PCR - Spring 2019

CR

Pingry Community Research Journal // Spring 2019

View our previous issues at www.pingry.org/pcr

Contents.

Reports CRISPR Gene Editing for Duchenne Muscular Dystrophy 6 Discussion: 21st Century Science Students with Mr. C. Coe 8

Research Papers Effects of Memory-Enhancing Supplements in a Drosophila Alzheimer’s Model 12 Studying the Behavior of Large Mammals in Pingry 17 The Effect of Increasing Soil Salinity on Peroxidase Distribution in Comet Radishes 19 Observing the Chemotaxis of D. discoideum 22 Effects of Increased Oxygen Exposure on D. magna Survival 25 Inhibiting Stress Response in D. magna 30

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Editor’s Note.

Welcome to the latest edition of Pingry Community Research Journal. We are

excited to showcase Pingry’s best scientific talent, both through research skills and through knowledge of scientific news and concepts. PCR gives students in advanced courses and extracurriculars, like AP Biology and Independent Research Teams, a chance to demonstrate their understanding of complex topics and their applications in collegiate-level research.

We also would like to bring your attention to PCR’s new look - featuring modern design, reflecting the modern and topical research that can be found in this issue.

This journal is meant to inspire students to get into science, educate students about topics that they can look forward to learning, and most importantly, to spark curiosity about topics that would normally fly under the radar.

Enjoy this issue of the new PCR - Pingry’s foremost journal of scientific research.

Aneesh Karuppur (IV), Associate Editor Brian S. Li (V), Chief Editor

Staff

Chief Editor: Brian S. Li (V) Associate Editor: Aneesh Karuppur (IV) Layout Editors: Noopur Bhat (V) & Noah Bergam (IV) Copy Editors: Jessica Hutt (V), Ashley Lu (V), Anushka Agrawal (IV), Thomas Henry (IV), & Julian Lee (IV) Faculty Advisor: Mr. D. Maxwell Page 4

Reports.

Concise and thorough summaries of recent developments in science, globally and in Pingry.

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CRISPR Gene Editing for Duchenne Muscular Dystrophy

by Caitlin Schwarz (III) Genetic editing and re-engineering are emerging practices in science, sparking widespread debates over this ethical dilemma. The bioengineering of crops, animals, and even children has aroused controversy around the world. However, its application in the treatment of genetic diseases could prove to be the future of medicine. A recent approach to genetic editing called CRISPR-Cas9 (clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9) is a faster, more accurate, and effective method of genetic editing. This method uses an RNA strand to guide the Cas9 enzyme to target a specific section of DNA and either add or remove a section of DNA. CRISPR is naturally found in bacterial genomes and splices parts of viral DNA or RNA. CRISPR has many applications for genetic disorders and is being used in many clinical trials for possible cures for many genetic disorders including hemophilia, blindness, cystic fibrosis, and Huntington’s disease. More recently, scientists have looked into its applications for for the treatment of Duchenne muscular dystrophy (DMD), a genetic condition that results from mutation in the gene that produces dystrophin, a protein chain that binds muscle fibers to its surrounding structure. The result of

these gene mutations is a degenerative muscle disease that causes progressive muscle weakening. DMD affects 250,000 boys worldwide, making it the most common deadly muscle disease.

In recent trials done by Duke University, investigators focused on the DMD model in mice and used the CRISPR treatment to target mutated genes and remove them from DNA. This allows the body to connect the remaining genes back together, effectively creating a functional version of the dystrophin gene. In the first eight weeks after administration, muscle strength increased and dystrophin levels were restored to almost normal levels. While there was a presence of antibodies and T-cells, they did not hinder the Cas9 from editing the genome. However, unintended edits can occur on the targeted genome, but the dystrophin gene is already defective and the errors would only make the treatment less effective. Nonetheless, CRISPR treatment is still a viable option for a cure but requires further research on the effects of prolonged use.

Human testing in the United States for simpler genetic disorders such as blood disorders and sickle cell disease are projected to start this year. Trials in China have begun on CRISPR-Cas9 applications on cancer and HIV. As this therapy is

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extremely new, no data is available on prolonged exposure to the treatment. Nonetheless, doctors have claimed that their patients’ conditions have improved due to this treatment. More research is necessary, but CRISPR Cas9 holds a promising future in cures for DMD and other genetic disorders.

References Duke University. (2019, February 18). “DMD: Single CRISPR treatment provides long-term benefits in mice: Genetic edits and protein restoration in mouse models of Duchenne muscular dystrophy remain viable one year after single CRISPR treatment” ScienceDaily.

Retrieved from www.sciencedaily.com/releases/2019/02/190218123207.htm Dongsheng D. (2017, July 1). “A New Kid on the Playground of CRISPR DMD” Therapy Human Gene Therapy. Clinical Development. Retrived from https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC5510048/ Kwon D. (2017, July 25). “Dogs with Duchenne Treated with Gene Therapy” The Scientist Magazine. Retrieved from https://www.the-scientist. com/news-opinion/dogs-with-duchenne-treatedwith-gene-therapy-31175 Offord C. (2018, August 31). “CRISPR Treatment for Duchenne Muscular Dystrophy Helps Dogs” The Scientist Magazine. Retrieved from https:// www.the-scientist.com/news-opinion/crisprtreatment-for-duchenne-muscular-dystrophyhelps-dogs-64740 Ramirez V. B. (2018, February 20). “New CRISPR Method Takes on Duchenne Muscular Dystrophy” Singularity University. Retrieved from https://singularityhub.com/2018/02/20/new-crispr-method-takes-on-duchenne-muscular-dystrophy/#sm.0001yctw91wbeemky172859aryexk U.S. National Library of Medicine (2019, February 12). “What are Genome editing and CRISPR-Cas9 ?” Genetics Home Reference. Retrived from https://ghr.nlm.nih.gov/primer/genomicresearch/genomeediting Image Source: Synthego.com

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Discussion: 21st Century Science Students with Mr. C. Coe Mr. C. Coe, Science in the 21st Century Students. Compiled by Brian S. Li (V) PCR always includes student commentary and opin- cause the claims directly supported the hypotheion on science in its Reports section. In this new ad- sis in a logical, systematic way, the conclusion was dition to Reports, we have included student respons- easily justified. Deduction is often used in science es to questions posed by Mr. Coe on his Science in to test existing theories, in which experimental the 21st Century page. Students in this class are evidence is collected to test these conclusions. frequently challenged to evaluate implications of science and the ideas that drive scientific explora- — Hannah Gruber (VI), Kathryn Jones (VI), and tion. Late last September. Mr. Coe posed a series of Nia Phillips (VI) prompts regarding science and the of notion of truth that were answered by himself and his students. Theoretical physicist and bongo enthusiast Richard Feynman stated that “science Science is usually inductive in its course is a way of trying not to fool yourself, and of discovery, rather than deductive like you are the easiest person to fool.” Science Euclidean geometry.

However, there is therefore as subject to bias as any othare deductive aspects to science as well. er activity engaged in by (flawed) humans.

A statement made in an argument to prove a solu- One of Feynman’s chief concerns was how eastion may be true. Science is inductive because ily it is possible to really badly want something it can prove reasoning to be incorrect. Even if to be true, and then manipulate your experithere is a concrete hypothesis, if the result does ments or your perceptions or your data to make not prove the hypothesis to be correct, then the it seem to be true. Feynman believed that only reasoning is ultimately inductive. For example, if through rigorous, careful adherence to the scienStacy draws ten white marbles from a bag, she tific method could we be sure that we were not might theorize that all the marbles in the bag are succumbing to our natural confirmation bias or white. At first glance this may seem correct, but emotional linkage to what we were investigatthe statement is not necessarily true. Through in- ing. Feynman went further, saying in a Caltech duction, observations are made and a probable commencement address, “We profoundly overesconclusion is drawn. In science, inductive rea- timate our ability to see things as they actually soning is used to formulate theories. The basis of are. After you’ve not fooled yourself, it’s easy not science comes from experiments, and from these to fool other scientists. You just have to be honresults scientists in the future are able to use gen- est in a conventional way after that.” Scientists eral trends to formulate new hypotheses to re- have to be just as aware as people in other prosearch. Science, however, is generally deductive fessions that there is bias inside them (and posdeductive reasoning, which reaches a necessarily sibly their institutions) and bend over backwards true conclusion from true premises. Returning to to be honest and follow their procedures. In scithe example of the bag, if all the marbles in the ence, this attitude is called skepticism. If you bebag are white and if Stacey draws a marble from lieve a claim, and then you test it against nature the bag, then Stacy’s marble must be white. Be- (who, in science, is the ultimate authority) and

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if it fails, and the test is repeatable and well-designed, it’s time to modify what you believe.

find trustworthy evidence themselves, and avoid drawing conclusions from simple statements.

Regina Nuzzo writes in Nature, “In today’s environment, our talent for jumping to conclusions makes it all too easy to find false patterns in randomness, to ignore alternative explanations for a result or to accept ‘reasonable’ outcomes without question—that is, to ceaselessly lead ourselves astray without realizing it.”

— Thomas Campbell (VI)and William Rothpletz (VI)

In other words, it’s way too easy to believe what we want to believe, even in science.

— Mr. Coe Our intuition about science is often misled by myth, repetition of questionable ideas, popular culture, and informal analogies. The truth of scientific claims is often clouded by emotional context, advocacy, and popular media reportage.

New scientific studies appear almost daily. They claim one should exercise, eat, or behave differently while providing influential evidence. Many of these studies are extremely beneficial, but it can be hard to tell what is the truth. Scientific claims are often intruded by myths, lies, or uncertain ideas. Unfortunately, not all of these are caught, and many of them are able to be spread and promoted until they become a false truth.

For example, one may have heard the myth that carrots will enhance your eyesight or even give you night vision. This had been spread by impatient parents, friends, and even news outlets.

However, this idea is incorrect. As stated on Snopes, “While carrots are a good source of vitamin A… eating them won’t improve vision.”

The article proceeds to explain the origin of the myth as a World War II military ploy. The British had developed a new radar that could detect non-visible targets. Instead of revealing their weapon, the pilots claimed the carrots in their diet were responsible. While this myth was easily disproved, some still believe it. One’s emotions can cloud their judgment and therefore listen to what they want to hear. These types of claims will always exist and mislead the public causing additional doubt on any new findings. Ultimately, one cannot believe anything they hear from the Internet or a friend. They must take time to

Scientific statements should be viewed skeptically but not cynically. Scientists are generally willing to concede that they may be wrong.

For us to trust scientific claims, we need to have an idea about how one could know such things. Sometimes this comes through sourcing, sometimes through “common sense.”

For scientists, it’s important to factor in all data and observations in order to find the correct solution, not the solution that backs up one’s independent beliefs. This is a hurdle that many scientists have to overcome, because confirmation bias plays such a huge role in the way that we solve problems. We want a certain outcome because of premeditated beliefs, so it is important to be impartial and neutral when trying to answer a question. This contributes to “common sense,” which is a collection of unproven theories which make sense but have no backing or validity. Science operates on theories, sourcing, and experiments in order to prove things that otherwise would have no explanations. “Common sense” throws all of that out the window, and operates on assumptions and unproven theories. This is a problem that leads to the spread of misinformation and a lack of continuity in the scientific community. In order to maintain order and fact in science, it is important to factor in everything, and to use concrete evidence to support claims, rather than mere intuition.

— Elliott LaGorce (VI) and Nick Spinelli (VI) The philosopher Karl Popper believed that “scientific statements must be precisely worded and falsifiable.” In other words, a statement that is not falsifiable is not scientific.

Karl Popper was a renowned philosopher of science who was best known for his rejection of conventional inductivist views and his support of the idea of falsification, which is the ability for an observation to be proven incorrect. Regarding his views of the conventional inductivist views, Popper said “I approached the problem of induc-

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tion through Hume. Hume, I felt, was perfectly right in pointing out that induction cannot be logically justified. Popper’s philosophy of falsification (a.k.a. critical rationalism) is a method of deduction. Karl rejected empiricism/induction (observation to theory), because he believed the conclusions made by induction are not always 100% true. Thus, according to Popper, a statement could not be deemed “scientific” if it was not falsifiable. For example, an example of a falsifiable statement would be if one claimed that all birds can fly. If the statement is false, it will be proven to be so through experiment and is therefore is scientific. Popper’s theory of falsifiability does not apply to statements which are not “scientific”.

For example, the equation 2+2=4 is 100% true and cannot be falsified as it is proven by mathematics, and therefore it is not scientific. Popper argued that statements and theories made in science should be scrutinized and examined through decisive experiments. Although a scientific statement can never be proven to be completely true, it can always be falsified. Popper further clarified his theory of falsification by saying that if a statement is falsifiable, it does not mean that it itself is false. Rather, it means that if a theory is falsifiable, it means that it simply be proved to be false through observation and experiment. Going back to the claim that all birds can fly, the statement is false and can be proven to be so through observation. Penguins and kiwis, both of which are flightless birds, prove this statement false.

Thus, while humans tend to form inductivist theories based off of observations, Popper supports the formation of theories first and observations second. Much of Popper’s philosophy deals with determining not only how much a statement is false, but how much of it is true as well.

— Derek Raskopf (V) and Kimberly Young (V) Science is not about proof—science doesn’t prove things true. Truth in science is probabilistic and provisional. That does not mean, however, that every claim is equally likely to be true. As Carl Sagan once put it, “Extraordinary claims require extraordinary evidence.”

The above quote is referred to as the Sagan Standard, popularized by Carl Sagan through the 1980 TV documentary series Cosmos. How-

ever, many similar claims have been made much earlier by Pierre-Simon Laplace (“the weight of evidence for an extraordinary claim must be proportioned to its strangeness”) and by Thomas Jefferson (a thousand phenomena present themselves daily which we cannot explain, but where facts are suggested, bearing no analogy with the laws of nature as yet known to us, their verity needs proofs proportioned to their difficulty). These aphorisms all attempting to make the same, general point: you must have evidence of value equal to or greater than your claims.

The Sagan Standard has been in frequent use in the psychology world being used to debunk experiments “proving” precognition, an ability to sense the future state of the world before it happens. However, there are flaws to this dictum as evidenced the fact that there is no standard for “extraordinary claims.” It really depends on what you already know and believe.

As comedian Elayne Boosler quipped, “Popcorn is magic if you don’t know how it happens.”

Speaking more broadly, the entire notion that science can have definite evidence for anything, thus proving it “true”, is flawed. Take the theories of gravity, evolution, and The Big Bang, for example: falling objects prove gravity, people, animals, and even stars prove evolution, and the expansion of the universe and cosmic microwave background prove The Big Bang. However, they don’t.

These discoveries definitely provide strong evidence for their respective theories, but they aren’t proof because they are all provisional; they are subject to change in the future. As humans advance scientifically, all theories are constantly evolving. The theory of spontaneous (or equivocal) generation is an obsolete principle concerning the origin of life from inanimate matter. The hypothesis was brought out by Aristotle who advocated the work of earlier natural philosophers. However, by the middle of the 19th century, experiments of Louis Pasteur and others refuted the traditional theory of spontaneous generation and supported biogenesis.

— Hanna Davis (VI) and Rashida Mohammed (VI)

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Research Papers.

Novel scientific research articles from students in Independent Research Teams and AP Biology.

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Effects of Memory-Enhancing Supplements in a Drosophila Alzheimer’s Model

by Naiyah Atulomah ‘18 AP Biology sophila compound eye. By measuring the extent Abstract

of disorder of these cells, the relative amount of Amyloid-beta-42 plaques (Aß42) induce neu- Aß42 expression can be determined. We are inrodegenerative deficits associated with Alz- terested in researching the varying effects of difheimer’s Disease. Knowing if common, over- ferent herbal “memory enhancing” supplements the-counter memory-enhancing supplements in a Drosophila melanogaster model of AD. have any effect on memory could be valuable knowledge for understanding how to combat Methods this threatening disease. Previous research has We can manipulate Drosophila melanogasindicated that photoreceptor-directed Aß42 ter to overexpress Aß42 in the eye by using overexpression causes a “rough-eye” pheno- the Gal4-UAS system To create the Drosophtype in Drosophila melanogaster, but no tests ila line, we will use the Gal4 system, crossing have used common memory supplements in the driver line (elavGal4;+;+) with the responda Drosophila model of Alzheimer’s disease. er line (Abeta42-alz3;Abeta42-alz8/TM6B).

Introduction Alzheimer’s disease (AD), the most prevalent form of senile dementia in humans, is diagnosed by the presence of neuritic plaques, composed mainly of amyloid-beta peptides, and neurofibrillary tangles, which are composed of tau protein.

Disease manifestation is age-dependent, with the incidence of AD in the general population rising from 6% in those over 65 years to 30% in those over 85 years. The amyloid precursor protein (APP) is a transmembrane protein most commonly found in neurons. APP is initially cleaved by γ-secretase and ß-secretase, with the ß-secretase cleavage further cut by γ-secretase. The resulting peptides are known as amyloid-beta-40 (Aß40) or amyloid-beta-42 (Aß42). Aß42 contains two extra hydrophobic amino acids that resist moving out of the lipid bilayer, and are the basis of the toxic plaques that are a hallmark of AD. When overexpressed in the eye of Drosophila, a “rough-eye” phenotype is produced. This phenotype manifests itself in the form of disorganized ommatidial cells of the Dro-

By creating a fly homozygous for Aß42, the rough eye will have a more severe effect and be more apparent underneath the microscope.

The Aß42 fly from the cross will be the fly used for the memory-enhancing supplement tests. For the first step of the experiment, we will put in a significant number of the flies (5-10) into vials containing equal amounts of fly food with the correct doses of Ginkgo biloba, acetyl-L-carnitine, and huperzine A. We will examine the F1 generation born in the supplement food to see if Aß42 expression changes at a different rate than that of the control F1 generation. We can examine the eyes through quantitative analysis using a program called Flynotyper. Created by researchers at Pennsylvania State University, this software uses images obtained by bright-field microscopy to provide a quantitative result of disorder in the eye. In the case of the rough eye, there is a malfunction in the arrangement of the photoreceptor neurons that form the ommatidium. With Flynotyper, we will be able to track the random-

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ness in the eye. This screening method allows us to determine whether or not the administration of the supplements lessens the severity of the rough eye phenotype in future generations.

Results Last year, our group determined effective concentrations for each supplement. We mixed varying concentrations of each supplement with 10 grams of fly food in vials and collected data detailing whether the original flies were able to ingest the supplement and reproduce a second generation, or if the supplement concentration was so high that first generation died before it could produce another. From this experiment, we determined that each supplement had a different ideal concentration: Gingko biloba was 0.5 grams, Rhodiola rosea was 0.25 grams, acetyl-L-carnitine was 0.1 grams. Since the flies were not able to survive in any amount of omega-3 fish oil, we eliminated it from further study.

We set up our second control experiment by keeping wild type flies in vials containing 10 grams of fly food mixed with fixed concentrations of each supplement, which we determined in our first experiment. We then analyzed the progeny of these flies, which were born in the various supplements. We collected quantitative data

about the survival of these flies as well as their ability to effectively produce another generation.

This year, we have completed the genetic cross of a fly homozygous for Aß42 in the eye. We chose to use the Gal4-UAS system to express Aß42 in the eye so we could manipulate and analyze the rough eye phenotype. This would allow us to evaluate the effects of our chosen supplements. After completing our first genetic cross to selectively express Aß42 in the eye of Drosophila melanogaster, we found that the rough eye phenotype was only apparent when the fly’s second and third chromosomes were homozygous. As we only wanted to analyze flies with the visible rough eye phenotype, we selected and mated male and virgin female flies that did not express Cyo or TM2 to ensure that the entire population would be homozygous with a noticeable rough eye phenotype.

In addition to that, we have taken pictures of the eyes of the flies and analyzed them using Flyntoyper. This software is able to analyze the “randomness” in the ommatidium of the eye and help us determine if the rough eye is improving, deteriorating, or staying the same, all of which are indicators of differing levels of Aß42 expression.

Discussion Our data from last year showed that the flies

Figure 1: Survival rates of wild type Drosophila in 0.10 grams of acetyl-L-carnitine and 10 grams of food.

The black line represents the average, and the vertical lines represent the standard deviation.

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Figure 2: Survival rates of wild type Drosophila in 0.25 grams of Rhodiola rosea and 10 grams of food. The black line represents the average, and the vertical lines represent the standard deviation.

Figure 3: Survival rates of wild type Drosophila in 0.50 grams of Gingko biloba and 10 grams of food. The black line represents the average, and the vertical lines represent the standard deviation.

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were able to survive in the dosages of supplement we put in their food for around 24 days.

This directly correlates to the flies we are genetically crossing to express Aß42 in their eyes.

These flies start to express the rough eye phenotype after around two weeks. This means that the flies will survive long enough for us to collect sufficient data on the change in their eyes.

We have finished crossing our desired fly and are currently creating an adequate stock to begin our supplement tests. We will be conducting these tests the same way we did with the Canton-S flies. However, in addition to taking survival data, we will also be taking pictures of the flies throughout their development in the supplement to analyze the randomness of their ommatidium. We hope to find a significant difference between the rate of change of Aß42 expression of the supplement flies and the flies in normal food.

Using Flynotyper, we can calculate the disorderliness of the fly eyes in the pictures. We will use as many flies from the supplement food as we can in order to have sufficient data to compare to a “normal”Aß42 fly and better quantitative results overall.

If there is a change in the progression

or Aß42 expression of the flies, it would prove that the memory-enhancing supplements have an effect on the plaques that contribute to Alzheimer’s Disease. If the progression of expression shows no change, we will test more supplements. However, if the supplements are able to slow down the rate of Aß42 expression, we will proceed to use the Gal4 method with a mushroom body-specific driver line to express Aß42 in the brain. We will follow up by testing memory recovery through behavioral assays.

Figure 4 (L): A wild type fly eye.

Figure 5 (R): A fly eye with rough eye phenotype.

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Figure 6 (L): Resulting image when Figure 5 is run through Flynotyper Figure 7 (R): Resulting image when Figure 4 is run through Flynotyper

Literature Cited Bahadorani, Sepehr, et al. “The Effects of Vitamin Supplementation on Drosophila Life Span Under Normoxia and Under Oxidative Stress.”

J Gerontol A Biol Sci Med Sci, https://doi. org/10.1093/gerona/63.1.35.

ated mechanisms.” Human Molecular Genetics, doi:10.1093/hmg/ddt478.

Prokop A. 2013. A rough guide to Drosophila mating schemes” G3 (Bethesda). 3(2): 353-358.

Cao, Weihuan, et al. “Identification of Novel Genes That Modify Phenotypes Induced

by Alzheimer’s ß-Amyloid Overexpression in Drosophila.” Genetics, doi:10.1534/genetics.107.078394.

Iyer, Janani, et al. “Quantitative assessment of eye phenotypes for functional genetic studies using Drosophila melanogaster.” NCBI, doi:http://dx.doi.org/10.1101/036368.

Moloney, Aileen, et al. “Alzheimer’s disease: insights from Drosophila melanogaster modelsTr.” Trends Biochem Sci, doi:10.1016/j. tibs.2009.11.004.

Prüßing, Katja, et al. “Drosophila melanogaster as a model organism for Alzheimer’s disease.”

BioMed Central, DOI:10.1186/1750-1326-8-35.

Shulman, Joshua M., et al. “Functional screening in Drosophila identifies Alzheimer’s disease susceptibility genes and implicates Tau-medi-

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Studying the Behavior of Large Mammals on Pingry’s Campus

by Ryan Feely ‘18 and Malcolm Fields ‘18 AP Biology

Abstract The woods on the Pingry campus are an area in which several different forms of wildlife can live comfortably. The goal of this project is to pinpoint exactly which animals, large mammals specifically, can be found on the Pingry campus.

Ethograms were created for whitetail deer and red foxes in order to track the mating behavior of the deer and the hunting behavior of the foxes.

The actions observed through the video footage were used to document how these animals behave. This information can serve as baseline data for potential future projects that involve the observation of animals on Pingry’s campus. The results showed that the camera placed behind the school was better for observing deer mating behavior and that the camera near the chicken coops was better for observing fox hunting behavior.

Introduction Camera traps are often used to track animal movement and behavior. Using camera traps for observation is a valuable technique because it eliminates the possibility that human presence will alter an animal’s behavior. Ken D. Tape and David D. Gustine conducted a study of the migration patterns of caribou and ptarmigan in Northern Alaska and the advantages and disadvantages of using camera traps to capture animal behavior. They found that camera traps proved useful for collecting data in a limited time frame. They write that “camera traps can distill long periods into short animations rich in observational data” (Tape 123). These cameras are placed in locations where there is a high likelihood of spotting the target animals. Once these cameras are set up, they gather data by taking a picture or short video when motion is detected. The cameras are left for as long as the experimenter sees fit. In order to collect the data, the cameras are removed and the information stored on them is transferred to

a computer. Camera traps can collect information such as what animals are present and diet and sleeping habits. We would be interested in collecting both of these types of information for our study. The purpose of this project is to use these camera traps to identify what large mammals can be found on the Pingry campus, as well as to monitor the behavior of these mammals.

Once the mammals are identified, ethograms will be created to track their behavior. These ethograms prevent bias in opinions about whether animals are expressing certain behaviors. There are definitive actions that correspond with certain types of behavior, and these behaviors will be recorded in in order to determine how whitetail deer and red foxes act on the Pingry campus.

Methods The materials used to collect data for this project were two camera traps which were placed in different areas on the Pingry campus. One camera was placed near the chicken coops that are found on campus, and another was placed in the woods behind the school. The cameras were set to capture a short video of whatever the camera detected with its motion sensor.

After several days of data collection, the videos were transferred to a flash drive so they could be analyzed. The cameras were then returned to their respective locations on campus. It was decided that the animal behaviors that would be analyzed were the mating behaviors of whitetail deer and the hunting behaviors of red foxes. An ethogram was created to determine whether the animals were performing one of these behaviors. The ethogram displayed all of the possible actions that a deer or fox could perform that would reflect mating or hunting behavior. Once the ethogram was created, the camera footage was watched and these actions were recorded.

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Results Whitetail Deer Results There were three instances of does separate from a group found on the camera near the chicken coop and four instances of a lonely doe found on the camera behind the school. There was one instance of a buck chasing a doe observed on the camera behind the school. No other actions specified on the ethogram were observed on the cameras for whitetail deer. There were eleven total deer sightings on the camera near the chicken coops and fifty-eight total deer sightings on the camera behind the school.

Red Fox Results There were six instances of a fox turning their ears captured on the camera near the chicken coops and no instances of a fox turning its ears on the camera behind the school. There were seven instances of a fox crouching captured on the camera near the chicken coops and three instances of a fox crouching on the camera behind the school.

There were twenty six total foxes captured on the camera near the chicken coop and fifteen total foxes captured on the camera behind the school.

Discussion There were fewer instances of the deer showing mating behavior than expected. This could be because although there were fifty-eight sightings of deer on the camera behind the school, the rutting season for deer was near its end by the time the camera was placed there and data was recorded. It is most likely that the camera near the chicken coops did not capture a lot of deer activity because this area is more open and closer to human exposure than the woods behind the school where the other camera was located.

There were more foxes spotted in the camera near the chicken coop than the camera behind the school, and there were also more hunting behaviors shown near the chicken coop than behind the school. Both these patterns can be explained by the fact that there are chicken coops near the camera. The foxes see the chickens as prey which draws them to the area in general, as well as increases their hunting behavior. If these observations were to be repeated, it would be best to place a camera in the woods behind the school in October or November. This would maximize the amount of mating behavior seen in whitetail

deer because there seems to be a greater chance of seeing deer in this area. In order to observe hunting behavior in red foxes it is best to put a camera by the chicken coops. However, the angle at which the camera was placed made it hard to observe the behaviors because the foxes were constantly walking directly away from the camera.

Appendix Whitetail Deer Mating Ethogram Actions that reflect mating behavior Bucks sparring Antler fights Buck pawing ground Urinating in scrape Bucks rub antlers on tree or bush Bucks make repeated vocalizations Female call when deer is ready to mate Rubbing head on trees/ shrubs Males Chasing Females Pregnant does: Separate from other does, Find suitable birthing area Flehmen response - Deer lifts head up and exposes upper gum Buck Run - neck extended, head low, mouth open, tail straight out behind Red Fox Hunting Ethogram Actions that reflect hunting behavior Turning ears Attempting to dig Cocking head Crouching low to the ground Diving in snow Literature Cited Tape, Ken D., and David D. Gustine. “Capturing Migration Phenology of Terrestrial Wildlife Using Camera Traps.” BioScience, vol. 64, no.

2, 2014, pp. 117–124., Madison, Richard T. “Red Fox-Vulpes Vulpes.” Liska’s EncycVulpedia: Red Fox Hunting Techniques, FoxStar Arts, 1997.

Gee, Ken. “In a Rut - Breeding Season Behaviors in Deer.” Noble Research Institute, 1 Nov. 2008.

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The Effect of Increasing Soil Salinity on Peroxidase Distribution in Comet Radishes

by Dhruv Govil ‘18 AP Biology

Abstract Excessively high soil salinity negatively affects nearly 20% of the world’s crop production. To engineer more salt tolerant plants, it is crucial to unravel the mechanisms by which plants respond to salt stress. One of the less understood mechanisms by which plants respond to salt stress is the increased activity of the peroxidase enzyme. The main purpose of this project was to examine how varying levels of soil salinity affect the distribution of the peroxidase enzyme in comet radishes, with the goal of understanding the specific role of peroxidase in responding to salt stress by observing its distribution pattern. 20 comet radishes were planted, four to each specific salt concentration group, with salt concentrations ranging from 0M to 0.2M, in equal increments of 0.05M. Samples from the lower stem, upper stem, and leaf sections were grinded and individually added into a cuvette with hydrogen peroxide and guaiacol solution, and the time taken for the solution to reach an absorbance of 0.3 mL was recorded.

Introduction Crop performance worldwide is severely affected by high soil salt concentrations (2,5). High soil salinity levels, largely caused by the presence of seawater, decreases a plant’s ability to absorb water.

This leads to a severely reduced crop yield (1,3).

Peroxidase enzymes act as detoxifiers in plants by breaking down hydrogen peroxide. While previous experiments have shown that the activity of peroxidase increases under salt stress conditions, its exact distribution in the plant and why its activity increases under salt stress are not clearly understood (4). It was hypothesized that radish plants exposed to greater soil salinity would show greater peroxidase activity in the lower stem regions compared to the upper stem and leaf regions; the plants will most likely minimize salt uptake

through the lower stem regions so as to prevent excess salt from reaching the rest of the plant.

Methods 20 comet radish seeds were planted, four to each specific salt concentration (0M, 0.05M, 0.1M, 0.15M, 0.2M). Miracle-Gro potting mix and tap water were used. One radish seed was planted in each cup. For the first three weeks, the radishes were given daily water doses of 50 mL. After three weeks, the watering schedule changed to include predetermined salt concentrations for each of the five salt concentration groups. Each plant was given 30 mL of water with a predetermined specific amount of non-iodized salt thoroughly dissolved into solution. Watering with salt occurred every two days for nine weeks. Upon plant maturity, tissue sections were grinded (Figure 2). Cuts of length 1 cm were made using dissecting scissors. 0.5 mL of 10 mM hydrogen peroxide and 0.5 mL of 25 mM guaiacol solution were added into a cuvette with the ground tissue. The amount of solution absorbed was measured using a spectrophotometer at a wavelength of 470 nm; the time taken for each solution to reach an absorbance of 0.3 mL was recorded.

Results The third test radish from the 0.15M group died midway through the watering process. All other radishes from the 0.1M, 0.15M, and 0.2M groups also died. A total of eight healthy radishes survived to maturity and were all from the 0M and 0.05M groups. The dead radishes had similar characteristics such as dry, thin stems and crumpled dried leaves. The alive radishes had similar characteristics such as bright green leaves and thicker stems, and the radishes in the process of dying had characteristics between those that were dead and those that were fully alive. The re-

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sults from the peroxidase assay are tabled below.

Discussion A major issue was that more than half of the total possible assay reactions did not occur, which was likely due to incomplete grinding of the radish tissue sections. This is supported by the fact that a greater percentage of the leaf tissue reactions were successful compared to the upper and lower stem tissue reactions: the leaf sections were generally drier and therefore easier to grind, and they covered a greater surface area, which allowed the guaiacol reaction in Figure 1 to proceed much quicker and with a greater rate of success. Some of the no visible reactions (NVR) had relatively high absorbances, but this was not due to the brown product forming in the cuvettes due to the reaction in Figure 1, as the cuvettes were still clear after the absorbance reading was taken. Most likely, the actual radish tissue had a particularly active absorbance at 470 nm.

Possible experimental errors include the varying conditions of the radishes when they were assayed, with some being alive, some in the process of dying, and some dead. Peroxidase activity might substantially differ between dead radishes and full healthy radishes. So, in order to control for this variable, the final peroxidase assay should be conducted using fully healthy, alive radishes. For a future study, this means using lower salt concentrations for the salt treatment groups. This will ensure that future data will be more meaningful and conclusive than the data obtained in this experiment.

The timing of the reaction to reach an absorbance of 0.3 mL was also a source of error and uncertainty, as human reaction times varied and the stopwatch was likely stopped too late on most

Figure 1: The chemical reasoning underlying the peroxidase assay. Peroxidase cartalyzes the decomposition of hydrogen peroxided into water and oxygen. The oxygen reacts with guaicol to form a brown product. The quicker the brown product was formed, the greater the peroxidase activity was in that specific tissue region.

occasions. Thus, it is reasonable and accurate to assume that the recorded numerical times are higher than their actual values. These experimental errors should be corrected in a future study to have more conclusive, statistically significant results, from which scientific knowledge in this particular research area may be furthered.

Figure 2: Diagram of a radish showing the three specific tissues that were assayed.

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Literature Cited Carillo, P., Annunziata, M. G., Pontecorvo, G., Fuggi, A., & Woodrow, P. (2011, September 22).

Salinity Stress and Salt Tolerance. Retrieved from https://www.intechopen.com/books/abiotic-stress-in-plants-mechanisms-and-adaptations/ salinity-stress-and-salt-tolerance Deinlein, U., Stephan, A. B., Horie, T., Luo, W., Xu, G., & Schroeder, J. I. (2014, June). Plant salt-tolerance mechanisms. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC4041829/

Yildrim, E. (2008, October 10). Mitigation of salt stress in radish (Raphanus Sativus l.) by plant growth promoting rhizobacteria.

Acknowledgements Thank you to Ms. Tandon and Mr. Maxwell for their assistance with the project.

Negrão, S., Schmöckel, S. M., & Tester, M. (2017, January). Evaluating physiological responses of plants to salinity stress.

Swapna, T. (2002, August 13). Salt stress Induced Changes on Enzyme Activities during Different Developmental Stages of Rice (Oryza sativa Linn.).

Figure 3: NVR stands for no visible reaction. The leftmost column gives the radish salt concentration followed by the trial number within that concentration group. The time to reach a solution absorbancee of 0.3 is given for each of the three tissue extracts.

If no reaction was visible, it was recorded as such.

Groups 0.2M:3 and 0.2:4M had no radish parts present, which likely dissolved in their soil. Out of the 50 samples that were assayed, 27 reactions were not visible. The radish 0.15M:3 dided before maturity and hence was not assayed.

Figure 4: A successful reaction between the decomposing hydrogen peroxide and the guaiacol solution indicator as evidenced by the final amber brown color of the solution

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Observing the Chemotaxis of D. discoideum

by Jared Lefkort ‘18 AP Biology Abstract Dictyostelium discoideum is an organism that moves towards higher concentrations of cyclic adenosine monophosphate (cAMP). This chemotaxis occurs when the organisms are in their multicellular form and under agarose. Multiple experiments have been performed and the procedure has been modified, yet no conclusive data has been recorded as no movement of the organisms have been recorded in any of the experiments.

Introduction Dictyostelium discoideum, also known as cellular slime mold, is a single cellular organism. The life cycle of cellular slime mold includes two phases, growth and development (Chibalina). During a normal growth stage, the organism is able to duplicate themself and eventually develop into a larger, functioning organism. However, when these organisms are starved they go through a more complex growth process. When starved in the single cell stage, these organisms both release and search for higher concentrations of cAMP (Chibalina). This allows the organism to determine where there may or may not be food sources available. Once a higher concentration of cAMP is recognized, each organism will move towards it (MetaMicrobe). This movement towards an increasing chemical gradient is known as chemotaxis. Studies have found that this chemotaxis occurs only in conditions under agarose (Woznika). In addition, this chemotaxis only occurs when the organisms are in their multicellular stage (Woznika). The purpose of this study is to examine the time it takes for the D. discoideum to reach a concentration of cAMP after different starvation times and with differing concentrations of cAMP. In the first round of testing, the concentrations of cAMP will be artificial, meaning that they will not be released by other D. discoideum

organisms. In the second round, multiple organisms will be introduced into the environment after starvation to observe noticable differences.

Methods Prior to each experiment, the D. discoideum was isolated and starved for a time period between 4-7 hours. To isolate the D. discoideum, an inoculating loop was used as it is both sterile and small enough to pick up one organism. Using the loop, one organism was picked up and placed on a sterile, empty plate. The organism was then starved for a time period between 4-7 hours. After that time period, the cAMP was prepared for addition to the plate with the organism. Because the amount of cAMP needed is so minute, it is not necessary to weight the amount. Rather, the smallest amount possible was picked up. To perform this step, a dissecting microscope was used.

First, a small piece of agarose is needed, as the chemotaxis will only occur under-agarose. Then, using a spatula, the smallest amount possible of cAMP is taken and added to the surface of the agarose which is present on the plate. After the cAMP is placed on the agarose, the organism is plated on the surface of the agarose and any movements are observed. If possible, the time taken by the organism to reach either the cAMP concentration or the other organism is recorded.

Results No conclusive data was found. For the first few experiments, the organisms were starved outside of the agarose and an attempt was made to observe chemotaxis in the same environment where the starvation occured. In later experiments, the organism was introduced to agarose after starvation and still no movement was observed.

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Analysis

These experiments have yielded no conclusive data. Chemotaxis was not observed in any of the trials. While the procedure has been modified after each trial, I do not expect that chemotaxis will be observed in any of the experiments. I attribute this to human error as well as the experimental environment. Human error could be attributed to the amount of cAMP used, the transfer of the organism, as well as in viewing minute movements of the organism.

Ideally, if data was able to be collected, I would analyze the differences in movement after the differing periods of starvation. In each experiment the same amount of cAMP would be used, therefore the only differing variable is the amount of time starved. If data was collected, the different starvation times would be collected and the movement would be collected and grouped along with the starvation time. Thus, further research is needed in order to draw a conclusion about the ideal starvation time to elicit chemotaxis in D. discoideum organisms.

Literature Cited Chibalina, M V, et al. “Gdt2 Regulates the Transition of Dictyostelium Cells from Growth to Differentiation.” BMC Developmental Biology., U.S. National Library of Medicine, 5 July 2004, www.ncbi.nlm.nih.gov/pubmed/15236669.

“MetaMicrobe.com/Dictyostelium Discoideum.”

Dictyostelium Discoideum, Model Organism, Social Amoeba: Facts, Life Cycle, References at MetaMicrobe, www.metamicrobe.com/dicty/.

Veltman, Douwe M., et al. “Four Key Signaling Pathways Mediating Chemotaxis in Dictyostelium Discoideum.” The Journal of Cell Biology, The Rockefeller University Press, 25 Feb. 2008, www.ncbi.nlm.nih.gov/pmc/articles/ PMC2265585/.

Woznica, D, and D A Knecht. “Under-Agarose Chemotaxis of Dictyostelium Discoideum.”Methods in Molecular Biology (Clifton, N.J.)., U.S. National Library of Medicine, www.ncbi. nlm.nih.gov/pubmed/16957299.

“Dictyostelium Discoideum - Details.” Encyclopedia of Life, eol.org/pages/197896/details.

Acknowledgements Olivia Tandon, Morgan D’Ausilio

Figure 1: D. discoideum starved for 7 hours

Figure 2: D. discoideum starved for 4 hours under agarose

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Figure 3: Experiment Dates, Times, and Results

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Effects of Increased Oxygen Exposure on D. magna Survival

by Cathleen Parker (VI) AP Biology Abstract In each experiment, Daphnia magna were placed in five bins and exposed to different amounts of oxygen. Each bin had an airstone that bubbled oxygen through the tank from an external air pump, and each airstone was connected to the air pump by a tube that sent the air through a valve. Each bin’s valve was opened to a different percent, with the control bin having a closed valve, and the others having a 25% open, 50% open, 75% open, and 100% valve. Populations of the D. magna and levels of dissolved oxygen in each bin were taken over the course of the experiment. The results were inconclusive in terms of relating the percent the valves were open to the amount of dissolved oxygen in the bins. Furthermore, after two trials of the experiment, the D. magna populations did not show any patterns as to which bins showed success in sustaining and growing a population.

Introduction In “Effects of Low Dissolved Oxygen on Survival, Growth and Reproduction of Daphnia, Hyalella, and Gammarus”, it was found that “all four species of tested crustaceans were very tolerant of low dissolved oxygen”(1) levels, including D. magna as one of the four tested species. While it is accepted that D. magna can withstand very low levels of dissolved oxygen (DO) in their water, effects of high oxygen levels in their environment have not been as deeply explored. Whether or not a large amount of DO (dissolved oxygen) is beneficial to the survival of the D. magna population has not been well studied either. Human populations, however, have been studied in depth, with considerable literature available regarding adap-

tation to high altitude, low oxygen concentration environments. It is difficult to draw parallels between these small D. magna and humans, but the premise of whether excess oxygen enhances or hinders survival has sparked researchers’ interest.

Researchers studied D. magna in an aquatic environment where it was believed that regulating DO levels were more controlled than oxygen levels in a gaseous environment with another organism.

Methods Throughout the experiments, all daphnia were housed in standard blue plastic bins. All were exposed to constant 24 hour light from LED bulbs placed uniformly above the bins, which were located within closed cabinets with no exposure to outside light. All daphnia bins were kept within the same cabinet at room temperature, which fluctuated throughout the experiment and was not accounted for.

Experiment 1 We ran our first batch of 50 adult daphnia placed in one plastic bin filled with 100 mL of tap water, awaiting transfer into their 5 different bins. They were fed 1 daphnia food pellet as well as 2 tbsp of Miracle Gro. The entire population did not survive more than 3 days.

Experiment 2 We ran our second batch of daphnia for 8 days until the population reached 0. The daphnia were all housed in one of the plastic bins awaiting transfer. They stayed in the same 1500 mL of water that they arrived in from William Tricker, Inc. The daphnia arrived with multiple other

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predators from their previous container, including Zygoptera (damselflies), Amphipoda (gammaridae) and hemiptera (backswimmer). They were fed 1 pellet of daphnia food every other day until the population reached 0, 8 days later.

Experiment 3 We ran our third batch of daphnia with an initial population of 50, with 10 placed in each plastic bin. On day 0, they were all placed in 1500 mL of water from Round Valley Reservoir at room temperature. One oxygen stone was placed at the front of each bin in order to increase oxygen levels within the water. Each bin had a different amount of oxygen added to the water via the oxygen stones attached to a 4 valve air flow valve, which was connected to a Tetra Whisper Aquarium Air Pump. The control bin had an oxygen stone attached to the same tubing, but not attached to the valve to receive additional oxygen. The 4 bins with functioning oxygen stones were exposed to 4 different levels of oxygen, with each valve at 25%, 50%, 75%, or 100% opened. Approximately every 48 hours, the daphnia were fed 1 pellet of daphnia food. Every 10 days, 500 mL of reservoir water was added to each bin to replenish the water levels. The daphnia population was counted every time they were fed and oxygen level data was collected using a dissolved oxygen level measurement device(Hach HQ30D probe).

Results

When populations were measured in the first trial of Experiment 3 (Fig. 1), the only bin that did not have all the D. magna die before the end of the 30 day period was the control bin, whose valve was 0% open. The 100% open valve and the 25% open valve also had population growth, but they both died off before the end of the experiment.The 50% valve bin and the 75% valve bin both saw only decreases in their populations, with the population never multiplying over its original 10 D. magna.

In the second trial (Fig. 2), however, the only bins that saw population growth after the second day were the 50% and 75% open valves, with the control and 100% open valve bins eliminating their populations early on, on Days 4 and 6 respectively.

The dissolved oxygen levels in both trials were very clustered, save for the 25% open valve bin in Trial 1(Fig.3),and the 0% open control bin inTrial 2 (Fig.4).

When analyzed through a regression analysis, the dissolved oxygen levels output the equation y = 0.038432 x + 4.6254. The regression analysis quantifies the relationship between values and how strong of a predictor one is for another value/variable. The equation output by the data shows that there is a positive relationship between the amount of DO and the percentage that the valves were opened. As the valves were opened at a higher percent, the DO levels tended to also increase. The coefficient of determination(R2) for Trial 1 is 5.000235025·10-1, putting the data almost exactly between the regression prediction perfectly fitting the data(which would be

Figure 1: D. magna population taken over a period of 20 days. Each line represents one bin with a set percent open valve pumping oxygen into the bin. Bins are labeled based on their valve open percent.

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Figure 2: Population during Trial 2 of D. magna taken over 25 day period. if the R2 = 1) and not fitting at all(R2 = 0). This ~0.5 value put the data at what could barely be considered a moderate predictor, as it is barely above 0.5, which could be attributed to chance.

For Trial 2, the regression equation was y = 2.353538462·10-2 x + 6.341692308 with an R2 value of 4.189358092·10-1. This put Trial 2 at a lower level as a predictor of the data because it is well under 0.5, which is even a moderate predictor.

Conclusions With the results of Trial 1 on the low end of what would be considered an even moderate predictor of the dependent variable(level of oxygen), based on the independent variable(what percentage open the valves were) and Trial 2 even below that 0.5 mark at approximately 0.42, the data does not show a strong correlation between how much the oxygen levels were varied within the bins in either trial. Since the data does not show a strong correlation between the percentage the valves were opened and the amount of dissolved oxygen in each bin, the ability to make any conclusions about lifespan in the presence of different levels of oxygen is lost, as even the levels of oxygen were not determined to be strongly correlated with the valve opening percentages. Additionally, the populations of D. magna were widely varied within each trial and did not show similar patterns in regards to which bins had sustained population growth and which had all their population die off early. The levels of DO were not varied enough

to draw conclusions about the impact that DO levels have on the lifespans of D. magna. However, the DO levels from the D. magna that were cultivated were fairly clustered in both trials, and the populations ranged a great deal between the trials. This indicated that other variables also affected the lifespans, reproduction, and survival of the D. magna. Age of the D.magna at the beginning of each trial was not known, and age varied between all D.magna in each bin. Thus, age was a variable that was not accounted for but may have changed the population success in each.

Further Directions Research on this project could be furthered by improving upon the experiment itself, and by taking more data and controlling it. Sex and age of the D. magna could be accounted for so that the existence of single-sex or entirely old populations that would be unable to reproduce could be avoided. In an improved version of this experiment, more time would be put into finding a way to significantly change the amount of DO within the bins in a way that would better correlate to the amount of oxygen that was being pumped in. If there was a clearer distinction between the DO levels of each bin, then more conclusions could be drawn about links between DO levels and the survival of D. magna populations.

Literature Cited Nebeker, A. V., Dominguez, S. E., Chapman,

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G. A., Onjukka, S. T. and Stevens, D. G. (1992), Effects of low dissolved oxygen on survival, growth and reproduction of Daphnia, Hyalella and Gammarus. Environmental Toxicology and Chemistry, 11: 373-379. doi:10.1002/etc.5620110311 Acknowledgements DavidMaxwell The Pingry School Pingry Biology Department

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Figure 3: Trial 1’s measures of dissolved oxygen over 17 day period, taken in each bin. Bins are labeled based on the percent their valve was open.

Figure 4: Trial 2’s levels of dissolved oxygen in each bin over 25 day period.

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Inhibiting Stress Response in Daphnia magna

by Matt Stanton ‘18, Cameron Wright ‘18, Jessie Carvelli ‘18 Abstract The fight or flight response is an integral part of human behavior: it is essential for survival, has been attributed to neurotransmitters in the brain - specifically norepinephrine - and is believed to be a direct effect of adrenaline regulation in the brain. The response encapsulates the idea that when a human is face to face with a stressor, that human will either fight the stressor or will leave the situation. Previous studies on the fight or flight response have failed to focus on organisms without a brain, but there is little understanding regarding the stress responses in these organisms.

The purpose of this paper is to better understand the role of neurotransmitters in organisms that don’t have a brain: specifically, to see if Daphnia magna would have a fight or flight response.

Signs of the activation of the fight or flight response that were focused on during this study were an increased heart rate and the upregulation of adrenaline. The hypothesis was that Daphnia magna would have a fight or flight response in reaction to a stressor due to neurotransmission. But, if this neurotransmitter was inhibited, the fight or flight response would not be present.

In this study, the average stressed heart rate of Daphnia magna was found to be 272.9 bpm while the average inhibited heart rate of Daphnia magna was found to be 191.6 bpm. The data supported the hypothesis in that once an inhibitor was introduced, the average heart rate of a stressed Daphnia magna was lowered to 81.3 bpm. Neurotransmitters such as acetylcholine are responsible for lowering heart rate, so

we hypothesized that an upregulation of acetylcholine was responsible for counteracting the up-regulation of adrenaline in this study. The lowered heart rate suggests the inhibition of the fight or flight response in Daphnia magna.

Introduction The fight or flight response has been an integral part of human behavior and has been well studied. The response encapsulates the idea that when a human is face to face with a predator or stressor, that human will either fight it or they will leave the situation. This primitive brain response is essential for survival and has been attributed to neurotransmitters in the brain, specifically norepinephrine.

However, previous studies on the fight or flight response have failed to focus on organisms without a brain. Little to no research has been conducted to further understand the stress response in these organisms. This raises the question of how organisms without a brain respond to and survive face to face interactions with a stressor or predator. The fight or flight response is widely believed to be a direct effect of adrenaline regulation in the brain. Interestingly, some organisms without a brain have neurotransmitters like adrenaline, such as Daphnia magna.

The purpose of this project was to better understand the role and function of neurotransmitters in organisms that lack a brain in order to further our knowledge regarding their survival methods in their natural environments. Based on previous studies, it is known that Daphnia magna have

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androgen receptors and norepinephrine in their bodies, but it remains unknown what a stressor might induce (1). Daphnia magna are known to exhibit increased heart rates in certain stressful conditions (2), but it is not clear whether or not Daphnia magna have a fight or flight response.

The main goal of this study was to see if Daphnia magna have a fight or flight response similar to humans despite their not having a brain. Signs of the fight or flight response include a higher heart rate and the upregulation of adrenaline - the two signs that this study primarily focuses on. The hypothesis for this study was that since Daphnia magna have androgen receptors and norepinephrine, they would have a fight or flight response in the presence of a stressor or predator. If norepinephrine was inhibited, this hypothesized fight or flight response would be non-existent.

Methods and Materials Growing a living culture of Daphnia magna Daphnia magna were split into four groups of fifty each and put into different environments containing varying amounts of Miracle Gro plant food and 2000 mL of water from the Round Valley Reservoir in Clinton, New Jersey. Group 1 was placed in an environment with water and 1 tsps of Miracle Gro. Group 2 was placed in an environment with water and 0.5 tsps of Miracle Gro. Group 3 was placed in an environment containing water and 1.5 tsps of Miracle Gro. Group 4 served as the control group and was placed in an environment containing only 2000 mL of water.

The best environment for growing a living culture of Daphnia magna was determined by calculating the survival percentage after two weeks in their respective environments. All Daphnia magna environments were fed 12 drops daily of the food sent along with the Daphnia magna shipment.

Measuring the heart rates of Daphnia magna The heart rates of Daphnia magna were measured under a microscope. One Daphnia magna was removed from its environment and placed on a slide with enough water to cover it but not so much that it could swim around freely. This kept it within the lens of the microscope long enough to measure its heart rate. The slide was then placed under a microscope and was positioned so that the heart could be seen. A computer key was

clicked for each heart beat of the Daphnia magna for ten seconds. This process was repeated until there was a sample size of 10 Daphnia magna, from which an average was taken to get the average heart rate. This process was repeated to measure both the resting heart rate and increased heart rate of the Daphnia magna. The increased heart rate was measured just after inducing the stressor.

Raising the heart rates of Daphnia magna The heart rates of Daphnia magna were increased by inducing a stressor. The stressor for this project was shaking a microscope slide with a single Daphnia magna on it. After placing one Daphnia magna on a slide, it was shaken for about ten seconds.

Dilution of inhibitor The inhibitor was diluted in 1 L of water to give it a concentration of 1M. Due to the size of the Daphnia magna, it was calculated that the concentration of the inhibitor needed to be lowered to 1.0 x 10-6 M. 9 mL were taken from the 1 M solution and put into a test tube with 1 mL of water to lower the solution’s concentration. This process was repeated until the solution reached the correct concentration.

Introducing Daphnia magna to inhibitor Daphnia magna were placed in an environment with 10 mL of water with the diluted inhibitor. The concentration of this solution was 1.0 x 10-6 M. Daphnia magna were left in this environment for five minutes before their heart rates were tested.

Testing inhibition efficacy on Daphnia magna The inhibition efficacy was tested on Daphnia magna by comparing the average resting, stressed and inhibited heart rates. The inhibitor was considered to be effective if the average inhibited heart rate was lower than the average stressed heart rate.

Results Best living culture of Daphnia magna The best population of Daphnia magna came from the control group, or Group 4. The control group lived in an environment with 2000 mL of water from the Round Valley Reservoir in Clinton, New Jersey and 0 tsps of Miracle Gro plant food. This group had the greatest survival percentage after two weeks in the en-

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Figure 1: Resting heart rates of Daphnia Magna (blue) and stressed heart rates of Daphnia Magna (red).

Average resting heart rate of 212.7bpm and average stressed heart rate of 272.9bpm. Change in average stressed heart rate to average resting heart rate is 60.1bpm. vironment. Daphnia magna in all other environments died within the two week time period.

(Fig. 2). The average change in heart rate from the stressed rate to the inhibited rate was 81.3 bpm.

Raising the heart rates of Daphnia magna The heart rates of Daphnia magna were successfully increased by inducing a stressor. The resting heart rates for 13 Daphnia magna were measured and had an average of 212.7 beats per minute (bpm) (Figure 1). After inducing the stressor, the heart rates for the same 13 Daphnia magna were measured and had an average of 272.9bpm (Fig. 1). The average increase for the heart rates of Daphnia magna was 60.1 bpm.

Discussion We hope that this research lays a foundation for further research into behavior analysis in organisms that lack a brain. Prior to this study, little information could be found on neurotransmitter function in organisms with no brain.

There is now data to support that Daphnia magna have some sort of stressor detection system as shown by their increase in heart rate when their microscope slide was shaken. The initial hypothesis was that this increase in heart rate was caused by the adrenaline-epinephrine pathway, which is the pathway that is referred to as the fight or flight response in humans.

Initially, we wanted to inhibit adrenaline in the phase of our study to inhibit the stress response of Daphnia magna, but we found that the drug we planned on using would prove toxic to the Daphnia magna. Instead, we used a much safer drug that was able to up-regulate neurotransmitters in the Daphnia magna such as acetylcholine,

Efficacy of Inhibitor The efficacy of the inhibitor was tested by comparing the change in heart rate from the average resting rate to the average inhibited rate as well as the change in heart rate from the average stressed rate to the average inhibited rate.

The inhibitor successfully lowered the stressed heart rates of Daphnia magna. After inducing a stressor to the environment, the Daphnia magna had an average inhibited heart rate of 191.6 bpm

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adrenaline, and serotonin. To our surprise, their stress response was inhibited through the administration of this drug, but we do not yet have data to explain why. Since so many neurotransmitters were upregulated, we cannot pinpoint the exact reason as to why the fight or flight response was inhibited. However, it can be inferred that the neurotransmitter(s) responsible for this inhibition were depressants that have an adverse effect on heart rate. Neurotransmitters such as acetylcholine are responsible for lowering heart rate, so we hypothesize that an upregulation of acetylcholine was responsible for counteracting the up-regulation of adrenaline. We hope that we have set a foundation for more research, which is needed to narrow down what exactly caused this inhibition of a fight or flight response.

Literature Cited Villegas-Navarro, Arturo, et al.

“The Heart of Daphnia magna: Effects of Four Cardioactive Drugs.”

Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology, vol. 136, no. 2, 2003, pp.

127–134., doi:10.1016/s1532-0456(03)00172-8.

Handy, Richard, editor. “Investigating Factors Affecting the Heart Rate of Daphnia.”

Society of Biology, 25 Jan. 2012, www.nuffieldfoundation.org/practical-biology/investigating-factors-affecting-heart-rate-daphnia.

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Figure 2: Stressed heart rates of Daphnia Magna (blue) and inhibited heart rates of Daphnia Magna (red).

Average stressed heart rate of 272.9bpm and average inhibited heart rate of 191.6bpm. Change in average stressed heart rate to average inhibited heart rate is 81.3bpm.

Figure 3: Average heart rates of Daphnia Magna. Average resting heart rate (left) of 212.7bpm. Average stressed heart rate (middle) of 272.9bpm. Average inhibited heart rate (right) of 191.6bpm.

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Join PCR today.

Learn about the latest scientific research in the Pingry community while applying your scientific knowledge.

Email Brian S. Li (V): bli2020@pingry.org Aneesh Karuppur (IV): akaruppur2021@pingry.org

Mr. D. Maxwell: dmaxwell@pingry.org

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