Cunningham
An Exploration of Bacterial Contamination Due to Kanamycin Resistance
Cathy Cunningham
Blaise Deluka, Kelly Foedinger, & __________
Leah Ferrante
Section 903
Cunningham
Introduction:
Chicken Farms usually add antibiotics, like kanamycin, to the food given to
chickens to prevent bad bacteria from penetrating the eggs. However, overuse of
specific antibiotics produces populations of chickens that are resistant to whichever
antibiotic is given. Such is the case with three chicken farms (Acme Eggs, Big Al’s
Poultry Farm, and Clucky’s Chickens), where an outbreak of kanamycin resistant
bacteria has occurred and thus produced contaminated eggs. There are three
different genes that are usually the cause of kanamycin resistance. Since the farms
are geographically isolated from each other, it is questioned whether or not they
shared a common source for the contamination or whether each occurrence of the
resistant bacteria is unique to each (Cyr, 2010).
To help identify the strain of resistant bacteria found at each farm, individual
bacteria samples from each of the three farms were collected. Coating a petri dish
with the collected bacteria would create a lawn or uncountable number of colonies
on the dish since the bacteria reproduce so quickly. However, by using serial
dilution before applying the bacteria to the petri dish the concentration of bacteria
is reduced. This process dilutes the bacterial sample to a point where individual
colonies can be easily counted (Cyr, 2010).
However, another process is needed to actually compare the DNA of the
different strains from the farms. Gel electrophoresis uses an agrose gel medium to
separate different DNA fragments based on their size using an electric field. The
DNA fragments are loaded into wells on the negative side of the apparatus, then 160
volts of electricity is applied causing the samples to move through the gel towards
Cunningham
the positive electrode (since the phosphate groups on the DNA have a negative
charge). How far the fragment moves is based on its size, with the larger pieces
moving slower and thus not as far as smaller pieces. However, a DNA ladder with
known fragment lengths can be used to compare the length of other bands of DNA
(Cyr, 2010).
Gel electrophoresis requires a large amount of DNA to run. To increase the
number of DNA, polymerase chain reaction (PCR) is used. DNA is added to a solution
of oligonucleotide primers, nucleotides, and taq polymerase, and then heated up.
The increased temperature caused the DNA to split into two different
complementary strands, which primers and nucleotides are added to by taq
polymerase to form new DNA fragments. Eventually, millions of new, identical DNA
molecules are formed since every round produces two more fragments than in the
original strand (Cyr, 2010).
The purpose of this experiment is to determine if the kanamycin resistant
bacteria found on each of the three farms with outbreaks come from the same
source, or more specifically if they come from the same gene that causes the
resistance. Also, the frequency of antibiotic resistance bacteria needs to be found so
that recommendations can be given to prevent any future contamination. The null
hypothesis is that the resistant bacteria all come from the same source, meaning
they come from a single strain and have the same DNA. While, the alternate
hypothesis is that the bacteria on the three farms come from different sources or
strains and therefore have different DNA.
Cunningham
Materials and Methods:
To begin, sample bacteria from each of the farms were collected. Then using
serial dilution, the group took 10 μl from each of the sample bacteria microtubes
and mixed it with 990 μl of water to create three 10^-2 dilutions. Then the group
took 10 μl from each of the 10^-2 solutions and added it to 990 μl of water, creating
three 10^-4 dilutions. Next, 10 μl of each of the 10^-4 dilutions were taken and
mixed with 990 μl of water, creating three 10^-6 dilutions. 100 μl of each dilution
was then evenly distributed on its own petri dish (labeled with correct
concentrations). This process was repeated using a non-resistant strain of bacteria,
creating another three dilutions for a total of six petri dishes. For 24 hours, all of the
dishes were incubated to allow the growth of colonies. Then the group counted the
number of visible colonies on each petri dish and found the number of original
bacteria and frequency of antibiotic resistant bacteria for each (Cyr, 2010).
Then, the group dipped three white pipette tips into three antibioticresistant colonies growing on a K-plate and put each one into their own microtube
with different DNA primers. Theses three tubes in addition to another three
containing a control plasmid and DNA primers were put into a PCR machine to
produce enough DNA for gel electrophoresis. After the machine was done, 2 μl of
loading buffer was added into each of the six tubes. Then, 15 μl of each tube and 5 μl
of a PCR DNA ladder, which was put in the first well and served as a control, was
injected into separate wells of a cast agarose gel. Then the gel was electrophoresed
and photographed in a UV light box so that the final DNA bands could be observed to
see how far they moved through the gel (Cyr, 2010).
Cunningham
Results:
Section Data Showing Numbers of Colonies, Numbers of Original Bacteria, and Freq
of Antibiotic Resistant Bacteria for All Farms
# of
Freq of
Bacteria in Antibiotic
# of Colonies Non-K
# of Colonies Kthe
FARM Plasmid
Resistant
Original
Plate
Plate
Bacteria
Sample
(Avg)
(Avg)
10^-2
10^-4
10^6
10^-2
10^4
10^6
A
5000 1051 39 2542 26
0
1667000 17.77%
B1
5000 5000 53
57
11
0
3450000
.45%
B2
5000 5000 51
85.5
0
0
3383333
.57%
C
5000 1401 23 2338
5
1
1250333 17.15%
According to this table, for both the non-K and K plates, as the dilution factor increases,
the number of colonies decreases. It is also seen that Farm B1/B2 has the highest number
of bacteria in the original sample (3450000 or 3383333) and the lowest antibiotic
resistance frequency (.45% of .57%). Farm C has the lowest number of bacteria in the
original sample and Farm A has the highest frequency of antibiotic resistant bacteria.
***How to calculate the original number of colonies
B=N/D, where B=Original number of colonies, N=Number of counted
colonies, and D = Dilution factor
I.E. -> For Farm A: 5000 + 1051 + 39
= 50010000/ 3 (to get an
10^-2
10^-4
10^-6
average) then multiply by .1
to account for the fact that
only .1 ml was plated
***How to calculate the freq of antibiotic resistant bacteria
# of Colonies K-plate/ # of Colonies Non-K plate
I.E. -> For Farm A: 2542 + 26
+ 0
= .53314/3 (to get an average) then
5000
1051
39
multiply by 100 to get a percent
Group/Individual Data Showing Numbers of Colonies, Numbers of Original
Bacteria, and Freq of Antibiotic Resistant Bacteria for Farm B2
Cunningham
# of Colonies Non-K
Plate
Partner
10^-2
10^-4
10^6
# of Colonies KPlate
10^-2
10^4
# of
Bacteria in
the
Original
Sample
(Avg)
Freq of
Antibiotic
Resistant
Bacteria
(Avg)
10^6
Cathy & Blaise 5000 1520 20
35
0
0
1190000
.23%
Kelly & ___
5000 5000 81
136
0
0
4383333
.91%
Average
5000 3260 51 85.5
0
0
3383333
.57%
According to this table, for both the non-K and K plates, as the dilution factor increases,
the number of colonies decreases. It is also seen that although the two groups had the
same strain of bacteria, Kelly & ___ had overall higher numbers for number of colonies,
number of bacteria in the original sample, and frequency of antibiotic resistant bacteria.
Photo of an Electrophoresed Gel of Bacterial DNA from Farm A
Plasmid C
Plasmid B
Plasmid A
Bacterial
Sample
DNA ladder
***Shows that the bacterial sample is the same length as plasmid A
Cunningham
Photo of an Electrophoresed Gel of Bacterial DNA from Farm B1
***Shows that the bacterial sample is the same length as plasmid B
Photo of an Electrophoresed Gel of Bacterial DNA from Farm B2
***Shows that the bacterial sample is the same length as plasmid B
Cunningham
Photo of an Electrophoresed Gel of Bacterial DNA from Farm C
***Shows that the bacterial sample is the same length as plasmid C
Data Table for Identification of Plasmid Responsible for Antibiotic Resistance in Bacterial Samples
FARM
A (Acme Eggs)
B1 (Big Al’s)
B2 (Big Al’s)
C (Clucky’s
Chickens)
Plasmid A
X
PLASMID
Plasmid B
Plasmid C
X
X
X
Discussion:
In conclusion, the results obtained from the lab did not match the null
hypothesis that the different outbreaks of kanamycin resistance on the three farms
came from the same source. Instead, it verified the alternate hypothesis that a
different strain of bacteria was contaminating each farm. This was determined from
looking at the different pictures of the gels. In the four gels, each bacterial sample
traveled the same distance as a certain control plasmid depending on the size of the
DNA fragment. Since each of bacterial samples from the different farms matched a
Cunningham
different control plasmid, they must be a different lengths and thus from different
genes.
Each strain from the three farms comes from different strains of kanamycin
resistant bacteria, so they have different resistance frequencies. Farm A had the
highest resistance frequency, which means that it has the most bacteria that can
resist kanamycin antibiotics. Farm B had the lowest resistance frequency and thus
the least bacteria that can inhibit the effects of kanamycin. Therefore, the gene that
codes for kanamycin resistance is most potent in Farm A, followed by Farm C and
finally Farm B.
All of the farms need to take actions to prevent future outbreaks even though
they have varying levels of contamination. All of them need to try and identify the
source of their contamination and eliminate it. Also, they need to change what
antibiotics they put in their chicken feed so that bacteria are no longer resistant to it
and, instead, are killed. In addition, they should monitor the farm weekly and check
the eggs for bacteria to make sure an outbreak doesn’t flare up again. Since Farm A
and Farm C both have resistance frequencies close to 18 percent, they need to not
only identify and kill the infected chickens but destroy all of the eggs until the
antibiotic resistance frequencies have been below 1 percent for 8 weeks. On the
other hand, Farm B has a contamination of less than 1 percent so they just need to
send their eggs to a pasteurization facility for 8 weeks as long as the levels stay the
same (Cyr, 2010).
The data collected could be incorrect for different reasons. If outside bacteria
entered the microtubes or petri dishes while the lids were removed, the petri dishes
Cunningham
could have more colonies than they should, producing wrong calculations. Also if the
bacteria dilutions were not mixed before being micropipetted out of the microtubes,
then the tubes using that concentration as a base for its dilution would contain
wrong amounts of bacteria and therefore create wrong data.
Another experiment that could be done is testing which antibiotics can be
used for the longest time without resistance forming. In general, antibiotic use
should be limited to help prevent the outbreak of resistance bacteria. Instead of
using one type of antibiotic, the farmers should switch types regularly to prevent
bacteria resistance.
References:
Cyr, R., Hass C., Woodward D., and Ward A., 2010. Using Genes for Antibiotic
Resistance to Trace Source(s) of Bacterial Contamination. In, Biology 110: Basic
concepts and biodiverity course website. Department of Biology, The
Pennsylvania State University. http://www.bio.psu.edu/