STUDENT CASE STUDY—WOLFSON
BLOOD DOPING: CHEATING OR LEVELING THE PLAYING FIELD?
Adele J. Wolfson, Schow Professor in the Physical and Natural Sciences; Professor of
Chemistry, Wellesley (MA) College
STUDENT CASE
Learning Objectives
1. Describe overall structure of proteins, including the amino acids that are linked
to make polypeptides (proteins). Describe how proteins can be modified by
addition of sugars. Describe how individual protein subunits can be combined
into larger units.
2. Interpret oxygen-binding curves for hemoglobin and myoglobin and discuss how
these are related to oxygen delivery.
3. Describe the steps involved in hormone signaling, particularly as related to
erythropoietin (EPO).
4. Evaluate clinical data on EPO treatment of anemia.
5. Explain the importance of statistical variability in a sample, and how it relates to
sample size
6. Assess methods for determining levels of EPO in body fluids. Explain how to
distinguish between signal and noise.
7. Summarize data graphically.
8. Evaluate conflicting interpretations of scientific data.
9. Consider a body of evidence to make a recommendation regarding use of
performance-enhancing drugs.
Preparatory Material
 Read carefully through this case study. You may also wish to refer to the Glossary in
Appendix A for definitions of terms used in this case.
 Review basic concepts of chemical bonding from previous chemistry course(s). Your
instructor will suggest texts if you need additional background.
 Watch Alex Gibney’s documentary, “The Armstrong Lie.” Your instructor will advise
about which portions of the two-hour film are most relevant.
 Read Malcolm Gladwell’s article in the New Yorker (September 9, 2013), “Man and
Superman.” Access it at
http://www.newyorker.com/arts/critics/atlarge/2013/09/09/130909crat_atlarge_gl
adwell?currentPage=all.
Introduction
In 2012, Lance Armstrong was stripped of his seven Tour de France titles and quickly fell
from his status as a venerated athlete to that of a despised cheater because of his use of
erythropoietin (EPO), a drug that raises a person’s red blood cell count. Contrast this
with the case of Eero Mantyranta, a Finnish cross-country skier, who in the 1960s was
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STUDENT CASE STUDY—WOLFSON
honored as an Olympic champion, winning a total of seven medals. Mantyranta’s
complexion was notably ruddy because he had a naturally high number of red blood
cells in his circulation.
What is the difference (physiologically and ethically) between being born with extra red
blood cells and taking a drug that increases your count? This case examines the data on
EPO: how is it used medically, and how effective is it? How is EPO detected in the body,
and why is it so difficult to tell if an athlete has taken the drug? Along the way you will
be introduced to the properties of the molecule EPO itself and how it acts, as well as to
hemoglobin, the oxygen-transport protein of blood, which makes up 95 percent of
protein in red blood cells. At the end of the case you will return to the question of the
legitimacy of using naturally occurring substances to enhance performance.
Exploring the Question 1
Proteins
The two important molecule “players” in this case are hemoglobin (Hb) and
erythropoietin (EPO). Hb is the oxygen-carrier in blood; it accounts for most of the
content of red blood cells. EPO is a hormone that signals bone marrow to make more
red blood cells.
Both Hb and EPO are proteins, one of the major classes of macromolecules in biological
systems. As the name macromolecule implies, these are very large molecules with
molecular weights ranging from thousands to hundreds of thousands. The synthesis of
such large molecules would be a real problem for the cell if they were put together as a
chemist might do it in the lab, bit by bit. Instead, for the most part, the macromolecules
are put together from subunits, or monomers. So, the macromolecules are polymers
made from monomers, of which there are a limited number, and these monomers are
strung together by the same reaction over and over again. Proteins are biological
macromolecules that are made up of one or more polypeptide, each of which is a chain
of amino acids.
1
Unless otherwise noted, figures are from Ahern, K. and I. Rajagopal. Version 2.0 Biochemistry Free and
Easy, 2012, 2013, available for download at: http://biochem.science.oregonstate.edu/biochemistry-freeand-easy.
Some questions/exercises are adapted from Loertscher, L., and V. Minderhout. 2010. Foundations of
Biochemistry. Lisle, IL: Pacific Crest.
For biochemistry topics, any textbook including the Ahern and Rajagopal reference cited above, is
appropriate. I also recommend, Biochemistry: A Short Course by Tymoczo, J. L., J. M. Berg, and L. Stryer.
2013. New York: W. H. Freeman.
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The monomers for proteins are amino acids. All amino acids have the overall structure
shown in Figure 1:
(http://biochemportal.wordpress.com/2013/03/17/amino-acids-and-meth/)
Figure 1. Generalized Structure of an Amino Acid.
There are 20 amino acids that occur naturally in proteins. The letter R in the general
structure refers to one of the 20 chemical groups that confer particular physical or
chemical properties (charge, interactions with water, etc.) to each.
In a protein, amino acids are connected by (strong) covalent bonds called peptide bonds
(Figure 2):
Figure 2. Generalized Structure of a Peptide.
Based on its unique amino acid sequence, each polypeptide folds into the most stable
three-dimensional shape, and it is this shape that determines the specific function
(activity) of the protein. The three-dimensional structure is stabilized by interactions
called non-covalent because they are weaker than covalent ones, but they are still very
important in the aggregate.
Some proteins have more than one chain (subunit), and these come together in specific
ways that are also stabilized by non-covalent interactions. Hb is an example of one such
protein. It is composed of four subunits, two of each of two kinds. Proteins with multiple
subunits can be regulated in complex ways. More details of this regulation are discussed
below.
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STUDENT CASE STUDY—WOLFSON
Additionally, some proteins have additional “decoration” added to them after they are
synthesized. EPO is an example of such a protein. It has sugar molecules added onto
some of its amino acids. Sugars account for approximately 40 percent of its weight. As
we will see in later sections, the pattern of these sugars can tell us about the origins of
the EPO.
Many proteins also have non-amino acid, or prosthetic groups. One of these is heme,
discussed on page 5, below.
Hemoglobin
Examine the binding curves below (Figure 3) for hemoglobin and the related protein
myoglobin (Mb). The units for pressure are torr, sometimes expressed as “mm Hg.”
Typical oxygen pressure in the lungs is ~100 torr and in body tissues is ~40 torr. Note
that “percent saturated with oxygen” can also be described as “percent of subunits that
have oxygen bound.”
Myoglobin can be considered an oxygen-storage protein in muscle tissue. Recall that
hemoglobin circulates in the blood. Answer the questions that follow:
Figure 3. Oxygen-binding Curves for Myoglobin and Hemoglobin.
Questions
1. What is plotted on the x-axis? On the y-axis?
2. How does the percent saturation change with increasing oxygen concentration
(O2 pressure) for both Mb and Hb?
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STUDENT CASE STUDY—WOLFSON
3. What does this graph tell you about how hemoglobin effectively delivers oxygen
from lungs to tissue?
4. We define p50 as the pressure (concentration) of oxygen when Hb or Mb is halfsaturated. Is the p50 for Mb greater than or less than that for Hb?
5. How is p50 useful for quantification of Hb or Mb binding of oxygen?
6. On the graph, draw a new curve for hemoglobin that is shifted to the right. How
does the value of p50 for the new curve compare to the original p50? How does
the oxygen-binding affinity of the hemoglobin represented in the new curve
compare to that of the original? What would that mean for delivery to the
tissue?
7. How would shifting the curve to the left affect p50? In what part of the body
would it be useful for this to occur?
The shape of the oxygen-binding curve of Hb is typical of cooperative binding. That is,
binding of one molecule of O2 to Hb makes it easier to bind the next molecule of O2.
Cooperativity almost always requires a protein with multiple subunits. The interactions
among subunits, along with binding of some small regulatory molecules, lead to the
cooperative effects. This behavior allows Hb to be fully loaded with O2 in the lungs, but
to unload O2 to Mb in the tissues. Mb, with only a single subunit, does not display
cooperativity.
The actual binding site for oxygen in Hb (and Mb) is the iron ion in the center of a
molecule called heme; Figures 4a and 4b display this structure:
Figures 4a and 4b. Structure of the Heme Prosthetic Group: (a) Chemical Structure of
the Isolated Group; (b) Shorthand Version of the Ring Structure with Coordinated
Amino Acid Side chains in Hemoglobin, with Oxygen Bound.
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STUDENT CASE STUDY—WOLFSON
Because iron is essential for synthesis of Hb, the increase in levels of Hb and red blood
cells is accompanied by a decrease in stored iron from the storage protein ferritin,
particularly in bone marrow, liver, and spleen.
There is one heme for each of the subunits in Hb (i.e., four total). When oxygen binds to
the iron in heme, it causes major changes in the overall structure of the protein, so
much so that crystals of Hb will crack if oxygen is diffused in. The changes in structure
on binding are responsible for much of the behavior of Hb related to binding oxygen in
the lung and releasing it to the organs as needed.
Erythropoietin
EPO is produced in the kidney in response to low oxygen levels and acts on bone
marrow cells to produce red blood cells (Figure 5).
http://classes.midlandstech.com/carterp/Courses/bio211/cha/Slide7.JPG
Figure 5. Physiological Role of EPO.
EPO is an example of a protein that signals cells to “turn on” specific genes and produce
more of a particular product, usually a protein. Figure 6 presents a general scheme for
such signaling molecules. As the diagram shows, the hormone or growth factor does not
enter the cell but rather attaches to a specific protein embedded in the outer
membrane, called a receptor. Binding of the hormone or growth factor generates signals
inside the cell that result in activation of one or more genes in the nucleus of the cell.
Each gene is a specific DNA region that encodes the information for one polypeptide.
When a gene is activated, thousands of copies of this genetic information are
synthesized (“transcribed”) and transported to the cytoplasm of the cell, where they
provide instructions for the synthesis of the polypeptide encoded by the gene.
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STUDENT CASE STUDY—WOLFSON
http://www.hartnell.edu/tutorials/biology/signaltransduction.html
Figure 6. Generalized Scheme for Binding of a Signaling Molecule to the Outer
Membrane, Triggering Events in the Cells via Second Messengers (“Relay Molecules”).
More details for EPO are shown below in Figure 7.
http://www.ijem.in/articles/2012/16/2/images/IndianJEndocrMetab_2012_16_2_220_
93739_f3.jpg
Figure 7. Specific Signaling Used by EPO.
JAK = Janus kinase and STAT = Signal Transducer and Activator of Transcription. Note
that there are many STATs.
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STUDENT CASE STUDY—WOLFSON
Questions
Use Figure 5 to help answer these questions about Figures 6 and 7.
1. Using the nomenclature from the Figure 6, above, what in Figure 7 might be
considered the “signaling molecule?” the “relay molecules”?
2. What do you expect is the target cell?
3. Which gene(s) do you expect will be “turned on” in these target cells?
As described above, EPO is a glycoprotein. The figure below shows that sugars are
attached to the protein. This occurs through bonds to specific amino acids making up
the protein. In the figure, the "ribbon" represents the protein portion, while the other
shapes identified in the key are different types of sugars (carbohydrates).
Wu, B. , J. Chen, J. D. Warren, G. Chen, Z. Hua, and S. J. Danishefsky. 2006. “Building
Complex Glycoproteins: Development of a Cysteine-free Native Chemical Ligation
Protocol,” Agnew. Chem. Int. Ed. 45: 4116–25. Copyright © 2006 WILEY-VCH Verlag
GmbH and Co. KGaA, Weinheim.
Figure 8. EPO as Naturally Occurring, with Sugars Attached.
The sugars do not seem to affect the way that EPO attaches to bone marrow cells to
signal production of Hb and red blood cells, but they do influence how long EPO can
circulate in the bloodstream.
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STUDENT CASE STUDY—WOLFSON
Interpreting Hb and EPO Levels in Groups and Individuals
Naturally occurring levels:
The amount of hemoglobin in a person’s body is reported in g/dL (grams per deciliter, or
100 mL, of blood). The normal ranges are:
Women: 12.3–15.3 (according to some sources, 12–16) (mean 13.8) g/dL
Men: 14.0–17.5 (according to some sources, 13.5–18) (mean 15.7) g/dL
(http://emedicine.medscape.com/article/2085614-overview, original reference:
Vajpaye et al. 2011; Dailey 2001)
Questions
1. Graph the values of hemoglobin for men and women in whatever format you
think will convey the information.
2. How would you describe in words the differences between men and women?
The differences within each of those groups?
3. If you were given a value for hemoglobin in a given sample, would that allow you
to determine whether that sample came from a male or female? Why or why
not?
You may also see reports of the hematocrit, the volume taken up by red blood cells
compared to the total blood volume.
Now let’s look at EPO and hematocrit distributions in people with diagnosed illnesses,
compared to normal values. Consider the graph below (Figure 9) and answer the
questions that follow:
Other
Bunn, H. F. 2013. Cold Spring Harbor Perspectives in Medicine, 3(3):a011619. © 2013
Cold Spring Harbor Laboratory Press.
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STUDENT CASE STUDY—WOLFSON
Figure 9. Plasma EPO Levels (milliunits/mL) in Patients with Different Types and
Degrees of Anemia and other Conditions.
Questions
1. What is on the x-axis? On the y-axis?
2. Note that EPO levels are plotted on a logarithmic scale. What does this mean?
Why would a researcher choose to use this scale?
3. Give the range for normal levels of EPO and for normal hematocrit readings.
4. Give the range for levels of EPO and for hematocrit readings for the condition
identified here as “uremia.”
5. Give the range for levels of EPO and for hematocrit readings for the condition
identified here as “PCV.”
6. If you were given a value for hematocrit for a particular patient, could you
predict EPO levels in that patient?
Figure 10 takes a closer look at just two groups: those with anemia and normal blood
donors.
From New England Journal of Medicine, Erslev, A. J. “Erythropoietin,” 324(10): 1340.
Copyright © 1991, Massachusetts Medical Society. Reprinted with permission from
Massachusetts Medical Society.
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STUDENT CASE STUDY—WOLFSON
Figure 10. Plasma EPO Levels in Normal Blood Donors and Patients with Anemia.
Triangles represent normal donors, squares those of various anemias. The dashed line
represents the limit of detection of the assay.
Questions
1. Compare Figure 10 with Figure 9, above. What are the differences in units? In
range?
2. What relationship between plasma EPO and hematocrit emerges more clearly
from this figure than from Figure 9?
3. What is meant by the “limit of detection”? If the researchers obtained a value of
plasma EPO of 2 U/liter, how confident could you be that this number is larger
than 1 U/liter? If the researchers told you that EPO was absent from the sample,
could you say with confidence that there was no EPO present? Sometimes,
distinguishing between “nothing” and “some particular value” is referred to as
the difference between “signal and noise.” Is this a good term for the
phenomenon? What is the “signal” and what is the “noise?”
Therapeutic use:
EPO has been used for at least 30 years to increase Hb and red blood cell production in
patients with anemia.
Consider these data (Table 1) from one of the earliest reports of use of EPO in anemia:
From J. W. Eschbach, J. C. Egrie, M. E. Downing, J. K. Browne, and J. W. Adamson. 1987.
“Correction of the Anemia of End-stage Renal Disease with Recombinant Human
Erythropoietin,” New England Journal of Medicine, 316: 75. Copyright ©1987
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STUDENT CASE STUDY—WOLFSON
Massachusetts Medical Society. Reprinted with permission from Massachusetts Medical
Society.
Questions
1. What is meant by “mean ± S.D.”? What does the “±” value tell you about
precision of measurements?
2. The means in Table 1 are estimates based on the hematocrits measured for a
limited number of patients. How would increasing the number of patients affect
your certainty about how good these estimates are?
3. What appears to be the relationship between dose of EPO and hematocrit?
4. Why was it important to establish a base line before administering the
therapeutic doses?
The patients entered into this study met the following criteria, among others:
- They were in a particular age range.
- They had hematocrit below a certain value.
- They had not lost blood due to any other reason than their anemia.
- They had no other diseases that might mask the effects of the treatment.
Questions
5. Think about what question the investigators actually wanted to answer. What
would the ideal (best case) data set look like to answer that question?
6. Given that the ideal experiment can never be done, what data set could they get
that would allow them to estimate the answer?
7. Is their set of patients closer to “ideal” or to “data they could get”?
Use of EPO in sports:
EPO has been banned from the Olympics since 1990. In order to determine whether or
not athletes were using EPO illegally, officials needed methods to measure or otherwise
detect its presence.
One way to measure EPO is to look for it in urine or blood. Natural and artificial EPO
could be distinguished in the early days of its production because the sugars attached to
the protein (see Figure 8) were slightly different when produced in the lab (in nonhuman cells) compared to those attached in the body. Some of these sugars have
charges (positive or negative) associated with them, so that the overall protein moves
differently in an electric field depending on the nature and number of these sugars2. The
amount of EPO protein, natural or artificial, can then be quantified. Figure 11 shows an
example of what the samples look like when subjected to the method:
2
You can read more about this technique (electrophoresis) in Tymoczo, Berg, and Stryer (2013), 72–74.
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STUDENT CASE STUDY—WOLFSON
Reprinted by permission from Macmillan Publishers, Ltd.: Lasne, F., and J. de Ceaurriz.
2000. “Recombinant Erythropoietin in Urine,” Nature, 405(6787): 635. Copyright 2000.
http://www.nature.com/nature/index.html
Figure 11. Patterns of Natural and Artificial EPO Obtained from Urine
(a) Natural Human EPO; (b) Recombinant (Artificial) EPO Source 1; (c) Recombinant
(Artificial) EPO Source 2; (d) Urine from Control Subject; (e) and (f): Urine from Two
Patients Treated with Recombinant EPO; (g) and (h): Urine from Two Cyclists in the
Tour de France.
Questions
1. Why is it important to include standards alongside the samples collected from
athletes?
2. Can you see a clear distinction between the natural and artificial standards?
3. Can you see a clear distinction between individuals treated with artificial EPO
and those who have not been treated? Which area(s) of the gel would you want
to examine in order to make that decision?
4. Does your answer to (3) allow you to draw a firm conclusion about whether or
not the cyclists have taken artificial EPO?
Unfortunately, this method is very time consuming, and the amount of EPO is
undetectable in 3–7 days after treatment. Furthermore, more recent versions of
recombinant EPO are indistinguishable by this test from the natural form.
Another method to detect artificial EPO use is to look for its physiological effects on
hematocrit, hemoglobin concentration, or amount of protein that transfers iron.
Because of doping scandals in sports, some associations have introduced an upper limit
for acceptable Hb concentrations in blood. Figure 12 comes from Nordic ski
competitions in the 1980s and 1990s:
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STUDENT CASE STUDY—WOLFSON
From Videman et al. 2008. “Changes in Hemoglobin Values in Elite Cross-country Skiers
from 1987 to 1999.” Scandinavian Journal of Medicine and Science in Sports, 10(2): 98–
102. Copyright ©2008 John Wiley and Sons.
Figure 12. Mean Values (bars), Standard Deviations (solid vertical line), and Maximum
Individual (dotted vertical line) for Hemoglobin Values from Cross-country Skiers. The
lower horizontal line represents the population mean and the upper horizontal line the
means for several ski nations.
Questions
1. What is your interpretation of the “mean” and “standard deviation”? How do
they relate to the ranges and separation of values you have seen earlier?
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STUDENT CASE STUDY—WOLFSON
2. Select one year prior to the “FIS Hb rule” introduction. Report the mean Hb level
in women that year, the standard deviation of that mean, and the maximum
value for any individual skier. Do the same for men in that same year.
3. How do the values for these female and male skiers compare to the population
means?
4. Could any of the skiers in the year you analyzed show a Hb value below that of
average people in the population sample?
5. If the FIS Hb rule eliminated from competition men with Hb levels greater than
170 g/L and women with levels greater than 160 g/L, would that exclude any of
the athletes? Any of the non-athletes who wanted to compete?
6. What problems do you see in a specific Hb cutoff for sports events? (Hint: go
back to data on normal ranges. You will need to convert between g/L and g/dL.)
7. Which of the methods you have seen for assessing EPO (Figures 11 and 12 and
related text) are direct measures, and which are indirect measures? What are
the advantages and disadvantages of each?
Final Assignment
Your last assignment for this case study is an “intimate debate.” To prepare for this
assignment, read the two papers included in a section on Current Controversies: “The
Banning of Sportsmen and Women Who Fail Drug Tests Is Unjustifiable.” 2013. Journal
of Royal College of Physicians of Edinburgh, 43(1): 39–43
(http://www.rcpe.ac.uk/journal/issue/journal_43_1/currentcontroversy.pdf):


Shuster, S., “Controversy about Drugs in Sport Is More about a Belief than
Reason.”
Devine, J. W., “Doping Is Bad in Sport because Doping Is Bad for Sport.”
You may also wish to read about the difficulty of catching athletes who dope with EPO:
http://hum-molgen.org/NewsGen/08-2004/000019.html.
Divide into pairs. Within each pair, one partner should take the position for banning
performance enhancing drugs, particularly EPO, and the other partner should take the
position against. Then, switch sides and argue the opposite. Having heard and
questioned one another, the pair should come to agreement and bring a
recommendation to the full class. The class overall will then summarize arguments and
develop a consensus position that might be presented to the World Anti-Doping Agency.
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References
Ahern, K., and I. Rajagopal. 2013. 2.0 Biochemistry Free and Easy, Version 2.
http://biochem.science.oregonstate.edu/biochemistry-free-and-easy.
Bunn, H. F. 2013. “Erythropoietin.” Cold Spring Harbor Perspectives in Medicine, 3(3):
a011619.
Dailey, J. F. 2002. Blood, Second Edition. St. Louis, MO: Cache River Science/Quick
Publishing.
Devine, J. W. 2013. “Doping Is Bad in Sport because Doping Is Bad for Sport.” Journal of
Royal College of Physicians of Edinburgh, 43(1): 41–43.
Erslev, A. J. 1991. “Erythropoietin.” New England Journal of Medicine. 324(10): 1339–
44.
Eschbach, J. W., J. C. Egrie, M. E. Downing, J. K. Browne, and J. W. Adamson. 1987.
“Correction of the Anemia of End-stage Renal Disease with Recombinant Human
Erythropoietin.” New England Journal of Medicine, 316(2): 73–78.
Lasne, F., and J. de Ceaurriz. 2000. “Recombinant Erythropoietin in Urine.” Nature, 405
(6787): 635.
Loertscher, J., and V. Minderhout. 2010. Foundations of Biochemistry, Lisle, IL: Pacific
Crest.
Shuster, S. 2013. “Controversy about Drugs in Sport Is More about a Belief than
Reason.” Journal of Royal College of Physicians of Edinburgh, 43(1): 39–41.
Tymoczo, J. L., J. M. Berg, and L. Stryer. 2013. Biochemistry: A Short Course, 2nd ed. New
York: W. H. Freeman.
Vajpayee N., S. S. Graham, and S. Bem. 2011. “Basic Examination of Blood and Bone
Marrow.” In Henry's Clinical Diagnosis and Management by Laboratory Methods, 22nd
ed., edited by R. A. McPherson and M. R. Pincus, 509–35. Philadelphia: Saunders.
Videman, T., I. Lereim, P. Hemmingsson, M. S. Turner, M. -P. Rousseau-Bianchi, P.
Jenoure, E. Raas, H. Schönhuber, H. Rusko, and J. Stray-Gundersen. 2000. “Changes in
Hemoglobin Values in Elite Cross-country Skiers from 1987 to 1999.” Scandinavian
Journal of Medicine and Science in Sports, 10(2): 98–102.
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About the Author
Adele Wolfson is the Schow Professor in the Natural and Physical Sciences and a
professor of chemistry at Wellesley College. She received her AB in chemistry from
Brandeis University and her PhD in biochemistry from Columbia University and has
worked or studied in Israel, France, and Australia. Her scientific research is in the area of
protein biochemistry, particularly the role of neuropeptidases in reproduction and
cancer. She also conducts research on educational and pedagogical topics, including
concept inventories for biochemistry and the ways that students connect learning
between science and non-science courses. Her major professional association is through
the American Society for Biochemistry and Molecular Biology; she currently chairs a
committee overseeing departmental accreditation of biochemistry programs. She has
been a workshop leader and consultant for Project Kaleidoscope and for AAC&U, a
member of the editorial board of Biochemistry and Molecular Biology Education, and on
the advisory boards of an ADVANCE program, POGIL Biochemistry project, and the
Biology Scholars Writing Project, among others. At Wellesley, Adele has held many
administrative positions, including director of the Science Center, director of the
Learning and Teaching Center, Associate Dean of the College, and director of the ThreeCollege Collaboration. In 2013, she was elected AAAS Fellow for her contributions to
undergraduate biochemistry and molecular biology education and increasing
participation of underrepresented groups. Professor Wolfson was appointed an
American Association of Colleges and Universities (AAC&U) STIRS Scholar in 2013.
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Appendix A
Glossary
Anemia: A medical condition characterized by lack of red blood cells.
Amino acids: The units that make up proteins.
Bond (see Chemical bond)
Chemical bond: The attraction between atoms that makes chemical compounds or
molecules. These may be covalent, in which electrons are shared, or non-covalent, due
to temporary or permanent electrostatic attractions.
Cooperativity: The influence that binding of one molecule of ligand has on binding of
another molecule of ligand at a separate site on a protein; results from interactions
among subunits of a multi-subunit protein.
Covalent bond (see Chemical bond)
Electrophoresis: A method for separating molecules in an electric field based on
differences in their charges.
Erythropoeitin (EPO): A hormone produced in kidney that signals production of red
blood cells.
Glycoprotein: A protein with attached carbohydrates (sugars).
Hematocrit: The ratio the volume of red blood cells to total blood volume.
Heme: The non-protein portion of hemoglobin and myoglobin, and the portion of the
molecule responsible for binding oxygen.
Hemoglobin: The oxygen-carrying protein in blood.
Macromolecule: A very large molecule. The term usually refers to molecules of
biological importance, such as proteins and DNA, with molecular weights upwards of
several thousand to several million.
Mean: A statistical term referring to the arithmetic average.
Monomer: A small molecule that can be combined to make larger ones. The term may
refer to units that are linked together covalently into a polymer (such as amino acids
into proteins) or to a larger unit such as a protein chain that is combined with other
subunits in a non-covalent way to make a multi-subunit complex.
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Myoglobin: The oxygen-binding protein in muscle.
Polymer: A molecule made by linking similar or identical subunits (monomers) together,
usually covalently.
Protein: A biological molecule made up of one or more polypeptide chains, which are in
turn made up of amino acids linked covalently to one another.
Receptor: A protein that binds a hormone or other signaling molecule and triggers
further responses within a cell.
Standard Deviation: A statistical term referring to how far data are spread out from the
mean.
Subunit: A single polypeptide chain in a protein that contains more than one such chain.
Transcription: The process by which the information in DNA is used to direct synthesis of
messenger RNA. This is often a control point in hormonal regulation.
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Appendix B (Optional)
A Brief Guide to Statistics3
This Appendix briefly describes three important statistical concepts: What is a P-value?
What is a 95% confidence interval? And what is statistical significance?
When evaluating a particular result from a research study, we must consider three
general explanations:
 the finding is real;
 the finding can be explained by bias (i.e., systematic flaws in the way a study
population was selected or variables were measured, or errors from being
unable to account for all relevant factors); and
 the finding occurred by chance.
Researchers use statistical testing to evaluate the role of chance (i.e., to figure out how
likely it is that chance might be accounting for the observed result). We call this “testing
for statistical significance.” Statistical significance can be evaluated from P (probability)
values and confidence intervals.
According to the Dictionary of Epidemiology (Last 2001), a P (probability) value is “the
probability that a test statistic would be as extreme as or more extreme than observed if
the null hypothesis were true.”
For example, a study of cross-country skiers (Morkeberg et al. 2009), following up on
the Videman et al. (2000) paper cited in the case study, examined hemoglobin levels in
elite skiers following the expansion of blood-testing programs at major events. The null
hypothesis was that there is no difference in these values before and after expanded
testing was initiated. In fact, when results were compiled, hemoglobin in both male and
female athletes decreased from the levels of the 2001–02 season, when the program
was first announced, to lower values in subsequent seasons. For males, there was a
decrease in hemoglobin from 2001–02 to 2002–03 of 0.5 g/dL. The P value was less than
0.001. This means that a difference of 0.5 g/dL (or more) would likely occur by chance
alone less than 1 in 1,000 times if the null hypothesis were true (that is, if there was
really no difference in the hemoglobin levels from one period to the next). Because 1 in
1,000 is very unlikely, we reject the null hypothesis and we say that the 0.5 g/dL
decrease observed difference is statistically significant. That is, it was unlikely to be
explained by chance. Researchers often use a 5 percent cutoff to evaluate statistical
significance; that is, if P < 0.05, then they will reject the null hypothesis and declare that
an observed difference is statistically significant.
3
Adapted from Hunting, Katherine. Organic Foods: Examining the Health Implications.
Case Study for AAC&U STIRS Project, 2014.
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STUDENT CASE STUDY—WOLFSON
Here is one definition of the confidence interval: “The confidence interval describes the
uncertainty inherent in [an] estimate, and describes a range of values within which we
can be reasonably sure that the true effect actually lies . . . [This] is based on the
hypothetical notion of considering the results that would be obtained if the study were
repeated many times” (The Cochrane Collaboration 2011). Considering the same
Morkeberg analysis of changes in blood profiles, if the 95% confidence interval around
the 0.5 g/dL difference were 0.42–0.58 g/dL, it would mean that if the research was
repeated, 95 percent of the time the observed difference would lie between 0.42–0.58
g/dL. The 95% confidence interval is also the range of values that fall within
approximately two standard deviations of the mean if the data are distributed normally.
In the Morkeberg example, a 95% confidence interval of 0.42–0.58 g/dL would definitely
exclude the null difference of 0 g/dL. This would support the conclusion already made
above from the P value, that we can reject the null hypothesis and declare there is a
statistically significant difference in the likelihood that the difference between the two
time periods is real.
References
Cochrane Collaboration. 2011. “Glossary of terms.”
http://www.support-collaboration.org/summaries/c.htm. Accessed September 17,
2014.
Last, J. (editor). 2001. A Dictionary of Epidemiology. New York: Oxford University Press.
Morkeberg J., B. Saltin, B. Belhage, and R. Damsgaard. 2009. “Blood Profiles in Elite
Cross-country Skiers: A Six-Year Follow-Up.” Scandinavian Journal of Medicine and
Science in Sports, 19(2): 198–205.
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