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Summary

The purpose of this study was to compare 100% polylactic acid (PLA) biodegradable plastic to a low-density polyethylene plastic (LDPE) in terms of their effectiveness against food spoilage.  The interests of this study were: 1) the type of plastic that is more effective in preventing food spoilage, and 2) the materials’ properties, which are key factors in preventing food spoilage.  Three trials were conducted testing the two plastics, in which an apple half was wrapped in either 100% biodegradable plastic, LDPE plastic, or no plastic at all (control).  Over a period of 11 days, the daily mass of the apple was measured to determine the type of plastic that was more effective in preventing food spoilage.  The results showed that in the long term (11 days), the LDPE plastic was more effective in preventing food spoilage than the biodegradable plastic. By day 11, the apples in LDPE plastic lost about 4.84% of their original mass, the apples in biodegradable plastic lost 18.25% of their mass, and the control apples lost about 56.11% of their mass.  However, in the short term (1-3 days), both the apples in LDPE plastic and the apples in the biodegradable plastic lost a similar amount of their mass (2-4%) while the control apples lost about 20% of their mass.  The results demonstrate that the biodegradable plastic can prevent food spoilage as effectively as the LDPE plastic in the short term.  Hopefully, this will increase the appeal of biodegradable bags to consumers due to its ability to reduce the amount of trash in landfills.

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Received: September 29, 2011; Accepted: November 1, 2011; Published: March 20, 2012

Copyright: © 2012 Zhang et al. All JEI articles are distributed under the attribution non-commercial, no derivative license (http://creativecommons.org/licenses/by-nc-nd/3.0/). This means that anyone is free to share, copy and distribute an unaltered article for non-commercial purposes provided the original author and source are credited.

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Introduction

Packaging containers, bags, and wraps made up 56% of the total plastic waste and 31% of the total solid waste in 2005, and this percentage is annually increasing (1).  Plastic packaging waste is a large contributor to the growing landfill problem.  In the past, much research has been done to test the biodegradability of these types of plastics; however, comparisons between biodegradable plastics and generic plastics in their effectiveness in the prevention of food spoilage, which interests the consumer, has not been deeply examined.  Hence, it is important to compare the effectiveness of a biodegradable plastic in preventing food spoilage with that of a generic plastic.

Food spoilage is when the original nutritional value, texture, or flavor of the food are damaged and the food becomes harmful to people and unsuitable to eat (2).  Food spoils mainly because of moisture, oxygen, light, and microorganisms.  Oxygen can have deteriorating effects on fats, food colors, vitamins, and flavors, and can create conditions suitable for the growth of microorganisms. Oxygen also causes oxidative spoilage, which is the main cause of quality loss in fats (2).  Oxidation is the loss of electrons.  Oxidation not only starts to degrade the apple’s nutrients, but atmospheric oxygen can react with some food components which may cause rancidity or color changes (2).  Certain enzymes in foods can speed up the chemical reactions between oxygen and food.  In apples, the specific enzyme that causes the brownish color to appear is called polyphenol oxidase. Exposure to light can cause food spoilage through photodegradation (degradation by UV light) in the pigments, fats, proteins, and vitamins of food.   In solid foods, photodegradation occurs where the light penetrates the outer layer of the food.  In liquids, light penetration can be greater, but the light penetration depends on factors such as the light source strength and the type of light emitted (2).

In this study, the focus will be on examining the influence of moisture on food spoilage.  Normally, barrier materials (such as non-biodegradable and biodegradable plastics) have the ability to restrict the passage of gases, vapors, and organic liquids through their boundaries.  In simpler terms, barrier materials prevent substances inside the barrier from escaping, and outside substances from entering the barrier.  Therefore, mass is the best indicator of the amount of substances moving in and out of the barrier.  However, mass loss can be caused by many factors, including moisture loss and microbial degradation (3).  Low-density polyethylene (LDPE) is gaining market share in food industrial applications and is now used in both Saran Wrap^® and Glad^® Cling Wrap.  LDPE is flexible, transparent, resists tearing, and acts as a moisture barrier.  It also has very good resistance to acids, bases, and vegetable oils.  Because of this excellent combination of properties, it is widely used for packaging applications (4).  The water vapor transmission rate (WVTR) is one of the key indicators for determining a plastic wrap’s effectiveness in preventing food spoilage. It is defined as the rate at which water vapor can move from one side of the barrier to the other.  The transmission rate of gases and vapors depends on both the solubility of the gases and their rate of diffusion through the barrier (which depends on the configuration of the barrier polymer) (3). Current biodegradable plastic wraps (ex. PLA) have a higher WVTR than LDPE plastic wrap (3).

Permeability is a function of both the permeance and the thickness of the barrier material. Permeation, which includes the rate at which a gas passes through a barrier material, is affected by the characteristics of the polymer, such as its chemical makeup.  Permeability is also affected by the molecular organization of the polymer, such as crystallinity: crystallites are impermeable, so a polymer with a higher degree of crystallinity will have a lower amount of permeation, resulting in it being a better barrier.  Permeability is also affected by temperature, humidity, and pressure.  For example, every 5^oC increase in temperature can result in a permeability increase of 30-50%, making it a worse barrier (3).

The interests of this study were: 1) which type of plastic wrap is more effective in preventing food spoilage: a generic, non-biodegradable plastic wrap or a 100% biodegradable plastic wrap; 2) the material properties which prevent food spoilage; and 3) the average amount of time for each plastic wrap that it takes for the apple to spoil by measuring the average accumulated rate of mass loss over time.  The purpose of this study was to compare the effectiveness of a 100% biodegradable plastic (BioMass^®) with a low-density polyethylene (LDPE) plastic (Glad^®) in preventing food spoilage.  Three experimental trials were conducted to test the LDPE wrap and the biodegradable wrap in a controlled environment covering a Gala apple to evaluate their effectiveness in preventing food spoilage.  Because LDPE has a lower WVTR than the biodegradable plastic, it was initially thought that the generic LDPE plastic would be more effective than the biodegradable plastic in preventing food spoilage.  In the short term (3 days), the LDPE and biodegradable wrap performed similarly in preventing food spoilage, while in the long term (11 days), the LDPE wrap performed more effectively in preventing food spoilage than the biodegradable wrap.

Results

The independent variable of this experiment was the type of plastic: a generic LDPE plastic versus a biodegradable BioMass^®plastic, while the dependent variable was the effectiveness of the plastic in preventing food spoilage (specifically measured by the average rate of accumulated mass loss).  The control was an apple with no barrier, which was used to compare the results of the apples wrapped in the plastics to an apple with no barrier.  The sample size was half of an apple and 3 samples in each condition (and 4 for the control, as there were 10 apple halves).  The purpose of using half an apple for experiments was to accelerate the apple spoilage because the skin protects the apple from exposure to light and oxygen and thus slows its spoilage process.  Multiple trials were conducted at once to make sure the conditions (temperature and air moisture) were the same and to reinforce the accuracy of the experimental results.

Two criteria were used for determining which barrier material was better in preventing food spoilage: 1) quantitative method—mass loss, measured by taking the mass of the apples daily (Table 1); and 2) qualitative method—visual observations (which includes changes in apple texture, color, and odor), measured by drawing and taking pictures of the apples.  More mass loss demonstrates a poorer barrier material.  When color and texture begin to change, it shows that the quality of the apples is starting to degrade, due to food spoilage because of oxidation and photodegradation.

There were 2 outliers in the data, but these outliers did not affect the data consistency too dramatically.  On Day 3, Apple 1A increased in mass from the day before by 0.262 grams and on Day 8, Apple 5A increased in mass from the day before by 1.173 grams.  Both the outliers were apples in LDPE.

In the analysis, the raw data of the mass of the apple every day was first changed to the accumulated mass loss each day by calculating the difference between the current mass and the original mass.  Then the mass loss was changed into the rate of mass loss by dividing the original mass by the accumulated mass loss at that day and then converting the decimal into a percentage. The average rate of accumulated mass loss for each experimental group was determined (Table 2) and then plotted on a graph (Figure 1). The data shows that on average, by Day 11 (the end of the experiment), the control apples lost 56.11% of their mass, the apples in Glad^® lost 4.84% of their mass, and the apples in the BioMass^® bags lost 18.25% of their mass.

Furthermore, oxidation also played a role in the experiment. This starts to promote food spoilage as the original nutrient value starts to break down.  For example, by Day 4 (Figure 2B), the food spoilage has even begun to occur on the apples wrapped with the generic non-biodegradable LDPE plastic and it shows that the color change occurred most severely in the apples in the control group, followed by the biodegradable and the generic groups.  On Day 8 (Figure 2C), the apples are spoiling even more.  At the end of the experiment, Day 11 (Figure 2D), all tested apples spoiled.  Slices of the apples were also observed daily under the microscope.  By Day 3, microorganisms were observed on the control apples.  By Day 4, microorganisms were also observed on the apples wrapped in both Glad plastic as well as the apples wrapped in biodegradable plastic, suggesting that both plastics are similar in preventing this aspect of food spoilage.

Since the WVTR of the biodegradable plastic is unknown, an attempt to determine the water vapor permeability of the biodegradable plastics based upon the experimental data has been carried out. Since the WVTR (W) is represented by the water vapor loss (G) in a unit of time (t) through a unit area (A) of body, W=G/(t·A). Therefore, all plots in Figure 1 can be fit as straight lines with G=(A·W)·t; in other words, the slope of the plot is k=(A·W).  Water vapor permeability (h) is defined as the rate of water vapor transmission through a material of thickness (d) induced by the unit vapor pressure difference (DP) between two specific surfaces i.e., h=W· d/DP. Therefore, the slope of the plot in the Figure 1 described above can be further presented as k=(A·W)=A·h·DP/d.  The thickness of LDPE wrap is measured as d₁=12 micron and the biodegradable bag has a thickness of d₂=28 micron. In the present study, we have the same packaging area (A) and same vapor pressure DP, thus the permeability of the biodegradable bag (h₂) can be determined from the two plot slopes displayed in Figure 1 (for Glad^® k₁=-0.0052, and biodegradable bag k₂=-0.0174) and the known water vapor permeability value of LDPE (h₁=1.0 grams/100 sq. in/day) (6).  With k₁/k₂=h₁·d₂/h₂·d₁,  substituting the known values of parameters gives h₂=k₂·d₂·h₁/k₁·d₁=(-0.0174 x 28 x 1)/(-0.0052 x 12)=7.8 grams/100 sq.in/day, which means that the water vapor permeability is almost 8 times higher for the biodegradable bag than the one made of LDPE.  Despite the thicker film of the biodegradable bag, which can better slow water vapor loss, the much higher water permeability of the biodegradable film leads to higher water loss in the apple.

Discussion

The study demonstrated that the generic LDPE Cling Wrap performed better than the biodegradable plastic in terms of preventing overall food spoilage.  The experimental results show that by Day 11, on average, the control apples lost about 56.11% of their mass, the biodegradable apples lost about 18.25%, and the Glad^® apples only lost 4.84% of their mass.  This result implies that the biodegradable plastic wrapped apples lost more water than the LDPE wrapped apples because biodegradable plastics have higher water vapor transmission rates than Glad Cling Wrap and LDPE plastics (as described above).  Due to the fact that the apples in the biodegradable plastic lost more mass than the apples in the generic LDPE plastic, the biodegradable plastic was not as effective of a barrier material as the generic LDPE, and therefore is not as effective in the prevention of food spoilage. However, the experimental results also show that in the short term (days 1-3), the LDPE plastic and biodegradable plastic performed very similarly, and only in the long term (days 4-11) does the LDPE plastic begin to perform better than the biodegradable plastic (Figure 1).  Most consumers are likely looking for a plastic that can protect their food in the short term (1 to 3 days).  As a result, biodegradable plastics can be as suitable for short-term conditions as a generic plastic, which can hopefully increase their appeal to consumers.

Secondly, the experimental observations in changes of apple color and texture also demonstrated that the generic LDPE has the better barrier property to resist the discoloration and oxidation of the food in the long term.  In Figure 2, oxidation is shown to be the worst in the control apple, however it is very similar in the LDPE wrapped apple and the biodegradable plastic wrapped apple.  However, by day 8, all of the apples are spoiled, displaying that though LDPE and the biodegradable plastic are very similar in preventing oxidation in the short term, in the long term, oxidation affected all of the tested apples.  However, although the BioMass^® plastic did not prevent the passage of vapors and organic liquids through their boundaries as well as the Glad^® Cling Wrap (LDPE), BioMass^® does slow down the apple spoilage speed compared with the unwrapped apples, which is demonstrated in the experimental results that the control apples lost about 56.11% of their mass, while the biodegradable apples lost about 18.25%.  Hence, the conclusion is that the BioMass^® biodegradable plastic is not as effective as preventing food spoilage compared with non-biodegradable generic Glad^® in the long term, but it is as effective as preventing food spoilage compared with a generic wrap in the short term, and can still reduce the speed of food spoilage in the long term.

The WVTR of the biodegradable bag was nearly eight times higher than that of the LDPE wrap.  The WVTR calculation could be affected by other factors besides moisture loss.  The mass loss, which was used in the WVTR calculation, also could have been caused by microbial degradation, the daily unwrapping of the apples, or other factors.

In summary, the experimental results show that the Glad^® Cling Wrap is more effective in preventing food spoilage compared with the BioMass^® Bag.  However, the BioMass^® Bag was still sufficient in preventing food spoilage in the short term.  Hopefully, this will increase the appeal of biodegradable bags to consumers due to its ability to reduce the amount of trash in the landfill. Meanwhile, hopefully, more scientific breakthroughs in replicating the molecular structure of LDPE will allow for more effective biodegradable plastics solutions.

There are a few areas that could be further improved upon in future study.  More types of plastics could be used in future studies. Another area to investigate is the influences of temperature and humidity on the permeability of LDPE and BioMass^® Plastic.  It is clear that there are many improvement opportunities for biodegradable plastics, specifically how to replicate the molecular structure of LDPE or using coatings in order to maximize the effectiveness in preventing food spoilage.

Methods

The apples used in the study were Gala apples. For the biodegradable plastic, BioMass^® Bags (PLA Flat Bag with 1” Lip & Tape 5.5”X8”) were used. For the LDPE plastic, Glad^® Cling Wrap was used. The permeability of the LDPE plastic used in this study is 1.0-1.5 grams/100 sq. in/day at 20^oC (3).  The thickness of the biodegradable bag was measured using a digital
micrometer, and the thickness of the bag was found to be 56 microns.  Because the thickness of the bag is 56 microns, the thickness of the biodegradable film was found to be 28 microns.  The thickness of the LDPE plastic was measured using a digital micrometer and found to be 12 microns.

First, each apple was cut in half in order to accelerate the speed of apple spoilage and then using a permanent marker, each apple half was labeled with a number followed by a letter, where the number represents which of the five apples it is (1, 2, 3, 4, or 5) and the letter represents which half of the apple it is (one half of the apple is A and the other is B).  The thicknesses of both plastic wraps were measured using a micrometer. The Glad^® Cling Wrap was measured and torn at a length of 30 cm.  One half of an apple was fully covered and wrapped in Glad^® Cling wrap.  The other half of the same apple was put into a BioMass^® Bag in order to eliminate possible variation from apple to apple.  The remaining apple halves were the control samples, and therefore did not have any packaging.  There were 3 samples in each condition (and 4 for the control, as there were 10 apple halves).

A picture was taken of both the skin side and the flesh side of each apple half.  Using a digital balance, the mass of each apple (including the bag/wrap) was recorded on a chart.  On the following day, another picture was taken of each apple half (both sides) and the mass was recorded.  Using a vegetable peeler, an extremely thin slice of each apple was sliced and placed on a slide with a cover slip to be observed (sketched and observations recorded) under a microscope.  Each apple slice was then returned to each apple and each apple half was rewrapped.  For the next 11 days, the process of taking a picture, recording the mass, and observing each apple half under the microscope was repeated.  After the 11 days, the apples were disposed of.  In the analysis, the raw data of the mass of the apple every day was first changed to the accumulated mass loss each day by calculating the difference between the current mass and the original mass.  Then the mass loss was changed into the rate of mass loss by dividing the original mass by the accumulated mass loss at that day and then converting the decimal into a percentage. Microsoft Excel was used to calculate the change in the mass of the apples, to determine the average rate of accumulated mass loss, and to create graphs.

Acknowledgments

I would like to thank Mrs. Jill Carter for supervising my experiment.  I would also like to thank Shrewsbury High School for allowing me to perform my experiment in their classrooms.  Finally, I would like to thank Mr. Allen King from BioMass^® Packaging for donating free samples of their PLA biodegradable plastic for my experimentation.

References

1. “Plastic 101.” Earth 911. N.p, 2010. Web. 11 Oct. 2010.
2.  “Describe Why Food Spoils.” Food Safety Organization. Department of Food Science and Human Nutrition of Clemson University, n.d. Web. 1 Oct. 2010.
3.  Massey, Liesel K. Permeability Properties of Plastics and Elastomers – A Guide to Packaging and Barrier Materials. Norwich, NY: William Andrews Publishing, 2003. Print.
4. Castellion, Mary. “Plastics and Other Polymers.” The New Book of Popular Science. Ed. Jerry Bennis. 2000 Edition. Vol. 6. Danbury, CT: Grolier International Inc., 1998. 201-203. Print.
5. Deacon, Jim. “Apple Rot and Other Fruit Rot Fungi.” The Microbial World. Institute of Cell and Molecular Biology. n.d. Web. 7 Jan. 2011.
6. Shogren, Randal. Water Vapor Permeability of Biodegradable Polymers. Journals of Polymers and the Environment. 5 (1997): 91-95. Print.

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Summary

In many cultures and for many centuries, the implications of birth order have been examined. Birth order has been shown to affect personality, accomplishments, and even career choice. This study investigated the impact of birth order and ethnicity on two measures of academic success in high school: a student’s grade point average (GPA) and the number of Advanced Placement (AP) classes he or she took. Based on previous studies, we hypothesized that birth order would have an effect on GPA and the number of AP classes taken. We also hypothesized that ethnicity would not affect this relationship. Survey results from 162 eleventh and twelfth grade students were analyzed by ANCOVA. Despite some prior research to the contrary, we found that birth order had no statistically significant effect on GPA or the number of AP classes taken. Although ethnicity affected GPA, there was no interaction between ethnicity and birth order effects, supporting our hypothesis that these particular birth order effects are not dependent upon ethnicity.

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Received: September 28, 2011; Accepted: November 1, 2011; Published: January 30, 2012

Copyright: © 2012 Geil et al. All JEI articles are distributed under the attribution non-commercial, no derivative license (http://creativecommons.org/licenses/by-nc-nd/3.0/). This means that anyone is free to share, copy and distribute an unaltered article for non-commercial purposes provided the original author and source are credited.

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Introduction

The implications of ordinal position, or birth order, have long been examined (1, 2, 3, 4, 5). As early as Biblical times, birth order implications can be seen. In the fourth chapter of Genesis, the term “firstborn” appears, displaying that the Israelites characterized the firstborn son by distinguishing him with special rights based on his high ordinal position (2). Sir Francis Galton began conducting studies on birth order in 1874, when he found more firstborn sons than lower order sons in prominent work positions (3, 4, 5). Galton attributed the result to chance, but multiple studies through the years have confirmed that birth order correlates with certain personality attributes and accomplishments (2,3,5,6,7,8,9).

While many studies related to birth order have been conducted on an international scale (1,5,6,7,8,9,10), relatively few have compared birth order effects across ethnic groups. When asked about the importance of race in birth order, Frank Sulloway answered that race and gender affect social aspects, but are not as important as birth order in determining family dynamics and rebelliousness (11, 12). Kantarevic and Mechoulan related income and educational attainment to birth order (7). Their study found that firstborns had greater incomes. They examined this result with respect to ethnicity and found that the results were significant for large African American families and White families of all sizes. Kantarevic and Mechoulan cited other studies in other countries that supported this finding (7).

Black et al in Norway found similar results (1). Black et al correlated family size and birth order with the degree of education received (1). They found a negative correlation between family size and the children’s education, meaning that the larger the family, the less educational opportunities the children in that family had. In larger families, children higher in birth order were more likely to receive more education (1).

A more difficult question to address is the fundamental relationship between birth order and intelligence. Kristensen and Bjerkedal found declining intelligence scores with a lower ordinal position (3). They reported that firstborns have a three-point advantage in IQ over the next eldest (13). The study suggested that the tutoring effect accounts for the difference. Not only do older siblings have more educational opportunities, they might also act as teachers for younger siblings. To teach material, the elder sibling must possess a greater understanding of that material. Younger siblings question their older sibling in the same manner they question their parents, and in teaching and rehearsing, the older siblings learn. Last-born children and only children lack the opportunity to learn by teaching (3,9).

Hester et al also addressed the relationship between ordinal position and academic achievement by examining student and parent expectations (6). They studied college students to compare their perceptions of expected GPA to the parents’ actual expectations. They found that the number of sibling cohabitants has a significant correlation with GPA expectations, and that GPA expectations were higher in homes with three or more siblings than in families with two children or one child (6). If children believe they must receive a higher GPA to please their parents, they might become better students. Likewise, as suggested by Sulloway, younger siblings might rebel against the expectations, not reaching their full scholarly potential (11, 12).

Because birth order is such a widely studied topic, some inconsistencies are present in research results. Longitudinal studies of individual families have discovered varied results, contradicting cross-sectional studies across multiple families. Rodgers examined three theories about birth order: Page and Grandon’s 1979 admixture hypothesis, Dowey’s 2001 dilution theory, and Zajonc’s 1976 confluence model (4). Rodgers suggested that because of the variance in results, nothing significant could be taken from birth order’s effect on intellect until further research was conducted. He acknowledged that genes, discipline (as it changes with age), and even hunger and social life change family dynamics (4). Rodgers discounted the effects of birth order even though his own longitudinal study found that family size affects IQ (a trend supported by the admixture hypothesis, the dilution theory, and the confluence model). Rodgers attributed this contradiction to the idea that parents with lower IQ levels tend to have larger families (4).

Zajonc offered a different perspective on the debate (9). Though his results were similar to Rodgers’, cross-sectional studies showed that birth order affected intellect while there was no effect found in longitudinal studies. He examined his own confluence model in explaining the variance. The confluence model suggests that results differ because family dynamics change as time changes, most significantly with the birth of another child, but also as children grow physically and intellectually. Zajonc used this model to explain the results based on his studies of children at various ages. He found that before the age of 11 (plus or minus two years), the results were random but after age 11, birth order affects intellect (9).

While Zajonc acknowledged the benefit of longitudinal studies, he also noted that cross-sectional studies have purpose and are good for establishing new trends (9). For the purpose of our study, a cross-sectional method was beneficial because ethnicity was compared to the effect of birth order and the larger sample size available in a cross-sectional model enabled more comparisons. The debate only gave more cause for this research.

To our knowledge, no study has examined the effect of birth order on a high school student’s GPA and then related it to ethnicity. Other studies, including the landmark Norwegian study, examined IQ rates, but IQ is based on one test on one day, while GPA is accumulated by years of choosing classes, completing homework and studying those classes, and taking multiple tests specific to the subject of the class (3). While Hester et al used GPA in their birth order study, they had tested different variables (6). Instead of testing parents’ GPA expectations compared to the actual GPA of the college student, our study looked at the actual data in relation to ethnicity. Many studies have been performed in many different nations with many different ethnicities of subjects. Though studies have attributed race and controlled for ethnicity, no study examined ethnicity as a factor of interaction.

This investigation examined GPA and the number of AP classes taken in a large, diverse public high school. While these two measures are not comprehensive indicators of academic achievement, the combination of GPA (which has been studied before [5] in relation to ordinal position) and enrollment in AP classes represents an outcome partially dependent on motivation, desire to please, and responsibility. These qualities have been theorized to vary depending on birth order. They also might be more indicative of potential personality differences than other potential indicators of academic success such as ACT or SAT scores, which represent a single day’s measure. Furthermore, ethnic bias has been shown to exist in standardized test scores such as the ACT and the SAT, which would confound this analysis (14, 15,16).

We tested the hypothesis that birth order will have an effect on measures of academic achievement in high school students. In particular, we hypothesized that students with higher ordinal positions would have higher grade point averages and take more AP classes than children with lower ordinal positions. Furthermore, we hypothesized that, in this setting, birth order effects would be consistent across ethnic groups. We predicted these birth order-specific results based upon past studies on IQ, the greater educational opportunities available to firstborn children (even in a public school), economic status, the tutoring effect, and a higher expectation to succeed. The ethnicity-related hypothesis was based upon the many birth order studies performed internationally. Furthermore, we reviewed studies that investigated ethnicity, birth order, and personality, although no studies specifically considered these factors alongside academic success.

Results

We distributed surveys (Appendix A) regarding birth order, family demographics, ethnicity, GPA, and the number of AP classes taken to students at a large public high school in an Atlanta suburb. The research protocol was examined by the school’s review board for research involving human subjects, and students were aware that by taking the survey they were participating in a research project. A total of 294 surveys were distributed to students in grades 11 and 12. The distribution of ethnic groups across the school’s student population at the time of the survey was 5% Asian, 36% African American, 13% Hispanic, and 46% White. Thirty-nine percent of the students qualify for “free or reduced price lunch” (a measure of the poverty level) (17).

One hundred sixty-two usable surveys were returned, giving a response rate of 55.1%. Twenty-three surveys could not be used for various reasons: the GPA was not filled in; there was more than a 10-year age gap between half-siblings and step-siblings (results like this were not included because such age ranges suggest that the siblings did not live together and therefore would not be applicable to the study; for example the largest age range between siblings was 42 years); the participants explicitly indicated that they did not live with all of their siblings; both parents of the participants were deceased; the participant reported more than 3 ethnicities; the participant was a twin; or they did not complete the survey. Thirty-one respondents indicated that they had divorced parents, single parents, half/step siblings, or same-gender parents. These results were included in the study.

The usable results included 24 only children, 70 firstborns, 33 middle-born children, and 35 lastborns. Specifically, useable surveys were received from 24 only children, 70 firstborns, 48 second-born children, 15 third-born children, and 5 who were fourth in the birth order. The results included 1 American Indian, 10 Asians, 40 African Americans, 4 Native Hawaiians/Pacific islanders, 76 Whites, and 20 Hispanic/Latinos. Eleven people indicated mixed ethnicities. The average GPA was 3.375 ± 0.566 and the average number of AP classes taken was 2.78 ± 3.56 (Table 1).

Because some ethnic minorities were very small, an analysis was conducted with three collapsed ethnic categories: African American (N=40), White (N=76), and Other (N=46). Similarly, birth order was categorized. When attempting to describe and analyze birth order, for example, a “third-born” child could be the youngest child in the family or somewhere in the middle. Consequently, birth order was compared to the total number of children in the family to identify placement into four levels: FIRST (first born among siblings, N=70), BETWEEN (neither firstborn nor youngest, N=32), YOUNGEST (last born among siblings, N=36), and ONLY (no siblings, N=24).

In these modified categories, mean GPA varied between 3.088 (White Only Children) and 3.781 (Other Youngest Children) (Table 1). Analysis of covariance for GPA with these collapsed categories showed significance for ethnicity (p=0.004) but not for birth order (p=0.841). Covariates of age and gender, factors that were not controlled in the study but could have affected the outcome, were not significant (p=0.738 and 0.327, respectively). Interaction between ethnicity and birth order was not significant (p=0.626). Plots of estimated marginal means, in which any effect of the covariates is removed, show non-significant differences in GPA due to birth order (Figure 1a) and significant differences due to ethnicity (Figure 1b). Two particularly low GPAs may have had an effect on the outcomes for firstborn and youngest children. We removed the outliers, but the difference was still not statistically significant (p = 0.529) (Figure 2).

Using the same categories, the analysis was repeated with number of AP classes as the dependent variable. In this analysis, birth order had a stronger effect (p=0.067) than ethnicity (p=0.339), though neither was significant. In a subsequent analysis of the number of AP classes taken between firstborns and younger children (with Only children excluded), the effect was significant (p=0.003); firstborns took more AP classes than students of lower ordinal position. There was a significant relationship between age and the number of AP classes taken. Plots of estimated marginal means again reveal differences between ethnicities and birth order categories, with the “other” category taking more AP classes than African Americans and Whitesm (Figures 3a and 3b).

Standard deviations were larger for the number of AP courses taken (Table 1) than they were for GPA, indicating more variability (Figure 4).

Discussion

The results indicate that birth order appears to have different effects on different indicators of high school achievement. While firstborn children consistently reported the highest number of AP classes taken, they did not demonstrate the highest grade point averages. The hypothesis regarding ethnicity was supported. Though there was a significant effect of ethnic category on GPA, there was not a significant interaction between birth order and ethnicity in the ANCOVA. The null hypothesis was supported as birth order did not affect GPA or the number of AP classes taken and the hypothesis that ethnicity would not affect these trends was supported. In general, these results suggest that current theories on birth order may not be accurate for GPA; the debate on the significance of birth order continues.

Statistically significant results were found in the relationship between ethnicity and GPA and in the relationship between age and number of AP classes. Age affects the number of AP classes taken because older participants had more opportunities to take AP classes, making their number of classes taken much higher. The two ethnicities with the highest mean GPAs (Asian, Hispanic) were in the Other category, potentially explaining the effect ethnicity had on GPA (Figure 5).

Though trends relating to birth order were not statistically significant, certain trends supported other studies. Youngest children displayed a higher mean GPA but firstborn children took more AP classes. The latter result was significant when firstborns were compared to younger children, removing only children from the analysis. This can be explained by the desire of firstborns to please and meet parent expectation levels. Baskett found that firstborns were characterized as achievement-oriented, antagonistic, anxious, assertive, conforming, extraverted, fearful, jealous, organized, plan-oriented, responsible, self-confident, and leaders (12). Furthermore, an older sibling might take an AP class offered by the school that is more difficult than other AP classes, and then suggest that their younger sibling should not take the class since receiving a higher grade is more challenging, thus affecting GPA. Our study did not compare to the findings of others with respect to only children.

Relating more to ethnicity, a reason for the trends could be explained by Herrera et al in their analysis of ethnicity in the perception of intelligence (2). Acknowledging ethnicity, they used African American or Hispanic college students who were young, childless, and unmarried as these parameters best let the study maintain and display ethnic diversity. Specifically related to birth order and intelligence, firstborn children were believed to be the most intelligent. However, they confirmed a perceived difference in intelligence and creativity as lastborn children were believed to be the most creative and firstborns were the least creative in the majority of perspectives. Furthermore, firstborns were described as obedient, stable, responsible, and the least emotional. GPA is built up of more than just intelligence (11). Stable and responsible characteristics (even if just perceived, there must exist some basis for a perception to be made) induce diligence, improving GPA. These results were true in people who were believed to best represent their ethnic group (2). High school students may be more representative of their ethnic group because they live at home and are influenced by their family, but our study found no interaction between ethnicity and the birth order effects. At the age of 16 and 17 students begin listening more to friends as they begin making decisions about college. This could be one reason for the lack of significance. Another reason could be that the students all attend the same school and for the most part, have a similar education that is not dependent on their ethnicity.

One reason for the lack of statistical significance could be that there exist so many confounding variables that were difficult to control. Dysfunctional families do not always produce children with birth order characteristics that resemble other families. While dysfunctional families have been studied (Kristensen and Bjerkedal’s study of 400 abused children and their 795 siblings revealed certain correlations among the abused), variables exist in studies not focused on dysfunction. Variables include the addition of siblings through remarriage or adoption; the death of a sibling or parent; the age of the mother in relation to the age of the child; the birth order of the parents; the mental, physical, and emotional abilities of siblings; and even the gender of the siblings (although as gender opportunities equalize, further differences in ordinal position due to gender shrink). It is possible that differences in socioeconomic standing between ethnic groups could influence variation in indicators of academic achievement; this potential confounder was not controlled for in the study.

The official records of the school are not accessible by students. Thus, GPA was self-reported anonymously through our survey. Although participants were not directly told the objective of the study, they still might have lied about their GPA, possibly influencing the study. Another limitation was the quasi-random method used. Every person in the 11^th and 12^th grade did not have an equal chance of being selected because students are grouped into their homeroom class alphabetically.

The controversy of the birth order effect continues, begging for more research to be completed to answer very basic questions. Perception is an important aspect in character traits; therefore a study focusing on the perception of traits correlated with birth order would be interesting. In the future, we could focus the study on the question “Which sibling, including yourself, do you think is the smartest?” to see if the general population believes that birth order affects their lives.

In conclusion, this study found that birth order played no statistically significant effect on the number of AP classes a student took or the GPA of the high school student. The effect was consistent across ethnic groups, supporting our hypothesis. So while this study adds to the debate on the simple question of birth order, it also hints at new trends.

Methods

To test our hypothesis, we created a survey (Appendix A). We did not include samples indicating mixed marriages, deceased siblings, or families with twins in the analysis. (This decision was based on previous studies (9, 13, 18) that determined that different family structure abnormalities nulled birth order’s effects). The survey did not directly ask for birth order to avoid any potential preconceived bias. Instead, the survey asked for respondents’ and siblings’ ages.

We distributed 294 surveys to homerooms in North Cobb High School in a quasi-random manner by selecting every fourth homeroom. Homerooms are divided alphabetically by students’ last names. We only selected 11^th and 12^th grade homerooms because upperclassmen’s GPAs are more reflective of their entire educational experience. Enrollment in Advanced Placement classes is not limited to any particular student group at this high school. The school offers a wide variety of AP subjects, and typically provides more than 18 AP classes each year.

We analyzed the results by performing two ANCOVAs, or analyses of covariance with more than one factor (SPSS, version 18). GPA and the number of AP classes taken were initially analyzed to determine the presence of outliers. We included a test of interaction to find ethnicity’s relationship with birth order. The analyses of covariance main effects were birth order and ethnicity. Covariates (uncontrolled factors that could possibly affect the outcome) were gender and age. Estimated marginal means were reported to account for any potential effect of the covariates. Two ANCOVAs were performed for the number of AP classes taken and for the GPA. Alpha levels for significance were set to 0.05.

Appendix

PDF: Student Survey

Acknowledgments

The authors would like to thank Dr. Mark Geil and Mrs. Melanie Shelnutt for their helpful contributions to the research and analysis performed.

References

1. Black, S. E., P. J. Devereux, and K. G. Salvanes. “The More the Merrier? The Effect of Family Size and Birth Order on Children’s Education.” Quarterly Journal of Economics 120.2 (2005): 669-700.
2. Herrera, N. C., R. B. Zajonc, G. Wieczorkowska, and B. Cichomski. “Beliefs about Birth Rank and Their Reflection in Reality.” Journal of Personality and Social Psychology 85.1 (2003): 142-50.
3. Kristensen, P., and T. Bjerkedal. “Explaining the Relation Between Birth Order and Intelligence.” Science 316.5832 (2007): 1717.
4. Rodgers, Joseph Lee. “What Causes Birth Order-intelligence Patterns? The Admixture Hypothesis, Revived.” American Psychologist 56.6-7 (2001): 505-10.
5. Eckstein, D. “A Review of 200 Birth-Order Studies: Lifestyle Characteristics.” Journal of Individual Psychology, 66(4) (2010): 408-434.
6. Hester, C., G. E. Osborne, and N. Trang. “The Effects of Birth Order and Number of Sibling and Parental Cohabitants on Academic Achievement.” Individual Psychology: The Journal of Adlerian Theory, Research & Practice 48.3 (1992): 330.
7. Kantarevic, J., and S. Mechoulan. “Birth Order, Educational Attainment, and Earnings.” Journal of Human Resources 41.4 (2006): 755-777.
8. Travis, R., and V. Kohli. “The Birth Order Factor: Ordinal Position, Social Strata, and Educational Achievement.” Journal of Social Psychology 135.4 (1995): 499-507.
9. Zajonc, R. B. “The Family Dynamics of Intellectual Development.” American Psychologist 56.6-7 (2001): 490-96.
10. Wahab, A., A. Winkvist, H. Stenlund, S. A. Wilopo “Infant mortality among Indonesian boys and girls: effect of sibling status.” Annals of Tropical Paediatrics 21.1 (2001): 66-71.
11. Shermer, M. “Rebel with a cause.” Skeptic 4.4 (1996): 68.
12. Sulloway, F. J. Born to Rebel: Birth Order, Family Dynamics, and Creative Lives. New York: Pantheon, 1996.
13. Kluger, J. “The Power of Birth Order.” TIME. 17 Oct. 2007. Web. 14 Apr. 2011. <http://www.time.com/time/health/article/0,8599,1672715,00.html>.
14. Thomas, K. M. “The SAT II: Minority/Majority Test-Score Gaps And What They Could Mean For College Admissions.” Social Science Quarterly (Blackwell Publishing Limited) 85.5 (2004): 1318-1334.
15. Everson, H. T., Millsap, Roger E. “Beyond Individual Differences: Exploring School Effects On SAT Scores.” Educational Psychologist 39.3 (2004): 157-172.
16. “Demographic Information: SAT: Mean Scores by Gender Within Ethnicity.” 2010 College-Bound Seniors: State Profile Report (Georgia). CollegeBoard, 2010. Web. 11 Nov. 2011.
17. “2009-2010 Report Card: North Cobb High School.” The Govenor’s Office of Student Achievment. The Georgia Department of Education. Web. 15 Nov. 2011. <http://reportcard2010.gaosa.org/(S(r2234vrxp0iea2yz0wpcuh55))/k12/demographics.aspX?ID=633:2056&TestKey=EnR&TestType=demographics>.
18. Epstein, J. “O, brother!” Commentary 103.4 (1997): 51+.

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“What is real science all about?”

What do we really mean when we talk about science, science education, and science research?  Our mission at JEI is to teach “science in its truest form”, but we have yet to explain our definition of science.   As we begin publishing your work, it’s important for us to tell you about our expectations.  So here is a brief list of what we think about when we think about science:

• Generating a testable hypothesis (or question) based on previous knowledge, observations, and data.
• Gathering information on what is known about your observation or question.
• Performing experiments and other tests to gather more information on your observation or question.
• Combining previous knowledge and your own data to come up with possible explanations for an observed phenomenon.

So that’s what we look for in an article.  If you think that something is missing, write us a letter and tell us!  Another important aspect of science is reaching out to other researchers to start a conversation about science.

Noticeably absent from our list is  “finding an answer.”  We hesitate to put this on the list because people may assume we mean “finding the right answer” or “finding an endpoint.”  Years of doing research have taught us that there is no “right answer” in science research, and there definitely is no endpoint.  Typically the most interesting answers and results are the unexpected, and more often than not, the answer from one experiment leads to more questions.

At JEI we want you to have the freedom to explore your questions.  Our job is to help guide you and keep you thinking about your results so that you can come up with the next question.

Our first article is published today, January 30^th 2012.  This article truly embodies the kind of science we expect.  Sarah Geil, the lead author, thought of an interesting question: does the order in which you are born into a family affect your academic success?  Sarah did very thorough background research to see what was already known about this topic.  Even with all of her background research she did not find studies that she felt explicitly answered her question, but it did provide her with enough information to come up with an informed hypothesis.  Through surveys and sophisticated statistical analysis, Sarah tested her hypothesis.  We don’t want to completely give away her results, but as with most scientific studies Sarah obtained results that were perhaps ambiguous and differed from previous studies.  Sarah also obtained some more straightforward results.  But what is most important to any scientific endeavor, and something that Sarah hit spot-on, is the dissection of the variables that could have affected the results and how these variables can be used to ask different questions for future studies.  So in the end Sarah’s research led to more questions, just as we hypothesized.

Sarah’s article is the first of what we hope will be many, many more publications that highlight the work of all levels of student scientists.  If you’re interested in publishing, take a look at our submission guidelines for help with putting your article together.  Don’t hesitate to email us with questions (questions@emerginginvestigators.org).  And keep checking the website, we’re continually updating and adding additional resources to help you in your science research.

Sincerely,

The JEI Editorial Team

Sarah, Chris, Lincoln, Amy, Bryan, Sean, Matt, Andrew and Dave

With cheerful personalities and encouraging messages about digestive health, advertisements on TV and internet often urge us to “eat yogurt!” Similarly, doctors often advise us to ingest probiotics – beneficial microorganisms found in yogurt –  when we’re under antibiotics treatments in order to help maintain the “good” bacteria, and combat the “bad” bacteria, in our intestines. Probiotics are even available as over-the-counter pills, to be taken daily just as we would a multi-vitamin. But does eating probiotics through yogurt or pills really help us? Two seniors from Quincy High School in Massachusetts, Peter Giunta and Eoin Moriarty, were skeptical, and decided to investigate the question.

Using the scientific method, Peter and Eoin began their investigation by focusing their broad question into more specific ones that are testable by experiments. To confer benefits to our digestive health, the probiotics we eat must be able to pass through our stomach to reach the intestines; they must also be able to keep harmful bacteria at bay. The two young investigators therefore asked: Can the types of bacteria we commonly ingest as probiotics survive conditions similar to the human stomach? If so, can these bacteria then inhibit the growth of harmful bacteria? Peter and Eoin hypothesized that the probiotic bacteria likely do not survive the stomach environment, which is highly acidic, presents vigorous physical agitations, and could therefore be hostile to microorganisms.

To test their hypothesis, Peter and Eoin first selected two common types, or strains, of probiotic bacteria as their “test subjects”: Lactobacillus acidophilus and Lactobacillus casei. They acquired these two strains of probiotic organisms in various forms, including from pills, capsules, and yogurt, and also as pure living cultures from a science supply store. This allowed them to study all the possible ways the probiotics can arrive in our stomach. The investigator pair also selected a strain of “bad” bacteria whose growth the probiotics are supposed to stop, Escherichia coli. The high school seniors were careful to handle disease-causing bacteria in a protected environment, so the version of E. coli they chose, called K-12, was actually not harmful to humans.

Finally, to mimic the hostile conditions of the stomach that the probiotic bacteria have to survive, the duo built a model “stomach.” To do so, Peter and Eoin used a beaker filled with water mixed with hydrochloric acid, an ingredient that makes our stomachs acidic, and pepsin, an enzyme in our stomach that breaks down proteins. They heated the mixture to 37 degrees Celsius, the normal temperature of the human body, and stirred the mixture with a magnetic stirrer that simulated the mechanical breakdown that takes place in the stomach. With these components in place, Peter and Eoin’s experimental design was beautifully straightforward: digest the two probiotic bacteria strains, in their various forms, in the model “stomach” for a few hours, then incubate the resulting product with E. coli to see if the growth of E. coli can be inhibited by the digested probiotics.

In less than a year, Peter and Eoin had made significant progress in their research project. The pair started experimentations in November 2010 after conceiving the project idea during the summer, and by March, they had preliminary results showing that L. acidophilus in pill and capsule forms, but not in yogurt and pure strain forms, can survive the stomach conditions. They submitted their results to the school science fair, and successfully advanced to first the regional, then the state-wise competition. They went on to win 2^nd place in the Team Division of the 2011 Massachusetts State & Engineering Fair. By conducting hands-on experiments, Peter and Eoin learned quite a lot about how certain probiotic bacteria may function in our digestive tract. More importantly, they got a taste for the demands of being microbiology researchers, and gained great appreciation for the joys and challenges of scientific research.

Peter and Eoin’s scientific journey was not without obstacles. For example, the pair knew early on that in order for their experiments to be conclusive, they had to perform proper scientific “controls,” a set of independent experiments that verify and validate their approach. These scientific controls would have to show that there were no causes other than the specific condition being tested that could explain the observed results. One such control experiment was to demonstrate that all their test subjects – the probiotics L. acidophilus and L. casei in the various forms – could inhibit the growth of E. coli K-12 under normal incubation conditions. This is an important control, because if the probiotics could not inhibit E. coli growth under normal conditions, then there would be no reason to expect that they could inhibit E. coli growth after stomach treatment. On the other hand, if the probiotics could inhibit E. coli growth under normal conditions, but not after stomach treatment, then the young scientists could attribute the incapacitation of the probiotics to the stomach treatment. Unfortunately, the team had a difficult time completing this control, because the optimal incubation conditions for L. acidophilus, L. casei and E. coli are all slightly different. In other words, if the team used incubation conditions that were ideal for growing E. coli, but not the probiotics, they might find that the probiotics were unable to prevent the E. coli growth not because the probiotics were ineffective after the stomach treatment, but because the E. coli simply outgrew them. To work around this problem, Peter and Eoin are actively looking into published reports to see how other scientists typically grow these bacteria. They are also consulting with microbiologists at Massachusetts General Hospital and Harvard Medical School for advices on alternative ways to test their hypothesis.

In addition to scientific complications, Peter and Eoin also had to overcome the challenge of limited resources during their project. The team conducted all their research in their high school, which means their experiments had to be designed with the limited space, equipment, and reagents of the school, as well as keeping the safety of all teachers and students in mind. Fortunately, their high school was recently renovated with a new science teaching facility, which provided a majority of the crucial equipment. The young scientists proudly recalled using the autoclave in one classroom to sterilize the petri dishes on which they would grow the bacteria, borrowing the magnetic stirrer from the chemistry classroom for their model “stomach,” and traveling to yet another classroom to measure the acidity of their model “stomach” with the pH meter.

As scholar-athletes, Peter and Eoin also had to balance their experiment schedule with classes, homework, sports, and other extracurricular activities. The high school scientists quickly learned the power of collaboration and teamwork. The two coordinated their respective schedules to complete the experiments: when Peter was at hockey practice, Eoin would often “cover” for him, and Peter would return the favor when Eoin was busy with soccer practice. Still, the pair often found themselves getting up early to start their experiments before their first-period classes, and staying after school – sometimes as late as 9 p.m. – to finish their lab works. The two became expert time managers and multi-taskers, completing homework assignments while they waited for certain steps of their experimental protocol to finish.

Fortunately, Peter and Eoin were blessed with supportive parents and teachers who helped and guided them along their scientific exploration. The pair especially credited their science teacher, Mr. Lawrence Johnson, for helping them translate their idea into doable experiments within the school environment. In Peter’s words, “He really… taught us everything we need to know about doing a legitimate experiment.” The pair also credited Peter’s mother, Mrs. Leah Giunta, for encouraging them to pursue their skepticism about probiotics and yogurt through active scientific investigation. Without her initial encouragement, and later tireless efforts in helping the team acquire the necessary reagents for their experiments, the project would not have progressed.

Was all their hard work worth the experience? Peter and Eoin both replied “yes” without hesitation. They might have had a self-professed interest in science prior to initiating their research, but it was only after they began actively pursing their curiosity through careful experiments that they realized what science actually entails. Unlike typical classroom laboratory lessons, science research does not necessarily have results that are clear-cut and easily predictable. This makes research challenging, but also makes the work exciting and worthwhile. In conducting their experiment and remaining committed to the challenges of the research process, Peter and Eoin learned first-hand the power that lies in a set of well-documented results generated from careful experiments. They also experienced the incredible reward of personally discovering the answer to their own question.

The high school seniors appear to be “hooked” by this experience. Currently applying to colleges, both Peter and Eoin want to major in biology or biochemistry, and continue doing science research in the future. Peter hopes to become a leading microbiologist one day, conducting cutting-edge research in a laboratory of his own. Eoin plans to apply his enthusiasm about science toward helping others through both research and medicine. In the short term, Peter and Eoin are looking forward to writing up their scientific report for publication in JEI. It is clear that no matter what the future holds, Peter and Eoin will both treasure their high school research experience, which provided them with unique insights into the life of a scientist and the power of discovery through scientific experimentations.

Mei Rosa Ng

Graduate Student

Harvard University

Lincoln Pasquina

Graduate Student

Harvard University

Antibiotic resistance of bacterial pathogens is an increasing public health concern. Patients with normally routine hospital-acquired infections are now at serious risk due to insufficient antibiotics that result in the death of thousands worldwide. One bacterial species that is partially responsible for these deaths is P. aeruginosa. As an opportunistic pathogen, P. aeruginosa normally does not cause infections, and is found in the normal environment. While many clinical P. aeruginosa strains are resistant to several antibiotics there have been no previous studies examining the antibiotic resistance of environmentally isolated strains. Here, we isolate 30 novel P. aeruginosa strains from the Boston area and characterize their susceptibility to the antibiotic rifampicin. While only two strains were resistant to rifampicin, we identified large variation at the sequence level between environmental strains. These results suggest that large heterogeneity among environmental populations of P. aeruginosa may lead to higher rates of antibiotic resistance.

Introduction

Over the last fifty years, the discovery of antibiotics has allowed humans to control potentially devastating infectious bacterial diseases. There are several different classes of antibiotics that have many different modes of action. Some antibiotics alter bacterial cell wall stability while others inhibit critical processes such as DNA transcription or translation (1-2). Over the last decade, however, there has been an emergence of antibiotic resistant bacterial strains that are becoming increasingly problematic for public health (3-4). There are several mechanisms that bacteria have developed to avoid the toxicity of antibiotics. One of the most common antibiotic resistant species is Pseudomonas aeruginosa. Some P. aeuriginosa antibiotic resistant strains are able to avoid toxicity by synthesizing an efflux pump that simply pumps the compound out of the bacteria (5). Other resistant strains have mutations in their DNA that prevent the antibiotics from working (6-7). For example, many of these bacterial strains have single point mutations (one base pair changes) that alter the amino acids coded by the DNA. These single amino acid changes can alter the antibiotic target and thus prevent the antibiotic from binding and working (7). One question that remains is: how prevalent are mutations in specific antibiotic targets in environmental bacterial populations? We hypothesized that all environmental P. aeuriginosa strains isolated would be sensitive to the antibiotic rifampicin, since they have never been exposed to the drug previously and thus have not evolved resistance. Rifampicin works by binding to one subunit of the bacterial RNA polymerase (RNAP) and inhibiting its essential function of transcribing DNA to RNA (8). Mutations at the DNA level, however, can result in a mutant polymerase which is resistant to Rifampicin (9). In order to test our hypothesis, we examined both the sensitivity of environmental strains to rifampicin as well as the mutation frequency in the RNA polymerase gene that encodes the RNA polymerase. To test for point mutations we used a technique known as a Cel1 digest. Cel1, isolated from common celery, is a restriction enzyme that cleaves mismatched DNA pairs, the hallmark of point mutations. Using this strategy we were able to screen many strains of P. aeruginosa in a short amount of time without the need for DNA sequencing. We found that even though most strains show the same susceptibility to antibiotics, many have point mutations throughout the RNA polymerase gene. This suggests that mutations can occur even in essential genes at low frequency. However, most of these mutations do not appear to result in resistance to antibiotics.

Results

Isolation of environmental P. aeruginosa strains

In order to examine the antibiotic sensitivity and mutation rates of environmental strains of P. aeruginosa we first needed to isolate and characterize several strains. We used Cetrimide selective agar to isolate unique environmental strains (10). We took samples from 50 locations around the Boston area including playgrounds, soil, and plants. From these 50 sample locations we were able to grow 30 P. aeuriginosa strains. In addition, we grew two laboratory strains in parallel as controls: one strain (R) is resistant to rifampicin and the other strain (S) is sensitive to rifampicin. We confirmed that these 30 strains were in fact P. aeruginosa through the use of bacterial strain identification strips. Furthermore, all strains smelled similar to the two control strains which have a grape scent, a common feature of growing P. aeruginosa on agar plates (11).

Most strains of P. aeruginosa are sensitive to rifampicin

We next wanted to test the sensitivity of these strains to the antibiotic rifampicin. Rifiampicin is an RNA polymerase inhibitor that is known to inhibit the growth of P. aeuroginosa. We first grew all 30 strains, along with our two control strains, in liquid culture in the presence and absence of varying concentrations of rifampicin. We then used the optical density (or the cloudiness) of these cultures as a readout of growth over time. In liquid culture 28 of the 30 strains and our S strain grew dramatically slower in the presence of antibiotic when compared to growth with no antibiotic present. The remaining two environmental strains and the R lab strain grew identically whether or not rifampicin was present in the culture media.

We next wanted to test whether the sensitivity to the antibiotic seen in liquid cultures was also observed when grown on agar plates. We first spread a lawn of each bacterial strain on two “blank” agar plates and placed an empty disk as a negative control on one plate and a disk containing high concentrations of rifampicin on the other plate. After a 48-hour incubation we measured the diameter of the zone of inhibition by the bacteria. Consistent with the results seen in the liquid broth, the growth of 28 of the 30 strains was inhibited greatly by the presence of rifampicin in the plate. The two strains that appeared resistant to rifampicin in broth culture were also resistant on agar plates. These results suggest that the majority of P. aeruginosa strains are in fact sensitive to the antibiotic rifampicin. However, since we identified two unique strains that are both resistant to this antibiotic, it suggests that there is some underlying frequency of mutations that allows a small percentage of the population to be resistant. From these results it also appears that growth on agar plates or in liquid broth culture does not alter the sensitivity (or resistance) of these strains to antibiotics.

Amplification of RNA polymerase gene by PCR

Since the majority of the strains we examined are resistant to rifampicin, we hypothesized that these strains would be identical at the sequence level. In order to test this, we first used a technique known as polymerase chain reaction (PCR) to make thousands of copies of the RNA polymerase (the target of rifampicin) gene- FIG. We first designed a forward and reverse primer from the genome sequence of P. aeruginosa (see Methods). We then amplified the RNA polymerase gene from each of the 30 strains we isolated and tested previously, as well as the two control strains. These PCR products were confirmed by running an agarose gel –FIG-. We were able to amplify the RNA polymerase gene from all 30 strains of P. aeruoginosa, as seen by a prominent band in all 30 lanes. We saved these products to use in the Cel1 digests.

Cel1 digest reveals many differences between sensitive strains of P. aeruginosa

We next needed a rapid method to determine whether there were any sequence differences between the 30 strains of P. aeruginosa. We developed an assay using a specific enzyme from common celery known as Cel1. The Cel1 endonuclease specifically cleaves DNA downstream of any single base pair mismatches present in heteroduplexes of DNA. This means that any DNA duplexes that contain specific point mutations will show specific cleavage products on an agarose gel. We used all 30 PCR products described above and mixed them in a 1:1 ratio with the sequence-validated laboratory strain of P. aeruginosa. Following the formation of heteroduplexes and subsequent Cel1 digestion, we separated the products on an agarose gel to determine if any point mutants (mutations?) exist. To our surprise, all 30 of our isolated P. aeruginosa strains had at least one point mutation compared to the laboratory strain. Both the S and R strains had at least one mutation as well. This suggests that there is great heterogeneity within even the same species of bacteria. These data show that even though the majority of P. aeruginosa strains are senstitive to the antibiotic rifampicin, these strains still contain polymorphisms in the genome that do not result in antibiotic resistance.

Discussion