and a dipole magnet. Higher order magnets, such as sextupoles and octupoles, are used for further corrections, given that the momenta of protons in a bunch can vary.

The LHC accelerates the protons at Point 4, where the RF system is located. The RF system uses an accelerating cavity to create an oscillating electric field so that protons will be attracted to the cavity as they approach it, and repelled from the cavity as they leave it. At the simplest level, the accelerating potential can be modeled as a sinusoid: V (t) = Vo sin(ωt). Each particle that enters the RF system is characterized by a phase φ with respect to the oscillating V . Particles that enter with different phases will be accelerated to different extents. A priori , it seems that this effect would destroy the bunch; in reality, the phases of the protons in each bunch are distributed about an equilibrium phase φ0 , which may be relativistic or non-relativistic, depending on the γ factor of the protons.

In the non-relativistic regime, φ0 lies on an upwardsloping region of the sinusoid (see Figure 2). Each kick from the RF system increases the speed of the protons and causes them to orbit around the LHC in less time,

The CMS Detector

The Compact Muon Solenoid (CMS) detector is a versatile detector capable of studying a broad spectrum of interesting physics, including supersymmetry. A silicon tracker surrounds the interaction point. The next layer is an electromagnetic calorimeter (made of scintillating Lead Tungstate crystals) and a brass hadronic calorimeter. Surrounding this is a solenoid that produces a 4 T magnetic field. The the muon chambers, which are made of drift tubes and resistive plate chambers, constitute the outermost layer.

In the electromagnetic calorimeter, incoming charged particles can experience ionization, compton scattering, pair production, and bremsstrahlung. At lower energies, ionization dominates. The energy loss due to ionization is greatest when particles have the lowest momenta.

Most of the charged particles’ energy is deposited at the end of their track (resulting in a “Bragg peak”). Beyond a critical energy, Ec , bremsstrahlung dominates.

Bremsstrahlung refers to the radiation emitted by electrons that accelerate as they pass by the ions in the detector and is responsible for the so-called “electromagnetic shower.” This shower consists of N stages, where N depends on the initial conditions. In the first stage, a sufficiently energetic electron of energy E enters the calorimeter and radiates a photon, doubling the number of particles in the shower. In the second stage, the photon decays to an e+ e− pair, and the electron radiates another photon, resulting in another doubling of the particle count.

The doubling continues until, in stage N , the energy of each of the 2N particles falls below Ec . The depth of the electromagnetic shower is therefore proportional to both

Fall 2013 | Rutgers Science Review | 29

3

Research µ Fake Rate for PT 20, Eta 0 Away Jet 20 Away Jet 25 Away Jet 30 Away Jet 35

0.5

0.4

Yield in Control Region

Fake Lepton Background Contribution to SS µ µ channel

500

400

300

0.3

Yield in SS Data

200

0.2

Away Jet 30 Away Jet 40 Away Jet 45 Away Jet 50

100 0.1

1

1.5

2

2.5

FIG. 3: The fake rate for a muon with pT = 20, η = 0 as a function of away jet pT and the isolation. The statistical error bars will shrink once we calculate the fake rate using 19 fb−1 of data. This plot was made with 5.3 fb−1 of data.

N and log EEc . Hadronic calorimeter showers penetrate deeper than EM calorimeter showers (as may be inferred from the relative sizes of the calorimeters). Muons, which are much more massive than electrons, travel farther because they lose much less energy to bremsstrahlung than electrons do. This is why the muon chambers are located outside the EM calorimeter.

A silicon strip detector detects charged particles through ionization. As charged particles move through the tracker, they create electron-hole pairs that move in an electric field outside wires that collect the charge and measure a current. Gas chambers can also be used to detect particles in this way, but silicon detectors are denser and thus offer better spatial resolution. The energy necessary to ionize the electron-hole pairs in solid state detectors is 10 times smaller than the average ionization energy in gas chambers; as such the energy resolution in the former is more precise. The energy needed to liberate an electron-hole pair is less than the thermal energy at room temperature, so the device must be cooled. Within the depletion zone formed with a p-n junction, there are no free charges from thermal excitations [7].

III.

0 0

3 3.5 Loose Lepton RelIso Cut

ANALYSIS

In this study, we search for pair-produced stops in a dileptonic final state. Our analysis was performed on 19.5 fb−1 of data. Each stop decays to a top and a neutralino, a massive invisible particle responsible for missing transverse energy (MET) in the detector. We assume that the two tops decay leptonically, leaving two opposite sign leptons, two b-quarks, and two neutrinos. Each b-quark is expected to produce a b-jet, which is characterized by a displaced secondary vertex. The branching ratio for pairproduced tops going to dileptons is about 1 % . We use

30 | Rutgers Science Review | Fall 2013

1

2

3

4

5

6 7 Loose Lepton RelIso Cut

FIG. 4: The horizontal black line is the yield from the data in the SS tight-tight dilepton region minus rare non-fake MC processes. The other lines represent predicted background yields in the SS tight-tight dilepton region from the tight-toloose method applied on SS loose-loose dilepton data with different away jet ET thresholds used to calculate the fake rate.

h_Muon_signal_pT_eta

Eta

0.5

Muon FR Away Jet 50, No Loose Iso

2.5

0.0238041

0.0746269

0.0740741

0.047619

0.0247096

0.048913

0.0289855

0.027027

0.0541911

0.047619

0.0416667

0.05

Entries 4999 Mean x24.93 Mean y1.586 RMS x 6.033 RMS y0.6034

0.07

2 0.06

1.5

0.05

0.04

1

0.03 0.5

0.0151271

0.0215686

0.0176211

0.037037

0.02 0 10

15

20

25

30

35 PT (GeV)

FIG. 5: The muon fake rate in bins of pT and η.

standard selection criteria for our muons and electrons, including pT > 20 GeV/c and |η| < 2.5 (2.4) for electrons (muons). Additionally, both leptons are required to have a relative isolation (relIso) of less than 0.15. RelIso is defined as the scalar sum of the momenta of all the tracks and calorimeter isolation deposits (both electromagnetic and hadronic) in a cone of ∆R < 0.3 divided by the momentum of the lepton. Lower relIso values correspond to more isolated tracks. Our event selection criteria are: • Choose events with opposite sign leptons from a DoubleElectron, DoubleMuon, or MuEG triggered sample.

• If both leptons are of the same flavor, veto the event if they lie in the Z window (76 to 106 GeV).

• Require the invariant mass of the leptons to be

Research4 greater than 20 GeV to eliminate contributions from low mass resonances.

• Require at least two jets and at least one b-jet.

• Require MET > 40 GeV in the same flavor final states to further eliminate the Z background.

We also define a signal region and a control region using the MT2ll variable, which will be discussed in the next section. The control region is defined for MT2ll < 80 GeV, and we expect this region to be dominated by Standard Model processes. The control region therefore allows us to validate our background prediction methods.

Once we reach sufficient control of the background, we analyze the signal region.

A.

The MT2ll Variable

The MT2ll variable is a generalization of the transverse mass variable. Transverse mass is a useful variable because it depends only on momenta projected onto the

transverse plane. The transverse plane is a useful reference because we know that the sum of all the momenta therein should be zero (our system might be boosted down the beampipe to an unknown extent).

We define the following, assuming that all particles are relativistic (that is, that their mass is negligible): η ≡ − log tan

1 |p| + pz E + pz 1 θ = log log ≈ (1) 2 2 |p| − pz 2 E − pz pz sinh η = T , (2) E

where the superscripts z and T indicate the projection on either the z axis or transverse plane.

Assume we wish to measure the mass of a W boson that decays to a lepton and a neutrino in an event with nothing but jets to balance the momentum of the W. We can measure the transverse momentum of the lepton and the missing transverse energy of the event, which is associated with the transverse energy of the neutrino. To find the mass of the W, we compute

m2W = m2l + m2ν + 2(Eν El − p Tl · p Tν − pzl pzν ) = m2l + m2ν + 2(EνT ElT cosh ην cosh ηl − p Tl · p Tν − ElT EνT sinh ην sinh ηl ) = m2l + m2ν + 2(ElT EνT (cosh ην cosh ηl − sinh ην sinh ηl ) − p Tl · p Tν ) = m2l + m2ν + 2(ElT EνT cosh ∆η − p Tl · p Tν ).

With the exception of the ∆η, we have written down the W mass in terms of the transverse variables we can measure. We now assume the lepton and neutrino masses to be negligible, therefore m2W

≤

2(| pTl || pTmiss |

−

p Tl

·

p Tmiss ).

(8)

We define the transverse mass as pTl , p Tmiss ) ≡ 2(| pTl || pTmiss | − p Tl · p Tmiss ). m2T (

(9)

Because the transverse mass must always be less than the actual mass of the W, we can look at many W events and plot a mT distribution that should have an endpoint at the W mass. The MT2ll variable is a generalization of mT for the case of two W bosons (and hence two leptons and two neutrinos). The key difference is that there T is only one measurable Emiss , which represents the vector sum of the transverse momenta of the two neutrinos. Our strategy is to decompose this sum into every possible combination of neutrino momenta p T1 and p T2 , calculate the maximum possible mT given the two lepton momenta p Tl1 and p Tl2 , and take the overall minimum

(3) (4) (5) (6) (7)

across all combinations. Thus, the result will certainly be a lower bound for the W mass. Mathematically, MT2ll is defined as M T 22ll ≡

min

p T pT pT 1 + 2 = miss

pTl2 , p T2 )]. pTl1 , p T1 ), m2T ( max[m2T (

(10) The most significant background in this analysis is t¯t.

Pair-produced stops and t¯t and pair produced stops look the same, save that stops carry neutralinos, which can increase the MET and make MT2ll larger than the W mass.

Since t¯t goes to WW and jets, MT2ll should remain below the W mass, which is about 80 GeV. In reality, t¯t events will have tails greater than 80 GeV due to mismeasurement of the MET and other quantities. Hence, our control region is defined for MT2ll < 80 GeV, and our signal region is defined for MT2ll > 80 GeV [1, 2].

B.

Estimating the Fake Lepton Background

ZJets is the second most significant background phenomenon, following t¯t. ZJets is estimated by using a

Fall 2013 | Rutgers Science Review | 31

102

5 Data 302 Data-NFMC 284 OJ5 290 VV 2 Rare 22 Non W/Z 31 Single 1 + -Top Z/ γ * l l 0 t t 14

MT2(ll) µ µ channel SS CMS preliminary, 19.58 fb -1 @ s=8TeV

Events

Events

Research

Data11955 Data-NFMC 1652 VV 37 Rare 161 DD 317 Single Top 425 + Z/ γ * l l 675 t t 9590

104 MT2(ll) µ µ channel OS

103

CMS preliminary, 19.58 fb -1 @ s=8TeV

102

10

10

1

1 -1

10-1 Obs/Exp

Obs/Exp

10

1.5 1 0.5

0

20

40

60

80

100

GeV

FIG. 6: Plot of MT2ll in the same-sign region with data minus the t¯t and Z Monte Carlo (blue points) and the prediction from the tight-to-loose method (red points). Note that there are actually no Z events in this plot; a new Z sample with more statistics is being generated. This plot uses an away jet pT of 50 and no isolation requirements for the loose muon. The data/MC discrepancy is being investigated.

“Non W/Z” refers to semileptonic t¯t and WJets backgrounds.

“Rare” refers to a collection of rarer backgrounds: tt¯+γ+jets, WW+γ+Jets, Higgs + WW, tt¯W+Jets, tt¯Z+Jets, W+γ, and Z+γ.

RelIso distribution of Loose Mu Events (Normalized)

hLep0RelIso_mumu_MT2ll_looseSel

Entries Mean RMS

10-1

35742 1.421 1.782

SS Data, MT2ll < 80

Away Jet 50

10-2

10-3

10-4

0

1

2

3

4

5

6

7

8 9 10 relative Isolation

FIG. 7: Normalized distributions of the muon isolation in the SS loose-loose dimuon channel and the QCD data in which we measured the fake rate. The relative agreement of these shapes supports the use of this tight-to-loose method. Note that the SS region has more leptons with small isolation because the SS region contains prompt leptons as well as fake leptons.

Monte Carlo (MC) and a data driven method. The MC gets the shape, and the data driven method is used to normalize events outside the Z window, given the number of events inside the Z window. After all other backgrounds are determined, the t¯t background is determined by normalizing the t¯t MC with respect to the remaining

32 | Rutgers Science Review | Fall 2013

1.5 1 0.5

0

20

40

60

80

100

GeV

FIG. 8: MT2ll distribution in the OS tight-tight dimuon channel with all backgrounds, including the data driven fake muon background.

events in the control region. Our focus is on the SM fake lepton background, wherein one lepton is fake and one lepton is real or “prompt.” Fake leptons are defined as leptons that do not originate from the primary vertex of the collision or are simply not leptons at all. Fake (or non-prompt) leptons include cosmic rays, real leptons that originate from meson decays within jets, and jets that deposit energy in the ECAL or punch into the muon chamber. Prompt leptons are leptons that originate from decays occurring close to the primary vertex. We cannot measure directly whether a lepton is prompt or fake, but we can measure whether it is “tight” or “loose.” Tight leptons are defined as leptons that satisfy the previously mentioned selection criteria. Loose leptons generally satisfy a weaker set of criteria. The loose-tight dilepton events contribute to our fake dilepton backgrounds. We attempt to measure this contribution by analyzing the number of loose leptons and extrapolating to the tight region, as most fake leptons will be loose.

For this analysis, we use the same criteria for tight and loose leptons, with the exception of the relIso cut.

Most fake leptons originate from jets, therefore we do not expect them to be isolated. The relIso cut was relaxed to different values and compared with the results. The method used herein should not depend strongly on the definition of the loose working point. The loose lepton isolation cut can be varied to check for robustness.

The tight-to-loose method relies on an accurate knowledge of two quantities: the fake rate f and the prompt rate p. The fake rate is defined as the number of tight leptons divided by the number of loose leptons in a sample of fake leptons (“loose” refers to all leptons that pass the loose criteria, so the tight-tight dilepton sample is a subset of the loose-loose dilepton sample by definition).

A QCD sample is a good example of a fake lepton sample.

The prompt rate is defined as the number of tight leptons divided by the number of loose leptons in a sample

Research6 of prompt leptons. We calculate the prompt rate from a set of leptons that result from Z decays. To ensure that we had a QCD sample with an adequate number of leptons, we started with a Single Lepton triggered sample and applied cuts to reduce the electroweak background (MET < 20 GeV and mT < 15 GeV). We also performed a cut on the “away jet” pT . The away jet is defined as a jet separated from the leptons in the event by at least ∆R = 1. Because many of the QCD events are dijet events, the away jet pT is correlated with the pT of the fake lepton because the away jet would recoil off the fake lepton. We perform the aforementioned cuts on the away jet to correct for the potentially different energy spectrum of fake-lepton QCD jets (as compared with that of the the jets in the WJets events that dominate the fake background in this analysis). These differing jet energies should impact the isolation distribution of the fake leptons. By performing cuts on the away jet, we can control the energy of the “near-side” jet that fakes the lepton.

The value of the away jet pT cut is chosen so that the background yield prediction in a same-sign dilepton sample (dominated by the fake background) agrees with the data. We can obtain a systematic error for our background prediction by varying the pT of the away jet and monitoring how the prediction changes.

The fake and prompt rates are calculated in bins of pT and η. The errors are calculated under the assumption that the rates follow a binomial distribution; that is, we assume there is a true probability q of receiving a tight lepton given n loose leptons. The error in the number

N00

of tight leptons observed is then nδq = nq(1 − q), so . The value of q is the fake or prompt rate δq = q(1−q) n of interest. Figure 3 depicts how the fake rate varies with the loose isolation cut and the away jet pT cut.

Once we have the fake and prompt rates, we return to the double-lepton data we use in our analysis. We apply the usual event selection criteria, but this time we require the presence of two loose leptons instead of two tight leptons. We weight the event by an appropriate scaling factor (a function of the fake and prompt rates), and we fill histograms using these weighted events. Each weight is to be interpreted as the contribution of that event to the tight-tight dilepton region, given that it has satisfied the loose-loose selection. The total yield in the tight-tight region is the sum of the event weights.

We now derive the actual weightings used in this analysis. Assume that we have leptons l1 and l2 with fake and prompt rates f 1, p1 and f 2, p2, respectively. The fake and prompt rates are binned as a function of the lepton pT and η. Each lepton either passes or fails the tight cut, so we can call the event “pass-pass,” “passfail,” “fail-pass,” or “fail-fail.” N11 refers to the total number of events where l1 passes the tight cut and l2 passes the tight cut, N10 refers to the total number of events where l1 passes the tight cut and l2 fails the tight cut, etc. Npp refers to all of the events wherein l1 and l2 are prompt, Npf refers to all the events wherein l1 is prompt and l2 is fake, etc. Therefore, we have the following simple relations:

N11 = p1 p2 Npp + p1 f2 Npf + f1 p2 Nf p + f1 f2 Nf f N10 = p1 (1 − p2 )Npp + p1 (1 − f2 )Npf + f1 (1 − p2 )Nf p + f1 (1 − f2 )Nf f N01 = (1 − p1 )p2 Npp + (1 − p1 )f2 Npf + (1 − f1 )p2 Nf p + (1 − f1 )f2 Nf f = (1 − p1 )(1 − p2 )Npp + (1 − p1 )(1 − f2 )Npf + (1 − f1 )(1 − p2 )Nf p + (1 − f1 )(1 − f2 )Nf f .

(11) (12) (13) (14)

We can express this as a matrix equation 

    N11 p1 p2 p1 f 2 f 1 p2 f1 f2 Npp p1 (1 − p2 ) p1 (1 − f2 ) f1 (1 − p2 ) f1 (1 − f2 )  N10     Npf  N =  N . )p (1 − p )f (1 − f )p (1 − f )f (1 − p 01 1 2 1 2 1 2 1 2 fp N00 (1 − p1 )(1 − p2 ) (1 − p1 )(1 − f2 ) (1 − f1 )(1 − p2 ) (1 − f1 )(1 − f2 ) Nf f

(15)

Inverting, we obtain 

  Npp (1 − f1 )(1 − f2 ) 1  Npf   −(1 − p2 )(1 − f1 ) N =  (p1 − f1 )(p2 − f2 ) −(1 − p1 )(1 − f2 ) fp Nf f (1 − p1 )(1 − p2 ) The total background of interest is Npf + Nf p , as this is the background in which one lepton is prompt and the

−(1 − f1 )f2 −f1 (1 − f2 ) p2 (1 − f1 ) f1 (1 − p2 ) f2 (1 − p1 ) p1 (1 − f2 ) −(1 − p1 )p2 −p1 (1 − p2 )

  N11 f1 f2 −p2 f1   N10  . −p1 f2   N01  p1 p2 N00

(16)

other is fake. We can read the event weights from this inverted matrix. For example, if an event has two tight

Fall 2013 | Rutgers Science Review | 33

Research

7

leptons, we give it a total weighting of 1 [(1 − p2 )(1 − f1 ) + (1 − p1 )(1 − f2 )]. (p1 − f1 )(p2 − f2 ) (17) In the code, the variables p and f were changed to = f 1−p 1−f and η = p . For each weighting, the errors were calculated by means of simple error propagation [9].

−

IV.

RESULTS

Before we can claim that we have accurately predicted the fake lepton background, we must define a control sample dominated by fake leptons where we know beforehand what the fake lepton background is. Then, we can apply our method to this sample and compare it with the actual background. The control sample is a set of data that satisfies all event requirements except that the two leptons be of the same sign (SS) rather than of opposite signs (OS). The dominant processes here are WJets and semi-leptonic t¯t. Rare non-fake backgrounds, such as WW and ZZ, can also contribute. We apply the tightto-loose method to the SS sample and compare it to the SS data, excluding the rare non-fake processes measured in the Monte Carlo. These non-fake processes are very small, so it is permissible to estimate them in the Monte Carlo.

Figure 4 shows agreement for an away jet pt cut of 50 GeV/c and no isolation cut on the loose muons. The muon fake rates are given in Figure 3. In Figure 6, we plot the actual MTII distribution, which is shown to agree nicely with the data. The prediction is smoother than the data because it consists of many more events (as only loose muons are required). We compare the isolation distributions of the muons in the SS and QCD data in Figure 7. The contribution to the OS data from the tight-to-loose method is shown in Figure 8.

The yield in the opposite sign control region is 317 events. This result is, of course, subject to statistical and systematic uncertainties. We estimate the systematic uncertainty by calculating the yield with away jet pT cuts at 45 and 55 GeV/c, and taking the uncertainty to be the largest difference of these yields with the central value. We also calculate a conservative statistical un[1] Alan Barr et al. 2003 J. Phys. G: Nucl. Part. Phys. 29 2343 [2] A. J. Barr et al. 2011 [arXiv:1105.2977v1]

[1] Alan Barr et al. 2003 J. Phys. G: Nucl. Part. Phys. 29

[3] LHC: The Machine. Stanford University. Retreived July 1, 2013, from 2343 http://www-conf.slac.stanford.edu/ssi/2012/Presentations/Zimmer[2] A. J. Barr et al. 2011 [arXiv:1105.2977v1] mann.pdf [3] LHC: The Machine. Stanford University. Retreived July

1, G. 2013, [4] Dvali, (Julyfrom 19, 2013). Beyond the Standard Model. Summer Studenthttp://www-conf.slac.stanford.edu/ Lecture Programme Course. Lecture conducted from CERN, Geneva. ssi/2012/Presentations/Zimmermann.pdf Dvali, G. (July 19, 2013).

Beyond the Student Standard Model.

[5] [4] Shears, T. (July 3, 2013).

Particle World.

Summer Lecture Summer Student Lecture Programme Course.

Lecture Programme Course. Lecture conducted from CERN, Geneva. conducted from CERN, Geneva.

34 | Rutgers Science Review | Fall 2013

certainty by re-calculating the yield with all of the fake rates shifted up and down by their standard errors. The results are shown in Table 1.

V.

FUTURE WORK

A variey of technical issues remain to be addressed.

The data we used to measure the fake rate came from single lepton triggered data; this data was subject to a skim in a later processing stage that required at least two offline reconstructed leptons. The resulting bias is currently being corrected. The prompt rates are currently Stat. Variation Sys. Variation

DOWN CENTRAL UP % error 165 317 469 47.9% 243 317 436 37.5 %

TABLE I: For the statistical uncertainty in the estimate for the yield in the OS region, we obtained the lower and upper estimates by shifting the fake rates down and up. For the systematic uncertainty, we obtained the lower and upper estimates by shifting the away jet pT cut up and down.

being calculated from Monte Carlo samples instead of data samples. Thus, the results shown here are still preliminary. We will shortly run the electron ntuples for the ee and µe backgrounds. The statistical error in the fake rates will be reduced by running the full 19.5 fb−1 rather than a 5.3 fb−1 sample.

VI.

ACKNOWLEDGEMENTS

I would like to thank all of the University of Michigan professors for their support over the summer, the summer student administrative team, and the summer student lecturers for the excellent lectures. I would also like to thank my advisor, Alberto Graziano, for his guidance and advice and for the opportunity to work on this interesting and educational project. Finally, I would like to thank the NSF for funding the University of Michigan REU at CERN. Edited by Alex DeMaio.

[6] Holzer, B. (July 10, 2013) Accelerators. Summer Student Lecture Programme Course. Lecture conducted from CERN, Geneva.

Shears,D.T.(July (July 3, 2013).

Particle World.

Summer [7][5] Bortoletto, 15, 2013) Detectors.

Summer Student LectureStuProdent Lecture Programme Course.

Lecture conducted gramme Course. Lecture conducted from CERN, Geneva. from CERN, Geneva.

[8][6] Holzer, B. (July 9, 2013) Introduction to Accelerator Physics.Student Summer Holzer, B. (July 10, 2013) Accelerators.

Summer Student Lecture Programme Course. Lecture conducted from CERN, Lecture Programme Course. Lecture conducted from Geneva.

CERN, Geneva.

[9][7] [CMS AN -2010/261].

Unpublished raw data.

Bortoletto, D. (July 15, 2013) Detectors. Summer Stu-

dent Lecture Work.

Programme Lecture conducted [10] How Accelerators European Course.

Coordination for Accelerator Refrom CERN, Geneva. search & Development Retrieved July 3, 2013, from http://eucard.web.

[8] Holzer, B. (July 9, 2013) Introduction to Accelerator cern.ch/eucard/activities/communication/public/magnets.html

“Stem Cell Transplantation for Neurodegenerative Disorders"

Eva Feldman, M.D., Ph.D.

University of Michigan, Department of Neurology

Friday,th December 6 , 2013

12:00 p.m.

Nelson Biological Laboratories, Room D-406, Busch Campus (Enter through door at Lot 56 Take elevator to 4R)

Luncheon for students following lecture!

Brought to you by RUWINS, a program cosponsored by the Douglass Project for Rutgers Women in Math, Science, and Engineering and the W. M. Keck Center for Collaborative Neuroscience

Questions or Comments: Please Email: RUWINS@dls.rutgers.edu Or search “RUWINS” on Facebook

Research Optogenetic Modulation of Parvalbumin-Expressing Interneurons in the R6/2 Mouse Model of Huntington’s Disease Ashley J. So1, 2 1

2

Department of Biomedical Engineering, Rutgers University, New Brunswick, NJ Department of Psychiatry and Biobehavioral Sciences Amgen Scholars Program, Los Angeles, CA

Huntington’s disease (HD) is an inherited neurodegenerative disorder characterized by motor alterations and cognitive and physical disturbances. As the disease progresses, medium-sized spiny neurons (MSNs) in the striatum and later in cortical pyramidal neurons (CPNs) in the cortex begin to degenerate particularly rapidly. Optogenetic techniques were used to target GABAergic parvalbumin (PV)-expressing interneurons, which regulate CPN firing in the cortex and MSN firing in the striatum, to reduce cortical hyperexcitability and striatal synaptic dysfunction. Single patchclamp recordings of CPNs showed that blue light stimulation of channelrhodopsin (ChR2) had no effect on the magnitude of the GABAergic response in R6/2 and WT mice. CPN responses in R6/2 mice demonstrated faster rise and slower decay times compared to WT. In the cortex, ChR2 was expressed in a heterogeneous population of cells, which may send diverse inputs to CPNs. In the striatum, we demonstrated that stimulation of PV interneurons led to a greater GABAergic response in R6/2 MSNs than in WT. In this study, we aimed to determine the contribution of PV interneurons in the striatal GABAergic response received by MSNs. Yellow light stimulation of enhanced natronomonas halorhodopsin (eNpHR) had no effect on the magnitudes of the MSN responses, although faster rise and decay times were observed in R6/2 mice. Modified kinetics recorded in MSNs and CPNs may indicate GABA dysfunction in HD mice. These results suggest that inhibition of PV interneurons using optogenetics is insufficient to reduce GABAergic responses in MSNs and CPNs; a more complex mechanism may be necessary to modulate communication.

I.

INTRODUCTION

Huntington’ disease (HD) is a fatal, inherited neurodegenerative disorder caused by a mutation in the IT15 gene. The alteration results in an expansion of polymorphic CAG repeats in exon 1 of the huntingtin (htt) gene, which leads to an expanded polyglutamine domain [16]. The main symptoms of HD, including motor alterations as well as cognitive and psychiatric disturbances, generally begin around 35 years of age. The involuntary movements (chorea) characteristic of HD may become bradykinetic as the disease progresses. Histopathologically, HD is characterized by the massive degeneration of striatal medium-sized spiny neurons (MSNs), which account for 90-95% of striatal neurons, and to a lesser degree, cortical pyramidal neurons (CPNs) (Figure 1) [15, 32].

The basal ganglia play a significant role in the regulation of movement. The striatum, a major component of the basal ganglia, is responsible for activating pathways that promote or inhibit movement. It is therefore logical to expect that malfunction would result in hypo- or hyperkinetic movement disorders, as is the case for HD [26].

Using mouse models of HD, it is possible to study the type of synaptic dysfunction that is observed in humans (Table 1). The first and most common transgenic rodent model, the R6/2, expresses exon 1 of the human mutant htt under the human htt promoter with ∼150 CAG repeats [20]. This model displays an aggressive phenotype and neuroanatomical abnormalities, specifically the progressive reduction of brain and striatal volume (atrophy) [13]. Affected animals display motor and cognitive

36 | Rutgers Science Review | Fall 2013

symptoms at about six weeks of age and die at about 12 weeks [20]. Based on genetic models of HD, electrophysiological studies performed in Dr. Levine’s laboratory showed biphasic changes in synaptic transmission.

During the presymptomatic stage, glutamatergic synaptic activity in the cerebral cortex progressively increases while GABAergic transmission is high, but decreases over time, leading to cortical hyperexcitability [9]. However, in the striatum, synaptic alterations are opposite to those in the cortex. Transient increases in glutamate synaptic transmission were observed early in the course of the disease, followed by decreases in synaptic activity as the disease progressed [6, 15]. In contrast, inhibitory GABA synaptic activity in striatal MSNs was increased [7]. Subsequent studies demonstrated that spontaneous GABA synaptic activity on MSNs is increased in multiple HD models (YAC128 and BACHD) even after action potentials are blocked, which suggests that the change is presynaptic [1, 4].

One possible source of altered inhibition is from GABAergic interneurons. In this study, we focus on parvalbumin (PV)-positive cells, which generate shortduration action potentials and high-frequency firing, and connect to other PV-positive neurons via gap junctions.

PV-positive interneurons receive strong cortical innervation and in turn, regulate CPN firing in the cortex or MSN firing through feed-forward inhibition in the striatum [11].

Optogenetics allows for the precise activation or inhibition of specific neuronal populations using microbial opsins that respond to different wavelengths of light [29].

These disparate opsins permit modification and adjustment of the specificity and temporal precision of the tech-

Research2 nique. Channelrhodopsin-2 (ChR2) is a channel that depolarizes cell membranes when activated with 470 nm blue light. Enhanced natronomonas halorhodopsin (eNpHR) is a fast, electrogenic Cl− pump that hyperpolarizes cell membranes when activated with 590 nm yellow light (Figure 3).

Our lab has investigated sources of increased inhibition in MSNs in HD by injecting ChR2 into the striatum. Selective optogenetic stimulations of PV-positive interneurons induced significantly larger amplitude responses in R6/2 mice than in WT mice, however stimulation of somatostatin (SOM)-positive interneurons had no appreciable effect on the magnitude of the evoked responses. We conclude that PV-positive interneurons are associated with the increased GABA synaptic activity of MSNs [5].

Our work was intended to continue the investigation and modulation of PV communication in HD mice, using optogenetics in the striatum and the cortex. The GABA synaptic activity of PV-positive interneurons was studied using single patch-clamp recordings during stimulated and non-stimulated states. The interneurons were selectively activated by light in mice expressing ChR2 or eNpHR to excite or inhibit the cell membrane, respectively.

By modulating inhibitory inputs onto CPNs, we expected to alter cortical hyperexcitability in HD. Specifically, using ChR2 to activate inhibitory PV-positive cells in the cortex should generate larger GABAergic responses and reduce CPN hyperexcitability. In the striatum, optogenetic activation of eNpHR in PV-positive interneurons will hyperpolarize the cells and reduce GABAergic activity to MSNs.

II.

MATERIALS AND METHODS

All procedures in this study were performed as described in [4, 5].

A.

Animals

All experimental procedures were performed in accordance with the United States Public Health Service Guide for Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee at the University of California, Los Angeles (UCLA). Every effort was made to minimize pain and discomfort. R6/2 mice and wild-type (WT) littermates were obtained from a colony at UCLA. The R6/2 mouse model of HD expresses exon 1 of the HD gene with ∼150 CAG repeats. The mouse population was maintained by crossing WT male C57BL/6xCBA mice with WT female C57BL/6xCBA mice that had transplanted R6/2 ovaries (both males and females were purchased from The Jackson Laboratory). The mice were genotyped twice, once at weaning and again after electrophysiological recordings.

R6/2 mice were crossed with PV-CRE animals.

B.

Injection

At 30 days of age, PV-CRE mice (wild type and R6/2) were injected stereotaxically with AAV2-DIOChR2-EYFP or AAV2-DIO-EF1a-eNpHR-EYFP (Gene Transfer Vector Core, University of Iowa, Iowa City, IA) in the cerebral cortex (AP +1.0, L +/- 2.0, V -1.0) or striatum (AP +1.0, L +/-2.0, V -3.3). Four weeks later, several mice were analyzed to characterize viral efficiency and specificity using immunocytochemistry (IHC) and to characterize MSN or CPN responses to light stimulation using electrophysiological recordings in slices.

C.

Cell Visualization and Electrophysiology

The mice were deeply anesthetized with isoflurane and sacrificed by decapitation four weeks after the injection.

The brains were rapidly removed and placed in ice-cold low-Ca2+ artificial CSF (ACSF) containing the following (in mM): 130 NaCl, 3 KCl, 1.25 NaH2PO4, 26 NaHCO3, 5 MgCl2, 1 CaCl2, and 208 sucrose. Coronal slices were cut (300 m) using a microslicer (Leica VT1000S; Leica Microsystems) and transferred to an incubating chamber containing ACSF (with 2 mM CaCl2 and 2 mM MgCl2) oxygenated with 95% O2-5% CO2 (pH 7.2-7.4, 290-310 mOsm), and recordings started after at least 1 h incubation at room temperature (recovery). The microscope (Olympus BX51WI) was equipped with differential interference contrast optics and fluorescence (LED470 and 530 nm, CoolLED).

Whole-cell patch-clamp recordings from CPNs in layers II/III and MSNs were obtained from neurons using a MultiClamp 700B Amplifier (Molecular Devices) and pCLAMP 10.2. The patch pipette was about 4 M resistance. We used a Cs-methanesulfonate-based internal solution containing the following (in mM): 130 Csmethanesulfonate, 10 CsCl, 4 NaCl, 1 MgCl2, 5 MgATP, 5 EGTA, 10 HEPES, 5 GTP, 10 phosphocreatine, and 0.1 leupeptin. This solution also contained biocytin (0.2%) for subsequent cell identification.

D.

Synaptic Stimulation

To shift the MSNs to the up state and evoke synaptic currents, a monopolar stimulating electrode (glass pipette filled with ACSF, impedance ∼1.5 MΩ) was placed in the striatum 300-400 µm from the recorded cell.

Inhibitory post-synaptic currents (IPSCs) were evoked with neurons voltage clamped at +10mV in the presence of glutamatergic antagonists, NBQX (20 µM, Sigma) and AP5 (50 µM, Tocr`ıs Bioscience). Test stimuli (0.5 ms duration) were every 60 s applied at increasing stimulus intensities (0.005-0.015 mA) to assess input-output functions, and intensities were set to evoke responses at 60% of maximal amplitude. For the eNpHR experiments, the cells were exposed to yellow light (590 nm) at 10 Hz for

Fall 2013 | Rutgers Science Review | 37

Research

3

3 s or 10 s or continuously for 1.75 s before the electrical stimulation.

At +10 mV holding potential, a brief blue LED (CoolLED) pulse (470 nm, 0.5 ms, 10 mW) stimulated the network of ChR2-EYFP interneurons surrounding the patched MSNs in the slice. All experiments comparing CPN or MSN responses from WT and R6/2 mice used the same intensity and duration of the light stimulus. No light was used as the baseline and control conditions.

E.

Immunohistochemistry

Four weeks after injection, mice (2 WT and 2 R6/2) were perfused with 4% paraformaldehyde (PFA), and their brains were extracted and left overnight in PFA solution. After rinsing with 30% sucrose, the brains were frozen in powdered dry ice, mounted in the coronal plane, and sectioned on a cryostat (30 µm). Double labeling for PV expression (Swant, 1:1000) was performed and visualized using a secondary antibody (Alexa- 594, Invitrogen, 1/1000). Slices were observed using a confocal microscope (Zeiss, LSM), double-labeled EYFP and Alexa-594 cells were manually counted, and cell density (cells/mm2) was calculated.

F.

Biocytin Staining

After the recording, the slices were transferred from PFA to 20% sucrose for one week. The slices were permeabilized for 2 h (Trition X-100, 0.7%) then stained with streptavidin blue or Alexa594 (Invitrogen, 1:1000) for 3 h at room temperature. The brain slices were mounted on R Gold antifade reagent (Life Techslides with ProLong nologies), and labeled cells were visualized using the Zeiss ApoTome.2 and the confocal microscope.

G.

positive cells in the left panel and green ChR2-EYFP cells in the center panel were merged in the right panel to determine the colocalization of the stains. Ninety percent of the ChR2-EYFP expressing cells (n = 117 total) also were positive for the PV-stain. The top row of images is included to show the distribution of PV-positive cells in the cortex (Figure 4A). The density of colocalized cells was similar between WT (24.5 ± 4.4 cells/mm2 ) and R6/2 (29.9 ± 6.4 cells/mm2 ) mice (Figure 4C).

Data Analysis and Statistics

Analyses of individual postsynaptic responses obtained during dual patch recordings were performed using the Clamp Fit Program. The statistics were performed using SigmaPlot software.

III.

RESULTS

A.

Optogenetic Activation of ChR2 in PV-Positive Interneurons the Cortex

1.

Colocalization of ChR2-EYFP with Cortical PV-Positive Cells

Immunohistochemistry staining was used to confirm the specificity of ChR2-EYFP in the PV-positive interneurons in the cortex (Figure 4). Images of red PV-

38 | Rutgers Science Review | Fall 2013

2.

Heterogeneous Population of Double-Labeled Cells in the Cortex

The morphology of cells that colocalized for ChR2EYFP and PV-Alexa594 in the cortex was heterogeneous, as described in [21]. Cells appeared to be one of three shapes: round, long, or triangular (Figure 5A). The highest percentage of double- labeled cells appeared round (79%) in both WT and R6/2 mouse models. Significantly fewer cells appeared long and cylindrical (11% in WT, 9% in R6/2), and similar proportions (9% in WT, 11% in R6/2) were considered triangular. The data for the triangular cells are not shown. The PV-Alexa594 appeared to be expressed in the soma and neurites of the round and long cells. By comparison, the PV stain was only expressed in the axons of the triangular cells.

3.

Blue Light Stimulation of ChR2 Shows Faster Rise and Slower Decay Times

We have shown that the optogenetic stimulation of ChR2 in PV-positive interneurons in the striatum produces significant changes in the GABA response of MSNs [5]. In this study, blue light (470 nm, 0.5 ms pulse) was used to stimulate ChR2 in PV-positive interneurons in the cortex to investigate the inhibitory inputs of PV-positive cells to CPNs. Glutamatergic antagonists (20 µM NBQX, 50 µM APV) were added to isolate the GABA response recorded in voltage clamp at a holding potential of +10 mV. There was no significant difference between the amplitudes of the CPN GABAergic responses in WT (1684.5 ± 203.3 pA) and R6/2 (2124.0 ± 218.2 pA) mice (Figure 6C). Interestingly, the charge was significantly different in R6/2 (159285 ± 20225 pA.ms in R/2; 107060 ± 16048 pA.ms). The R6/2 CPN response exhibited faster rise times (1.6 ± 0.3 ms) and slower decay times (157.7 ± 10.4 ms) when compared to those taken from WT mice (rise: 2.5 ± 0.3 ms; decay: 103.7 ± 12.1 ms).

Research4 B. Optogenetic Inhibition of eNpHR in PV-Positive Interneurons the Striatum 1.

Specific Expression of eNpHR Is in Striatal PV-Positive Cells

To evaluate the specificity of eNpHR in the striatum, all of the eNpHR-EYFP cells (green) also stained positive for PV (red), which confirms that the eNpHR is selectively expressed in PV-positive cells in the striatum (Figure 7). PV interneurons in the striatum appear to be a homogenous population (one shape in accordance with the literature).

2.

Yellow Light Stimulation of eNpHR Showed No Effect on the MSN Response

Previously, we activated ChR2 in striatal PV-positive interneurons and reported that PV-positive and SOMpositive interneurons are primarily responsible for the increased GABA activity in MSNs of HD mice [5]. PV interneurons are not spontaneously active in the slice. In order to determine their contribution we used intrastriatal electrical stimulation to evoke a response. Glutamatergic antagonists (20 µM NBQX, 50 µM APV) were used to isolate the GABA response recorded in voltage clamp mode at +10 mV. Activation of MSNs and striatal GABAergic interneurons was responsible for this response.

We hypothesized that the activation of eNpHR could be used to inhibit PV-positive interneurons and visualized by a decrease in the GABAergic response in the MSNs. Different protocols were used to inhibit eNpHRexpressing PV-positive interneurons. These were: • Protocol 1 consisting of 10 Hz yellow light stimulation (590 nm, 1 mW) for 3 s • Protocol 2 consisting of 10 Hz light stimulation (590 nm, 1 mW) for 10 s • Protocol 3 consisting of continuous yellow light stimulation (590 nm) for 1.75 s at 1 mW or 4 mW. Yellow light stimulation (10 Hz, 590 nm) for 3 s or 10 s before the electrical stimulation showed no significant difference on the amplitude of the MSN response. With no light (baseline), the mean MSN response in the R6/2 mice (n = 9) had a larger amplitude (439.2 ± 63.2 pA) relative to those observed in the WT mice (n = 10) (402.4 ± 55.8 pA) in accordance with the literature. During the 1 mW stimulation periods, the magnitudes of the MSN responses in the R6/2 mice (3 s: 95 ± 2% of baseline; 10 s: 83 ± 4%; 1.75 s: 98 ± 5%) were similar to those recorded in the WT mice (3 s: 90 ± 6%; 10 s: 88 ± 6%; 1.75 s: 98 ± 3%) (Figure 8A). While small decreases in amplitude were observed during the different optogenetic stimulation protocols in the WT or R6/2 mice, the

changes were not significant when compared to the baseline values (Figure 8C). The continuous light protocol showed that the power of the light used to stimulate the PV-positive interneurons had no apparent effect on the evoked MSN response (R6/2: 230.3 ± 103.2 pA at 1 mW, 227.0 ± 93.4 pA at 4 mW) (Figure 9). The cells in both the WT and R6/2 mice recovered to amplitudes larger than or near baseline values (data not shown).

The magnitudes of the GABAergic responses recorded in MSNs did not appear to affect the inhibition of PVpositive interneurons. The kinetics of the responses evoked in R6/2 MSNs appeared slightly faster than those recorded in WT, although these differences were not statistically significant. The recorded cells were stained, and imaging revealed that they were surrounded by EYFPaxon staining. (Figure 8B).

IV.

DISCUSSION

In this study, we aimed to use optogenetic techniques to target PV-positive interneurons in the cortex and striatum to evaluate the contribution of these cells to GABAergic responses in CPNs and MSNs, respectively.

In the cortices of both WT and R6/2 mice, the shapes of the cells expressing ChR2- EYFP and PV-Alexa594 were not uniform. The large majority of the cells were round, but the remaining cells were equally divided between long and triangular shapes. There are two main classifications of PV-positive interneurons in the cortex, basket and chandelier cells. Based on morphology, the round PV-positive cells could be classified as basket cells, which account for 50% of all inhibitory PV-positive interneurons. Basket cells innervate the soma and proximal dendrites of pyramidal cells, although there are several different subtypes of basket cells that may be associated with regional specializations [27]. Found in comparatively much smaller quantities, chandelier cells may be long shaped cells that were colocalized in the cortex [18, 21]. Chandelier cells project to the axon initial segments of pyramidal cells, where action potentials are generated, which suggests that the cells may act as circuit switches. Interestingly, GABA transmission in these cells may have a depolarizing or hyperpolarizing effect, depending on the level of circuit activity [25, 33]. The triangular shaped cells may be a specific type of basket cell distinct from the rounded cells observed. Additional studies to investigate the physiological, molecular, and synaptic characteristics of the cells are necessary to confirm these classifications.

The diverse population of cells expressing ChR2-EYFP in the cortex suggests that the synapses on CPNs may vary for each cell type. The distinct subtypes of PVpositive interneurons may send differential inhibitory inputs to the CPNs, thereby creating a complex GABAergic response. Furthermore, certain cell types may be more sensitive to the optogenetic stimulation while others are affected comparatively minimally by the light. In

Fall 2013 | Rutgers Science Review | 39

Research other words, the density of cells expressing ChR2-EYFP in the cortex may not represent the inhibitory effects of the optogenetic stimulation on the CPN response. The pathways by which these interneurons inhibit the MSNs may be modified by light stimulation. The heterogeneity of the PV-positive cells expressing ChR2-EYFP in the cortex may explain the variable peak amplitude responses of the cells in response to light stimulation.

We have shown that optogenetic stimulation of ChR2 expressing cells using a pulse of blue light (470 nm, 0.5 ms) produces no significant difference between the amplitudes of the CPN responses in WT and R6/2 mice.

However, the increased rise time and decreased decay times in R6/2 mice with respect to to WT mice suggest a modification in the release and reuptake of GABA. The charge of the response recorded in the R6/2 mice was larger than WT, which suggests that a larger amount of GABA is released by the action potential. Increased GABA or increased sensitivity of GABA receptors prior to synaptic firing would also induce faster rise times. The mechanism for terminating GABA action is a complex system primarily involving GABA transporters (GATs), which mediate the reuptake of GABA into nerve endings and glial processes [22]. Slower decay times in R6/2 mice may indicate a GAT dysfunction that reduces the speed or efficiency of GABA reuptake in the cortex of HD mice.

During the three optogenetic stimulation protocols, there were no significant effects observed in the MSN responses of WT and R6/2 mice. The duration, 3 s or 10 s, of the 10 Hz pulses of yellow light (1 mW) appeared to have no effect on the strength (peak amplitude, area) or kinetics (rise, decay times) of the MSN response. Additionally, neither 1.75 s of continuous light nor a power increase (to 4 mW) ultimately changed the MSN response with respect to baseline conditions.

The lack of a significant effect of eNpHR stimulation on the magnitude of the GABAergic evoked MSN response suggests that the optogenetic protocol may not be optimized for this type of modulation. Depending on the target cells, several publised protocols use continuous light stimulation for various durations in slices, from 150 ms for retinal ganglion cells [17] to 30 s in cortical neurons [34]. The continuous yellow light stimulation of eNpHR in cortical neurons showed that the inhibition is stable and effective over a period of 30 s [19, 34]. Activation of eNpHR using continuous illumination of orange light (585 nm, 50 mW/mm2 , 10 s) has also been shown to strongly inhibit firing in cerebellar Purkinje cells [28].

Furthermore, in those PV-positive cells expressing the opsin, the opening of chloride channels may modify the mechanisms controlling the release of GABA to MSNs. Nevertheless, the eNpHR opsin functions as a Cl− pump, and decreasing the membrane potential of already hyperpolarized PV cells may have deleterious effects [12].

GABA, the primary neurotransmitter of PV-positive interneurons, hyperpolarizes the cell by moving chloride ions into the cell. When GABA is released and binds to the GABA-A receptor, chloride channels open to hy-

40 | Rutgers Science Review | Fall 2013

5 perpolarize the cell. Therefore, there is a large influx of chloride ions into the naturally hyperpolarized PV interneurons from the movement of chloride through the eNpHR pump and the opening of chloride channels. The resulting high intracellular concentration of chloride may be toxic to the cell and change the normal inhibitory PV activity. It has been shown that the sustained pumping of Cl− into the cell causes the electrochemical gradient across the membrane to shift, and Cl− flows through the GABA-A receptor openings out of the cell [24, 30].

Other photoinhibitory opsins should be considered to modulate the communication between PV-positive interneurons and MSNs. Light-driven proton pumps (eBr, Mac, Arch) have been shown to generate larger photocurrents than light-driven chloride pumps and have yielded promising results as inhibitory optogenetic tools [10]. Mac and Arch have been shown to be extremely effective inhibitory opsins in recordings from hippocampal and cortical neurons. For example, Arch displayed rapid kinetics (8-20 ms rise and decay times), large magnitude photocurrents, and an impressive post-light recovery. Following continuous yellow light (575 nm, 5 s) illumination, Arch-evoked responses spontaneously recovered within seconds, similar to the patterns observed in ChR2 [8]. However, the application of proton-motive pumps in mammalian cells raises concerns about longterm functionality and tolerability. Specifically, if pumping protons into the extracellular space causes non-celltype-specific effects, decreased response in the damaged tissue may be mistaken for photoinhibition [10].

While eNpHR may effectively silence PV-positive interneurons in the striatum, there may be a compensatory mechanism to counterbalance the effects of PV inhibition. PV-positive interneurons are also connected to other PV-positive interneurons by functional gap junctions. It was reported that gap junction coupling between PV-positive cells allow them to change their bursting patterns without marked shifts in firing rate [3]. Therefore, even if several PV-positive cells express eNpHR, the rapid communication between cells may enable other interneurons to overcome the effects of optogenetic stimulation and restore the normal function of the eNpHR-EYFP expressing cells. Future experiments to investigate gap junction connectivity through direct manipulation and dual patch clamp recordings will help illuminate the extent of communication and influence of PV-positive interneurons on other PV-positive cells.

PV-positive interneurons are classified as phasically active neurons (PANs), which are generally inactive (no spontaneous firing), with the exception of burst firing during learning and voluntary actions [14]. Optogenetic stimulation of eNpHR in vivo and in vitro has been studied in Alzheimer’s disease to investigate the function of hilar GABAergic interneurons to spatial learning and memory. Fiber optic electrodes were inserted into surgically implanted guide cannulae to provide laser illumination during behavioral testing [2]. In this case, PV-positive cells interact with CPNs to generate corti-

Research6 contributes to the inhibitory inputs of PV cells to CPNs. In the striatum, the inhibition of PV-positive interneurons appears more complicated and is still under investigation. The difficulty in silencing PV synaptic activity may be attributed to the complexity of neuronal networks in the striatum. These findings provide strong evidence for a compensatory mechanism that enables PV-positive cells to survive until advanced stages of HD.

cal gamma oscillations (30-80 Hz) that are associated with sensory information processing [31]. Therefore, it may be more applicable and physiologically accurate to record the activity of PV-positive interneurons in vivo during behavioral tasks involving working memory when these cells are typically active. In HD, PV-positive interneurons are largely spared relative to other cell types, which degenerate extensively. The cells play a major role in the regulation of firing in a large number of MSNs, which suggests that PV interneurons are extremely important in the striatum and are preserved even in diseased states [5, 32]. It follows that PV cells could have a unique property or compensatory mechanism to preserve the cells until very advanced stages of the disease.

In general, whether via a compensatory mechanism or natural robustness, PV-positive interneurons may not be blockable; this conclusion is supported by the survival of the cells in HD patients and symptomatic mice.

In this paper, we have demonstrated that communication between PV-positive interneurons and CPNs in the cortex is modified in the HD mouse model. A heterogeneous population of basket and chandelier interneurons

Thank you to the Amgen Foundation for supporting the Amgen Scholars Program as well as Dr. Tama Hasson, Dr. Patty Phelps, and Cindy Mosqueda of the University of California, Los Angeles for their support and guidance during the program. Thank you to My Hyunh for her help with brain sectioning and immunohistochemistry.

Edited by Stephanie Marcus and Alex DeMaio.

[1] Andre, V.M., Cepeda, C., Fisher, Y.E., Huynh, M., Bardakjian, N., Singh, S., Yang, X.W., and Levine, M.S. (2011). Differential electrophysiological changes in striatal output neurons in Huntington’s disease. The Journal of neuroscience : the official journal of the Society for Neuroscience 31, 1170-1182.

[2] Andrews-Zwilling, Y., Gillespie, A.K., Kravitz, A.V., Nelson, A.B., Devidze, N., Lo, I., Yoon, S.Y., Bien-Ly, N., Ring, K., Zwilling, D., et al. (2012). Hilar GABAergic interneuron activity controls spatial learning and memory retrieval. PLoS One 7, e40555.

[3] Berke, J.D. (2011). Functional properties of striatal fastspiking interneurons. Front Syst Neurosci 5, 45.

[4] Cepeda, C., Cummings, D.M., Andre, V.M., Holley, S.M., and Levine, M.S. (2010). Genetic mouse models of Huntington’s disease: focus on electrophysiological mechanisms. ASN Neuro 2, e00033.

[5] Cepeda, C., Galvan, L., Holley, S.M., Rao, S.P., Andre, V.M., Botelho, E.P., Chen, J.Y., Watson, J.B., Deisseroth, K., and Levine, M.S. (2013). Multiple sources of striatal inhibition are differentially affected in Huntington’s disease mouse models. J Neurosci 33, 7393-7406.

[6] Cepeda, C., Hurst, R.S., Calvert, C.R., HernandezEcheagaray, E., Nguyen, O.K., Jocoy, E., Christian, L.J., Ariano, M.A., and Levine, M.S. (2003). Transient and progressive electrophysiological alterations in the corticostriatal pathway in a mouse model of Huntington’s disease. The Journal of neuroscience : the official journal of the Society for Neuroscience 23, 961-969.

[7] Cepeda, C., Starling, A.J., Wu, N., Nguyen, O.K., Uzgil, B., Soda, T., Andre, V.M., Ariano, M.A., and Levine, M.S. (2004). Increased GABAergic function in mouse models of Huntington’s disease: reversal by BDNF. J Neurosci Res 78, 855-867.

[8] Chow, B.Y., Han, X., Dobry, A.S., Qian, X., Chuong,

A.S., Li, M., Henninger, M.A., Belfort, G.M., Lin, Y., Monahan, P.E., et al. (2010). High-performance genetically targetable optical neural silencing by light-driven proton pumps. Nature 463, 98-102.

Cummings, D.M., Andre, V.M., Uzgil, B.O., Gee, S.M., Fisher, Y.E., Cepeda, C., and Levine, M.S. (2009). Alterations in cortical excitation and inhibition in genetic mouse models of Huntington’s disease. The Journal of neuroscience : the official journal of the Society for Neuroscience 29, 10371-10386.

Fenno, L., Yizhar, O., and Deisseroth, K. (2011). The development and application of optogenetics. Annu Rev Neurosci 34, 389-412.

Gittis, A.H., Nelson, A.B., Thwin, M.T., Palop, J.J., and Kreitzer, A.C. (2010). Distinct roles of GABAergic interneurons in the regulation of striatal output pathways.

The Journal of neuroscience : the official journal of the Society for Neuroscience 30, 2223-2234.

Gradinaru, V., Thompson, K.R., and Deisseroth, K. (2008). eNpHR: a Natronomonas halorhodopsin enhanced for optogenetic applications. Brain Cell Biol 36, 129-139.

Heng, M.Y., Detloff, P.J., and Albin, R.L. (2008). Rodent genetic models of Huntington disease. Neurobiology of disease 32, 1-9.

Inokawa, H., Yamada, H., Matsumoto, N., Muranishi, M., and Kimura, M. (2010). Juxtacellular labeling of tonically active neurons and phasically active neurons in the rat striatum. Neuroscience 168, 395-404.

Joshi, P.R., Wu, N.P., Andre, V.M., Cummings, D.M., Cepeda, C., Joyce, J.A., Carroll, J.B., Leavitt, B.R., Hayden, M.R., Levine, M.S., et al. (2009). Agedependent alterations of corticostriatal activity in the YAC128 mouse model of Huntington disease. The Journal of neuroscience : the official journal of the Society for

V.

[9]

[10]

[11]

[12]

[13]

[14]

[15]

ACKNOWLEDGEMENTS

Fall 2013 | Rutgers Science Review | 41

Research Neuroscience 29, 2414-2427.

[16] The Huntington’s Disease Collaborative Research Group.

(1993). A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. The Huntington’s Disease Collaborative Research Group. Cell 72, 971-983.

[17] Kaneda, K., Kasahara, H., Matsui, R., Katoh, T., Mizukami, H., Ozawa, K., Watanabe, D., and Isa, T. (2011). Selective optical control of synaptic transmission in the subcortical visual pathway by activation of viral vector-expressed halorhodopsin. PLoS One 6, e18452.

[18] Kelsom, C., and Lu, W. (2013). Development and specification of GABAergic cortical interneurons. Cell Biosci 3, 19.

[19] Li, N., Downey, J.E., Bar-Shir, A., Gilad, A.A., Walczak, P., Kim, H., Joel, S.E., Pekar, J.J., Thakor, N.V., and Pelled, G. (2011). Optogenetic-guided cortical plasticity after nerve injury. Proc Natl Acad Sci U S A 108, 88388843.

[20] Mangiarini, L., Sathasivam, K., Seller, M., Cozens, B., Harper, A., Hetherington, C., Lawton, M., Trottier, Y., Lehrach, H., Davies, S.W., et al. (1996). Exon 1 of the HD gene with an expanded CAG repeat is sufficient to cause a progressive neurological phenotype in transgenic mice. Cell 87, 493-506.

[21] Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., and Wu, C. (2004). Interneurons of the neocortical inhibitory system. Nat Rev Neurosci 5, 793-807.

[22] Melone, M., Barbaresi, P., Fattorini, G., and Conti, F. (2005). Neuronal localization of the GABA transporter GAT-3 in human cerebral cortex: a procedural artifact?

J Chem Neuroanat 30, 45-54.

[23] Minelli, A., Brecha, N.C., Karschin, C., DeBiasi, S., and Conti, F. (1995). GAT-1, a high-affinity GABA plasma membrane transporter, is localized to neurons and astroglia in the cerebral cortex. J Neurosci 15, 7734-7746.

[24] Olsen, R.W., and Tobin, A.J. (1990). Molecular biology

Figure 1: Cross section of the brain of a latestage HD patient. Extensive atrophy is visible in the striatum and cerebral cortex (Harvard Brain Tissue Resource Center).

42 | Rutgers Science Review | Fall 2013

7 of GABAA receptors. Faseb j 4, 1469- 1480.

[25] Povysheva, N.V., Zaitsev, A.V., Gonzalez-Burgos, G., and Lewis, D.A. (2013). Electrophysiological Heterogeneity of Fast-Spiking Interneurons: Chandelier versus Basket Cells. PLoS One 8, e70553.

[26] Rubchinsky, L.L., Kopell, N., and Sigvardt, K.A. (2003).

Modeling facilitation and inhibition of competing motor programs in basal ganglia subthalamic nucleus-pallidal circuits. Proceedings of the National Academy of Sciences 100, 14427-14432.

[27] Rudy, B., Fishell, G., Lee, S., and Hjerling-Leffler, J. (2011). Three groups of interneurons account for nearly 100 [28] Tsubota, T., Ohashi, Y., Tamura, K., Sato, A., and Miyashita, Y. (2011). Optogenetic manipulation of cerebellar Purkinje cell activity in vivo. PLoS One 6, e22400.

[29] Tye, K.M., and Deisseroth, K. (2012). Optogenetic investigation of neural circuits underlying brain disease in animal models. Nat Rev Neurosci 13, 251-266.

[30] Tønnesen, J. Optogenetic cell control in experimental models of neurological disorders. Behavioural Brain Research.

[31] Volman, V., Behrens, M.M., and Sejnowski, T.J. (2011).

Downregulation of parvalbumin at cortical GABA synapses reduces network gamma oscillatory activity. J Neurosci 31, 18137- 18148.

[32] Vonsattel, J.P., Myers, R.H., Stevens, T.J., Ferrante, R.J., Bird, E.D., and Richardson, E.P., Jr. (1985). Neuropathological classification of Huntington’s disease. J Neuropathol Exp Neurol 44, 559-577.

[33] Woodruff, A., Xu, Q., Anderson, S.A., and Yuste, R. (2009). Depolarizing effect of neocortical chandelier neurons. Front Neural Circuits 3, 15.

[34] Zhang, F., Wang, L.P., Brauner, M., Liewald, J.F., Kay, K., Watzke, N., Wood, P.G., Bamberg, E., Nagel, G., Gottschalk, A., et al. (2007). Multimodal fast optical interrogation of neural circuitry. Nature 446, 633-639.

Figure 2: Two types of optogenetic tools.

Channelrhodopsin (ChR, left) is an inward cation channel that depolarizes neurons when stimulated by blue light. Halorhodopsin (HR, right) is an outward chloride pump that hyperpolarizes neurons when activated to yellow light (Tye and Deisseroth, 2012).

Research

Figure 3: Standard model of the direct (D1) and indirect (D2) pathways of the basal ganglia. Dark arrows denote inhibitory connections, white arrows show excitatory connections, and striped arrows show the dopaminergic innervation of the striatum. Cortical activation of the D1 pathway promotes movement, and cortical activation of the D2 pathway inhibits movement.

(GPi - internal globus pallidus; SNr - substantia nigra pars reticulata; GPe - external globus pallidus; STN - stubthalamic nucleus) -(Rubchinsky et al., 2003).

Table 1: Summary of the main mouse models of HD from Menalled et al. (2009).

Fall 2013 | Rutgers Science Review | 43

Research

44 | Rutgers Science Review | Fall 2013

Research

Fall 2013 | Rutgers Science Review | 45

Advertise Here!

The Rutgers Science Review is a premier undergraduate research journal that publishes semesterly, with a circulation of 3000.

Our primary readership consists of students in the sciences, many of whom are interested in graduate, medical, or dental school.

For more information about rates, email us at: team@thersr.com 46 | Rutgers Science Review | Fall 2013

Help science spread its wings.

Write • Design • Publish

We’re interested in your article proposals, editorials, research papers, art, and photography.

Submission Deadline

Sunday January 5th contact us: team@theRSR.com

Rutgers Science Review

Visit Us: theRSR.com