Wings of Discovery 2005-2006

2005–06 EDGEWOOD HIGH SCHOOK STUDENT SCIENCE JOURNAL

Featuring the work of: Matthew Belopavlovich Chelsey D’Alessandro Henry Duwe III Alexandria Hall Emily Hanson Amanda Heller

Stephanie Hird Tae Hoon Kim Kyle Kinzel Rachel Larson Han-Kyung Lee Kimberly Leonard

Melanie Meyer Elise Meyers Nicholas Richardson Jessica Sanderson Chuan Wang Courtney Zwick

In Loving Memory of Joseph E. Zaiman, Jr.

Wings of Discovery is an annual, independent journal of original science research by Advanced Science students at Edgewood High School. All contributions constitute the students’ own work and reproduction in whole or in part of any article without permission is prohibited.

Wings of Discovery Š 2006 Edgewood High School of the Sacred Heart.

Edgewood High School, 2219 Monroe Street, Madison WI 53711.

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Foreword As President of Edgewood High School, I write today to tell you how proud I am to have our annual science research journal, Wings of Discovery, in your hands at this moment.

This journal is a credit to our Advanced Science students, who care so much about adding to the knowledge we have about our world, and are capable of doing so.

It is also a credit to our award-winning science teachers, who nurture in their students not only current scientific understanding, but the confidence to become part of creating it. Edgewood strives to form young scientists in the Dominican Catholic values on which we were founded — truth, compassion, justice, community and partnership — and Wings of Discovery is part of that effort.

Few high schools around the country publish a journal like the one you now hold.

For this achievement, I would like to thank our science teachers — Ms. Alana Brennan, Mr. Jonathan Hessler, Mr. Eric Pantano, Mr. Derek Ralph, Mr. Robert Shannon and Mrs. Mekel Wiederholt Meier — for their guidance and leadership to our students. I also want to remember that this annual journal is a tribute to our beloved teacher Mr. Joseph Zaimann, whose dream this was, a dream that has come true in his memory.

In addition, I want to thank all EHS science supporters, without whom this annual journal would not have come to fruition in its first year nor in this, its second.

Your continued support will allow this exceptional growth experience for our science students to continue into the future and become another great Edgewood tradition.

The discipline of science embodies the search for truth. But without the additional essential values we instill in our students — compassion, justice, community and partnership — new knowledge of scientific truths may add little to the quality of life for the inhabitants of God’s earth. We strive to form the whole person of our young scientists, endowing them with a complete and honorable value system that will serve to create good through science. Our prayer is that this journal has been, and will continue to be for years to come, a means to this end.

Sincerely,

Judd Schemmel President

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In Loving Memory of Joe E. Zaiman, Jr. 1953-2003 For 23 years, Joseph E. Zaiman, Jr. was a science teacher at Edgewood High School in Madison, Wisconsin. During his time there he not only taught life science, but by his example he taught about life itself. Through his love and enthusiasm for teaching, he earned the admiration and respect of his students, colleagues, friends and family. Joe’s motto to learn constantly, laugh a lot, and love others is the best way to describe the way he lived his life.

Joe was a man who savored constant learning. Whether he was reading a scientific journal, identifying a rare aquatic insect, digging for fossils, or learning the stars and constellations, Joe wanted to know everything about the world around him. But he most enjoyed learning from his students. Likewise, his infectious desire to learn inspired his students as well. Joe always thought that the best way for students to understand nature was to teach through hands-on experiences. His students will never forget their DNA models, hiking the rain forest in Belize, bog walking on Madeline Island, marveling at newly hardened lava in Hawaii or canoeing the St. Croix River.

All of these activities made science fun and inspired students to strive to learn more.

When one thinks of Joe, one has to think of laughter. It was his trademark. A day would not go by when any student or friend of Joe’s did not hear him laugh. Joe’s sense of humor, like his love of learning, was contagious. His unforgettable jokes, pranks, and wonderful “magic tricks” brightened even the gloomiest day.

Joe emulated the Gospel teachings through his love of others. He showed an unconditional love for everyone and everything. His love for his students could be seen and felt immediately upon entering his classroom. Joe reached out to all students.

However, it seemed that he was in his glory helping those who struggled or did not quite fit in. Being around Joe and his warmth made you want to be a better person.

Joe was also a man of many dreams. Some were fulfilled in his lifetime while others were not. The publication of a scientific journal specifically designated for high school students was one dream he planned to pursue. It is our hope that this journal will in some way be a tribute to Joe, a man, a teacher, and friend who lived life to learn, love, and laugh and inspired all around him to do the same.

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Index Proving the Putative Targeting Sequence to the Mitochondria in SUR2A Regulatory Subunit of the ATP-sensitive Potassium Channel ..........................................................PAGE 6

by Elise Meyers DoME: Digitally Probing Proteins....................................................................................................................PAGE 9

by Henry Duwe III Effects of Rain Gardens on Soil ......................................................................................................................PAGE 12

by Chelsey D’Alessandro, Jessica Sanderson and Tae Hoon Kim Testing Water in Search of Bacterial Pathogens and E-coli..............................................................................PAGE 15

by Alexandria Hall Environmental Risk Factors for Asthma in High School Students ..................................................................PAGE 18

by Melanie Meyer and Matthew Belopavlovich The Effects of Acid Rain and Fertilizer Runoff on Lake and Aquatic Life ......................................................PAGE 22

by Emily Hanson and Nicholas Richardson The Relationship of Child IgE to Parental and Environmental Effects ..........................................................PAGE 25

by Kimberly Leonard and Amanda Heller Evaluation of Chemicals for Androgenic Potency ..........................................................................................PAGE 29

by Courtney Zwick and Stephanie Hird Synthesis of Benzonitriles and Their Use in the Captodative Stability of Tetraphenylethylene ........................PAGE 31

by Kyle Kinzel The Effects of Dams on Water Quality and River Ecology ............................................................................PAGE 34

by Rachel Larson & Han-Kyung Lee Involvement of α-Amylase in Transitory Starch Breakdown in Plants ............................................................PAGE 38

by Chuan Wang Acknowledgements ........................................................................................................................................PAGE 42

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Proving the Putative Targeting Sequence to the Mitochondria in SUR2A Regulatory Subunit of the ATP-sensitive Potassium Channel Elise Meyers Elise Meyers entered the College of Wooster in the fall of 2005. There she hopes to continue conducting independent science research and being published. She also plans to pursue her passion for poetry and fiction. She desires to eventually combine her practical and emotional (or left and right brain) creative pursuits.

Elise acknowledges her always-willing and kind mentor, Dr. Jonathon Makielski, at the University of Wisconsin-Madison as well as Stacie Kroboth, who patiently taught Elise about the basic techniques for her project.

by Elise Meyers The adenosine 5’-triphosphate sensitive potassium channel (KATP) exists on the cell membrane (sarcKATP) or mitochondria surface (mitoKATP) in cardiac, skeletal and smooth muscle as well as brain tissue and the β-cells of the pancreas. KATP channel exhibits a heterooctameric subunit structure with four KIR6.x subunits forming the pore and four SUR2A subunits surrounding for regulation. KIR6.2 poreforming subunit and SUR2A regulatory subunit constitute the mitoKATP channel of myocytes. The mitoKATP channel plays a vital role in the cardiac protection mechanism, ischemic preconditioning. Ischemic preconditioning reduces damage to the myocardium during longer periods of ischemia or hypoxia by subjecting the myocardium to harmless bursts of ischemia. In an ongoing global project to describe and regulate the chemical mechanism of ischemic preconditioning, the role of SUR2A subunit in the mitoKATP channel remains unknown.

Researchers have hypothesized an amino acid sequence in SUR2A that putatively directs it to the mitochondria instead of the cell membrane for maturation and channel formation. The goal of this project was to prove or disprove that hypothesis through insertion mutagenesis of the Lumio Tag, cloning, transfection and con-focal imaging. Results are pending.

Introduction he adenosine 5’-triphosphate-sensitive potassium channels (KATP) are present in a variety of tissues, including cardiac, skeletal and smooth muscle, brain and the β-cell of the pancreas. The KATP channel’s ability to couple metabolic stresses with membrane excitability results in its key role in regulating physiological processes such as muscle contraction and insulin secretion (Chutkow 13656). A sulfonylurea receptor (SUR1, SUR2A or SUR2B) and a pore-forming subunit (KIR6.1 or KIR6.2) constitute the KATP channel. In a hetero-octameric subunit structure, four KIR6.X subunits create the central pore with four SURX subunits encircling for regulation (Kono 692). KIR6.1 exists ubiquitously in KATP channels, and KIR6.2 acts as a β-cell inward rectifier. Of the subunits, SURX has the larger and more complicated structure. SUR is a sulfonylurea receptor and a member of the ATP-binding cassette (ABC) protein super-family.

ABC proteins transport such things as ions, lipids, and secondary metabolites of drug molecules across the cell membrane (Shi). SUR2B contains exon 40 and exists primarily in smooth muscle while SUR2A contains exon 39 and exists primarily in cardio myocytes. The latter, when coupled with KIR6.2, form the mitochondrial KATP subunit in myocytes on which this research focuses.

It has been determined through voltage clamp studies and autofluorescence of flavoprotein oxidation that the mitoKATP channel plays an important role in ischemic preconditioning (IPC). Ischemia refers to oxygen deprivation (hypoxia) of the heart that leads to potentially lethal angina, myocardial infarction, and arrhythmias such as ventricular tachycardia and ventricular fibrillation. Injury occurs because ischemia leads to the formation of oxygen-derived free radicals and overloads the cardiac cell with calcium (Asimakis).

The myocardium has a mechanism known as ischemic preconditioning (IPC) by which it can protect itself from further ischemic injury. The paradoxical phenomenon of ischemic preconditioning is that short

T

periods of ischemia result in marked reduction of infarct size and incidence of cardiac arrhythmias following a prolonged period of ischemia (Gross 280). In essence the heart hurts itself a little to ensure that when it hurts itself for longer, only minor injury will occur.

There are few certainties regarding the role of mitoKATP in IPC. The scientific community agrees that mitoKATP must be open prior to IPC in order to facilitate cardioprotection (Schulz 271). MitoKATP channels exist downstream of PKC, but research has yet to determine whether PKC directly activates mitoKATP channels or indirectly activates through a downstream tyrosine kinase-mediated pathway (Sato 287). Once activated mitoKATP channels decrease the mitochondrial membrane potential, which decreases mitochondrial calcium uptake. Opening mitoKATP channels also regulates mitochondrial volume and interferes with the production of oxygen free radicals (Schulz 271).

Whether or not these characteristics of mitoKATP channels are involved in IPC, however, remains unknown. When coupled with other research, the role of mitoKATP channels in cardioprotection may be one of the following hypotheses. The first hypothesis is that increased K+ conductance through the channel results in inner membrane depolarization that reduces mitochondrial Ca2+ entry and blunts mitochondrial Ca2+ overload. The second is that mild uncoupling and oxidation of flavoproteins induced by diazoxide, a chemical that opens mitoKATP channel, will lower free radical production in the mitochondria. The changes in the mitochondrial membrane potential may alter glycolytic pathways during ischemia, favoring myocyte protection (Sato 287).

The purpose of this research is to determine a particular aspect of the molecular composition of the mitoKATP channel. Recall that most research points to KIR6.2 and SUR2A as the pore and regulatory subunit, respectively, of mitoKATP channel. Yet what directs SUR2A to the mitochondria instead of to the cell membrane (sarcKATP channel) remains unknown.

PROVING THE PUTATIVE TARGETING SEQUENCE TO THE MITOCHONDRIA IN SUR2A REGULATORY SUBUNIT OF THE ATP-SENSITIVE POTASSIUM CHANNEL

Previous research indicates a sequence within the SUR2A protein of a mouse that might represent the mitochondrial targeting signal. Once the sequence directs the regulatory subunit to the mitochondria it disassociates and comes to maturity within the mitochondria. To prove the putative mitochondrial targeting sequence, one must mutate it. This will be done by introducing the Lumio tag instead of previously-favored GFP because Lumio will not affect the conformation of the channel. The mutated SUR2A will then be transfected into a stable cell line containing the pore KIR6.2 to form a mitoKATP channel. By staining the transfected cells with MitoTracker dye and a dye attractive to the Lumio tag, one can determine the accuracy of the putative targeting sequence.

Methods/Results In order to determine the accuracy of the putative mitochondrial targeting sequence, one must perform mutagenesis in order to insert the Lumio tag, DNA sequencing to determine the presence of the mutation, cloning to create a complete SUR2A protein, transfection into a cell line with KIR6.2 to form the channel, and immunochemistry to detect the channel and Lumio tag.

Analysis of the data involves DNA sequencing, immunochemical detection, and interpretation of a wild-type mitoKATP channel cell line, an out-of-frame mutated mitoKATP channel cell line and an in-frame mutated mitoKATP channel cell line.

Creation of Template from SUR2A The complete SUR2A protein, 4640 base pairs long, resides in the 5.4 kilo base pairs (kb) pcDNA3.0 vector manufactured by Invitrogen. In order to effectively mutate SUR2A, a smaller template of approximately 1 kb was created and named pEM1. The procedure for template creation followed the typical procedure used in laboratory manipulation of DNA. Restriction Enzyme Digest SUR2A full was of SUR2A with Nde digested with enzymes HindIII, ClaI, PstI and ScaI to no avail. Enzyme NdeI (Fischer) effectively cut a 1.3 kb segment from SUR2A full. The desired vector for insertion, pcDNA3.0 was also digested with NdeI. Prior to ligation, the hydroxyl groups must be removed using Rouche’s CIPing process The 1.3 kb segment of SUR2A and pcDNA3.0 were ligated. Using heat-shock transformation, the vector plus DNA, pEM1, entered DH5α cells (Invitrogen). The cells were plated on LB plus ampicillan antibiotic plates and incubated at 37°C overnight. Twenty cell colonies were picked and

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isolated for mini prep. A mini prep (Qiagen) utilizes vacuum filtration through a specific resin to increase the recovery of DNA. Following the mini prep, a check digested was performed on pEM1. The digest ran through gel electrophoresis to ensure the DNA had not bonded to the vector backwards. For further checking, pEM1 was sequenced by density. pEM1-4 had the correct sequence and was, thus, re-transformed. It was plated and cultures grew overnight. Then pEM1-4 was maxi prepped to increase yields to 1mL, following Qiagen’s procedure. By spectroscopy in Eppendorf ’s BioPhotometer, pEM1-4’s concentration was determined to be 5.0_g/mL. The low concentration necessitated a re-transformation. The second spectroscopy concentration result was an acceptable 927_g/mL. A final check sequencing was performed. (NB: The complete sequence of pEM1 in pcDNA3.0 is too long to be provided in this paper, but can be obtained from the Makielski Lab.) Insertion Mutagenesis and DNA Sequencing Following Stratagene’s procedure for QuikChange® Site-Directed Mutagenesis, which utilizes the thermal cycler, the Lumio tag was inserted into exon 5 of pEM1.

Prior to mutagenesis, Nian Qing-Shi designed primers for the mutation and replication off of the human codon usage chart. DNA polymerase incorporated Lumio, with the following code: Gly-Cys-Cys-Pro-Gly-Cys-Cys-Gly or GGC TGC TGC CCC GGC TGC TGC GGC. Immediately following mutagenesis, the mutated pEM1, termed pEM2, was sequenced using the BigDye and CleanSeq protocol. The BigDye protocol involves thermal cycling and the T7 primer. The CleanSeq protocol requires the use of Agencourt’s CleanSeq beads and a magnetic plate.

Though not realized until after the purification process, the initial mutagenesis of pEM1 to form pEM2 failed to insert the Lumio tag at the correct site in exon 5 of SUR2A. The primers used in mutagenesis were to dictate Lumio insertion at position 750 but resulted in Lumio insertion at 751. The following is a schematic to illustrate the misinsertion: CACACTT correct

A

TC incorrect

SUR2A Sequence Lumio Insertion

This created an out-of-frame mutation in pEM2.

An accidental mistake, pEM2 will be used as a secondary control during the immunochemical studies. The mutagenesis and sequencing process was repeated on

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REFERENCES Askimakis, Gregory K. “Research Interests.” The University of Texas Medical Branch: Department of Human Biological Chemistry and Genetics. www.hbcg.utmb.edu 3 November 2004.

Chutkow, William A, et al.

“Alternative splicing of sur2 exon 17 regulates nucleotide sensitivity of the ATP-sensitive potassium channel.” The Journal of Biological Chemistry.

Vol 274: 13656-13665. 7 May 1999.

Gross, Garrett J. “The role of mitochondrial KATP channels in cardioprotection.” Basic Research in Cardiology. Vol 95: 280284. August 2000.

Kono, Yutaka, et al. “The properties of the Kir6.1-6.2 tandem channel coexpressed with SUR2A.” European Journal of Physiology. Vol 440: 692698. 5 July 2000 on-line.

Loubani, Mahmoud and Manuel Galinanes.

“Pharmacological and ischemic preconditioning of the human myocardium: mitoKATP channels are upstream and p38MAPK is downstream of PKC.” BMC Physiology: 2002. 3 November 2004. www.biomedcentral.com Sato, Toshiaki and Eduardo Marban. “The role of mitochondrial KATP channels in cardioprotection.” Basic Research in Cardiology. Vol 95: 285-9.

August 2000.

Schulz, Rainer. “Ischemic Preconditioning.” Basic Research in Cardiology. Vol 95: 271. August 2000.

Shi, Nian-Qing, Bin Ye and Jonathon C. Makielski.

“Function and distribution of the SUR isoforms and splice variants.”

Unpublished work.

Department of Medicine: University of WisconsinMadison.

pEM1 to create the correctly-inserted or in-frame mutated pEM3. The partial results of sequencing pEM2 (underlined) and pEM3 (italicized) by density with Lumio tag highlighted are as follows:

Transfection

ATGAACACACTTAGGCTGCTGCCCCGGCTGC TGCGGCTCATAT CAAAGCTACTTACTGGTGGATGAACACACTT GGCTGCTGCCCCGGCTGCTGCGGCATCAT ATCAGCTCACAGGAAACCTA Increasing Yields with Mini and Maxi Prep pEM2 and pEM3 were transformed, using the heatshock procedure, into XL1-Blue Supercompetent (Invitrogen) cells, plated on LB and ampicillan plates and incubated at 37°C overnight. Free colonies were picked and purified in preparation for a mini prep.

Following the mini prep, the pEM2 and pEM3 were sequenced to determine which sub-labeled tube contains the correctly-mutated DNA. The DNA from that sublabeled tube will then be re-transformed. The retransformation and growing and picking of cell colonies increases the yield of the DNA. Finally pEM2 and pEM3 were maxi prepped, resulting in 1mL of DNA. Cloning Out-of-frame and In-frame into SUR2A Full The out-of-frame control and the in-frame experimental must be exchanged into the complete SUR2A sequence. pEM1 that did not go through mutagenesis must also be ligated back into SUR2A full. The procedure is proposed but not performed for this step.

First pEM1, the out-of-frame mutated plasmid and the in-frame mutated plasmid (for ease, referred to solely as pEM1) are cut with NdeI to remove the fragment with the Lumio mutation. SUR2A in pcDNA3.0 vector (called pSUR39F) is also cut with NdeI, gel purified and CIPed. The mutated fragment and pSUR39F are ligated together. Through heat shock transformation the DNA enters bacterial cells. Cell colonies are grown overnight and picked. Then the complete pEM1 is mini-prepped for sequencing. A culture is grown of the clone with the correct sequence. Then the clone is maxi-prepped and sequenced a final time.

Discussion/Conclusion This project accomplished the creation of an easilymutated template, pEM1, consisting of the first exons in SUR2A regulatory subunit. The template may be used for repeat trials of this project or further study regarding the first exons of SUR2A and their importance to the conformation of the mitoKATP channel and the channel’s role in ischemic preconditioning. A mutation of the template with Lumio Tag was also successfully created. At first the

primers to integrate Lumio into pEM1 worked incorrectly, creating an out-of-frame mutation as a secondary control. Reanalysis of the human codon usage chart provided accurate primers for a corrected integration of Lumio. Currently pEM1/wild type, pEM2/out-of-frame and pEM3/in-frame are subcloned into the complete SUR2A ready for further studies.

In order to definitively prove the targeting sequence to the mitochondria in SUR2A, the wild type, out-offrame and in-frame must be transfected into a stable cell line and imaged with a con-focal microscope. Research in the near future will focus on accomplishing these tasks. The African Green Monkey kidney cell line, which already contains the KIR6.2 pore, will be used.

Proposed Results for Transfection and Immunochemistry Studies

Transfection provides for the creation of a KATP channel on the mitochondrial membrane. The wild type, out-offrame and in-frame cell lines will next be treated with the reagent for Lumio. The reagent will bind to the recognition sequence for Lumio and provide green fluorescence. The cell lines will also be stained with MitoTracker, a red dye that stains only the mitochondria. If the image under the con-focal microscope is red or green, then the mutated SUR2A is not present in the mitochondria. This is the image that would prove the putative targeting signal. If the image appears yellow, then mutated SUR2A is present in the mitochondria, and researchers must start analyzing sequences again.

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DoME: Digitally Probing Proteins

by Henry Duwe III Several different forces are responsible for a protein binding with another macromolecule (e.g., another protein). Research has focused on the forces occurring between the individual particles of a protein complex, including hydrogen bonding, electrostatic forces, van der Waals forces, solvent related forces, and steric repulsive forces. The goal of the Docking Mesh Evaluator, or DoME, is to create a better computational method for predicting protein-protein interactions. This particular research project has included gaining familiarity with the DoME software, learning the basics of the C programming language, writing several subfunctions in C (to sort data and manipulate matrices), and preparing sets of data structures for use in DoME. While this research project has been completed, the DoME project will next focus on running .pdb format protein structures through a modified solvent accessible surface area program to optimize parameters.

Another program using iterative least squares will approximate the solvent accessible surface area calculation. Then work will proceed on deriving atomic solvation parameters.

Introduction

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ith the documentation of protein structures, the prediction of proteinprotein interactions has become a central focus of computational chemistry. (Mandell, et al.) Numerous and varied computer programs have been designed to predict protein-protein interactions. A wide range of success has resulted although none of these programs has been “perfected.” While DoME attempts to predict conformations of protein-protein complexes near the actual ‘solution,’ its true purpose is investigating the various definitions of energy terms it uses. This particular research project will aid in the improvement and development of the DoME program.

Several different forces are responsible for a protein binding (i.e., docking) with other macromolecules, such as other proteins. Electrostatic forces are a result of the electrical charges on the atoms in matter, due to either charged atoms or dipoles. A charge-charge interaction can occur over a greater distance than either a chargedipole or dipole-dipole interaction. (Kaapro, et al.)

An electric field can induce a normally neutral atom into a dipole. Van der Waals interactions occur when two such induced dipoles in very close proximity interact. (Kaapro, et al.) The properties of the solvent in which a protein complex exists also must be taken into consideration when determining how proteins dock with other macromolecules, because the solvent interacts with the complex. (Butzlaff ) Water is often the solvent and, due to its polarity, can form hydrogen bonds with the complex, affecting its structure. (Kaapro, et al.)

When the interacting proteins are in water, the water in the binding sites must be displaced to allow docking.

This takes some amount of energy, most notably around charged or polar atoms. This force is opposed by the hydrophobic effect, which occurs when the highly organized water molecules around the non-polar portion of a protein’s surface are displaced. Atoms cannot occupy the same physical space, so steric repulsive forces also must be considered. (Mitchell) These are the forces that affect and ultimately cause the binding of a protein complex.

The quantum mechanical Schrödinger equation theoretically could be used to predict protein interactions; however, practically, this does not return an explicit answer and computationally is too expensive.

Thus research has focused on the forces occurring between the individual particles of a protein complex.

(Kaapro, et al.) When attempting to predict the configuration of a protein-protein complex, one must consider as many of these forces as possible. (Butzlaff ) Shape complementarity, also known as geometric fit, maps the surfaces of the interacting proteins to find favorable binding sites, which have opposite surface features, such as protrusion to crevice. This is a relatively simple method and does not account for flexibility, thus it does not work well for unknown protein complexes. (Smith, et al.) Geometric fit is most useful for refining results from other search methods. (Law, et al.) An exhaustive search method samples the configuration space of interacting proteins in order to find low-energy regions.

This works best on rigid protein docking, returning only ranked lists of possible configurations. Thus postprocessing often has to be performed on the results from an exhaustive search. One method entails the grouping of similar low-energy configurations into clusters based on their binding geometry. This is called cluster analysis.

Knowledge-based potentials, another way of postprocessing, compares known binding sites to potential binding sites to rate the plausibility of interaction. This only can be done if there are known binding sites of other complexes with similar characteristics to those of the predicted complex. Using global optimization of energy functions involves searching for the protein complex that has the lowest energy (theoretically most stable). There are many local minima or stable states at specific sites of interaction. Not only does this cause global optimization to be computationally intensive, but also the results gained often do not agree with the biologically correct answer. It is important to note that all the previously described methods use rigid protein structures to predict protein interactions. In reality, proteins are dynamic, flexible molecules that allow for ‘induced’ binding. Basing these methods on flexible protein structures would be impractical due to the high computational costs involved in evaluating all the possible configurations of a protein. The rigid structures

Henry Duwe III Henry began attending the University of Wisconsin-Madison in fall 2005, and hopes to major in biochemistry and computer sciences.

Henry’s project mentors were Prof. Julie Mitchell and Erick Butzlaff.

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can be aligned relative to each other with six degrees of freedom (three translational and three rotational).

Results have shown that no single one of these methods works in all situations. Thus hybrid methods have been developed. Hybrid methods combine complementary methods (i.e., use different methods whose individual advantages make up for the individual disadvantages of each other) to achieve more accurate results. (Mitchell) The Critical Assessment of PRedicted Interaction, or CAPRI, used a number of methods to obtain positive results. CAPRI first used DOT (Daughter of TURNIP) to estimate interaction energies. (Law, et al.) DOT’s energy function is the sum of Poisson-Boltzmann electrostatic energy (electrostatic energy in an ionic solution) and van der Waals energy. (Mandell, et al. and Fogolari, et al.) Then, the most favorable results from DOT (the top 1,500 to 100,000 results) were postprocessed using geometric fit, cluster analysis, proximity filters, and visual examination. The results showed that improvement in the fine-tuning of parameters like distance measure and proximity analysis was needed to reduce the abundance of “false-positive solutions.”

(Law, et al.)

Docking Mesh Evaluator, or DoME, attempts to improve upon previous methods, utilizing a modified Poisson-Boltzmann equation for calculating electrostatic energy:

and a Lennard-Jones 12-6 potential to calculate van der Waals forces:

While DoME was moderately successful in accounting for only these forces, it is projected that by adding a hydrogen bond energy term as well as a solvation energy term, the success of these results will be improved greatly. DoME utilizes an exhaustive search method to analyze rigid protein structures.

Post-processing is done by cluster analysis and global optimization of energy functions, employing a gradientbased optimization algorithm. DoME accounts for hydrogen bonds by evaluating whether or not the specific conditions occur under which hydrogen bonds form. Thus it only considers polar hydrogen atoms that are within 0.45nm of their acceptors and would form hydrogen bond angles of greater than 90º. A desolvation model is currently being added to DoME. This model will compute how a solvent, such as water, interacts with and affects the structure of the protein complex. The model will be computed using the solvent accessible surface area of the complex to find the free energy of the solvent. (Butzlaff et al.) The current research attempts to further optimize parameters for the approximation of the solvent accessible surface area calculation for use in the DoME system.

Report of Research In this research project much time was devoted to learning the C programming language and the general method for the optimization of parameters. A few subfunctions were written that manipulate matrices by adding and multiplying two matrices of variable size.

These functions will be employed in the DoME program for various uses.

Another tasked performed for this research was taking known protein-protein complexes, using their protein data bank structures (stored as .pdb files), as well as the structures of their component parts and putting them together, in preparation for running them through a program to aid in the optimization of parameters. Before these structures could be used, polar hydrogen atoms were added using Swiss PDB Viewer (an application which allows the visualization and manipulation of a .pdb file). It is necessary to include the polar hydrogen atoms for the prediction of hydrogen bonds, while non-polar hydrogen atoms are physically accounted for through slightly increased radii on the molecules with which they are bonded. Next, any errors in this preparation process needed to be corrected. Certain files were declared unusable, while others needed minor fixing.

One such “repair” was pruning out certain atoms of the combining proteins. Occasionally when the two subunit files are combined, have hydrogen atoms added, or are otherwise modified, one atom is duplicated on top of itself, causing an error if it were to be run through the program (no two atoms can occupy the same physical space due to steric repulsion). To locate these errors, a modified version of the solvent area surface area program was used. This testing ensures that all atoms are in their proper locations and positions, attached where they are supposed to be. Various properties of carbon, nitrogen, and oxygen atoms for each of the 20 amino acid side chains were entered as parameters. The information entered was dependent on the atom’s hybridization, which affects the structure of the molecule as Sp3 hybridization has a tetrahedral shape, Sp2 planar, and Sp3 linear.

The next stage in the research involved working with disk files, so more training in the specific application of these was necessary. A new sort subfunction was written to rank the results of the main program. This subfunction uses the bubble sort method to sort strings of numbers. Programs were written using this subfunction that can sort thousands of entries on as many as fifteen data columns in one pass. The original subfunction sorted a column in a disk file. This was later modified to sort many rows based on a column. This subfunction is used to order the output of the model to show the rows most likely to be significant first. It will also be useful in many future output programs.

Two more subfunctions were written to manipulate matrices. One added any two addable matrices, and the other multiplied any two multiplicable matrices. These subfunctions were used to process the .pdb file data

DOME: DIGITALLY PROBING PROTEINS

through the equations. Up to this date, an initial set of parameters has been prepared, and at least one set of proteins has been processed by DoME using these new parameters.

Although this particular research project has concluded, the DoME project is still in progress. Soon it will begin running the protein structures (.pdb format) through another modified solvent accessible surface area (SASA) program, which will aid in the optimization of parameters. From this, the next priority of the research is the further optimization of parameters for the approximation of SASA calculation itself, which uses an iterative version of the least squares method. This will involve a comprehensive search for information pertaining to the proteins that are being used to optimize the parameters in SASA. The derivation of atomic solvation parameters for use in protein-protein docking algorithms, specifically DoME, is necessary to complete the optimization of parameters. After this has been accomplished, a set of parameters for the solvent accessible surface area will be needed.

As this research is only a part of the larger DoME project, it is difficult to assess the success at this time.

The final measure of this research’s success will be determined by the performance of DoME. At this point,

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however, an interim evaluation can be made. The preparation of the files can be considered successful, as over fifty of them were in an acceptable condition to be used by the DoME program. The sort subfunction has been very successful in that it functions as anticipated and reduces the time it takes to find the most energetically favorable locations. As a whole, DoME is able to render results with low energies close to the biologically correct solution. However, the sampling function that creates the original “seed points” (Butzlaff, et al.) is not as effective as it needs to be, which drastically reduces DoME’s accuracy.

Conclusion DoME attempts to account for some of the flexibility of proteins involved in docking by factoring in electrostatic energy and van der Waals forces. The addition of a desolvation model is in progress. This particular research project involved C programming training and usage, the writing of some subfunctions that sort data and manipulate matrices, enhancing protein data files, and data entry. The DoME project is still in progress.

It is further optimizing its parameters, with a focus on better calculating SASA.

REFERENCES Butzlaff, Erick A., et al.

“Improving the Potential Energy Function of the Docking Mesh Evaluator.” (poster) Fernández-Recio, Juan, Maxim Totrov, and Ruben Abagyan. “Soft proteinprotein docking in internal coordinates.” Protein Science. 11 (2002): 280291.

Fogolari, Federico, Pierfrancesco Zuccato, Gennaro Esposito, and Paolo Viglino.

“Biomolecular Electrostatics with the Linearized PoissonBoltzmann Equation.”

Biophysical Journal. 76 (1999): 1-16.

Kaapro, Aatu, and Janne Ojanen. “Protein Docking.”

November 27, 2002.

Law, Dennis S., et al.

“Finding Needles in Haystacks: Reranking DOT Results by Using Shape Complementarity, Cluster Analysis, and Biological Information.” Proteins: Structure, Function, and Genetics. 52 (2003): 33-40.

Mandell, Jeffrey G., et al.

“Protein Docking Using Continuum Electrostatics and Geometric Fit.” Protein Engineering. 14.2 (2001): 105-13.

Mitchell, Julie C. “Protein-Protein Interactions: Prediction.”

Nature Encyclopedia of Science. Article reference code: A4107.

Smith, Graham R., and Michael J. E. Sternberg.

“Prediction of ProteinProtein Interactions by Docking Methods.”

Current Opinion in Structural Biology. 12 (2002): 28-35.

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Effects of Rain Gardens on Soil

by Chelsey D’Alessandro, Jessica Sanderson and Tae Hoon Kim How do rain gardens affect our environment? This question can be answered through an examination of organic, pH, and soil-dispersion tests. Rain garden data will be compared with data from other urban soil samples tested.

Chelsey D’Alessandro

I Jessica Sanderson

t is theorized that the organic content of rain gardens is elevated compared to urban lawn soil, as well as being more porous due to larger particles sizes. Over the past four months, samples have been obtained from various testing sites and analyzed. The three procedures done to analyze the soil were as follows: loss on ignition (LOI), pH testing, and particle size distribution. The eight locations from which the samples were obtained included the front yard of Edgewood High School, the established rain garden located near the back of the Edgewood campus, the newly grown rain gardens in front of the Mazzuchelli Science Center, Lake Wingra shoreline, Henry Vilas

Park, an urban lawn (14 St. Lawrence Cr.), a prairie (located near Old Sauk Road), and the marsh next to Door Creek. The samples were taken in November and April using a core sampler; each sample contained about five core samples.

A rain garden is a bio-retention pond that is built in a shallow depression on your property and is designed to capture and treat run-off (Green Venture). Rain gardens consist of native water-loving plants, which help to aerate the soil. The roots extend into the soil, and absorb most of the nutrients and organic material present in the soil. This helps to loosen particles in the soil, allowing better infiltration of water into the ground. It is hoped

Table1: Samples taken April 4, 2005 (Trial 1) Tae Hoon Kim Chelsey D’Alessandro, a 2006 graduate of Edgewood High School, is planning to major in pre-veterinary medicine.

Throughout her research, she and her group have had two mentors: Jim Lorman and Dan Olson of the Edgewood College Natural Science Department.

Jessica Sanderson is currently a senior at Edgewood High School.

Her interests include a future in engineering following graduation in 2006.

Tae Hoon Kim is currently attending Edgewood High School as a senior. He is planning to major in architecture after graduation in 2006.

Edgewood front yard

Old rain garden

New rain Chelsey’s Chelsey’s Lake garden front yard back yard front yard

Weight of crucible + sample

18g

19.5g

19.5g

20.5g

21.0g

Weight of crucible

10g

10g

11g

10g

Weight of Crucible + Sediment 500

17.5g

18.5g

18.0g

Weight of Sediment (A-B)

8.0g

9.5g

Percentage of water loss

6.3%

10.6%

Park

Marsh

18.5g

20.0g

23.0g

10g

9.5g

9.5g

10.5g

19.5g

20.5g

18g

19g

22.5g

8.5g

10.5g

11g

9.0g

10.5g

12.5g

21.2%

9.5%

4.6%

5.5%

9.5%

4.0%

Park

Marsh

Table 1: Samples taken April 4, 2005 (Trial 2) Edgewood front yard

Old rain garden

New rain Chelsey’s Chelsey’s Lake garden front yard back yard front yard

Weight of crucible + sample

17.0g

17.0g

19.5g

24.0g

19.0g

26.0g

23.0g

21.5g

Weight of crucible

9.0g

9.5g

11g

10g

10g

9.0g

9.5g

9.5g

Weight of Crucible + Sediment 500

17.5g

18.5g

18.0g

19.5g

20.5g

25g

22.5g

21.0g

Weight of Sediment (A-B)

8.0g

9.5g

8.5g

10.5g

11g

17.0g

12.5g

11.5g

Percentage of water loss

7.1%

5.3%

18.5%

4.8%

5.2%

5.9%

4.0%

4.3%

EFFECTS OF RAIN GARDENS ON SOIL

13

that the LOI testing will prove this by revealing that the organic content is higher in places that have rain gardens.

The samples were placed in separate 500 ml beakers, and placed in the drying oven to remove moisture.

These dried samples were used for each experiment. The procedure for the LOI tests is as follows. First, the samples were placed onto a sheet of waxed paper and separated into four sections. One section was then divided in half and one of the halves placed into a 50 ml beaker. This was to ensure a “random” sub-sample. Small amounts of sub-sample, about 20 grams each, were measured into a crucible and weighed. The crucibles were placed into a crucible tray and placed in the muffle furnace for 24 hours. After this period, the crucibles were weighed again and subtracted from the original weight of the sediment before. The resulting number was the weight of organic material lost. Two sub-samples from each sample were tested and an average percent was found.

The procedure for the pH tests is as follows. Ten grams of each sample were placed into a 50 ml beaker and then 10 ml of distilled water were added. Samples were stirred until the sediment was fully dispersed throughout the water. The pH electrode was then placed in the solution, after being stabilized with the buffer solution. The reading was then recorded. This was done for all eight samples.

The procedure for the particle size distribution tests is as follows. Subsamples of 100 g each were measured out, and placed into the top screen of the sieve set. The sieve set was then locked down onto the rotary shaker and turned on for 10 minutes. After the 10 minutes elapsed, the material in each of the separate sieves was poured onto waxed paper and a soft brush was used to loosen pieces of sediment that got caught in the screens.

The sediment was weighed and recorded. This procedure was done for each of the eight samples.

Table 3: pH Testing, May 2005

Particle size

Original weight of sediment: 41.5g

2.00mm 1.00mm .50mm .25mm .125mm .063mm .045mm <.045mm Total

0.1g 10.5g 7.0g 7.5g 8.5g 7.5g 2.0g 1.0g 41.1g

Particle size

Original weight of sediment: 117g

2.00mm 1.00mm .50mm .25mm .125mm .063mm .045mm <.045mm Total

11.5g 36.0g 29.0g 18.5g 9.5g 4.0g 3.0g 1.0g 112g

Particle size

Original weight of sediment: 112g

2.00mm 1.00mm .50mm .25mm .125mm .063mm .045mm <.045mm Total

Location

pH level

Edgewood front yard

6.04

Old rain garden

8.18

New rain garden

7.03

Particle size

Chelsey’s front yard

8.48

Chelsey’s back yard (prairie)

7.88

Lake front yard

6.87

Park

4.98

Marsh

6.79

2.00mm 1.00mm .50mm .25mm .125mm .063mm .045mm <.045mm Total

60.0g 19.5g 15.5g 7.5g 4.5g 2.0g 1.5g 0.5g 111.0g Original weight of sediment: 115.5 g 20g 21.0g 16.5g 13.0g 12.0g 19.0g 10.5g 3.5g 115.5g

Percentage of each particle layer

Table 4: Edgewood front yard particle size distribution test results

0.2% 25.5% 17.0% 18.2% 20.7% 18.2% 4.9% 2.4%

Percentage of each particle layer

Table 5: Chelsey’s back yard (prairie) particle size distribution test results

10.3% 32.1% 25.9% 16.5% 8.5% 3.6% 2.7% 0.9%

Percentage of each particle layer

Table 6: Park particle size distribution test results

54.1% 17.6% 13.9% 6.8% 4.1% 1.8% 1.4% 0.5%

Percentage of each particle layer 17.3% 18.2% 14.3% 11.3% 10.4% 16.5% 9.1% 3.0%

Table 7: Old rain garden particle size distribution test results

EDGEWOOD HIGH SCHOOL STUDENT SCIENCE JOURNAL 2005-06

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Table 8: Chelseyâ&#x20AC;&#x2122;s front yard particle size distribution test results

Table 9: New rain garden particle size distribution test results

Particle size

Original weight of sediment: 115.5 g

2.00mm 1.00mm .50mm .25mm .125mm .063mm .045mm <.045mm Total

31.5g 18.0g 10.5g 15.5g 12.5g 12.5g 5.5g 8g 114g

Particle size

Original weight of sediment: 124g

2.00mm 1.00mm .50mm .25mm .125mm .063mm .045mm <.045mm Total

23.0g 16.5g 13.5g 16.0g 13.5g 7.0g 5.0g 19.5g 114.0g

Percentage of each particle layer 27.3% 15.6% 12.6% 13.4% 12.6% 10.8% 4.8% 8.7%

Percentage of each particle layer 20.2% 14.5% 11.8% 14.0% 11.8% 6.1% 4.4% 17.1%

The LOI testing results have shown that the soils from the new and old rain gardens was about 10 to 20 percent as compared to other soils where the organic content was below 10 percent. This clearly proves that the old and new rain gardens percolate water more efficiently than urban soils. For example, Henry Vilas Park had a cumulative of 6.75 percent organic matter as compared to the new rain garden, which had a cumulative of 19.85 percent organic matter. This clearly shows how rain gardens help to benefit the quality of the soil.

The next test was the pH test, which helped to evaluate the acidity of the soil. Results showed that soils from the new rain garden and the old rain garden were above the standard pH level, which indicated that the soils were not acidic, resulting in an above standard for soil pH. For example, the old rain garden had a result of 8.18 as compared to the Edgewood High School front lawn, which had a result of 6.04.

Finally, the last test that was conducted was the particle size distribution. These results were also clearly distinguishable. The soils from the rain gardens had more portions of larger particles, which help to filter the water through the soil. For example, the new rain garden had 23 percent, or â&#x20AC;&#x201C;1.0 phi, which consisted mainly of granules. This can be compared to the Edgewood High School front lawn, which was 0.2 percent or â&#x20AC;&#x201C;1.0 phi, consisting mainly of medium and fine sand particles.

It is theorized that the organic content of rain gardens is elevated compared to urban lawn soil, as well as being more porous due to larger particles sizes. This hypothesis clearly fulfilled the testing that had been conducted over the course of this experiment. As a result, the importances of the rain gardens are clear, because of their benefits to the quality of the soil and protection of people from flood zones (DNR).

Although this experiment proves that rain gardens do have benefits, there may have been possible errors in terms of accuracy due to lack of time and facilities. More samples and equipment could have been obtained.

Instead of conducting the particle size distribution tests, the soil dispersion tests could have been conducted with more time. Other possible miscalculations could have occurred by other organic contents within the soil. These tests also could have been more accurate if more trials were done upon each sub-sample.

As cities and suburbs grow, they replace forests and agricultural land and increased storm water runoff from impervious surfaces becomes a problem (DNR). It is strongly encouraged that homeowners or cities build rain gardens to help with these problems. Not only does this benefit the environment, but also humans. More research and interest should be put into this subject, and more information brought to the public attention.

15

Testing Water In Search of Bacterial Pathogens and E-coli

by Alexandria Hall In Lake Vilas, a search for bacteria and E-coli was performed on four different days, concluding in four different results. The first day it was cold, and snow and ice were present. The second day was also cold and there were geese present. The third day was a very nice day with the sun shining and a great deal of geese present. The fourth day was raining and the lake sample was thought to have been diluted.

Each day had a different weather condition, which affected the number of bacteria and E-coli present. Another factor of the numbers of bacteria and E-coli present was the geese and the droppings that they left. To find the bacteria and E-coli present, the Quanti-Tray Method was used and a MPN Table was used to find the most probable number present in a given sample. To begin each test, water was placed in one-hundred milliliter containers. A reagent was added and shaken until it dissolved. After the reagent dissolved, the water was then placed into a Quanti-Tray where it was sealed by a Quanti-Tray sealer. The Quanti-Trays were placed in an incubator at a temperature of 35º C and kept for twenty-four hours.

O

nly seven percent of Wisconsin’s bodies of water are considered exceptional or outstanding and are free of pollution.

The most common pollutants of streams and lakes are phosphorus nutrients, nitrogen nutrients, sediments, and fecal bacteria. The Environmental Protection Agency (EPA) requires the water to be tested for bacteria and E-coli. The EPA set up quality assurance programs to create maximum time limits at which water samples can be held. The time was set at 30 hours. At the Wisconsin State Laboratory of Hygiene, researchers have been able to prove that water can stand 48 hours, 18 hours more than the 30-hour holding time in the past. The time was set so the bacteria would not die off before testing. With the increase in time allowed, researchers are able to receive water samples from places farther out and still test them within the 48-hour time limit. If the sample exceeds the time limit, it cannot be tested for an accurate bacteria count. There is no set holding time created for E-coli because it can die off before testing and could be considered safe when it is not (Sonzogni).

Escherichia coli, also known as E-coli, is a bacteria that normally lives in the intestines of people and animals. E-coli can cause inflammation of the stomach and bowels and is extremely harmful to the intestines.

There are five classes of the intestinal E-coli, also known as Enterovirulent E-coli, or ECC. The five classes consist of Enteroinvasive E-coli, which invades the intestinal wall and causes severe diarrhea; Enterohemorrhagic E-coli, which causes bloody diarrhea; Enterotoxigenic E-coli, which produces toxins that attack the intestinal lining; Enteropathogenic E-coli, which causes diarrhea in newborns and can lead to outbreaks; and Enteroaggregative E-coli, which can cause long lasting diarrhea.

Ways to prevent E-coli contamination include cooking all ground beef thoroughly, drinking pasteurized milk, drinking municipal water, not swallowing lake water while swimming, and washing hands after going to the bathroom (CSI).

Bacteria cannot be detected by sight, smell, or taste (Skipton). It must be sent to laboratories to be tested for the presence of bacterial pathogens and then further tested for E-coli. The frequency of testing depends on the number of citizens in a municipal area and the number of people using the facility. At the Wisconsin State Laboratory of Hygiene, seventy thousand samples are taken each year. Minor adjustments have been taken in order to fulfill the research requirements, but mainly have remained the same as the Wisconsin State Laboratory of Hygiene criteria. Testing the water at different holding times and temperatures are used to test sensitivity.

Water was collected near the dam or dropout of Lake Vilas. The water was collected at four different times during the year. The dates of collecting were February 2, February 22, March 29, and April 12, 2005. February 2 was a cold winter day with snow and ice along the lake, except for the area around the dam. February 22 was also a cold winter day with snow and ice, although it was not as cold and geese seemed to be living in the area for a short period. March 29 felt like a summer day with the sun shining and a great number of geese around the dam. April 12 was a rainy day and the geese were present.

The Quanti-Tray method was used to test for bacterial pathogens and E-coli in Lake Vilas. Water was collected in pint containers in the morning on the first day of every test session. For the first test, four samples were taken. The leftover water was then split up into two categories, the 0º C temperatures and the 20º C temperatures. After seven hours, a second test was done on those same samples. Two samples from the 0º C temperatures and two samples from the 20º C temperatures were taken. The pattern continued similar to the second sample for the third sample (after 24 hours), fourth sample (after 32 hours), and fifth sample (after 48 hours). For every test date, the same overall pattern was used to test the water for bacterial pathogens and E-coli (Allen Degnan).

Alexandria Hall Alexandria Leigh Hall graduated from Edgewood High School in May of 2005. She is currently attending Carroll College in Waukesha and hopes to pursue a degree in physical therapy. In her Advanced Placement Environmental Science Research class, she did research on testing water in search of bacteria and E-coli in Lake Vilas with the help of her mentor, Allen Degnan, who works at the State Laboratory of Hygiene. Mr. Degnan helped by providing supplies for the research and information about bacteria and E-coli in our waters today.

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Graph 1a: The number of bacteria present in water samples on February 2, 2005 Graph 1b: The number of E-coli present in water samples on February 2, 2005

Graph 2a: The number of bacteria present in water samples on February 22, 2005 Graph 2b: The number of E-coli present in water samples on February 22, 2005

To begin each test, water was placed in 100 milliliter containers. A reagent, Colilert, used to determine the number of bacteria and E-coli present, was added and shaken until dissolved. After the reagent was dissolved, the water was then placed into a Quanti-Tray which was sealed by a Quanti-Tray sealer so the water would not escape. The Quanti-Trays were placed in an incubator at a temperature of 35ยบ C and kept for 24 hours. After 24 hours, the trays were removed to see if bacteria were present. If the well was white or clear, the water was considered safe, but if the water was yellow, the water contained bacteria. The lights were then turned off to see if any E-coli were present by using a black light. If the wells did not change color, there were no E-coli, but if the wells were the color of a blue florescence, E-coli was present. The number of bacteria and E-coli present were recorded and coverted to a number that represented the levels of bacteria and E-coli present in a given sample. The numbers were taken by using a MPN (Most Probable Number) Table, which takes into consideration the number of large wells and the number of small wells. After locating the number of small wells and sliding down the list to the number that matches the number of large wells, the number at the intersection was recorded as the number of bacteria or E-coli present in a given sample (Allen Degnan).

The first test, taken on February 2, 2005, was a cold winter day with snow and ice still on the ground. There were geese present, though their numbers were minimal.

Bacteria counts were not extremely high and there were very limited numbers of E-coli present. Graphs 1a and 1b show the number of bacteria and E-coli that were present throughout the entire test. E-coli had a total of three present throughout the test. For the first test, there was no hour 48, which is thought to affect the graph little or not at all because the number of bacteria and E-coli was too low at this point to make a difference.

The second test, taken February 22, 2005, (Graphs 2a and 2b) was also a cold winter day and compared to the first test, the number of geese present was much higher. The presence of the geese is thought to have affected the number of bacteria and E-coli because of the bacteria and E-coli present in goose droppings.

Therefore, the numbers in test two were a great deal higher than test one. In the test for bacteria, hour 31, sample three, the number was 1,986, which was extremely high for the entire test. Therefore the number was dropped and replaced with an asterisk. The same problem was found in hour 31, sample four, in which the number of E-coli present was 179, which was also replaced with an asterisk. The asterisk was placed at these points because when a count is dramatically different from the rest of the results, it is thought to be a false number that only affects the graph at its time interval. Without the abberant numbers included, it is easier to see the differences between the other given hours. At these points the graph is at zero which makes the graph smaller in size and clearer to read.

The third test, taken March 20, 2005, (Graphs 3a and 3b) was a warm summer-like day with the sun shining bright and the number of geese present was extremely high in comparison with the first and second tests. The number of bacteria and E-coli were higher than the second test, and at the end had a noticeable change in numbers due to the different water holding temperatures. The number of bacteria present dropped off towards the end in the warmer temperatures.

In warmer temperatures, bacteria are easily killed off because of the lack of food able to grow. In colder temperatures, bacteria are able to grab onto food in the water because it strives to stay alive when the water is cold. When the water is warm, the bacteria are unable to grab onto food available because there is none present.

Therefore when the water temperatures are colder the number of bacteria is expected to be higher than when the water is warmer (Allen Degnan).

The fourth test, taken April 12, 2005, (Graphs 4a and 4b) was a rainy day that was not very warm, and the geese were still present, but in limited numbers. The water could quite possibly have been diluted because of the rain. The number of geese on the lake and the bacteria and E-coli found could have caused the results to be lower than the third test.

The same pattern occurred with the fourth test that happened with the third test. As time passed, the

TESTING WATER IN SEARCH OF BACTERIAL PATHOGENS AND E-COLI

bacteria count and E-coli counts became lower in the warmer temperatures than the counts in the colder temperatures.

The next step was to take the averages of the first sample of each test. The averages of bacteria ranged from 43 to 254, and the averages of E-coli ranged from one to 117. The averages represented the number of bacteria

17

REFERENCES

Graph 3a: The number of bacteria present in water samples on March 29, 2005 Graph 3b: The number of E-coli present in water samples on March 29, 2005

CSI. “Certified Testing: Stream and Lake Water.” www.communityscience .org/teststream.htm.

The Clean Water Act: Thirty Years Later. Know Your Environment and Roland Wall, Environmental Associates, Academy of Natural Sciences.

Oct. 2002.

“Coliform Bacteria Fact Sheet.” Suburban Water Testing Labs, Inc: 19962004. Nov 2004. www.h2otest.com/factsheets /coliform.html.

Degnan, Allen. ESS Micro Method: Total Coliform/Ecoli. Dec. 2004.

Degnan, Allen. Project Abstract. Dec. 2004.

and E-coli present at the time the water was taken from Lake Vilas. The weather and presence of geese were factors that led to the great variance between each test day.

In conclusion, the important factors that cause the variety of numbers of bacteria and E-coli are the weather and the presence of geese. When the day was summerlike and the number of geese was high, the bacteria and E-coli were at their highest levels as well. During the first test, there were hardly any geese present and there was still ice on the lake, which resulted in low numbers. On the fourth test, the rain could have diluted the water, resulting in lower numbers of bacteria and E-coli even though there were still geese on the lake.

It was interesting to learn how researchers find bacteria and E-coli in water by using a reagent, an incubator and a black light, and how researchers used their findings to help the Environmental Protection Agency determine set holding times for bacteria. Overall, the project was a fun and exciting learning experience.

Graph 4a: The number of bacteria present in water samples on April 12, 2005 Graph 4b: The number of E-coli present in water samples on April 12, 2005

“Escherichia coli O157:H7.” Centers for Disease Control and Prevention. 27 Jan. 2004. www.cdc.gov/ncidod/ dbmd/diseaseinfo/ escherichiacoli_g.htm# What is Escherichia coli O157:H7.

Meine, Curt, Ph.D., ed.

Wisconsin’s Water: A Confluence of Perspectives.

Vol. 90. Madison, Wisconsin: Wisconsin Academy of Science, Arts and Letters, 2003.

Rose, Joan B., Paul R Epstein, Erin K. Lipp, Benjamin H. Sherman, Susan M. Bernard, and Jonathon A. Patz. “Climate Variability and Change in the United States: Potential Impacts on Water- and Foodborne Diseases Caused

by Microbiologic Agents.”

Environmental Health Perspectives. May 2001: V 109. EBSCO, Oct 2004.

Skipton, Sharon, Extension Educator. Drinking Water: Bacteria. Cooperative Extension, Jan 1999 (electronic version). Oct 2004. http://ianrpubs.unl .edu/water/g989.htm# sources.

WASAL. 2003. Water of Wisconsin: The Future of Our Aquatic Ecosystems and Resources. Madison, Wisconsin: Wisconsin Academy of Science, Arts and Letters.

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Environmental Risk Factors for Asthma in High School Students

by Melanie Meyer and Matthew Belopavlovich Melanie Meyer

Matthew Belopavlovich

An asthma questionnaire was given to students at an urban (Edgewood High School, Madison, WI) and rural (Riverdale High School, Muscoda, WI) high school to determine the prevalence of asthma and factors that may increase or decrease the risk of having asthma. The responses were entered into a database, and statistical analysis was performed to generate odds ratios and identify significant risk factors for asthma. The prevalence of asthma for both student groups was 20 percent. The presence of other allergic disorders (allergic rhinitis or eczema) significantly increased the risk of having asthma. Other significant risks included having family members with asthma, ever having smoked cigarettes, and living in a building that housed two or more families. Exposure to a farm environment when less than 5 years of age or to a pet animal had odds ratios that were less than one but did not have a significant protective effect against having asthma. The results showed a high prevalence of asthma in the study population but did not identify a protective effect of living on a farm (the “hygiene hypothesis”) or exposure to pet animals as young children.

Introduction xposures to substances in the environment may either cause or prevent lung disease. Asthma is a condition where irritation and inflammation of the bronchial tubes cause them to narrow (Barnes 352). Asthma is a very common problem for children and adults and is the most common lung problem that has been linked to environmental irritants and air pollution. People with asthma, which is considered to be almost always an allergic disease, typically have episodes of wheezing, cough, and shortness of breath. An acute asthma attack can be lifethreatening and lead to hospitalization.

Although a positive family history of allergies and asthma is the strongest risk factor for developing it, many other risk factors (Table 1) have been linked to asthma and probably combine with genetic predisposition to cause the disease (Kaiser 7). These exposures include a positive family history of allergies or asthma, positive skin test reactions to allergens, having allergic rhinitis or eczema, sensitization to animals (dog, cat, other fur-bearing animals, birds), maternal smoking while in utero, exposure to tobacco smoke including second-hand exposure, exposure and sensitization to pollens or molds, having a small family (few siblings), exposure to air pollution (e.g. diesel exhaust and fine particles), obesity, and exposure to wood-burning heating systems. Certain exposures may protect against the development of asthma. These include having episodes of rhinitis, herpes, and measles early in life, early attendance at a day care center, exposure to a farm environment early in life, exposure to livestock and poultry early in life, exposure to pets (cat or dog) early in life, exposure to farm milk early in life, and having multiple siblings.

Identifying environmental risk factors and protective factors is very important for efforts to prevent asthma because the prevalence of asthma seems to be on the rise.

As stated by Ownby et al., “The increasing prevalence of asthma in the United States and other developed countries over the last few decades has been a cause for concern” (Ownby 963). Although exposures to things like pollens, molds, and fur-bearing animals can cause people to develop allergies or asthma, recent studies have

E

suggested that exposure to animals such as cats or dogs or to a farm environment early in life may protect people from developing allergies and asthma (Kabesch and Lauener 383, Adler 67, Ownby 963, Gern 307).

As stated by Kabesch and Lauener, “Growing up on a traditional farm seems to confer protection against the development of asthma and atopy” (Kabesch and Lauener 383). These observations have led to the “hygiene hypothesis,” which suggests that living in a very tidy environment where lack of exposure to things like common childhood infections, endotoxin (a bacterial product), other bacterial products, or antigens commonly found in high amounts in a farm environment may make a person more likely to develop an allergy and/or asthma (Kabesch and Lauener 2004, Kaiser 7, Ownby 963).

Although the increased incidence and prevalence of asthma in urban environments has been linked to higher levels of air pollution (Ring 2), an alternative explanation is that exposures in a rural environment early in life, especially a farm environment, may protect against sensitization to allergens, including common indoor allergens. People raised in an urban environment may be more easily sensitized to common indoor antigens such as dust mites and more likely to develop asthma.

Exposures to pets early in life may have a similar protective effect. As stated by Gern et al., “The consequences of owning pets are of particular interest because exposure to cats and dogs has been reported to either promote or inhibit the risk of subsequent atopy” (Gern 307).

However, living in a rural environment does not seem to be protective in itself (Kaiser 7, Kabesch and Lauener 383). The protection from developing allergies and asthma appears to require contact with farming, especially exposure to farm animals. Scientists suspect that certain exposures early in life can protect from allergies and asthma by affecting the immune system.

Priming of the immune system by certain infections or by intimate exposures to animals may keep it from developing allergies (Kaiser 7). This beneficial effect seems to occur early in life only, and it may not occur if these exposures are limited.

ENVIRONMENTAL RISK FACTORS FOR ASTHMA IN HIGH SCHOOL STUDENTS

The hypothesis of this research was that a questionnaire given to a large sample of high school students could detect environmental risk factors for asthma including some that may protect against the development of asthma. A questionnaire was given to students in Edgewood High School and in a rural high school (Riverdale) to examine various common exposures and living environments, including those experienced early in life, to determine whether any of these can be linked to the presence of asthma or to protection from developing asthma. The goal of the project was to examine the prevalence of allergies, asthma, smoking, and various environmental exposures in a population of high school students using a questionnaire to solicit anonymous responses. These responses were examined for relative risks for asthma.

Riverdale High School was included because of its rural location near Richland Center, Wisconsin, and the high likelihood that many respondents would have been raised on farms and therefore allow the investigators to determine whether the hygiene hypothesis could be validated in the study population.

Thesis By performing statistical analysis on survey results pertaining to asthma and environmental risk factors gathered at one urban and one rural high school, this study showed a link between asthma and the presence of other allergic disorders, family members with asthma, smoking cigarettes, and living in a multi-family dwelling, but it did not support a link between asthma and exposure to farm life or pet animals.

Report of Research The questionnaire (at end of article) was circulated among all the students who were taking biology classes at Riverdale and to homeroom classes at Edgewood HS. A total of 103 surveys were returned from Riverdale and all were complete. A total of 177 responses were returned by Edgewood students, but three were not adequately completed and were discarded. An additional 7 questionnaires were not complete, but most questions were answered, and these questionnaires were considered acceptable for entry into the data set.

The data were entered into an Excel (Microsoft, Inc.) spreadsheet and performed statistical analysis of the data using Excel programs. More sophisticated data analysis was then performed using a statistics program (Epi Stat) that was obtained from the Center for Disease Control website. Odds ratios were constructed for the incidence of asthma for exposed versus non-exposed groups for each risk factor to determine relative risk and whether these factors were associated with asthma in the study population. Logistic regression analysis was performed to determine whether the relative risk values for individual factors were statistically significant. A p value of ≤0.05 was considered significant. The data analysis was guided

by Ms. Lorna Will (State of Wisconsin Department of Public Health epidemiologist) and by Dr. Keith Meyer (Professor of Medicine at the University of Wisconsin).

19

Response

Riverdale

Total number of respondents analyzed

Edgewood Combined

103

174

277

58:45

86:80

144:125

Age

15.6±1.1

16.1±1.4

15.9±1.3

Body mass index Physical condition: High Medium “Couch potato” Number of siblings

22.8±1.1

22.0±3.4

22.3±3.7

Female:Male

Has lived in city larger than Madison

54 (52%) 45 (44%) 4 (4%) 2.3±1.7 17(17%)

The data are shown in Tables 1-4. Demographic data that describes the populations studied are given in Table 1. The two populations were nearly identical in age, body mass index, and in self-perceived physical condition. Twice as many Edgewood students had lived in a city larger than Madison as compared to the Riverdale student population, and the female to male ratio was slightly greater in the Riverdale subjects.

The prevalence of asthma in both the Riverdale and Edgewood study groups was essentially identical (Table 2). The prevalence of asthma was essentially identical for Response

Riverdale (N=103)

60 (34%)

77(28%)

Table 1.

Characteristics of the study groups.

Table 2. Prevalence of asthma, atopy (allergic rhinitis and eczema) allergies, and exposures to animals or a farm environment.

Edgewood Combined (N=174) (N=277)

Asthma diagnosed by health care professional Asthma at <5 years of age* Asthma symptoms currently* Medications used to treat asthma* ER visited for asthma treatment* Hospitalized for treatment of asthma*

20 7 16 16 10 3

Allergic rhinitis

27 (26%)

15 (9%)

42 (15%)

1 (1%)

8 (5%)

9 (3%)

Eczema

(19.4%) (35%) (80%) (80%) (50%) (15%)

88 (53%) 142 (53%) 68 (41%) 113 (42%) 10 (6%) 14 (5%) 1.9±1.3 2.0±1.5

36 13 15 31 10 8

(20.7%) (36%) (42%) (86%) (28%) (22%)

56 (20.2%) 20 (36%) 31 (55%) 47 (84%) 20 (36%) 11 (20%)

Allergies to environmental allergens: Any allergy Cat Dog Pollens Birds

16 (16%) 8 (8%) 6 (6%) 14 (14%) 3 (3%)

47 26 15 40 5

(27%) (15%) (9%) (23%) (3%)

63 (23%) 31 (11%) 21 (8%) 54 (19%) 8 (3%)

Wheeze noticed (no diagnosis of asthma)

25 (24%)

28 (16%)

53 (19%)

Lived on a farm at age <5 yrs

34 (33%)

5 (3%)

39 (14%)

Currently living on farm

23 (22%)

4 (2%)

27 (10%)

Farm exposure at age <5 yrs

49 (48%)

20 (11%)

69 (25%)

Pet in home at age <5 yrs

78 (80%) 100 (57%)

178 (64%)

Family member with asthma

33 (32%)

58 (33%)

91 (33%)

Family member with allergic rhinitis

51 (50%)

30 (17%)

81 (29%)

9 (9%)

12 (7%)

21 (8%)

62 (60%)

76 (44%)

138 (50%)

Family member with eczema Family member with atopy

*Values indicate number of individuals with asthma who have these characteristics

EDGEWOOD HIGH SCHOOL STUDENT SCIENCE JOURNAL 2005-06

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Table 3.

Exposures linked to asthma and other lung diseases

the two high school populations and similar to the prevalence in a recently published study of children in Wisconsin (Adler 67). However, the prevalence of allergic rhinitis in Riverdale students was three times that of the Edgewood students, while Edgewood students had a higher prevalence of eczema. Interestingly, 19 percent of the combined study group had observed wheezing in their chest but had not been given a diagnosis of asthma, suggesting that some students may have undiagnosed asthma. One third of the Riverdale group lived on a farm prior to age 5, but only 3 percent of Edgewood students had lived on a farm.

Response Current smoker Tried smoking Tried smoking (but <100 cigarettes total) Secondhand smoke at age <5 Routine secondhand smoke currently Wood-burning stove used in home Diesel exhaust exposure Family vehicle with diesel engine Mold problem in home Living in building that houses 2+ families

Table 4.

Odds ratios and relative risks for asthma

Riverdale 9 (9%) 29 (28%) 28 (27%) 53 (51%) 48 (47%) 25 (24%) 46 (45%) 26 (25%) 16 (16%) 6 (6%)

Edgewood 3 (2%) 29 (17%) 25 (14%) 32 (18%) 31 (18%) 7 (4%) 27 (16%) 15 (9%) 18 (10%) 11 (6%)

Combined 12 (4%) 58 (21%) 53 (19%) 85 (31%) 79 (29%) 32 (12%) 73 (26%) 41 (15%) 34 (12%) 17 (6%)

Very few students smoked cigarettes, although there were more smokers in the Riverdale group (Table 3).

Nearly twice as many Riverdale students had tried smoking versus Edgewood students, although very few of those that tried smoking in either group had smoked more than a total of 100 cigarettes. Significantly more Riverdale students had been exposed to secondhand smoke as young children or routinely exposed to secondhand smoke at the present time. Similarly, more Riverdale students live in homes heated by burning wood and reported exposure to diesel exhaust fumes.

Table 4 shows various risk factors for asthma. An odds ratio greater than one indicates an increased risk

Risk Factor Family member with asthma Presence of allergic rhinitis Presence of eczema Ever smoked cigarettes Lives in building that houses 2+ families Home heated by burning wood Home has problem with mold Routine exposure to diesel fumes Lived in city larger than Madison Lived on farm at age <5 Current farm exposure Pet animal in home Exposure to any animal at age <5 Physical fitness: exercises often vs. other

Odds Confidence Significant?

Ratio Interval 5.40 2.77-10.61 Yes (p<0.01) 2.41 1.17-4.19 Yes (p=0.01) 5.50 1.43-21.40 Yes (p=0.01) 1.99 1.02-3.89 Yes (p=0.04) 5.25 1.74-15.97 Yes (p=0.001) 1.10 0.93-13.0 No 1.05 0.39-2.74 No 0.57 0.25-1.26 No 1.19 0.059-2.36 No 0.86 0.32-2.19 No 0.85 0.41-1.74 No 0.65 0.26-1.71 No 0.74 0.37-1.47 No 0.76 0.39-1.47 No

for having asthma, while an odds ratio less than one indicates protection against having asthma. However, the confidence interval must not pass through zero if the relative risk indicated by the odds radio is significant, and the p value by regression analysis had to be less than or equal to 0.05 to be considered unlikely to have been due to chance alone. Some factors significantly increased the risk of asthma, while most of the factors examined had no significant risk. Exposure to a farm environment or pet animals did not confer protection from asthma for these study groups. None of the other risk factors that are not shown in the table were significant.

Conclusions The prevalence of asthma was similar to that reported for children in Wisconsin (Adler 67). Having atopic problems (allergic rhinitis or eczema) was a risk factor for also having asthma and demonstrated the association of asthma with allergies. Asthma risk was also increased when other family members had asthma, which indicates the influence of genetic factors and predisposition. Two additional risk factors that the analysis found were that living in a building that has more than one family or having ever smoked cigarettes. Although exposure to secondhand smoke as a child and current routine exposure to secondhand smoke were relatively high, especially for the rural student population, these exposures were not significantly linked to the presence of asthma. The data did not support the hygiene hypothesis, which states that residing on a farm with exposure to a farm environment early in life protects against asthma. However, the sample size may have been too small to detect this as a protective factor against the development of asthma.

This project had a number of flaws. The sample size was relatively small. A larger sample size would have provided a more representative sampling of the two populations and more reliable data. Better wording could have been used in the questions on the questionnaire. The data reported by respondents to the questionnaire may not be entirely accurate, and there is no way to make a definite diagnosis of asthma in the study population without doing objective testing such as lung function testing. However, some interesting trends were seen in these data, and the study did identify allergies and a positive family history of asthma as risk factors for developing asthma, reflecting the role of genetic susceptibility in the development of asthma.

The prevalence of asthma that was found by this study matches the prevalence found in a recent large study of Wisconsin children. However, this pilot study raised the possibility that exposure to a farm environment may not protect against the development of asthma.

ENVIRONMENTAL RISK FACTORS FOR ASTHMA IN HIGH SCHOOL STUDENTS

21

ASTHMA AND THE ENVIRONMENT QUESTIONNAIRE

REFERENCES

Asthma is a chronic lung disorder caused by inflammation of the bronchial tubes in the lungs, and people who have asthma tend to have recurring attacks of symptoms such as wheezing in their chest, coughing, a sensation of chest tightness, and sometimes shortness of breath. Asthma is one of the most common chronic diseases that affect children in the United States and a leading cause of missing school or work, as well as having to be hospitalized.

This anonymous response questionnaire is being distributed by Melanie Meyer and Matt Belopavlovich for their Environmental Science Research Project. We want to conduct an epidemiologic study to determine if asthma can be linked to environmental exposures. Your response to the following questions would be greatly appreciated.

1. Have you ever been told by a doctor, nurse, or other health care professional that you have 1a. Asthma?

Yes No 1b. Allergic Rhinitis?

Yes No 1c. Eczema?

Yes No 2. Has anyone in your immediate family (parents or siblings) been told by a doctor, nurse, or other health care professional that they have 2a. Asthma?

Yes No 2b. Allergic Rhinitis?

Yes No 2c. Eczema?

Yes No 3. If you have been told that you have asthma, 3a. Was it before your 5th birthday?

3b. Do you currently have asthma symptoms?

3c. Have you taken prescription asthma medicines?

3d. Have you had to go to an ER for treatment?

3e. Have you ever been hospitalized for an asthma attack?

3f. Does your asthma only happen when you exercise?

4. If you have not had asthma, 4a. Have you ever noticed wheezing in your chest?

4b. Did you have wheezing before age 5?

Yes Yes

No No

Yes

No

Yes

No

Yes

No

Yes

No

Yes Yes

No No

5. Have you ever had tests that showed you are allergic to: 5a. Cats?

Yes No 5b. Dogs?

Yes No 5c. Birds?

Yes No 5d. Pollens?

Yes No 5f. Other allergies?

Yes No Please list type of allergy: ______________________________________________ 6. Do you smoke cigarettes?

6a. If yes, have you smoked more than 100 cigarettes?

7. If you don’t smoke currently, 7a. Have you ever smoked?

7b. Are you routinely exposed to secondhand cigarette smoke?

7c. Were you exposed to secondhand smoke in the home before age 5?

8. Have you ever lived in a city larger than Madison?

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

9. Home environment: Farm and animal exposures 9a. Did you live on a farm before age 5?

Yes No 9b. Do you currently live on a farm?

Yes No Please list type of animals exposed to before age 5, if known ____________________________ ______________________________________________

10. If you have not lived on a farm, 10a. Did you have frequent exposure (more than once per month) to a farm environment before age 5?

10b. Do you currently have frequent farm environment exposure (more than once per month)?

Yes

No

Yes

No

11. Have you ever had pet animals in your home?

Yes No Please list type of animals __________________________ ______________________________________________ 12. If you have had pet animals, 12a. Did you have them before age 5?

Yes No Please list type of animal (dog, cat, hamster, bird, etc.) ______________________________________________ ______________________________________________ 12b. Did you keep them in the house?

Yes No 12c. Did you keep them in your bedroom?

Yes No 13. Home environment: Inside air 13a. Does your home have carpeting?

13b. Is it a one-family home?

13c. Is it a building for 2 or more families?

13d. Have you had mold or mildew problems in your home?

13e. Is a wood-burning stove used to heat the house?

14. Outdoor environment: 14a. Does your family own a vehicle with a diesel engine?

14b. Are you exposed frequently to diesel exhaust (school bus, your own or a family car, other vehicle more than once per week)

Yes Yes Yes

No No No

Yes

No

Yes

No

Yes

No

Yes

No

15. Physical fitness: Would you describe yourself as (please choose only one) 15a. In good shape and exercising often 15b. In medium shape and exercising occasionally 15c. Basically a “couch potato” 16. What is your 16a. Height in feet and inches?

16b. Weight in pounds?

16c. Gender?

16d. Age in years?

16e. How many brothers and sisters do you have (total)?

Any comments that you wish to make?

_____________ _____________ Female Male _____________ _____________

Adler, A., Tager, I., Quintero, D.R. “Decreased prevalence of asthma among farm-reared children compared with those who are rural but not farmreared.” Journal of Allergy and Clinical Immunology 2005; 115:67-73.

Barnes, P.J. Asthma. In Pulmonary Biology in Health and Disease, Ed. Bittar, E.E. New York, Berlin, and Heidelberg: SpringerVerlag, 2002.

Burney, P.G.J. “Asthma Epidemiology.” British Medical Bulletin 1992; 48:10-22.

Gern, J.E., Reardon, C.L., Hoffjan, S., et al. “Effects of dog ownership and genotype on immune development and atopy in infancy.” Journal of Allergy and Clinical Immunology 2004; 113:307-314.

Kabesch, M., Lauener, R.P. “Why Old McDonald had a farm but no allergies: genes, environments, and the hygiene hypothesis.”

Journal of Leukocyte Biology 2004; 75:383-387.

Kaiser, H.B. “Risk factors in allergy/asthma.” Allergy and Asthma Proceedings 2004; 25:7-10.

Ownby, D.R., Johnson, C.C., Peterson, E.L. “Exposure to dogs and cats in the first year of life and risk of allergic sensitization at 6 to 7 years of age.”

Journal of the American Medical Association 2002; 288:963-972.

Ring J., Eberlein-Koenig, B., Behrendt, H. “Environmental pollution and allergy.” Annals of Allergy Asthma and Immunology 2001; 87 (6 Suppl 3); 2-6.

EDGEWOOD HIGH SCHOOL STUDENT SCIENCE JOURNAL 2005-06

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The Effects of Acid Rain, Manure, and Fertilizer Runoff on Lake Water and Aquatic Life

by Emily Hanson and Nicholas Richardson Emily Hanson This project examines how acid rain, fertilizer and manure in varying amounts changed the pH of lake water. The results were then coupled with results from other experiments on runoff of acid rain, manure and fertilizer. Experiments were then performed with fish to examine how these changes would affect aquatic life.

Nicholas Richardson Emily graduated in May 2005 from Edgewood High School. She is attending the University of Wisconsin-Madison, striving for a degree in medicine, specifically pharmacy.

Nicholas Richardson graduated from Edgewood High School in 2005.

He currently attends Vanderbilt University in Nashville, Tennessee.

Nicholas and Emily’s mentor on this project, Mark Wilson, is the Vice President of Minitube of America, a worldwide research facility.

I

n this research project, the effects of the addition of acid rain, manure and fertilizer to aquatic ecosystems were studied. The hypothesis was that the addition of acid rain to the aquatic environment would have little effect on the water quality or the health of the aquatic species, while the addition of fertilizer and manure would produce some adverse effects.

The results supported this hypothesis, with little change in the pH of water samples after the addition of acid rain, and lack of fatality in aquatic fish when acid rain was added to their tanks. With the addition of manure and fertilizer to soil samples, the osmolarity of the water samples from the run-off changed dramatically.

There was also a high fatality rate observed among the aquatic fish in tanks where fertilizer and manure were added. These results show that the addition of acid rain to aquatic environments is of little importance to the overall health of the ecosystem while other factors such as runoff from farms (such as manure and fertilizer) cause dramatic changes in the health of aquatic ecosystems. This means that to protect aquatic ecosystems a major concern must be controlling run-off from farms and other agricultural industry.

The first set of data obtained in this experiment was the average pH of four different bodies of water in Dane County. These bodies of water were Lake Wingra, Lake Mendota, Lake Monona, and Shoveler’s Sink. The average was calculated using water samples from each of the four sites and a standard pH meter. The osmolarity of each sample was also taken. Osmolarity is a measure of the amount of particles in the water. This is measured using an osmometer which freezes the water sample and measures the particles in the water by determining the water’s exact freezing point.

The pH of each sample was taken three times to ensure accuracy, and then these three numbers were averaged. After this, calculations were done to find a volume for a one-inch and two-inch rainfall. This was done by using a model lake. The model lake had a depth of 10 feet and a surface area of one acre. The volume of this lake was calculated. A volume for both a one-inch

and two-inch rain was also calculated. The ratio of total water volume to rain volume was used to find the amount of acid rain that needed to be added to a 10 mL sample of water to simulate a one-inch and two-inch rain. Using this data, a mixture of distilled water and sulfuric acid was created to mimic real acid rain. The pH for this mixture was measured at two different levels.

The acid mixtures in both jars were created through trial and error, varying the amount of water and acid until the correct pH was reached. The samples were measured several times with a pH meter to ensure accuracy.

Jar 1 was mixed to have a pH of 6.5. It had the higher pH of the two jars and was meant to simulate the type of acid rain that would be expected near big cities or mining operations. Jar 2 was mixed with a pH of 6.9 to simulate the type of acid rain that would be expected in most areas including small cities, farmland, and other areas exposed to normal levels of pollution.

The amount needed for a one-inch rain from both jars was added to the 10 mL water samples, and the pH of each sample was tested three times. The results of this were then averaged. This same experiment was repeated using a 10 mL sample of each of the bodies of water, mixed with what would be equivalent to a two-inch rain from both jars. The pH of this mixture was again measured three times for each sample and the results were averaged. From this data it is possible to see the effect that acid rain has on the pH of bodies of water.

This will show whether or not acid rain has a large effect on water quality by itself. The data that was collected showed that though the addition of acid rain to lake water did change the pH of the water, the change was very slight. It was also noted that the addition of acid rain did not make the water more acidic, but rather, made it more basic. This suggests that nutrients and other components of the lake water are actually neutralizing the acid; this then increases the alkalinity of the water. Using this information, it was hypothesized that the addition of acid rain to lake water on its own would have little effect on the aquatic life in the body of water.

THE EFFECTS OF ACID RAIN AND FERTILIZER RUNOFF ON LAKE AND AQUATIC LIFE

After these results were compiled, a second component to the research was done. This component dealt with the runoff of manure and fertilizer. Six pans were filled with different types of soil. Two pans were filled with sand, two were filled with black dirt, and two were filled with squares of sod. Holes were poked in the bottom of one end of each pan and the pans were placed on an incline so water would drain out of them. Empty pans were placed underneath each pan to catch the drainage. First, plain water was poured over the top of the contents of each pan and allowed to drain through for twenty-four hours. The water that drained into the empty pans was collected in jars and labeled. Next, the acid rain mixture was poured through the pans. The runoff was collected and labeled. This same experiment was repeated with the addition of fertilizer to the water poured through the samples. The drainage was again collected and put in jars. The water runoff was repeated with manure added to the pans. When all the samples had been collected and labeled, the samples were run through an osmometer to see if the addition of acid rain, manure, or fertilizer changed the amount of debris in the water. This set of results led to the last component of this experiment.

In the last part of this experiment, fish were again used. Four different tanks were set up. Each tank contained three fish. Two tanks were filled with lake water, while the other two tanks were filled with distilled water. This time 0.5 gram and 1.0 gram of fertilizer was added to each tank, with the exception of the control group, and the fish were monitored for a period of 24 hours. Some fish died with the addition of fertilizer to the tank, but the deaths were few and only in the tanks of lake water. This was done again with the addition of manure to the tanks. The fish were again monitored for a period of 24 hours and the results were recorded. The addition of the manure to the water caused some death as well, but again no deaths were seen in the distilled water tank. After this was done, the experiment was run again, by adding 1.0 gram of fertilizer to the fish tanks.

The fish were monitored for a 24-hour period. An increased number of deaths were seen among all the fish, Mendota

Wingra

Monona

Shovelerâ&#x20AC;&#x2122;s Sink

Distilled Water

Jar 1 (pH 6.5)

Water Source

Table 1.

Change in pH with addition of simulated acid rain

1 inch

+0.1

+0.5

+0.14

+0.14

-1.36

2 inch

+0.07

+0.2

+0.13

+0.29

-1.38

Jar 2 (pH 6.9)

The next stage of this experiment involved the addition of a one-inch and two-inch acid rain, of both varieties (pH of 6.5 and 6.9), to tanks containing goldfish. The goldfish used in this experiment were comet goldfish. Comet goldfish are a member of the carp family. Carp are present in almost every lake in the United States. Carp are a well-established species that live on many different continents and constitute a wide range of species and varieties. The fish were chosen because they are hardy and have long studied and established behavioral patterns. Comet goldfish are omnivores, eating plants and small crustaceans, insect larvae and worms. They tend to swim in the middle of an aquarium setting and can live in a wide range of water temperatures, though their optimum temperature is 20 to 22 degrees Celsius or 68 to 72 degrees Fahrenheit. These fish can live upwards of 20 years if properly cared for.

Four different tanks were prepared. Each tank contained three fish. Two tanks were filled with lake water, while the other two tanks were filled with distilled water. After calculating the volume of each tank, the amount of acid rain mixture that needed to be added to simulate a one- and two-inch rain was calculated. A oneinch volume of the acid rain mixture was added to both tanks containing lake water, and to one of the distilled water tanks. The second distilled water tank was left as a control and had no water added to it. The fishesâ&#x20AC;&#x2122; behavior was observed over a 24-hour period to look for any changes. No changes in behavior were observed in any of the fish. This was done for both jars of acid mixture, the 6.9 pH and the 6.5 pH. This suggests that a small acid rain of either acidity will have little effect on the fish in the lake.

The experiment was repeated using the same set-up, but a volume of each jar was used that would simulate a two-inch rain. The fish were again observed over a 24hour period. The fish in the two lake water tanks again showed no change in behavior for the addition of a twoinch rain of either acid mixture. The fish in the tank of distilled water became more lethargic and tended to swim near the bottom of the tank for the first three to four hours following the addition of either acid mixture.

After this period, the fish returned to their normal behavior. This data suggests that the addition of a greater amount of acid rain changes the pH of the water more significantly. This can be deduced from the data because the fish in the distilled water were not affected by the one-inch rain but were affected by the addition of a twoinch rain. The fish in the tanks containing lake water were not affected by the addition of a greater amount of acid rain. This suggests that though the pH of the water will change more with the addition of the acid rain, the nutrients and other components of the lake water neutralize enough of the acid so that the change is not great enough to affect the fish.

23

1 inch

-0.01

+0.2

+0.13

+0.29

-1.27

2 inch

+0.25

+0.48

+0.27

+0.31

-1.37

EDGEWOOD HIGH SCHOOL STUDENT SCIENCE JOURNAL 2005-06

24

Contaminants

Regular Water

Manure

Acid Rain

Phosphorus Fertilizer Phosphorus-Free Fertilizer Table 2.

Runoff Osmolarity Results

Dirt

Sand

Grass

18

15

16

1155

1209

284

14

12

3

645

733

350

29

72

71

including the distilled water tanks. This was done again using 1.0 gram of manure. The fish were monitored for the same 24-hour period. An increased number of deaths were seen by the addition of an increased mass of manure, including deaths in the distilled water tank.

The results of this experiment show that acid rain seems to have little effect on the quality of water or the aquatic species in it. The change in pH caused by acid rain in lake water was very slight. The change was nowhere near enough to cause a noticeable change in fish behavior. This may be because components in the lake water are neutralizing the acid. Though pH is important for aquatic ecosystem health, acid rain does not seem to affect pH enough to be detrimental to the species living in the aquatic ecosystem. The results of this experiment show that fertilizer and manure have a much greater effect on the aquatic ecosystem. The addition of fertilizer and manure drastically changes the environ-ment of the aquatic ecosystem, changing the osmolarity of the water drastically.

25

The Relationship of Child IgE to Parental and Environmental Effects

by Kimberly Leonard and Amanda Heller Kimberly Leonard Rationale: If the IgE shift into overdrive truly does occur early in life, then a pattern of overproduction would develop in some of the COAST children. Additionally, parent IgE level and reaction to skin testing (both mom’s and dad’s) might predict the level of IgE production and reaction to skin testing in the children.

Methods: FEIA [laboratory procedure in which a small part of the blood sample is mixed with different allergens and analyzed for the chemical reaction]. Allergy Skin Test (SPT). [The procedure consisted of placing fourteen drops of dissolved extract of common allergens on the back or forearm.]

Results: A positive correlation was found between three and five year (3/5yr) IgE [r= +0.79] [p=<. 0001]. A slight positive correlation was found between mom’s total IgE and child 3/5yr IgE. [3 year r= +0.13 p=0.049] [5 year r=+0.14 p= 0.056]. Positive correlation found between dad’s total IgE and child 3/5yr IgE. [3 years r=+0.25 p= 0.0002] [5 years r= +0.31 p= <0.0001. When IgE levels increased with age, those children who had a SPT+ had significantly higher average IgE level than children with a SPT-. Children at age 3/5yr whose mothers had a SPT+ tended to have higher average levels of IgE than those whose mothers had a SPT-.

Conclusion: Levels of IgE antibody is increased in all children, especially those with: high IgE levels at young age; parents with high IgE levels; dad’s IgE level was more predictive than mom’s; child’s SPT+ to common allergens and parents with SPT+ to common allergens.

Background he Childhood Origin of ASThma (COAST) study was designed to further explore both environmental (virus infections and aeroallergen exposures) and genetic (immune response dysregulation) factors that contribute to the inception of childhood asthma. COAST began as a local study in Madison, Wisconsin, by enrolling 300 families before the birth of the infant. To qualify for the study, at least one parent had to have allergies (defined by allergy skin testing), asthma, or both, which is thought to put a child at high risk for developing these same conditions.

The COAST study has been collecting data on recruited children and their families for approximately six years. Involvement in the study began with the collection of umbilical cord blood after the birth of the infant and additional collection of data has been ongoing.

Nasal mucus samples were also obtained from children during episodes of wheezing and/or significant lower respiratory infection. For infants and young toddlers, two milliliters (about half a teaspoon) of sterile salt water were administered to each nostril with a modified bulb syringe and then suctioned back out to help wash out the nasal secretions that were present in his/her nose. In older children, saline nose spray was squirted into each nostril and the child was asked to blow his/her nose into a square of plastic wrap.

T

Specific Aims of this Analysis IgE is a molecule that is formed to protect the body from invasion by foreign particles. However, the response it creates when activated in the body also contributes to the symptoms that are known as allergies or asthma. In fact, in people with allergies and asthma, IgE is frequently overproduced. It is also hypersensitive, responding to substances in the body that do not pose

a threat, or over responding to relatively innocuous substances with a full-scale attack, when a smaller assault maybe be all that is needed. It is probable that people with asthma are simply born with IgE systems that are predisposed to overproduce the allergic antibody. If this is the case, some doctors believe that exposure to allergens such as tobacco smoke, dust mites, animal dander, and other asthma triggers very early in life may permanently shift the IgE system in people with asthma into overdrive (Edelman 23).

IgE level can be measured directly in a blood sample.

Amount and reactivity of IgE can also be assessed indirectly through skin prick testing. With skin prick testing, a small amount of allergen is introduced into the skin. Redness and swelling in reaction to the allergen suggests higher levels of potentially more reactive IgE in a patient.

It was hypothesized that if the IgE shift into overdrive truly does occur early in life, that a pattern of overproduction would be seen developing in some of the COAST children. Additionally, COAST wanted to explore how parent IgE levels and reaction to skin testing (both mom’s and dad’s) might predict the level of IgE production and reaction to skin testing in the children.

To test the hypothesis, the following analyses were performed: • Explored the correlation between the child’s IgE level at age three years and at five years to determine whether or not a pattern of IgE overproduction was forming.

• Calculated the correlation between Paternal Total IgE and child’s IgE level at ages three years and five years to determine whether or not parent IgE production could predict production in their children.

• Investigated the relationship between parent and child positive skin test result.

Amanda Heller Kimberly and Amanda, graduates of Edgewood High School in 2005, conducted their research at the University of Wisconsin -Madison under the supervision of Robert Lemanske M.D., and Kathy Roberg of the COAST project. Kimberly now attends the University of Indiana at Bloomington.

Amanda attends the University of Colorado at Boulder.

They received additional assistance in this project from K.A. Roberg, D.F. DaSilva, M.D. Evans, D.K. Sullivan, L.E. Pleiss, C.J. Tisler, T.E. Pappas, E.L. Anderson, L.N. Peterson and J.E. Gern.

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Clinical Methods Two particular research procedures that were used to test the hypothesis will be explained in greater detail: • The FEIA test for total IgE and specifically selected allergens was performed using umbilical cord blood, annual COAST subjects, and parental blood samples. FEIA testing is a laboratory procedure in which a small part of the blood sample is mixed with different allergens and analyzed for the chemical reaction. Portions of the blood samples were tested specifically for this research project.

• Children had an allergy skin test at their five-year visit (not all five-year visits have been completed at the time of this paper). This test was also completed on all parents and was a part of determining eligibility criteria for inclusion in the study. The procedure consisted of placing 14 drops of dissolved extract of common allergens (such as house dust mite, pollen, grass) on the back or forearm. The underlying skin was then lightly pricked with a sterile disposable needle and the level of reaction was assessed. The COAST staff completed this procedure.

Statistical Methods Estimating correlations The correlation coefficient, abbreviated r, can range from –1 to +1. The closer r is to +1 or –1, the more closely the two variables are related. If r equals 0, it means there is no relationship between the variables.

If r is positive, it means that as one variable gets larger the other gets larger. If r is negative it means that as one gets larger, the other gets smaller, this is often called an “inverse” correlation. Correlation can be viewed graphically as in the examples in Figure 1.

Hypothesis Testing The hypothesis test is designed to determine whether or not results found in a small population might hold true for the larger population. For example, if it were found that 80 children in a group of 100 had asthma along with their parents, then a hypothesis test would be done in order to see if this same relationship is likely to hold true in the larger population. This chance that you make a false assumption about the larger population Figure 1

based on your small sample, is the p-value of a hypothesis test. If the p-value is less than .00001, then the results in a sample likely represent what you would also see in the larger population. If the p-value is above .05, then the likelihood of the relationship being the same in the larger population is much smaller.

Results IgE Levels Several tests were done with the data that included three and five year IgE levels in children, Mom total IgE, and Dad total IgE. In COAST’s sample of 167 children, it was expected that the total IgE levels would be very similar at both the three-year and five year time points.

To better explore this idea, a scatter plot of the threeyear versus the five-year data was developed.

Figure 2. IgE Levels

Using a normal scale made it difficult to analyze the data points since the points became clustered in the lower left corner of the plot. To be able to better examine the data, a logarithmic transformation of the plot scale was performed.

Figure 3. IgE Levels - Logarithmic Distribution

THE RELATIONSHIP OF CHILD IGE TO PARENTAL AND ENVIRONMENTAL EFFECTS

This transformation compressed the upper end of the data while stretching out the lower end. The logtransformed plot shows a strong positive relationship between year three and year five IgE levels. The correlation coefficient calculated for the data was +0.79 confirming the relationship. The p-value for this relationship was found to be <.0001, which suggests that there is strong evidence that the IgE relationship found in the 167 COAST children, also holds true for the larger population of children with at least one atopic parent. Specifically, it suggests that most children with high IgE levels at age three years will also have high IgE levels at age five years.

An analysis of the relationship between parent and child IgE was done in a similar manner to the three and five year IgE data. Scatter plots of the data follow:

27

Figure 4c.

Child/father IgE, Year 3

Figure 4a.

Child/mother IgE, Year 3

Figure 4d.

Child/father IgE, Year 5

Figure 4b.

Child/mother IgE, Year 5

The first two plots look at mom’s IgE and child’s IgE. Here, no relationship is immediately evident, but the correlation coefficients of +0.13 for year three and +0.14 for year five indicate that there is actually a slight positive relationship between the two IgE measurements.

The p-values are very close to 0.05, which is considered by many statisticians to be the cut-off point for being able to draw conclusions about the larger population from the sample. Therefore, it is uncertain with this data that that the same pattern would be found in a larger group of children with at least one atopic parent.

The plots in Figures 4c and 4d compare the relationship between dad’s IgE and child’s IgE. The graphs show that there might be a positive relationship between the two measurements, but the pattern is not nearly as clear as what was shown in the graph of the child’s year three versus year five IgE. The correlation coefficients for the data reflect this fact and are much smaller (+0.25 at year three and +0.31 at year five versus the +0.79 for the three versus year five IgE). The calculated p-values for the correlation are very small at .0002 for year three IgE and .0001 at year five IgE. These numbers predict that the pattern shown in the sample is likely to be the same pattern that would be found in the larger population of children with at least one atopic parent.

In the parent versus child IgE data, the calculated correlation coefficients are all fairly small. This indicates that, although a parent’s IgE level is somewhat predictive of the child’s level; many other factors are influencing the relationship.

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Skin Prick Testing The results of skin prick testing in children at five years were compared with their IgE levels at ages one, three and five years. Results are listed in the table below.

Child’s IgE year 1 Child’s IgE year 3 Child’s IgE year 5

Child’s Skin Test negative positive 9.2 16 19 39 27 80

It was found that IgE levels increased with age and that children who had a positive skin test had significantly higher average IgE levels than children with a negative skin test. In the COAST research, the result of the parents’ skin tests with the IgE levels of their children were compared. The results of the mothers’ tests were first looked at.

Child’s IgE year 1 Child’s IgE year 3 Child’s IgE year 5

Mom’s Skin Test negative positive 15 14 19 34 31 53

The data shows that at years three and five, children whose mothers had a positive skin test tended to have higher average levels of IgE than those whose mothers had a negative skin test.

Child’s IgE year 1 Child’s IgE year 3 Child’s IgE year 5

Dad’s Skin Test negative positive 12 14 19 33 33 47

The same trend holds true for fathers and their children — at ages three and five years, average IgE levels tended to be higher in children whose fathers had a positive skin test.

While the relationship between a child’s skin test and their IgE levels is significant, it may not be a good idea to generalize the results comparing parent test to child IgE levels to a larger population. For the parent-child comparisons, the differences in IgE levels that exist between groups indicates a trend, but are not large enough to be convincing that we would find this same relationship in non-COAST children.

Conclusion Levels of IgE antibody increase with age in all children and tend to be higher in children with the following profile: • When child has high IgE levels at a young age (specifically, at one year) • When the child’s parents have high IgE levels; dad’s IgE level was more predictive than mom’s • The child’s has positive skin tests to common allergens • The child has parents with positive skin tests to common allergens The effect between the link of the child’s total IgE and paternal total IgE may be weakened as a result of the environmental exposure over time. There is convincing evidence that parents can be used as a predicative quality for child’s total IgE. However, as the child becomes older the environment will gradually become the largest predictor.

29

Evaluation of Chemicals for Androgenic Potency

by Courtney Zwick and Stephanie Hird

R

ecent discoveries have shown that estrogenic (female sex hormone) compounds affect fish.

These compounds are mostly natural and are being introduced into the lakes and rivers after having gone through wastewater treatment plants. Hormone molecules are naturally excreted through urine and body oils. This is suspect to the reason why the fish, located in Boulder Creek, Colorado, have both male and female sex tissues.

A Colorado biologist named John Woodling found white suckers, a fish native to Colorado, swimming downstream from Denver’s largest sewage plant. The concentrations of chemicals found in Boulder Creek are high enough to cause the feminizing effect in other fish (Stein). This concern has led to the thought that androgenic (male sex hormone) compounds may also be a problem.

When hormones enter a body, each individual hormone molecule travels through the blood until it reaches a cell with a receptor that matches it. Then, the hormone molecule latches onto the receptor and sends a signal into the cell. These signals may instruct the cell to multiply, to make proteins or enzymes, or to perform other vital tasks. Some hormones can even stimulate a cell to release other hormones. Hormones are so important because they are involved in just about every biological process (Stein). When the wrong hormones enter a system, there is a potential problem concerning whether or not they will then start to produce this wrong kind of hormone.

In order to determine if these hormone molecules are androgenic, it must be determined if they can bind with their receptors. A yeast reporter system is used to determine this. Yeast cells do not naturally have androgenic receptors. They are single-celled organisms; therefore they have no need for cell communication.

However, this system includes putting DNA that codes for receptors and receivers into a yeast cell. Then it can be observed because it acts like a normal human cell in the recepting and receiving hormone aspect. If the hormones bind to the receptor and causes the batagalactocidase1 to be synthesized, they can then be tested to see how potent the androgenic chemicals are.

Copper sulfate treatment is used to cause yeast to express the receptor. Chemicals are put into a template, after the Bata-gal has been added to the yeast, copper sulfate is added, which breaks open the yeast cells and

1

The enzyme produced in the yeast that metabolizes the ONPG. Substrate for the Bata-gal and when it is metabolized it can form a yellow color that can be measured by the spec.

3 Measures the absorbance of light at a specific wavelength.

2

chews up the ONPG2 and turns the solution yellow.

This activates the Bata-gal. The amount of Bata-gal shows the potency of androgens. The activity is measured using a spectrophotometer.3 The more yellow the solutions are, the more apt they are to absorb the light from the spectrophotometer. When the activity is determined, the potency can be determined. This project was designed to help determine how potent is “too” potent.

This project will try to find how well the androgen activity is removed by wastewater treatment plants.

Then the potency must be determined of the androgenic compounds, such as testosterone, androsterone, and 5a androstane 3 17-dione. This potency will tell if they are strong enough to be responsible for the androgenic activity.

The EC50 of all three chemicals mentioned above was determined from the dose response curve, which measures the activity of yeast in accordance to the amount of concentration in different androgens that were created from a spectrophotometer (Hemming, J, personal communications). The EC50 is the concentration of a chemical where half of the activity is present (Graph 1). The higher the activity is, the less potent the chemical. The average EC50 for testosterone was 15.07.

Because the activity was so high at such a low concentration, it was concluded that this is the most potent chemical of the chemicals in the study. Testosterone was the most effective at binding with the yeast cell receptors. Androsterone was found to have an average EC50 of 495.4, the least potent chemical because it had the highest activity at a high concentration.

5a Androstane 3 17- dione will be referred to as “androstane.” Androstane had an average EC50 of 166.9, placing it in the middle.

Courtney Zwick

Stephanie Hird Courtney Zwick and Stephanie Hird graduated from Edgewood High School in 2005. Stephanie is currently attending St. Thomas University, while Courtney is at Madison Area Technical College.

Their research mentor for this study was Jocelyn Hemming, a technician at the Wisconsin State Laboratory of Hygiene in the Department of Biomonitoring. From this project, they learned how to use bioassays and chemical analyses to evaluate the removal of endocrine disrupting chemicals during water treatment.

Relative Potencies of Androgenic Compounds

Testosterone

Androsterone

Graph 1.

The results show the mean of the EC50’s of the tested androgenic chemicals. These results are from six separate experiments. The bars on top of the columns indicate the standard deviation of each chemical.

Androstane

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REFERENCES Stein, Theo and Miles Moffeit (2004). â&#x20AC;&#x153;Mutant Fish Prompt Concern.â&#x20AC;?

The Denver Post.

Figure 1. Testosterone dose response curve showing androgenic activity in yeast cells.

Figure 2: All three chemicals; adrosterone, androstane and testosterone. It shows testosterone being the most active at the lowest concentration point, and androsterone first showing activity at the lowest concentration point.

In order to get these averages, the experiment was repeated at least three times (Figures 1 and 2). After determining how potent each chemical was, wastewater treatment samples were looked at to see if testosterone could be the one responsible for any activity found.

Because these chemicals cannot be tested on humans, it is important that they are not in our water supply because the effects are unknown and assumed serious.

Three samples were taken from random wastewater treatment plants in order to determine if there were androgenic compounds present. When looking at these three samples, it was determined how effective the wastewater treatment plants were at removing androgenic compounds. These three samples had a final concentration of 341.8 ng/L, 241.5 ng/L, and 233.51 ng/L. Although these amounts sound minute, when introduced to the body they can cause catastrophic effects. The testosterone levels in these samples were tested and came out relatively low. Because the androgenic activity was moderately high, this biological test indicated that more androgens were present. By performing chemical tests on these three samples, the

discovery for androgenic chemicals was sought out.

Testosterone equivalents were responsible. The other two chemicals, androsterone and androstane, had lower activity levels than testosterone. However, these two chemicals could still be contributing to the overall activity.

Future testing for the thousands of other compounds that make up androgenic hormones should be done to find out what is causing this high concentration in some wastewater treatment plants. The yeast assay used detected androgenic effluents in wastewater treatment plants after removal. It would be important to note the effects on any and all aquatic life introduced to these treated waters. It would be interesting to find out what would happen during reproduction of these transgendered fish in the polluted waters they live in.

Concluding the final results of the androgenic chemicals, it was found that testosterone was the most potent, androstane was the second most potent and androsterone was the least potent.

31

Synthesis of Benzonitriles and Their Use in the Captodative Stability of Tetraphenylethylene

by Kyle Kinzel Kyle Kinzel

The research of captodative stability, a enhanced form of stability observed during a switch of tetraphenylethylene from its cis to its trans isomerization, has been preformed before both with and without electron donating/accepting groups. The goal of this research is to investigate a possible correlation of phenyl torsional angle with the magnitude of captodative stabilization of the biradical transition state of thermal cis-trans isomerization of para electron donor/accepting, ortho/meta methyl substituted tetraphenylethylene.

The specific research done was the synthesis of substituted benzenes to be used in the Grignard reaction and then the McMurry coupling to form tetraphenylethylene. Three reactions were preformed: p-tolualdehyde to p-tolunitrile, 5-bromo-m-xylene to 3,5dimethylbenzonitrile, and aniline to benzonitrile. The p-tolunitrile reaction turned out to have poor yields, while the 3,5-dimethylbenzonitrile reaction had very good yields. At the time of this paper, results for the benzonitrile reaction had not been obtained. These products will be used for the ultimate goal of this research.

C

aptodative stability, an enhanced form of stability, was first proposed by Dewar in 1952.1 The effect of captodative stability is most apparent in a cis-trans isomerable ethylene like that of a substituted tetraphenylethylene. The captodative stability is observed during a switch of the molecule from its cis to its trans isomerization.

“Although captodative stability was proposed in 1952, the use of radicals was introduced in the 1970’s and 1980’s.”2 The effect that an electron donor radical and an electron acceptor radical would increase the power of the captodative stability was proposed.

In 1986, Neumann worked with para-substituted tetraphenylethylene, but found that acceptor-acceptor substitutions were greater at captodative stability than donor-acceptor substitutions, except for a few combinations of donor-acceptor groups.3 One of these pairs was a methoxy and nitrile group. The electron donor/acceptor groups have an influence on the captodative stability through their effect on the torsional angle of the phenyl groups, and the shifting of electron fields to create partial charges.

The specific point of this research is to investigate “a possible correlation of phenyl torsional angle with the magnitude of captodative stabilization of the biradical transition state of thermal cis-trans isomerization of para electron donor/accepting, ortho/meta methyl substituted tetraphenylethylene.4 This research will use sixteen different substituted tetraphenylethylenes.

X

R2

R3

Y

R2 1. X = Y = H

R1 R4

R3

Figure 1.

R4 R1

R1 R4

2. X = CH3O, Y = H 3. X = CN, Y = H 4. X = CN, Y = CH3O a. R1 = Me, R2, R3, R4 = H

R3 b. R1 , R4 = Me, R2, R3 = H

R4 R1

R2 Y

R3

R2

X

c. R2 = Me, R1, R2, R3 = H d. R2, R3 = Me, R1, R4 = H

Kyle Kinzel is a 2005 graduate of Edgewood High School who is currently attending the University of WisconsinGreen Bay. Kyle’s mentor for this project was Dr. Louise Stracener, a member of the chemistry faculty in the Natural Science Department at Edgewood College.

The presence of the ortho/meta methyl groups is the key part in this study to preformed, as Neumann did not have these substituted methyl groups. It is proposed that these methyl groups will have an effect on the torsional angle.

There is a multi-step process to make the necessary tetraphenylethylenes. The first step is to create two substituted benzenes according to the combinations listed above, one a bromobenzene and the other a benzonitrile.

Figure 2.

Br

X Y

O Grignard Reaction

Y + NC

McMurry Coupling

Y

X Y X

X

These two substituted benzenes are the put together to form a benzophenone using a Grignard reaction.5 The substituted bromobenzene is converted to a Grignard reagent when in the presence of Magnesium and ethoxyethane.

R-Br + Mg + Ether

R-Mg-Br 5

The Grignard reagent then reacts with the nitrile to form benzophenone (see Figure 3).6 Two benzophenones are then combined to make an ethylene using a process known as the McMurry coupling (see Figure 3).7 The main aim of this portion of the research was to determine ways to synthesize all 16 combinations for the tetraphenylethylenes, and then to synthesize them.

The trouble was that many of these compounds were not for sale and therefore had to be made. Many of these were very difficult to develop a plan to synthesize. It has yet to be determined how to synthesize a few of the 16 combinations.

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Figure 3.

Grignard Reaction Mechanism MgBr N R

MgBr

R’

C

N

H

C R

N

R

R’

C O

H

C R

O

O

H R’

MgBr

N

MgBr R

R’

C

R’

OH + 2

H

McMurry Coupling O

2

R

C•

C R

O-

O-

2

e-

R

R’ OR

C

R R’

-O2

C R’

OC

R’

R’

OC

R R’

! R C

C R’

The research began with the synthesis of p-tolunitrile from p-tolualdehyde. This was done using hydroxylamine in a formic acid solution8.

For this reaction, 1.2015 g p-tolualdehyde, 0.9 g hydroxylamine hyrdrochloride, and 10 g of formic acid were placed in a 25 mL round bottom flask.

A heating mantle was placed on top of a lab jack and filled with sand. The round bottom flask was placed in the heating mantle and was connected with a reflux valve. The p-tolualdehyde, hydroxylamine hydrochloride, and formic acid solution were allowed to reflux for approximately 30 minutes. Then the heating mantle was turned off, and the solution was allowed to cool. The solution was then poured into a 250 mL Erlenmeyer flask. 100 mL of ice water was added to the flask.

Using pH paper to test the pH of the solution periodically, the solution was neutralized with a 5% sodium hydroxide solution. The solution was extracted with t-butyl methyl ether twice. Magnesium sulfate was used to dry the solution. The remaining solution was concentrated by the use of a rotary evaporator machine.

The p-tolunitrile was tested with an IR and an NMR. After inspection by IR and NMR, it was determined that not much of the reaction took place. On the NMR, there was a signal indicating the presence of much aldehyde. There was also a very large spike for remaining ether.

This led to the inspection of the reaction to determine what caused it to not completely react. It was determined that the age of the formic acid caused the very low yields.

The reaction was performed a second time. This time, more formic acid was used, and the reaction was allowed to reflux for a longer amount of time.

An IR was taken of the second product. Although it showed that more ether was removed, there was actually a smaller ratio of p-tolunitrile to p-tolualdehyde.

Therefore, it was concluded that the reaction time was not a determining factor of the yield. This could also mean that the formic acid was expired. Because this reaction was not very reliable, the research continued on to a different reaction.

The second reaction that was performed, the cyanization of an aromatic halide in a dimethylformamaide solution,9 was the hardest to get started due to difficulty in obtaining one of the necessary chemicals, copper (I) cyanide. This is a very poisonous chemical and very dangerous. When mixed with a strong acid, cyanide gas is released.

Once the CuCN was allowed to be obtained, the reaction started. This time 3,5-dimethylbenzonitrile was be synthesized from 5-bromo-m-xylene.

0.027 mol (5 g) 5-bromo-m-xylene, 0.031 mol (2.78 g) CuCN, and 9.5 mL dimethylformamaide (DMF) were added to a 50 mL round bottom flask.

The flask was placed on top of a heating mantle filled with sand and a magnetic stirring plate. The solution was allowed to reflux for 6 hours.

The solution was added to a solution of 9 g FeCl3, 2.3 mL concentrated HCl, and 14 mL H2O. This solution was kept at a temperature of about 60° for approximately 30 minutes.

The aqueous layer was extracted with toluene in two batches. This was combined with the organic layer, washed with dilute hydrochloric acid, water, and 10% aqueous sodium hydroxide. It was then dried with magnesium sulfate and rotary evaporated to obtain the 3,5-dimethylbenzonitrile. An IR was taken, and it was determined that this reaction had worked the best. On the IR there were very clean signals that suggested a nitrile compound. Due to the lack of signals from another compound, a high yield was determined.

The third and final reaction that was completed is known as the Sandmeyer reaction.10 This reaction was used for cases 3 and 4. Since 3 and 4 already have a nitrile on them, it is impossible to specifically attack only one the nitriles with the Grignard reagent. By changing aniline to benzonitrile, therefore leaving the bromine alone, the bromine may then be cyanized after the reaction.

1.86 g aniline, 5 mL ≈7.7 M HCl, and 20g ice were added together. In another flask, 1.53 g NaNO2 and 4.2 mL H2O were added. These two solutions were added together and formed a suspension that was difficult to work with. To neutralize this solution, 1 g of Na2CO3 was slowly added to the solution to bring its pH up to about 7.5.

In another flask, 5.71 g CuCN, and 38 mL H2O were added together and cooled. While being stirred, 5 mL of toluene were added to this solution while stirring and adding ice. The aqueous layer was then drawn off. To date, there has been no IR or NMR taken of this product.

SYNTHESIS OF BENZONITRILES AND THEIR USE IN THE CAPTODATIVE STABILITY OF TETRAPHENYLETHYLENE

Rosenmund von-Braun Reaction Br CuCN

33

REFERENCES

Figure 4.

CN

1. Dewar, M. J. S. J. “A Molecular Orbital Theory of Organic Chemistry. IV. Free Radicals.” Am. Chem. Soc., 1952, 74, 3353.

DMF, reflux

Mechanism:

2. Viehe, H. G.; Jonaousek, Z.; Merényi, R. “The Captodative Effect.” Acc. Chem. Res., 1985, 18, 148.

2 Ar Br + CuCN [Ar-CN]2CuBr + CuBr 2 Ar CN + CuBr [Ar-CN]2CuBr Conversion of Aldehyde to Nitrile O

3. Neumann, W. P.; Uzick, W.; Zarkadis, A. K. J. “Sterically Hindered Free Radicals. 14.

Substituent-Dependent Stabilization of Parasubstituted Triphenylmethyl Radicals.” Am. Chem. Soc., 1986, 108, 3762.

CN

H H2N-OH HCl

•

formic acid, reflux

O O

Mechanism:

O

N R

N

H2N-OH

R C H

OH

H C OH

O

H

C

R C

C HO

N+

H

C OH

Sandmeyer Reaction N HCl, NaNO2 N+ +Cl- CuCN

NH2

2 HONO

Ar

NH2 + N2O3 +

N+ N

O + NO2-

H N N O Ar H + Ar N N + H2O

Ar

Ar

N N

Ar

H N N O H + N N Cl-+ CuCN

Ar Ar•+ CuClCN

H

N2O3 + H2O

H Mechanism:

CN

Br

Br

Br

Ar

4. Stracener, L. “Synthesis and Study of Substituted Tetraphenylethylenes: Effect of Torsional Angle on Captodative Stabilization.”

R C H

O

Ar

N N O H

Ar•+ N2 + CuClCN CN + CuCl

There was a problem discovered with this reaction. The research calculated a molarity for the CuCN solution, finding out that it was two times as strong after being made. Therefore, more water was added to decrease the molarity of the CuCN solution. However, all of the solution was added, therefore doubling the CN. Also, the molarity was calculated off of the CN, when that was in excess. Molarity should have been calculated through Cu. In conclusion, the most effective method of cyanization is with the copper cyanide.

The aldehyde conversion reaction was not found to be reliable. The Sandmeyer reaction appeared to work very well.

This research is still in its very beginning steps. There have been some hard times to try to get things going. Some of the reactions have worked very well, while others have not really worked at all. This researcher thinks that if kept on, there will be some very important discoveries towards the end of the research.

5. Clark, Jim. “Grignard Reagents.” 2003. May 15, 2005. www.chemguide.co.uk/ organicprops/haloalkanes/ grignard.html 6. “Organic Chemistry Portal.”

2005. May 15, 2005. www.organic-chemistry.org/ frams.htm www.organic-chemistry.org/ namedreactions/ grignard-reaction.shtm 7. McMurry, J. E. “CarbonylCoupling Reactions Using Low-Valent Titanium.” Chem.

Rev., 1989, 89, 1513.

8. Olah, G. A., T. Keumi.

“Synthetic Methods and Reaction; 60. Improved OneStep Conversion of Aldehydes into Nitriles with Hydroxylamine in Formic Acid Solution.” Communications.

Feb 1979, 113.

9. Friedman, L. and H. Shechter.

“Dimethylformamide as a Useful Solvent in Preparing Nitriles from Aryl Halides and Cuprous Cyanide; Improved Isolation Techniques.” 1960, 26, 2522.

10. Clarke, H. T., R. R. Read. “o-tolunitrile and p-tolunitrile.” Org. Syn. Coll. 1, 514.

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The Effects of Dams on Water Quality and River Ecology

by Rachel Larson & Han-Kyung Lee Rachel Larson

Han-Kyung Lee Rachel Larson was born in Seattle, Washington, and came to Madison the summer after 8th grade.

She graduated from Edgewood High School in 2005. She is attending South Dakota State University and plans on majoring in Athletic Training. The research mentor for this study was Amy Kamarainen, a graduate student focusing on limnology at the University of WisconsinMadison.

Han-Kyung Lee was born in Seoul, South Korea, and came to the United States in 2000 to study.

She graduated from Edgewood High School in May of 2005 and plans to study biology and premedicine at the University of Wisconsin-Madison.

Han-Kyung would like to thank Ms. Kamarainen for her time and assistance in conducting this research.

Dams have become an important part of many river ecology systems. There are about 4000 dams located on the rivers of Wisconsin.

Thousands of fights have taken place against dam construction led by the environmental movement for decades. Recently, the removal of dams has become a new strategy in dealing with river conservation. Given careful background research of the dam, and the possibility of improving the ecosystem, this nation’s view on dams is slowly changing as the importance of a free-flowing river is recognized. The purpose of this study was to compare and contrast the water qualities of Maunesha River and Mills Creek to Token Creek at their dams. Three sites were designated for each river, one 75m upstream from the dam, one at the dam and another 75m downstream from the dam. Each site was tested for flow, temperature, river width and depth, dissolved oxygen level, pH, conductivity, suspended sediments and macroinvertebrates. Through the research findings, one can assume Token Creek had the highest water quality followed by Mills Creek and Maunesha River. This assumption is based mainly on the macroinvertebrates present in the water.

Background river is a body of water that flows from high to low elevations on land. When a dam is built on a river, it backs up the water behind the dam and floods the land, thus forming a lake or a pond, commonly referred to as an impoundment. There are about 4000 dams in Wisconsin and most of these dams are “run-of-the-river” dams, meaning the amount of water entering the impoundment is equal to the amount of water flowing over the dam. These dams have no way of flood control or the level of the impoundment. Not only do dams impede the natural flow of water in the system, they also block the flow of sediment in the river. Rivers naturally erode, carry, and deposit sediment and this is what shapes the river, and forms meanders, pools, and riffles. When a dam is present on a river, it blocks the river from depositing the sediment which instead builds up in the impoundment. The river downstream becomes deprived of the sediment because the sediment has been trapped, and the water flowing from the outlet of the dam may carry little sediment. Another impact of a dam on the ecology of the river is the fact that it blocks traffic of the biota; fish and other organisms are not be able to move past a dam. The splicing of a river due to dams is referred to as “fragmentation.” The fragmenting of a river affects the types of fish found upstream and downstream of the dam.

A

Materials and Protocol The dams to be studied were selected by conducting online research about dams in the area and by asking other people who may have authoritative knowledge about the existence of small dams in the Dane County area. Thus, three dams were chosen: one on Token Creek, one on the Maunesha River, and another on the Mills Creek in Governor Dodge State Park. Three sites were designated for each dam; Site 1 is located 75 m upstream from the dam, Site 2 is located directly at the dam, and Site 3 is located 75 m downstream from the

dam for all ecosystems. The dams were compared on the basis of water flow, temperature, clarity, width, depth, dissolved oxygen level, pH, conductivity, suspended sediment amount, and macroinvertebrates present in the water.

Data The first dam to be analyzed was located on Token Creek on April 2 from approximately 1:30 p.m. to about 6:00 p.m. in sunny weather with scattered clouds in the sky. It was a small dam for it did not have a reservoir upstream from the dam and the impoundment pool created by the force of the water was not too large or deep. Site 1 had a bottom uniformly covered in silt so that one sank up to their hips when entering the stream.

The surrounding area was wetland, although there appeared to be farms nearby. Debris and algae floated down the stream. The macroinvertebrates found here were isopods, leeches and midge larvae.

At Site 2, the water moved faster near the dam, which was made of rocks and concrete. Algae were seen all along the surface of submerged rocks. The macroinvertebrates found included amphipods, crayfish, Dytiscus beetles, isopods, and orb snails.

At Site 3, the stream had narrowed considerably after widening for an island at the base of the dam. Vegetation grew in the water and larger trees grew along the shore.

Not as much algae was found floating down the stream.

The macroinvertebrates found were amphipods, backswimmers, diving beetles, isopods, leeches, orb snails, planarian, pond snails and pouch snails. Also found on the riverbeds of Site 3 were several unidentified mussel shells.

The dam at Marshall on the Maunesha River was analyzed on April 24 from about 11:00 a.m. until about 1:15 p.m. Immediately upstream from the dam, there is a small lake seriously affected by erosion, which the local residents tried to block by placing large rocks along the banks. According to one person, the area used to be very clean about 20 to 30 years ago, but due to development

THE EFFECTS OF DAMS ON WATER QUALITY AND RIVER ECOLOGY

further upstream, loose soil was now being washed downstream. The weather was once again sunny with scattered clouds and relatively windy breezes.

Site 1 had very rocky shores with some vegetation growing along the banks of the lake. The water was very murky and brown; after about 0.75 m in, one could no longer see through the water. The macroinvertebrates found here are bloodworms, spiders, threadworms, and water boatmen.

At Site 2, the flow of the water was concentrated on one side of the dam and the opposite side is completely closed off. This seemed to be a popular hangout spot for the local teenagers and fishermen (oil spills were found along the banks as well as litter). The macroinvertebrates found here were the same as Site 1, with an addition of a fish. A farm was located directly across the sampling site.

At Site 3, there was much algae floating down the river and the river became much shallower. An island segmented the river, and cows were found drinking from the water just downstream from the sampling site and fields surround the site. The water was still very murky, but the river had become shallow enough to be able to see to the bottom. There were no macroinvertebrates caught at this site.

The last dam, located on Mills Creek in Governor Dodge State Park, was analyzed on May 14 in scattered rain showers and very windy conditions from 4:00 p.m. to about 6:00 p.m.

At Site 1, the edge of the lake consisted mainly of rocks with plenty of duckweed floating on the surface of the water. Some litter was found washed ashore. On the lake, many recreational activities were taking place such as fishing, sailing and swimming. Visibility was about half a meter deep into the water. Macroinvertebrates found here were amphipods, coiled shells, pond snails, threadworms and whirligig beetles.

At Site 2, fallen tree branches could be found along the stream forming natural dams and vegetation was thriving. For macroinvertebrates, the majority was determined as black fly larvae, which were found in 100+ numbers in a single sampling.

Lastly, Site 3 was located downstream from some very picturesque waterfalls. There was lush vegetation growing all along the shore made up of large rocks. For macroinvertebrates, there were amphipods, coiled shells, mayfly nymphs and spiral snails (a small fish was also caught in the net). The water was fairly clear here and one could see all the way to the bottom through the fast moving currents.

Data Analysis The flow of a stream is mainly determined by the shape and size of the stream. This defines the stream flow, the sediment load that is deposited and how much the channel can erode. Over time, the stream channels naturally erode and deposit sediments making twists and turns in a formerly straight stream. The average flow at the Token Creek dam was around 0.25m per second.

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The average flow of the Maunesha River was 2.98m/s and for Mills Creek it was 0.64m/s.

The temperature of a river system fluctuates seasonally and daily. The average temperature of the water at Token Creek averaged 14.1°C. Temperatures were 9.73°C at Maunesha and 13.8°C at Mills. These temperatures could have been affected by the amount of cloud coverage in the sky, for the sun would have heated up the water. The lakes of Maunesha and Mills would have also affected the average temperature, for it is harder to heat up large bodies of water.

The width and depth of all three bodies of water varied and Maunesha and Mills had lakes that were difficult to measure.

The amount of dissolved oxygen present in an aquatic ecosystem may depend on the temperature of the water for oxygen is more soluble in low temperature waters. The dissolved oxygen in a system influences the rates of degradation of waste materials in a water body as well as the smell and taste of the water. The oxygen enters the water through either direct dissolution from the air or as a by-product of photosynthesis from aquatic organisms. The amount of dissolved oxygen peaks at noon when photosynthesis is at its greatest, thus in order to collect an accurate measurement of the dissolved oxygen level, one must have a station that would collect dissolved oxygen numbers throughout the day and then average the values. Low levels of dissolved oxygen can threaten the survival of fish and other aquatic organisms.

The average percent saturation of oxygen at Token Creek was 118.1%, at Maunesha, 117.4% and at Mills, 117.2%. The percent saturation of oxygen was significantly higher at Token Creek compared to the other sites indicating high levels of dissolved oxygen present in the water.

The pH values of a river ecosystem usually remain consistent throughout the year varying only by a couple tenths of a pH unit in a season. pH is the measurement of the hydrogen ion concentration of a solution.

Solutions with a pH of 7 are considered neutral while those with lower pH are acidic and higher levels of pH are considered basic. The average pH at Token Creek was at 8.1 (slightly basic), 8.8 at Maunesha (also slightly basic) and 8.4 at Mills Creek. However, note that Site 3 of Mills Creek, the pH was at a neutral 7.5. This was the only site to have reached such a neutral level in pH. The conductivity of water measures the water’s ability to conduct electrical current, a function of the concentration of dissolved salts in the water. Waters with high conductivity are usually located at roadside ditches in areas where salt is applied to the roads and in groundwater that has dissolved minerals during a long residence time at the subsurface. Other factors that influence conductivity are fertilizer, septic waste, barnyard runoff and leachate from landfills. The average conductivity at Token Creek measured 690μs, 739μs at Maunesha and 475μs at Mills Creek. The high level of conductivity at Maunesha River is not surprising since

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Table 1 TOKEN CREEK

Site 1

Site 2

Site 3

Flow (m/s)

0.14

0.16

0.45

Temperature (˚C)

14.9

14.2

13.1

Width (m)

19

10.7

4.4

Depth (m)

0.12

0.3

0.38

118.20

118.60

117.40

pH

8.1

8.1

8.1

Conductivity (μs)

659

698

714

-0.001

-0.001

0

Site 1

Site 2

Site 3

Flow (m/s)

0.04

8.50

0.40

Temperature (˚C)

9.35

9.75

10.1

Width (m)

n/a

24

21

Depth (m)

0.75

1.12

0.12

119.00

116.50

116.70

pH

8.9

8.9

8.7

Conductivity (μs)

733

739

744

0.049

0.027

0.014

Site 1

Site 2

Site 3

Flow (m/s)

n/a

0.77

0.51

Temperature (˚C)

13.9

13.7

13.9

Width (m)

n/a

4.75

3.5

Depth (m)

n/a

1.1

0.45

124.3

117.9

109.5

pH

9.0

8.7

7.5

Conductivity (μs)

473

477

474

0.006

0.003

0.005

Dissolved oxygen (%)

Suspended sediments (g/L) Table 2 MAUNESHA RIVER

Dissolved oxygen (%)

Suspended sediments (g/L) Table 3 MILLS CREEK

Dissolved oxygen (%)

Suspended sediments (g/L)

the site was directly across a farm with cows that visited the river right along its banks and the river is surrounded by agricultural fields. The lowest level of conductivity was seen at Mills Creek for it is located in a state park with no direct contact with any farms or agricultural fields.

The suspended sediment in the water column determines the clarity of the water as well. Measured in grams per liter, the higher the value most likely the murkier the water. The data collected at Token Creek cannot be evaluated for the numbers came out less after the lab process than before going through the lab. The average suspended sediment at Maunesha River was 0.03g/L and 0.0047g/L at Mills Creek. The high concentration of suspended sediments correctly indicated the murkiness of the water and the relatively low level at Mills Creek indicated high clarity. Judging from these numbers, it can be estimated that Token Creek had a suspended sediment concentration of at least 0.0047g/L (negative numbers were derived from the experiment indicating a definite error in calculation).

Lastly, the macroinvertebrates present in an aquatic ecosystem serve as an important indicator of water quality. These biotic indicators are important for they reflect the overall ecological health of a stream by integrating both physical and chemical stress. Aquatic insects are most often used as indicators for water quality for they are ubiquitous, abundant, easily collected and exhibit a wide range of responses to environmental stress. The presence of planarians and mussels shells at Token Creek indicated a semi-healthy water quality for these species are semi-sensitive to pollutants. The macroinvertebrates at Maunesha River were mostly those that are semi-tolerant to tolerant of pollutants in the water, the abundance of carp in the reservoir also indicated bad water quality. Mills Creek, with the exception of the area directly beneath the dam, indicated habitats for mayfly nymphs that are semisensitive to pollutants thus indicating semi-healthy water quality.

It can be concluded from the data that Token Creek had the most healthy water ecology. The aquatic insects and other organisms present in the water indicated a healthy environment. Also, Token Creek is surrounded by the wetlands of Token Creek County Park that have the potential to store runoff water and mitigate flooding.

Next came the Mills Creek at Governor Dodge State Park. Other than the impoundment area directly below the dam, the water quality tests indicated a healthy aquatic ecosystem. Maunesha River came in last with the least healthy aquatic ecosystem. The waters were greatly affected by accelerated erosion and the effects of unregulated manure entering the system by the nearby farms.

THE EFFECTS OF DAMS ON WATER QUALITY AND RIVER ECOLOGY

Error Analysis There is much room in this research for error.

Most of the errors could have been made when taking measurements in the field. Most of the equipment used was not quite state-of-the-art. In the process of calibrating these machines, if not precise, the measurements that the machine indicated would be off. There was a definite error in the lab analyzing the suspended sediment samples. An error had to have occurred somewhere during the lab work for some of the suspended sediment filters had less weight after going through the lab than before. The field data collected could have been affected by the recent rain showers.

Rain is mostly slightly acidic and this addition may have caused the water to be slightly more acidic than natural.

Also, the wind factor may have affected the data collected at Maunesha River and Mills Creek, raising the dissolved oxygen level.

Conclusion In conclusion, by analyzing water samples at the Token Creek dam, Maunesha River dam and the Mills Creek dam, it was determined that Token Creek had the

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supreme water quality followed by Mills Creek and then the Maunesha River. To come to this conclusion, the waters were tested for flow, temperature, river width and depth, dissolved oxygen level, pH, conductivity, suspended sediments and macroinvertebrates. To preserve the quality of the waters, it is recommended to restore surrounding wetlands and riparian zones to filter out pollutants present in the water before entering the stream and to eliminate agricultural and manure runoff from surrounding fields and farms. Macroinvertebrate sampling will also help to indicate water quality. Yet another way to improve water quality is to restore surrounding wetlands and riparian areas that will help filter out pollutants in the water before it enters the stream. In order to protect the aquatic ecosystems, there needs to be continual interest and motivation in managing watershed organizations. Already at the Maunesha River dam, local residents are thinking of somehow restoring the area surrounding the river and positively affect the ecosystem. The condition of the streams rest upon the people and organizations in the area and upon their level of commitment to restore the waters to their original

REFERENCES “Biology.” Dam Repair or Removal: A DecisionMaking Guide. 11 Jan. 2005. www.ies.wisc.edu/ research/wrm00/educbio.htm “Dam and River Ecosystem Basics.” Dam Repair or Removal: A DecisionMaking Guide. 11 Jan. 2005. www.ies.wisc.edu/ research/wrm00/ higheduc.htm David Blair, et al. Water Resources Atlas for Token Creek: A Water Resources Management Study.

Madison: Office of Publications, Information, and Outreach, 1997.

Rosenberg, David M. “Global-Scale Environmental Effects of Hydrological Alterations.”

American Institute of Biological Sciences. 14 Jan. 2005.

“Small Dams in Wisconsin.” Fly Fishing Journal. 11 Jan. 2005. www.flyfishingjournal.com/ archives/cn199904.htm Souers, Amy. Ed. “10 Ways Dams Damage Rivers.”

American Rivers: Restore, Protect, Enjoy. 11 Jan. 2005. www.amrivers.org/index .php?module=HyperConten t&func=display&cid=759 Stanley, Emily H. and Martin W. Doyle. “Trading off: the Ecological Effects of Dam Removal.” A Special Section on Dam Removal and River Restoration. 2003: 15-22.

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Involvement of α-Amylase in Transitory Starch Breakdown in Plants Chuan Wang Chuan Wang is a 2005 graduate of Edgewood High School. He came to Madison, Wisconsin from Beijing, China in 1998, and has been studying at Edgewood for the past six years. He is currently attending the University of Wisconsin-Madison for undergraduate education.

For this project, he worked in the Botany Department at UWMadison; his mentors were Sean E. Weise and Thomas D. Sharkey.

by Chuan Wang Transitory starch is the starch formed in the chloroplasts of leaves during the day and broken down at night to make sucrose.

Transitory starch serves two functions; it can act as a carbon overflow allowing photosynthesis to proceed faster than that of sucrose synthesis, but more importantly it acts as an energy reserve for the night providing the plant with carbohydrate when there is no photosynthesis. Although the pathway and regulation of transitory starch synthesis is well understood, little is known about the starch breakdown during the night (Zeeman et al., 2004a).

It is now becoming clear that starch is broken down hydrolytically and exported from the chloroplast in the form of maltose and glucose (Niittylä et al., 2004; Weise et al., 2003). Maltose and glucose are produced in the chloroplast by the enzymes α-amylase and D enzyme. They are then transported out of the chloroplast on separate transporters (Rost et al., 1996) to ultimately make sucrose in the cytosol (Figure 1). The starch degradation pathway upstream of maltose and glucose synthesis is still unclear. It is thought that α-amylase and D enzyme are unable to attack the intact starch granule and must work on a smaller maltodextrin (Takaha et al., 1993; Lizotte et al., 1990).

The enzymes that catalyze the initial attack on the starch granule making starch accessible to β-amylase and D enzyme are uncertain (Zeeman et al., 2004a). It has long been thought that Figure 1. The primary pathway of transitory starch breakdown at night. It is hypothesized that α-Amylase is involved in the initial α-amylase is required in this process either by catalyzing the degradation of starch granule. initial attack on the starch granule or by secondary processing (Sun et al., 1995). α-amylase, also referred as endoamylase, is an enzyme which hydrolyses α-1,4 linkage in starch, glycogen and other glucose polysaccharides producing glucose or shorter chain maltodextrins as its product. In Arabidopsis, the plant used in our experiments, there are three α-amylase proteins. One is located in the chloroplast, while the other two are thought to be located in the cytosol (Yu et al., 2005). Since transitory starch breakdown takes place in the chloroplast, we focused on the chloroplast α-amylase.

To further elucidate the role of plastidic α-amylase, we used a T-DNA knockout mutant of Arabidopsis thaliana that completely lacks transcript for the plastidic α-amylase (AMY1-2). We investigated the effect of α-amylase on starch degradation and maltose and glucose6-phosphate production. We propose that plastidic α-amylase does not play an essential role in hydrolytic starch breakdown, but may provide substrate for phosphorolytic starch breakdown.

Material and Method Use of Arabidopsis thaliana Plants he plant selected for this experiment was Arabidopsis thaliana. Arabidopsis is a small flowering plant that is widely used as a model organism in plant biology. It is a member of the mustard or Brassicaceae family, which includes species such as cabbage and radish. Arabidopsis does not have any agricultural significance, but it offers important advantages in studying genetics and molecular biology.

Arabidopsis has a rapid live cycle of six weeks from germination to maturing seeds and can be grown in small areas, such as growth tubes. It contains five chromosomes and its genome has been fully sequenced.

For this experiment, we obtained T-DNA KO (AMY 1-2) seeds from Dr. Charles Guy of University of Florida, Gainesville, who obtained them from Salk lines (SALK_005044). The T-DNA KO (AMY 1-2) disrupts the At1g69830 gene which is responsible for the synthesis of plastidic α-amylase enzyme (AtAMY3).

AMY 1-2 and corresponding wild-type (WT) are in the Columbia background.

T

Plant Material and Growing Conditions Arabidopsis wild-type and α-amylase knockout plants were grown under a short day (10-hour photoperiod) in a Conviron (Pembina, ND, USA) growth chamber.

Two metal-halide lamps, supplemented by six 60-watt incandescent bulbs, were shaded by wire screen to provide a photon flux density of 150 µmol m-2 s-1.

The temperature of the chamber was 22°C during the day and 20°C at night. The humidity was maintained at 60%. All plants samples were taken between three and four weeks of growth.

Metabolite/Starch Extraction Whole leaf samples were taken in the middle of light and dark period for maltose, glucose-6-phosphate, and glucose assays. Detached leaves were quickly placed into a 1.5 mL microfuge tube and frozen in liquid nitrogen.

Then, leaf material was then ground to a powder while frozen and 300 µL of 3.5% perchloric acid was added to the leaf to stop metabolism and extract soluble carbohydrates. Samples were then vortexed and centrifuged for five minutes. After centrifugation, 200 µL of the

INVOLVEMENT OF α-AMYLASE IN TRANSITORY STARCH BREAKDOWN IN PLANTS

supernatant was pipetted off and saved for metabolite assays. The supernatant was then neutralized to a pH of 7 by adding 68 µL of neutralizing buffer containing 2 M KOH, 150 mM HEPES, and 10 mM KCl. The samples were then frozen in liquid nitrogen to precipitate salt, centrifuged, and the supernatant was placed in a new microfuge tube.

For starch assays, whole leaf samples were taken at beginning and end of the dark periods. Sample treatment was the same as for carbohydrate assays. For starch assays, the pellet was saved following the perchloric acid extraction of soluble carbohydrates. The pellet was then washed by adding 1 mL of 80% EtOH vortexed and centrifuged. Following centrifugation the EtOH was pipetted off and discarded. Samples were then placed in an oven at 40°C for 30 minutes to evaporate any remaining alcohol. Following alcohol removal, 500 µL 0.2 M KOH was added and the pellet was broken up using a sonicator. The samples were then incubated at 95°C for 30 minutes to gelatinize the starch. The samples were then allowed to cool at room temperature for five minutes. The pH was then brought to 5 by adding 115 µL of 1 M Acetic acid. An enzyme cocktail of amyloglucosides and α-amylase, 6 and 625 units respectively, were added to each sample (Figure 2A).

The starch samples were then incubated on a shaker at 150 rpm for two days. Following incubation, samples were heated at 95°C for 20 minutes to kill all enzymes.

Samples were then centrifuged and the supernatant recovered.

Metabolite Assays – Maltose, G6P, and Glucose Metabolite determinations were made using NAD(P)(H) linked assays in a Sigma ZFP 22 dualwavelength filterphotometer (Sigma Instrumente, Berlin, Germany). Maltose empimerase (MER), Maltose phosphorylase (MPL), and β-Phosphoglucomutase (β-PGM) (Kikkoman Corporation, Tokyo, Japan) were used. These enzymes allow accurate and precise measurements of maltose. Assays were done in a 50 mM KH2PO4 buffer, pH 7.4, containing 20 mM KCl, 10 mM MgCl2, 2 mM EDTA, 500 µM NADP, 500 µM ATP. One unit ml-7 of MER was added to mix at beginning of each day. A total of 800 µL buffer was added to a self-masking cuvette with 10 µL plant sample. The cuvette was placed in the spectrophotometer for 5 minutes before beginning the assay to allow the mixture to come to a room temperature. The reaction

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Figure 2.

NADP(H) linked starch and metabolite assays.

(A) Starch and glucose assay using hexokinase.

(B) Metabolite assay using MPL, bPGM, and G6PDH.

was started by adding 1 unit of Glucose-6-phosphate dehydrogenase (G6PDH) to obtain the G6P concentration followed by 2 units of MPL and 8 units of β-PGM to obtain the maltose concentration (Figure 2B).

Due to high concentrations of glucose in our samples, glucose was determined separately by first diluting our sample 100 times and then assaying with one unit G6PDH and one unit hexokinase (HXK) (Figure 2A).

We used 334 nm for the measuring wavelength and 405 for the control wavelength in the dual wavelength measurement system to allow very sensitive measurements.

Starch Assay Starch determination was also made using an NAD(P)(H) linked glucose assay. Samples were assays in a 50 mM KH2PO4 assay buffer, pH 7.4, containing 20 mM KCl, 10 mM MgCl2, 2 mM EDTA, 500 µM NADP, 500 µM ATP. One unit ml-1 of G6PDH was added to mix at beginning of each day. A total of 800 µL was added to a self-masking cuvette with 5 µL of 40 times diluted starch sample. The reaction was then started by adding one unit of hexokinase (HXK) (Figure 2A). The measuring and control wavelength were same as in the metabolite assay.

Results We did not observe any phenotypic differences between the wild type (WT) and α-amylase (KO) plants (Figure 3). All plants had a similar growth rate and completed their life cycle in the same amount of time.

There were no observed differences in seed yield between WT and α-amylase plants (data not shown).

Figure 3.

Arabidopsis thaliana plants. Top three plants are WT; bottom three are α-amylase KO (AMY 1-2) plants obtained from Salk lines (SALK_005044).

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A

B

Figure 4. Starch degradation and maltose accumulation in WT and α-Amy mutant.

(A) Amount of starch broken down at night. (B) Amount of maltose accumulated at night.

Values are mean + SE (n=3).

Figure 5. Day and night levels of G6P in WT and αamylase KO plants. Values are mean + SE (n=5).

Figure 6. α-amylase may provide substrates for the phosphorolytic pathway.

Starch could be broken down as supplement to the Calvin Cycle when photosynthesis is unavailable during light fluxes or in the morning.

Starch samples were taken at the beginning and end of dark period. We did not observe any significant difference in absolute starch levels or degradation rates between WT and α-amylase KO plants (Figure 4A).

Maltose was then measured from samples taken in the middle of the light and dark period. WT and α-amylase KO plants did not show any significant differences in maltose levels (Figure 4B). Glucose levels were also assayed in middle of the light and dark periods and no signifigant differences were observed (data not shown).

G6P levels of WT and α-amylase KO plants were measured in the middle of the light and dark period.

In WT plants G6P levels increased slightly from day to night by 26 nmol g-1 FW (Figure 5). However, G6P levels in the α-amylase KO plants decreased significantly from day to night by 34 nmol g-1 FW (Figure 5).

Discussion Because the growth rate and phenotype of the α-amylase KO plants were indistinguishable from the wild type throughout the plants’ life cycle, we concluded that α-amylase is not essential for plant growth, survival, and reproduction under our growth conditions. Absence of the plastdic α-amylase in Arabidopsis has little to no effect on transitory starch degradation during the night.

The rate of starch breakdown in α-amylase KO plants was the same as WT plants (Figure 4A). Maltose is the primary product of starch degradation at night, and maltose levels are a useful indicator of starch breakdown (Weise et al., 2003). We found maltose levels to be similar in the WT and α-amylase KO plants (Figure 4B). Since we did not observe any differences in starch or maltose levels between the mutant and WT, our results are in contradiction to the hypothesis that α-amylase is one of the first essential enzymes in starch degradation (Trethewey & Smith, 2000; Beck & Ziegler, 1989; Steup, 1988; Preiss, 1982).

INVOLVEMENT OF α-AMYLASE IN TRANSITORY STARCH BREAKDOWN IN PLANTS

While we did not observe any differences in starch levels or the products of hydrolytic starch breakdown, we did observe difference in the amount of G6P between the mutant and WT. In WT plants we measured a small increase in G6P from day to night, while we measured a significant decrease of G6P in the α-amylase KO plants at night (Figure 5). Starch phosphorylase catalyzes the phosphorolysis of the terminal residue from the nonreducing end of α-1,4-linked glucan chains, liberating glucose-1-phosphate (G1P) (Zeeman et al., 2004b). Glucose-1-phosphate is then converted to G6P by the action of phosphoglucomutase (PGI). Glucose-6phosphate could then be used by the oxidative pentose phosphate pathway (OPP) at night or by the Calvin cycle during the day (Figure 6). Glucose-1-phosphate levels in plants are typically below our level of detection; we therefore used G6P as a proxy for starch phosphorylase activity. We suggest that the decrease in G6P levels observed at night in the α-amylase mutant could indicate that the role of α-amylase is to provide substrate for starch phosphorylase. Whole leaf levels of G6P alone are not enough to completely deduce the activity of starch phosphorylase since significant levels of G6P are also produced in the cytosol by action of hexokinase in glycolysis. This hypothesis could be further tested through the use of nonaqueous fractionation, a method to separate the subcellular components of tissue, to determine the levels of G6P in the chloroplast only (Gerhardt & Heldt, 1984).

In conclusion, we determined that α-amylase is not necessary for plant growth and development under our growth conditions. Further, α-amylase is not required for the breakdown of transitory starch in Arabidopsis leaves. However, we suggest that α-amylase may provide substrate for starch phosphorylase.

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REFERENCES Beck E, Ziegler P. 1989. “Biosynthesis and degradation of starch in higher plants.” Annual Review of Plant Physiology and Plant Molecular Biology. 40: 95-117.

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Lizotte PA, Henson CA, Duke SH. 1990. “Purification and characterization of pea epicotyl α-amylase.” Plant Physiology.

92: 615-621.

Niittylä T, Messerli G, Trevisan M, Chen J, Smith AM, Zeeman SC. 2004. “A previously unknown maltose transporter essential for starch degradation in leaves.” Science. 303: 87-89.

Preiss J. 1982. “Regulation of the biosynthesis and degradation of starch.” Annual Review of Plant Physiology. 33: 431-454.

Rost S, Frank C, Beck E. 1996. “The chloroplast envelope is permeable for maltose but not for maltodextrins.” Biochimica et Biophysica Acta. 1291: 221-227.

Steup M. 1988. “Starch degradation.” In: Preiss J, ed.

Biochemistry of Plants, Vol. 14. Carbohydrates. New York, NY, USA: Academic Press, 255-296.

Sun Z, Duke SH, Henson CA. 1995. “The role of pea chloroplast α-glucosidase in transitory starch degradation.”

Plant Physiology. 108: 211-217.

Takaha T, Yanase M, Okada S, Smith SM. 1993.

“Disproportionating enzyme (4-alpha-glucanotransferase – EC 2.4.1.25) of potato – purification, molecular-cloning, and potential role in starch metabolism.” Journal of Biological Chemistry. 268: 1391-1396.

Trethewey RN, Smith AM. 2000. “Starch metabolism in leaves.” In: Leegood RC, Sharkey TD, von Caemmerer S, eds.

Advances in photosynthesis, Vol. 9. Photosynthesis: physiology and metabolism. Dordrecht, The Netherlands: Kluwer Academic Publishers, 205-231.

Weise SE, Weber APM, Sharkey TD. 2004. “Maltose is the major form of carbon exported from the chloroplast at night.”

Planta. 218: 474-482.

Yu T-S, Zeeman SC, Thorneycroft D, Fulton DC, Dunstan H, Lue W-L, Hegemann B, Tung S-Y, Umemoto T, Chapple A, Tsai D-L, Wang S-M, Smith AM, Chen J, Smith SM. 2005. “α-Amylase is not required for breakdown of transitory starch in Arabidopsis leaves.” The Journal of Biological Chemistry 280: 9773-9779.

Zeeman SC, Smith SM, Smith AM. 2004. “The breakdown of starch in leaves.” New Phytologist 163: 247-261.

Zeeman SC, Thorneycroft D, Schupp N, Chapple A, Weck M, Dunstan H, Haldimann P, Bechtold N, Smith AM, Smith SM. 2004. “Plastidial α-glucan phosphorylase is not required for starch degradation in Arabidopsis leaves but has a role in the tolerance of abiotic stress.” Plant Physiology 135: 849-858.

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Acknowledgements Managing Editors Eric Pantano and Mekel Wiederholt Meier Science Editors Alana Brennan, Eric Pantano and Mekel Wiederholt Meier Editorial Team Robert Growney, Anna McManus, Amy Schiebel, Robert Shannon and Rebecca Volkman Lead Research Teachers Alana Brennan, Eric Pantano and Mekel Wiederholt Meier Layout Jim Ottney – OpenWindow Design Printing and Production Manager Michael Elliot – Suttle-Strauss, Inc.

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