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Spring 2017

A Survey of β-lactam Antibiotic Resistance Genes

and Culturable Ampicillin Resistant Bacteria in
Minnesota Soils
Emily M. Wollmuth
Hamline University

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Wollmuth, Emily M., "A Survey of β-lactam Antibiotic Resistance Genes and Culturable Ampicillin Resistant Bacteria in Minnesota
Soils" (2017). Departmental Honors Projects. 53.
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A Survey of β-lactam Antibiotic Resistance Genes and Culturable Ampicillin Resistant Bacteria
in Minnesota Soils

Emily Wollmuth

An Honors Thesis Submitted

for partial fulfillment of the requirements for graduation with honors in
Biology from Hamline University

April 20, 2017

 Table of Contents

ABSTRACT…………….…………………………………………………………………………3

INTRODUCTION……………….………………………………………………………………..4

Antibiotics…………………………………………………………………………………4

Antibiotic Resistance……………………………………………………………………...6

Environmental Antibiotic Resistance……………….………………………………..…...9

Land Cover and Surficial Lithology……………………………………………..………12

Purpose and Significance…………………………………………………………...……13

METHODS……………..………………………………………………………………………..14

Sample Collection………………………………………………………………………..14

Culturing of Bacteria……………………………………………………………………..14

DNA Isolation……………………………………………………………………………15

PCR and Gel Electrophoresis…………………………………………………….………15

Data Analysis…………………………………………………………………………….16

RESULTS………..………………………………………………………………………………17

Ampicillin Resistance in Culturable Bacteria …………………………………………...17

Frequency and Location of Antibiotic Resistance Genes…………...…………………...19

Culturable Ampicillin Resistance and Antibiotic Resistance Gene Presence……...……21

DISCUSSION……..……………………………………………………………………………..23

FUTURE DIRECTIONS…..…………………………………………..…………………...……27

WORKS CITED……………….………………...………………………………………………29

ACKNOWLEDGMENTS…………...……………………………………………….…...…..…35

APPENDECIES……….………………………………………………….………………...……36

2
 ABSTRACT

Since the discovery of penicillin, antibiotics have been an essential tool in the treatment

of bacterial infections and diseases. It is estimated that antibiotics and antibiotic resistance genes

have existed for billions of years. With the increasing appearance of resistant pathogenic

bacteria, there has been growing concern. β-lactam antibiotics make up the largest portion of the

global market, so resistance to these antibiotics is especially alarming. It has been theorized that

frequency and type of antibiotic resistance genes vary by area. Previous studies suggest that

these differences may be related to antibiotic use in agricultural and urban areas. To survey this

variation, soil samples were collected from 180 Minnesota locations and plated on LB media

with and without ampicillin. Plates were incubated for 24 hours at 32˚C. DNA was isolated

directly from soil samples, and each was tested for several antibiotic resistance genes using

polymerase chain reaction. Samples were classified by land cover and surficial lithology using

data from the United States Geological Survey. By comparing colony counts on plates with and

without ampicillin, proportions of ampicillin resistant bacteria in different environments were

identified. There was not a statistically significant difference in the percentage of colonies

resistant to ampicillin between areas of different land cover or surficial lithology. The genes

bla-1 and bla-TEM were observed at the highest frequency, appearing at percentages of 14.4 and

12.2 respectively, but the appearance of these genes was not correlated with land cover or

surficial lithology. Antibiotic resistance genes are found in the human microbiome and

mathematical modeling has shown that the evolution of antibiotic resistance genes is not related

to the hospital environment, indicating that the increase in antibiotic resistant infections may be

the result of existing antibiotic resistance genes being brought into hospitals where selection

pressure results in them being maintained and spread.

3
 INTRODUCTION

Antibiotics

Over 500 million years old, antibiotics—also called antimicrobials—date back to the

Cambrian period (Baltz, 2008). They are produced by microorganisms and are a natural part of

soil, marine, and other environments at typically sub-minimal inhibitory concentrations.

Microbes in nature are found in multi-cellular communities and complete a huge variety of

metabolic activities. They have evolved continuously as the environment and selection pressure

have changed. In conditions of nutrient starvation, microorganisms secrete secondary

metabolites, a variety of low molecular weight organic molecules (Davies, 2009). A small

portion of these molecules have been identified to have antimicrobial activity, thus these

secondary metabolites have been studied primarily for their potential in medicinal applications.

However, antibiotics were recently discovered to play roles in other physiological phenomena

ranging from control of transcription and translation to growth and survival of antibiotic

producing organisms in microbial communities. Despite these findings, the ecological role of

these compounds remains largely speculative (Sengupta, Chattopadhyay, & Grossart, 2013).

Microbes capable of producing antibiotics are believed to benefit from this capability as

it may be crucial for providing a competitive advantage over neighboring microbes. Antibiotics

are theorized to serve as weapons against naturally occurring cohabiting microorganisms and

allow the producing species to thrive and thus dominate a habitat (Davies, 1990). In addition,

antibiotics appear to play a role in microbial communication in some ecosystems. Quorum

sensing is a mode of bacterial communication in response to release of chemical messengers

which regulate bacterial gene expression (Bassler, 1999). The quorum sensing signaling network

initiates metabolic changes which lead to morphological differentiation and synthesis of

4
secondary metabolites, such as antibiotics. Structural similarity between antibiotics and

intracellular signaling molecules has been observed in several cases (Sengupta, Chattopadhyay,

& Grossart, 2013). In addition, there is evidence that indicates a variety of promoters are

activated in response to low concentration of different antibiotics—including those involved in

virulence, metabolic, and adaptive functions (Goh et al., 2002).

Despite their many potential ecological roles, antibiotics have gained the most attention

for their medical relevance. For at least 2,000 years, healers and modern medical professionals

have relied on antibiotics to treat infections and diseases caused by bacteria and other microbes.

Beginning in early Egyptian, Chinese, Greek, and Indigenous Central American civilizations,

molds, soils, and plant extracts were used to treat and prevent infections. Some of these remedies

were used to prevent infections in serious cuts or to treat rashes (Colditz, 2015).

Since the discovery of penicillin by Alexander Fleming in 1928, antibiotics have become

an essential tool in the treatment of bacterial infections and diseases. Although researchers had

previously observed the antimicrobial properties of Penicillium, Fleming is credited for the

discovery of modern antibiotics because he spent years trying to convince chemists to work

toward resolving issues with purification and the stability of the substance. In 1942, Howard

Florey and Ernest Chain published a paper describing a purification technique for penicillin that

could produce quantities sufficient for clinical testing. Just five years later, in 1945, the

widespread use of penicillin began. With this, Fleming voiced concerns about the potential for

resistance to penicillin if used at too low of a concentration or for too short a period of time

during treatment. He was one of the first scientists to point out this potential issue (Aminov,

2010).

5
Antibiotic Resistance

Antibiotic resistance is the ability of a microbe to survive after exposure to an antibiotic

at a typically lethal concentration. This ability can either be intrinsic or acquired (Allen et al.,

2010). In the case of intrinsic resistance, certain species possess a physiological trait that

prevents the antibiotic’s mode of action from being effective. For example, gram-negative

bacteria have an outer membrane that allows them to resist the effects of the antibiotic

vancomycin which acts by preventing the formation of peptidoglycan linkages that forms the cell

wall. Because the outer membrane prevents the drug from reaching the cell wall, gram-negative

bacteria are intrinsically resistant to vancomycin (Pehrsson et al., 2013).

Alternately, in the case of acquired resistance, mutations in certain genes or mobile

genetic elements can be responsible. Plasmids—small, circular, double stranded pieces of

DNA—are the most common mobile elements that have been shown to be capable of conferring

antibiotic resistance. These molecules can contain a variety of antibiotic resistance genes and are

easily transferred among microbes through horizontal gene transfer which has been linked to the

spread of antibiotic resistance genes in the environment. Examples of possible

resistance-conferring mutations include mutations in the gyrase causing resistance to

floroquinolones, RNA polymerase subunit B causing resistance to rifampicin, and the 30S

ribosomal subunit protein S12 causing resistance to streptomycin (Allen et al., 2010).

Microbes employ a wide range of mechanisms to achieve resistance such as efflux

pumps, modifications to target proteins, and inactivation of an antibiotic. Cells that employ

efflux pumps are able to survive presence of an antibiotic by removing the drug from the cell.

Efflux pumps are transmembrane protein complexes that are capable of transporting an antibiotic

out of the cell. Other antibiotic resistant cells survive through a mutation to the target protein of

6
an antibiotic. When an antibiotic-binding site is altered but maintains cellular functionality the

cell is able to survive exposure to that antibiotic. Finally, some bacteria achieve resistance by

secreting enzymes that destroys the chemical structure of an antibiotic resulting in loss of its

antimicrobial properties. This is the most common resistance mechanism employed against β-

lactam antibiotics. Bacteria that are capable of secreting β-lactamases, enzymes that cleave the β-

lactam ring thus rendering the drug ineffective, are able to survive the presence of many β-lactam

antibiotics (Allen et al., 2010).

Just as antibiotics are an ancient part of the environment, antibiotic resistance dates back

up to billions of years. Structure-based phylogeny of serine and metallo-β-lactamases indicates

that these enzymes originated more than two billion years ago (Hall & Barlow, 2004). In

addition, these genes may have been present on mobile elements for millions of years. Evidence

points to the presence of some serine β-lactamases on plasmids for millions of years (Garau et

al., 2005). In addition, even before the widespread use of penicillin, observations suggested that

some bacteria could destroy it by enzymatic degradation (Abraham & Chain, 1940).

Today, β-lactams—including penicillin, ampicillin, and many others—make up the 50%

of the global market for antibiotics, making resistance to these antibiotics especially alarming

(Livermore, 1998). Over the years, β-lactam resistance has produced growing concern

worldwide, particularly with the emergence of methicillin-resistant Staphylococcus aureus

(MRSA), a bacterial strain that is resistant to the entire class of β-lactams. In 1961, the first case

of MRSA was identified in the United Kingdom (Johnson, 2011); then, in 1968 the first case was

reported in the United States. Today, MRSA is prevalent in all parts of the world (Sengupta,

Chattopadhyay, & Grossart, 2013).

7
 Multidrug-resistant bacterial infections, such as MRSA, are responsible for thousands of

deaths each year. In the European Union, about 25,000 patients die annually from infections with

multidrug-resistant bacteria, and in the United States an estimated 2 million people acquire a

serious bacterial resistant infection, with about 23,000 of those individuals dying as a result

(“The Bacterial Challenge,” 2009; “Antibiotic Resistance Threats,” 2013). Additionally, the

economic toll of multidrug-resistant bacteria is high. An estimated 1.5 billion euros are spent

each year on additional healthcare costs and productivity losses from multidrug-resistant

infections in the European Union, and in the United States the estimate for economic toll of these

infections is even higher at 20 to 35 billion dollars annually (“The Bacterial Challenge,” 2009;

“Antibiotic Resistance Threats,” 2013).

Despite the long history of antibiotics in nature, the emergence of antibiotic resistant

pathogens is often suggested to be directly correlated with the extensive use of antibiotics in

healthcare and agriculture. It is well established that bacteria with antibiotic resistant phenotypes

and antibiotics present before the start of the antibiotic era in 1945 coexisted without producing

deadly resistant pathogens like those that exist today (Sengupta, Chattopadhyay, & Grossart,

2013). Bacteria are most frequently exposed to antibiotics at sub-minimal inhibitory

concentrations in some clinical instances such as incomplete treatment of an infection, patient

non-compliance, and reduced drug accessibility to certain types of tissues. For example,

sub-inhibitory concentrations would be experienced by colonic bacteria in a patient during the

start and end of oral antibiotic treatment. In addition, certain areas within a patient being treated

with antibiotics, such as the epidermis, lungs, or joints, may be exposed to significantly lower

antibiotic concentrations than the blood stream (Sengupta, Chattopadhyay, & Grossart, 2013).

Additionally, in agricultural areas, the use of manure from livestock fed antibiotic supplemented

8
feed can result in sub-minimal inhibitory concentrations in soil, exposing the microbes present in

nearby soils and water sources to these antibiotics (Sengupta, Chattopadhyay, & Grossart, 2013).

Over the years, concern about the increasing problem of antibiotic resistance has grown.

In 2009, a database indicated the existence of over 20,000 potential resistance genes of nearly

400 different types in the available bacterial genome sequences (Liu & Pop, 2009). With the

growing fear of antibiotic resistance over the past few decades, intensive investigations have

been conducted globally to attempt to determine ways to slow the spread of antibiotic resistance.

Nevertheless, no significant discoveries have been made so far (Sengupta, Chattopadhyay, &

Grossart, 2013).

Environmental Antibiotic Resistance

Antibiotic resistance in the environment has been well documented. Genes conferring

resistance to tetracycline and β-lactam antibiotics have been observed in rivers, lagoons, and

groundwater in the United States and China (Aminov, 2002; Chen, 2012). One study found that

glacier cores taken from different countries had different types of antibiotic resistance genes

present. These genes also varied by depth (Segawa et al., 2012).

The results of some studies have pointed to the possibility that human activity plays a

role in the patterns of antibiotic resistance genes in nature. A 1999 study found that on average,

strains of E. coli from Mexican wild mammals were similarly resistant to antibiotics as those

found in cities while Australian wild mammalian strains carried lower levels of antibiotic

resistance, an outcome which the authors believed could be explained by the higher population

and prevalence of antibiotic use in Mexico (Souza et al., 1999). Another study found that since

the use of antibiotics in aquaculture has increased, antibiotic resistant fish pathogens have

appeared. This indicates that the rise of these resistant pathogens may be a result of antibiotic use

9
(Cabello, 2006). In addition, the presence of many types of resistance genes have been detected

worldwide in agricultural areas and wastewater treatment plants and a variety of β-lactamase

genes have been observed in fecal slurry and water from dairy farms, water and sediments from

aquacultural areas, and surface water from a variety of bodies of water (“Antibiotic Resistance

Threats”, 2013; Chen et al., 2012; Zhang, X., Zhang, T., & Fang, 2009).

Several studies have examined the impact of manure treatment of soil on antibiotic

resistance. A study conducted in 2014 comparing soil treated with manure and soil treated with

inorganic fertilizer, found that the soil treated with manure contained a higher abundance of

β-lactam-resistant bacteria. Metagenomic analysis identified a higher frequency of β-lactam

resistant bacteria in the manure-treated soil, but β-lactam resistance genes were observed in both

treated and untreated soil (Udikovic-Kolica et al., 2014). In fact, this finding could explain how

the supplementation of animal feed on farms results in increased antibiotic resistance. Antibiotics

and heavy metals have been observed at elevated levels in the manure of animals whose feed is

supplemented with these components, indicating how the soil around farms may become

concentrated with antibiotics resulting in the selection of resistance genes (Zhu et al., 2012).

In addition, organic and antibiotic-free farming practices have been attributed to lower

antibiotic resistance in poultry. When litter, feed, and water samples were analyzed for the

presence of multidrug-resistant Enterococci, the percentages of resistant Enterococcus faecalis

and resistant Enterococcus faecium were significantly lower among organisms isolated from

organic versus conventional poultry farms. In addition, 42% of E. faecalis isolated from

conventional poultry farms were multi-drug resistant, compared to only 10% from organic

poultry farms (Sapkota et al., 2011).

10
 In addition to farming, trends in antibiotic resistance tend to correlate with other areas of

high human impact. For example, along a river in Spain, the relative abundance of the bla-TEM,

bla-CTX-M, and bla-SHV genes was greater in samples taken from both a water treatment plant

and downstream from an antibiotic production plant than in samples collected upstream

(Sidrach-Cardona et al., 2014). Another study that examined historic soil archives from The

Netherlands spanning from 1940 to 2008 found that antibiotic resistance genes from all classes

of antibiotics, but especially tetracycline resistance genes and bla-TEM genes, have significantly

increased since the widespread use of antibiotics began in 1940 (Knapp et al., 2009).

Additionally, there is some evidence of the environmental origin of clinically relevant resistance

genes. For example, a study found that multidrug-resistant soil bacteria containing resistance

genes against five classes of antibiotics—including β-lactams, aminoglycosides, amphenicols,

sulfonamides, and tetracyclines—possess genes identical to many human pathogens (Forsberg et

al., 2012).

As the link between agricultural and clinical use of antibiotics and antibiotic resistance

has been established, it has been theorized that the frequency and type of antibiotic resistance

genes vary between different areas, often based on human activities. Because bacteria are

ubiquitous and not easily isolated to particular areas, there is a constant flow of genetic

information between different areas. Even in small numbers and low frequencies, selection

resulting from high use of antibiotics in humans and livestock may encourage the rapid spread of

these genes when antibiotic resistance genes are introduced into the commensal or pathogenic

human or animal microbiota (Aminov, 2010).

11
Land Cover and Surficial Lithology

Many researches have

shown high levels of antibiotic

resistance in certain types of

environments including

feedlots, farms, and sewage

treatment plants (Chen et al.,

2012; Sidrach-Cardona et al.,

2014) However, relatively little

Figure 1: The map shows land cover classifications
research has been done to throughout the state of Minnesota from The National Map
Viewer, United States Geological Survey
monitor the presence of

antibiotic resistance across a large region and among natural variations in land type. Thus, this

study aims to examine the variation between both differing types of natural areas and human

land usage through classification by land cover and surficial lithology. Land cover is a

description of land use that is derived from aerial photographs (Land Cover Institute, 2016). It is

determined by factors including type of vegetation and percentage of area covered by vegetation.

See Appendix 2 for descriptions of the land cover classifications for samples in this study.

Surficial lithology provides a physical description of the surface layer of soil. It is determined by

texture, internal structure, thickness and environment of deposition or formation of materials. In

turn, substrate types can influence the distribution of vegetation at the local, regional, and

continental scale (Sayer et al., 2009). Refer to Appendix 2 for more information about surficial

lithology. Both land cover and surficial lithology vary throughout Minnesota and can be used to

examine the presence of antibiotic resistance in the natural environment.

12
Purpose and Significance

With the evidence that points to the over prescription and heavy agricultural use of

antibiotics as a factor in the emergence of new antibiotic resistance strains, this study aimed to

map antibiotic resistance in soils. The goal of this research was to determine if the frequency of

β-lactam resistance in Minnesota soils is related to particular geographic features, including land

use and surficial lithology, and to determine if there was significant spatial variation in the

presence of specific β-lactamase genes present in the soil. This research relied on both bacterial

culturing methods and genetic analysis of soil samples. The percent of resistance to ampicillin of

bacteria culturable on LB and the frequency of antibiotic resistance genes associated with the

production of β-lactamases were both recorded and compared to available land cover and

surficial lithology data. This survey of both culturable bacteria and antibiotic resistance genes in

the environment provides insight on the prevalence of antibiotic resistance in nature and how

antibiotic resistance may be transferred between the natural environment and the clinical setting.

13
 METHODS

Sample Collection

Surface soil samples were collected during

May and June of 2015 from 180 locations

throughout Minnesota as shown in

Figure 1. Locations were selected

approximately every four to ten miles

based on accessibility, and GPS

coordinates were recorded. A metal

scoopula disinfected with 70 percent

ethanol which had been allowed to dry

was used to scoop soil into a sterile 50-mL

Figure 2: The map shows locations where falcon tube. Upon collection, the samples
samples were collected throughout Minnesota
were stored on ice until return to the

laboratory, at which point the samples were stored at 4o C until processing. All samples were

processed within 72 hours of collection.

Culturing of Bacteria

Each sample was suspended in sterile water in a 15-mL falcon tube, containing 14 mL of

water and 1 g of soil. The tube was inverted vigorously 15 times and the soil was left to settle for

several minutes. The samples were serially diluted, and 100 µL of each dilution was plated on

LB and LB with ampicillin added at a concentration of 100 µg/mL. Plates were incubated for 24

hours at 32˚C. The colonies were counted and converted to CFU/mL. To account for even low

levels of antibiotic resistant colonies present and to ensure that the colony counts from plates

14
with and without ampicillin came from a sample that had been stored for the same amount of

time, plates with 1 to 310 colonies were considered countable.

DNA Isolation

DNA was isolated directly from the soil samples using the PowerSoil DNA Isolation Kit

by MoBio Laboratories following the protocol outlined in Appendix A. All extracted DNA

samples were stored in 1.5-mL microcentrifuge tubes and stored at -23o C until PCR reactions

were performed.

PCR and Gel Electrophoresis

All soil samples were tested for a variety of genes related to the production of

β-lactamases and β-lactam antibiotic resistance. These genes included bla-1, bla-TEM, bla-SHV,

OXA-23, OXA-24, OXA-51, and OXA 58. The primers and PCR programs for the genes were

obtained from previous studies (Chen et al., 2012; Monstein et al., 2007; Woodford et al., 2006).

Each PCR reaction tube contained 12.5 µL of 2x GoTaq® Green Master Mix from

Promega, 1 µL of each forward and reverse primer at a concentration of 10 µM, 7.5 µL of sterile

nuclease free water, and 3 µL of extracted DNA sample for a total volume of 25 µL. For the

negative controls the DNA was replaced with additional nuclease free water. All PCR reactions

were completed in an Eppendorf Mastercycler® Gradient instrument using the PCR programs

shown in Appendix B.

All PCR products were resolved by electrophoresis in 1.5 percent agarose gel stained

with ethidium bromide. The gels were placed in a gel box in 1X Tris-Acetate-EDTA (TAE)

buffer. Ten microliters of each sample was loaded into the gel wells along with 5 µL of a 100-bp

DNA ladder. The gels were run at 90 to 100 mV for about 30 to 45 minutes then were visualized

under UV light in a Gel DocTM XR+ System Instrument by BioRad. The samples were compared

15
to the ladder to determine the sizes of DNA fragments amplified during the PCR reactions in

order to determine if the DNA samples contained the targeted antibiotic resistance gene. A

sample was considered positive if a band of the expected length was visible on the gel during UV

light exposure. A sample was considered negative for a gene if no band or a band of unexpected

length was observed in the respective lane.

Data Analysis

The CFU/mL calculations were used to calculate percentage of antibiotic resistant

colonies for each sample using the following equation:

CFU mL on LB AMP plate

× 100 = % of antibiotic resistant colonies
Average CFU mL of countable serial dilution plates on LB

Samples were classified by land cover and surficial lithology using The National Map

Viewer, a tool to acquire data from the United States Geological Survey (USGS). Average

percent of antibiotic resistant colonies for each classification was calculated by averaging the

percentages calculated for each sample. A one-way ANOVA was used to determine the

statistical significance of the difference in percent of colonies resistant to ampicillin between

areas of different land cover or surficial lithology.

16
 RESULTS

Ampicillin Resistance in Culturable Bacteria

Soil samples from all 180 collection sites were plated on LB plates to determine total

culturable bacteria and on LB AMP to find the fraction that were resistant to ampicillin. See

Appendix 4 for a list of all samples and the fraction of resistant colonies. The average percentage

of antibiotic resistant colonies amongst the samples was 10.6% with a range from 0.0% to

93.8%. In addition, the majority of samples had at least one colony resistant to ampicillin; 98.9%

of samples had one or more ampicillin resistant colonies and only 1.1% had none. The range of

percentages of ampicillin resistant colonies among the samples was large. See Figure 5 for a

histogram of this range. As shown in Figures 3 and 4, the samples varied greatly in the

percentages of ampicillin resistant colonies between the locations. However, this variation was

not correlated with land cover or surficial lithology. Using a one-way ANOVA, the results for

Figure 3: The graph above shows the average and range of percentage of colonies resistant to
ampicillin by surficial lithology. The large error bars indicate that the samples varied widely,
but this variation was not correlated with surficial lithology. The differences in mean resistant
percentages were not statistically significant (one-way ANOVA, p=0. 425).

17
the analysis of land cover compared to percentage of ampicillin resistant colonies were not

statistically significant (p=0.673). In addition, a two sample T-test was used to compare all

samples classified as developed (open space, high intensity, medium intensity, and low intensity)

or cultivated crops to all other land cover types in order to examine the potential impact of

human activities in urban areas or farms. The difference between these two groups of areas with

higher and lower human interaction was not statistically significant (p=0.4213). In addition, a

one-way ANOVA was used to examine the relationship between percentage of ampicillin

resistant colonies and surficial lithology; the result was not statistically significant (p=0.425).

Figure 4: The graph above shows the average and range of percentage of colonies resistant
to ampicillin by land cover. The large error bars indicate that the samples varied widely, but
this variation was not correlated with land cover. The differences in mean resistant
percentages were not statistically significant (one-way ANOVA, p=0.673).

Additionally, the size of the population of culturable bacteria varied widely among the

samples. The total CFU/mL on LB plates ranged from 3.65 X 103 to 1.12 X 106 with an average

18
of 1.37 X 105. A similarly wide variation was observed in the CFU/mL on the LB AMP plates.

These ranged from 0 to 2.89 X 105 CFU/mL with an average of 1.19 X 104.
number of samples

percent resistant

Figure 5: The above histogram shows the distribution of the percentage of ampicillin
resistant colonies. The one-tailed distribution indicates that a lower percentage of
ampicillin resistant colonies was more common among the samples.

Frequency and Location of Antibiotic Resistance Genes

The 180 soil samples were also tested for a variety of genes—including bla-1, bla-TEM,

bla-SHV, OXA-23, OXA-24, OXA-51, and OXA 58—related to the production of β-lactamases

Figure 6: An example of positive bands for Figure 7: The above table shows the
bla-1 are shown circled in red on this gel. The frequency of each antibiotic resistance gene
negative and positive controls are show circled amongst the 180 soil samples.
in blue.

19
and β-lactam antibiotic resistance using PCR and gel electrophoresis. Among the samples, 22.8%

were positive for one or more antibiotic resistance gene. A summary of which samples were

positive for each gene can be found in Appendix 4, and the frequencies of positive samples for

each gene are shown in Figure 7. A high rate of bla-TEM (12.2%) and a low rate of bla-SHV

(0.0%) were observed. Meanwhile, the frequencies of bla-TEM (12.2%) and bla-1 (14.4%) were

similar.

In comparison, the rates of the carbapenemase genes appeared at a lower frequency than

the rates of general β-lactamases genes. The most commonly appearing carbapenemase gene was

OXA-58 at 3.89% of the samples. However, OXA-23 and OXA-51 appeared at much a lower

rate of 0.56%, while none of the samples were positive for OXA-24. To examine the relationship

between land cover and the presence of antibiotic resistance genes, a chi-square test was used to

compare the number of samples with one or more antibiotic resistance gene detected between

developed (open space, high intensity, medium intensity, and low intensity) and cultivated crops

compared to all other land cover types. The result was not statistically significant (p=0.1933). In

addition, a chi-square test was completed to determine if the presence of antibiotic resistance

genes was correlated with surficial lithology. Surficial lithology and the pattern of antibiotic

resistance genes was analyzed for correlation using a chi-square test and was found to be not

statistically significant (p=0.581).

20
Culturable Ampicillin Resistance and Antibiotic Resistance Gene Presence

In order to determine

how the culturable ampicillin

resistance compares to the

presence of β-lactamase

genes, an independent

samples t-test was conducted.

This measurement was used to

determine if there is a

difference in means between

samples that had one or more Figure 8: The map shows the geographic distribution of
samples and their corresponding antibiotic resistance genes
positive result for the and percentages of colonies resistant to ampicillin. An
interactive version of this map can be viewed at
β-lactamase genes or negative https://public.tableau.com/profile/presley.martin -
!/vizhome/Book1-EmilyARGresults/Sheet2
results. Although the results

were not statistically significant (p=0.1254) at a 95% confidence interval, they suggest that a

relationship between these factors may exist. Further investigation would need to be conducted

to determine if a relationship exists. A visual representation of these data can be seen in Figure 8

which shows the geographic distribution of the percentage of colonies resistant to ampicillin and

the presence of β-lactamase genes. The size of the marker identifies the percentage of colonies

resistant to ampicillin, and the color identifies which antibiotic resistance genes, if any, were

present in each sample.

The samples were also classified by the antibiotic resistance genes detected. For example,

a sample that was positive for bla-TEM and bla-1 was classified as bla-1+bla-TEM. These

21
classifications were referred to as resistance patterns. A Kolmogorov-Smirnov test was used to

compare the distributions of the percentages of colonies resistant to ampicillin for the samples

classified into each resistance gene pattern group. The

distributions in percentage of colonies resistant to

ampicillin, shown in Figure 9, were statistically

significantly different between the various resistance

gene patterns (p=0.004). Thus, there appears to be a

notable relationship between the presence of antibiotic

resistance genes and the percentage of colonies

resistant to ampicillin. The samples with no resistance

genes identified tended to have lower percentages of

colonies resistant to ampicillin, while the samples

positive for bla-TEM or bla-1+bla-TEM tended to

have higher percentages of colonies resistant to

ampicillin.

Percentage of colonies resistant

Figure 9: The samples were

classified by resistance pattern and
the percentage of colonies resistant to
ampicillin for each sample are shown
above. The samples with no
resistance genes identified tended to
have lower percentages of colonies
resistant to ampicillin, while the
samples positive for bla-TEM or
bla-1+bla-TEM tended to have higher
percentages of colonies resistant to
ampicillin. The the y-axis shows the
number of samples and the scale is
adjusted and differs for each
resistance pattern. 22
 DISCUSSION

The presence of β-lactamase genes have been previously observed in a variety of

environments ranging from soil to water treatments plants (Udikovic-Kolica et al., 2014; Chen et

al., 2012). Bla-TEM is a commonly encountered β-lactamase in gram-negative bacteria and is

typically observed in the environment at frequencies tenfold higher than any other type of

β-lactamase. It is responsible for as much as 90% of ampicillin resistance in E.coli. Both

bla-TEM and bla-SHV are common on plasmids (Livermore, 1995). In this study, a high rate of

bla-TEM and a low rate of bla-SHV, two different types of extended spectrum β-lactamases,

were observed. In addition, the frequencies of bla-TEM and bla-1 were similar. In fact, bla-1 is a

bla-TEM gene that is inserted into a plasmid. The bla-TEM primers detect bla-TEM genes either

in a plasmid or in the bacterial chromosome. Alternately, the bla-1 primers should only detect

bla-TEM genes in a plasmid because one of the primers binds to a sequence that is in the plasmid

adjacent to the site of the ampicillin resistance gene. Therefore, most samples that are positive

for one gene were positive for the other. However, several samples were exceptions to this,

presumably a result of the diversity of bla-TEM type genes, which is a large group of related

genes (Leflon-Guibout, Heym, & Nicolas-Chanoine, 2000).

In comparison, carbapenemase genes appeared at a lower frequency than the rates of

extended spectrum β-lactamases genes. Three of four OXA genes conferring resistance to a class

of β-lactams called carbapenems were detected in the environment. The most commonly

appearing gene was OXA-58. However, OXA-23 and OXA-51 appeared at much a lower rate,

while none of the samples were positive for OXA-24. The observation of these genes in this

study has been predated by the observation of carbapenemase genes in a variety of environments.

Previously, carbapenemase genes have been observed in water treatment plants and in rivers

23
downstream of waste water treatment plants (Yang, 2016; Girlich, 2010). In addition, similar

genes have been observed in clinical isolates of Acinetobacter species and, in the past decade,

there have been many recorded instances of carbapenem resistant pathogenic bacteria, especially

in clinical and hospital environments (Mostachio et al., 2009; Gupta et al., 2011; Papp-Wallace

et al., 2011; Woodford et al., 2006). Carbapenemase genes have also been observed in several

species of bacteria isolated from soil (Gudeta et al., 2016). However, very few studies have

attempted to identify such genes in soil. Carbapenems are broad spectrum antibiotics, meaning

they are able to be used in treatment against both gram-negative and gram-positive bacteria.

Because of their broad spectrum activity and the clinical relevance of these drugs, resistance to

carbapenem β-lactam class antibiotics is especially alarming. Carbapenems are considered a drug

of “last-resort” and are typically reserved for the treatment of severe bacterial infections.

Among the samples, the range in percentage of colonies resistant to ampicillin was large,

serving as evidence that antibiotic resistance might vary on a small scale. The samples in this

study were collected at about 5 mile increments, so it is difficult to determine at what distance

samples might differ in their antibiotic resistance and other properties. Perhaps samples as little

as feet apart could possess distinct populations of antibiotic resistant bacteria. While there was a

wide range in percentages of colonies that were resistant to ampicillin between locations, this

variation did not appear to be correlated to a particular type of area. However, the lack of

variation between various land cover and surficial lithology classification can perhaps be

explained by a variety of factors.

One factor playing a role in the appearance of ampicillin resistant bacteria across the

various land cover and surficial lithology types is the age of antibiotic resistance. As previously

mentioned, antibiotic resistance has been present in nature for as long as billions of years (Hall &

24
Barlow, 2004). One study that looked at antibiotic resistance genes in 30,000-year-old Beringian

permafrost sediments and found that many of the vancomycin resistance, beta-lactamase, and

tetracycline resistance genes they observed were similar to modern variants of these genes. From

these results the researchers concluded that the rapid emergence of antibiotic resistance in the

clinic results from the selection of pre-existing resistance genes that have been present in the

microbial pangenome for millions of years rather than the mutation of novel antibiotic resistance

genes in the hospital environment (D’Costa et al., 2011). A mathematical modeling study that

tested for the hospital influence on the prevalence of particular types of antibiotic resistance

genes also supported this assertion. Using a statistical hypothesis test called the Nosocomial

Evolution of Resistance Detector (NERD), which calculates the significance of resistance trends

occurring in a hospital, they found that for 13 of the 16 antibiotics that were examined, the

hospital environment did not have a significant effect on the resistance genes present. Several

β-lactams—ampicillin, piperacillin, and several cephalosporin antibiotics—were included in

these not significantly altered (Seigal et al., 2017). Thus, the presence of antibiotic resistance

genes across a wide variety of land types is less surprising.

In addition, metagenomics studies show that human gut microbiota contain many bacteria

that possess antibiotic resistance genes. Many of these genes have been observed to be capable of

being horizontally transferred to pathogenic bacteria in the gut (Rolain, 2013). This could be a

potential explanation of how environmental antibiotic resistance genes are transferred to the

hospital environment. Patients may pick up antibiotic resistance genes through contact with soil

or water in their daily lives and bring these antibiotic resistance genes into clinics and hospitals

where there is selection pressure for them to be maintained and spread. In fact, a study of

Swedish exchange students returning from programs in Central Africa or the Indian peninsula

25
showed an increase in antibiotic resistance genes in their gut microbiomes. This increase

included a 2.6-fold increase for β-lactam class antibiotic resistance genes among the subjects and

all but one of the subjects acquired one or more extended spectrum β-lactamase gene that was

not detected in the specimens collected before the exchange program (Bengtsson-Palme et al.,

2015).

There are also several theories about how the hospital environment might encourage the

spread and maintenance of antibiotic resistance genes. One theory about the role of antibiotic

production in nature could explain the prevalence of antibiotic resistance in both natural

environments and hospitals. Some scientists have proposed that antibiotic-resistant bacteria are

not only able to avoid predation by antibiotic producers but may also benefit from nutrients

released when cells are killed by antibiotic-producing bacteria. Therefore, antibiotic resistance

may be driven by predation in conditions of nutrient deprivation and suggests that environments

with low nutrient content—such as clean rooms or intensive care units in hospitals—may serve

as a source of antibiotic-resistant bacteria (Leisner et al., 2016). Another study identified that

there is an increase in the exchange of genetic material in the presence of antibiotics. In fact, they

observed more than a 1000-fold increase in conjugation rates of E. faecalis in the presence of

tigecycline, a glycylcycline class antibiotic that is similar to tetracycline (Beabout et al., 2015).

However, the belief that antibiotics promote conjugation has been debated, and other studies

have identified little to no difference in conjugation rates among cells treated and untreated with

antibiotics (Lopatkin et al., 2016).

Finally, cycling antibiotics has been shown to be beneficial in reducing the total

incidence rate of hospital-acquired infections and resistant infections (Wiesch et al., 2014).

Because antibiotic cycling appears to reduce the incidence of resistant infections, this supports

26
the theory that when one antibiotic is overused, the hospital environment tends to select for

bacteria resistant to that particular antibiotic. Alternatively, if multiple antibiotics are used, the

selection pressure for resistance to one particular antibiotic will not be as high. Thus, the role of

selection pressure in the prevalence of antibiotic resistance is great and the over prescription and

overuse of antibiotics is still an issue of concern despite the prevalence of antibiotic resistance

across a wide variety of natural environments.

FUTURE DIRECTIONS

This study could be expanded in the future to include samples from more pristine areas

away from major roadways and other human activities. Extending the diversity of samples could

provide further insight into the distribution of antibiotic resistance genes in the environment. In

addition, future research could include analyzing soil samples for nutrient content, metal content,

or other abiotic factors to identify the potential relationship between these factors and

percentages of ampicillin resistant colonies or antibiotic resistance gene frequencies. Previous

studies have indicated that nutrient starvation and high content of heavy metals can be correlated

with the presence of antibiotic resistance (Leisner et al., 2016; Knapp et al., 2011).

To further explore the antibiotic resistance genes present in the environment, it would be

beneficial to sequence the amplified PCR fragments to confirm that the bands observed in the

gels were in fact the antibiotic resistance genes that the primers were intended to identify. Also,

additional genes associated with production of β-lactamases could be identified. While this study

only included six genes, a genomic analysis found over 340 types of β-lactamases that all

function by breaking the characteristic β-lactam ring in β-lactam class antibiotics (Monstein et

al., 2007).

27
 It would also be beneficial to learn more about the specific species of bacteria present in

the soil samples that possess the antibiotic resistance genes. Although the species of individual

colonies can easily be identified using 16S ribosomal RNA, it is challenging and time-intensive

to identify individual antibiotic resistant colonies that possess the antibiotic resistance genes

being studied. Thus, a metagenomics study could be more feasible. A future study could involve

comparing the species composition of samples with particular antibiotic resistance genes or

varying levels of culturable ampicillin resistant colonies.

Considering the clinical relevance of carbapenems and the lack of knowledge about the

presence of these genes outside the clinical environment, further study of these genes would be

potentially useful. Carbapenem containing media could be used to identify culturable

carbapenem resistance in soils using the plating methodology employed in this study. In

addition, if individual colonies containing OXA genes could be identified, it would be interesting

to identify the species of these bacteria compared to those present in clinical environments.

28
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 ACKNOWLEDGEMENTS

There are many individuals and organizations that this work would not have been

possible without. First, I would like to thank the Hamline University Biology Department and the

Howard Hughes Medical Institute (HHMI) for the the funding to do this work. In addition, I

would like to thank my fellow student researchers for their collaboration, Lori Thrun for her

many contributions to our research group, and Frank Shaw for guidance regarding statistics. I

would also like to thank Kathryn Malody for her teaching and support in the laboratory over the

years, Betsy Martinez-Vaz for teaching me much of the microbiology that I know, the English

Department for helping me to develop further as a writer and learn to communicate my scientific

work to broad audiences, and my friends and family—especially Austin Conery and Tim and

Stacy Wollmuth—for their support. Finally, I would like to acknowledge Pres Martin for his

mentorship and encouragement as this work developed as well as for his support in my

development as a scientist through his many roles in my education as my academic advisor,

research advisor, and biology seminar advisor.

35
 APPENDECIES

Appendix 1: Summary of Antibiotic Resistance Genes and Primers

Primer Set Gene Target Forward Primer Reverse Primer Amplicon Size Reference
bla-1 bla-1 CAAGCAGCAGATTACGCG TAGCTTCCCGGCAACAAT 480 Chen (2012)
bla-TEM bla-TEM TCGCCGCATACACTATTCTCAGAAT ACGCTCACCGGCTCCAGATTTAT 445
Monstein (2007)
bla-SHV bla-SHV ATGCGTTATATTCGCCTGT TGCTTTGTTATTCGGGCCAA 747
OXA-23 OXA-23 GATCGGATTGGAGAACCAGA ATTTCTGACCGCATTTCCAT 501
OXA-24 OXA-24 GGTTAGTTGGCCCCCTTA AA AGTTGAGCGAAAAGGGGATT 249
Woodford (2006)
OXA-51 OXA-51 TAATGCTTTGATCGGCCTTG TGGATTGCACTTCATCTTGG 353
OXA-58 OXA-58 AAGTATTGGGGCTTGTGCTG CCCCTCTGCGCTCTACATAC 599

Appendix 2: Definitions for Land Cover and Surficial Lithology Classification

Land Cover Description

Developed Areas characterized by a high percentage (30 percent or greater) of
constructed materials (e.g. asphalt, concrete, buildings, etc.)
Cultivated Crops Areas characterized by herbaceous vegetation that has been planted or is
intensively managed for the production of food, feed, or fiber; or is
maintained in developed settings for specific purposes. Herbaceous
vegetation accounts for 75-100 percent of the cover.
Deciduous Forest Areas dominated by trees where 75 percent or more of the tree species shed
foliage simultaneously in response to seasonal change.
Grasslands/Herbaceous Areas dominated by upland grasses and forbs. In rare cases, herbaceous cover
is less than 25 percent, but exceeds the combined cover of the woody species
present. These areas are not subject to intensive management, but they are
often utilized for grazing.
Pasture/Hay Areas of grasses, legumes, or grass-legume mixtures planted for livestock
grazing or the production of seed or hay crops.
Woody Wetlands Areas where forest or shrubland vegetation accounts for 25-100 percent of
the cover and the soil or substrate is periodically saturated with or covered
with water.
Emergent Herbaceous Areas where perennial herbaceous vegetation accounts for 75-100 percent of
Wetlands the cover and the soil or substrate is periodically saturated with or covered
with water.
Evergreen Forest Areas dominated by trees where 75 percent or more of the tree species
maintain their leaves all year. Canopy is never without green foliage.
Mixed Forest Areas dominated by trees where neither deciduous nor evergreen species
represent more than 75 percent of the cover present.

36
Surficial Lithology Description
Glacial Till, Loamy Result of erosion by glacial movement; rich soil containing mixture of sand,
silt, and a smaller proportion of clay
Glacial Till, Clayey Result of erosion by glacial movement; made up primarily by a fine-grained,
firm soil
Glacial Outwash and Sand and gravel deposited by water melting from a glacier; “gritty” texture
Glacial Lake Sediment, with large particles
Coarse-Textured
Glacial Lake Sediment, Sediment near or within a lake formed by a melting glacier
Fine-Textured
Alluvium and Fine- Deposit of clay, silt, sand, and gravel by a river or stream;
Textured Coastal Zone
Sediment
Colluvial Sediment loose sediments deposited by rain water or downhill creep

37
Appendix 3: Protocol for MoBio PowerSoil DNA Isolation Kit

38
Appendix 4: Summary of Sample GPS Coordinates, Land Cover, Surficial Lithology,
Percentage of Colonies Resistant to Ampicillin, and Antibiotic Resistance Genes

39
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