The clash of the modern crises: A study investigating the development of antibiotic resistance in Escherichia coli under increasing carbon dioxide concentrations

The clash of the modern crises: A study investigating the development of antibiotic resistance in Escherichia coli under increasing carbon dioxide concentrations

Tahlia Martignago, Menai High School

The global crisis of increasing atmospheric carbon dioxide concentrations has resulted in changes in biological processes and systems, whereby carbon dioxide has been shown to affect mutation in organisms such as bacteria and fungi. In the context of the increasing prevalence of antibiotic resistance, it is unclear whether carbon dioxide may impact the development of antibiotic resistance in bacteria. This primary study aimed to investigate if differing concentrations of carbon dioxide affected the rate and extent of antibiotic resistance developed by Escherichia coli over 4 generations using the Kirby Bauer disk diffusion method and MIC test strips. It was found that increased carbon dioxide exposure resulted in significant increases in both the extent and rate of antibiotic resistance acquired by E.coli, where P=0.04 and P=0.008 respectively. Further investigation through refining and replicating the method is required to support the link between increases in global atmospheric carbon dioxide concentrations and increases in antibiotic resistance of bacteria, which may pose a major public health issue in terms of the treatment of infectious disease.

LITERATURE REVIEW

Carbon dioxide is a large factor in the climate emergency due to its contribution to the greenhouse effect, responsible for approximately ¾ of emissions of GHG. In November 2020, global atmospheric CO2 concentrations were at 415ppm, approximately 47% greater than preindustrial levels in 1850 (NASA, 2021). Increases in CO2 concentrations have been shown to increase mutation frequency (H. P. Charles & Gillian A. Roberts, 1967; Ezraty, B et al., 2011), cell membrane permeability (Endeward, V. et al., 2017) and promote the dissemination of antibiotic resistance genes in bacteria (Liao, J., Chen, Y., & Huang, H., 2019). Worst-case projections predict an increase from current concentrations by 1000 - 2100ppm (Nakicenovic et al, 2000), which would result in greater impacts than already seen in biological systems. Antibiotic resistance is one of the largest growing public health issues globally and is a result of bacterial mutation. The increase in both atmospheric CO2 concentrations and prevalence of antibiotic resistance alongside the known mutagenic effect of CO2 on biological systems, has attracted the investigation of the effects of carbon dioxide on the development of antibiotic resistance.

Carbon dioxide has been shown to act as a growth factor for some mutations of the fungi Neurospora (Reissig & Nazario, 1962; Charles & Broadbent, 1964). A study conducted by Charles & Roberts (1968) investigated whether the effects observed are limited to Neurospora, testing on E.coli. The study found that mutations of E.coli were largely seen in gas phases containing 20% CO2. It also found that the same mutations occur in E.coli as in Neurospora, which could suggest an impact on a wider variety of microorganisms due to increased CO2 involvement. However, the way in which CO2 stimulates mutations was not established, and the investigation failed to mention the rate or quantity of overall mutations over the range of CO2 concentrations tested, thus reducing the validity of the investigation.

These findings were supported in an investigation of mutation frequencies of E.coli in the presence of CO2 and hydrogen peroxide (Ezraty, B. et al., 2011). Higher concentrations of CO2 increased mutation rates significantly, and was shown to be linked to increased reactive oxygen species (ROS). This study suggests a link between CO2 increases and bacterial mutation, and a mechanism for how CO2 increases can result in increased mutation, however it investigated a multitude of independent variables that convoluted the aim of this investigation.

Supporting findings by Ezraty, B. et al. (2011), a 2019 study also showed that samples treated with CO2 produced more reactive oxygen species (ROS) compared to controls, indicating that CO2 promoted ROS production in E.coli (Liao, J., Chen, Y., & Huang, H. 2019). The increase of ROS production, induced by CO2 exposure, resulted in DNA and cell membrane damage, which affected the structure of the cells and increased cell membrane permeability, and promoted the transformation of antibiotic resistant genes (ARG) by 1.5 - 5.5 and 1.4 - 4.5 fold in two strains of E.coli respectively after being treated with CO2 (Liao, J., Chen, Y., & Huang, H. 2019). Since ROS can be attributed to DNA damage and mutation, this study provided a mechanism for the development and transfer of ARGs between bacteria, thus suggesting that exposure to CO2 would increase how many bacteria obtain these genes through horizontal gene transfer. Although this study consistently referred to antibiotic resistant genes, and acknowledged that further study was required to explore the effect of increased ARG transformation, it failed to show the resulting change in the extent of antibiotic resistance.

Considering the increases in mutation frequency shown in these studies, it can be inferred that carbon dioxide exposure could result in increased antibiotic resistance, however research generally focuses on the factors that could lead to antibiotic resistance particularly the effect of carbon dioxide on mutation, as well as cell damage and membrane permeability rather than the specific degree of developed antibiotic resistance. Thus, this primary investigation aims to investigate how increasing carbon dioxide concentrations affect the extent of antibiotic resistance in Escherichia coli over 4 generations to quantify the impact of mutation and other processes that occur in organisms as a result of increases in CO2 exposure outlined in these studies.

RESEARCH QUESTION

Will increased concentrations of carbon dioxide affect the extent of antibiotic resistance acquired by Escherichia coli over 4 generations?

SCIENTIFIC HYPOTHESIS

The cultures of E.coli exposed to increased carbon dioxide concentration are expected to develop more resistance to the antibiotic in comparison to E.coli cultures incubated in lower carbon dioxide concentrations, in accordance with studies that found increased carbon dioxide resulted in higher transformation efficiencies of antibiotic resistant genes (Liao, J., Chen, Y., & Huang, H., 2019).

VARIABLES

Independent variable: Concentration of carbon dioxide (see appendix B and E).

Dependent variable: Extent of antibiotic resistance acquired over 4 bacterial generations (diameters of zones of inhibition and MIC values).

Controlled variables:

• Type + strain of bacteria (Escherichia coli, K-12 strain)

• Antibiotic (tetracycline)

• Agar plates (Mueller Hinton, 100mm)

• Incubation temperature (35 degrees Celsius)

• Time in between inoculation and measurement (24 hours)

• Amount of initial bacteria (100 μL)

• Generations of bacteria (4 generations)

Control: Mueller Hinton agar plate inoculated with bacteria and tetracycline susceptibility disc, incubated at 35 degrees Celsius with no additional exposure to carbon dioxide.

METHODOLOGY

All methods were undertaken in a lab environment, following aseptic techniques throughout (see appendix A). Equipment setup is pictured in appendix C.

Generation 1

28 Mueller Hinton agar plates were each inoculated with 100 μL of Escherichia coli and spread to create a bacterial lawn. A tetracycline antibiotic susceptibility disc was placed in the centre of each plate (see appendix D). The 28 plates were split into 7 groups of 4 cultures. All agar plates of each group were placed in a container (7 containers in total). Carbon dioxide was pumped into each container for the required time interval detailed below before closing valves to seal the environment (see appendix B). All containers were stored in the same incubator set to 35 degrees Celsius. After 24 hours, the diameters of the zones of inhibition were recorded with a ruler and tabulated.

Table A – CO2 setup for bacterial groups

Generation 2 & 3

Swabs were taken of the edges of the zones of inhibition from each of the generation 1 control cultures and inoculated onto a control Mueller Hinton agar plate (see appendix F). This was repeated for each plate within each experimental group (see appendix C). Tetracycline antibiotic susceptibility discs were placed into the centre of all plates. Each group was placed into the same carbon dioxide environments as in generation 1 and incubated at 35 degrees Celsius for 24 hours. The diameters of the zones of inhibition were recorded with a ruler and tabulated. This was repeated to create generation 3.

Generation 4

Swabs were taken of the edges of the zones of inhibition for the generation 3 cultures and inoculated onto a corresponding agar plate as performed in generation 2 & 3. This was repeated for each experimental group. Tetracycline MIC e-test strips were placed into the centre of all plates (see appendix G). Each group was placed into the corresponding carbon dioxide environments as done in generation 1 and incubated at 35 degrees Celsius for 24 hours. The MIC values of each culture were recorded and tabulated.

RESULTS

Zones of inhibition of all cultures were measured for generations 1-3 with a centimetre ruler 24 hours after inoculation, and generation 4 was measured through recording the MIC of each culture 24 hours after inoculation (see appendix G).

T-tests were conducted in order to compare the 2 averages of the control and the experimental groups to determine the presence of a statistically significant link between CO2 and antibiotic resistance, however the test was limited by the lack of data provided. The alpha

value chosen was 0.05, thus results that are statistically significant have at least a 95% confidence interval (see appendix H). Two-tailed P-values were used to determine if the experimental mean was significantly less or greater than the control mean.

Figure 1a) – Average zones of inhibition of each experimental group were measured at bacterial generation 1, 2, and 3.

Figure 1b) – Average zones of inhibition of each experimental group were graphed at bacterial generation 1, 2, and 3.

It was found that zones of inhibition of all groups generally decreased as generations progressed, whereby generation 1 had the largest zones of inhibition. The first and second bacterial generations possessed larger zones of inhibition as CO2 concentration increased. The third generation had the largest zones of inhibition at lower CO2 concentrations of 1 and 2 seconds in comparison to third generation controls. After 3 seconds, the zones of inhibition then stabilised and decreased as CO2 concentration increased in groups 4, 5, and 6 seconds. Bacterial cultures exposed to 3, 4, 5, and 6 seconds of CO2 had decreased zones of inhibition compared to controls. Third generation bacterial cultures expressed the most antibiotic resistance when they were exposed to 6 seconds of CO2.

Figure 2a) – Average minimum inhibitory concentration of each experimental group was measured at bacterial generation 4 through e-test strips.

Figure 2b) – Average MICs of each experimental group at bacterial generation 4 were tabulated.

Minimum inhibitory concentration initially decreased in experimental groups exposed to 1 and 2 seconds of CO2 compared to controls, then increased as CO2 concentrations increased. Increased minimum inhibitory concentrations were seen at higher CO2 concentrations (4, 5 and 6 seconds) in comparison to that of control cultures, indicating that bacterial cultures exposed to increased CO2 concentrations possessed more antibiotic resistance. Highest Minimum inhibitory concentrations were expressed in 4 second cultures indicating that this group was the most resistant.

Figure 3a) – Rate of change between the 1 st and 3 rd bacterial generation was calculated through subtracting zones of inhibition of the 3 rd generation from the 1 st generation zones of inhibition.

Figure 3b) – Rate of change of antibiotic resistance of each experimental group between the 1 st and 3 rd bacterial generation was tabulated

The rate of antibiotic resistance initially decreased, whereby both 1 and 2 second experimental groups possessed negative rates of change between the first and the third generations, whereby the first generations of these 2 experimental groups possessed smaller zones of inhibition and were more resistant than the third generation of these same groups as shown in Figure 1a) and 1b). The rate then increased as concentration increased, whereby the 6 second experimental group had the greatest rate of resistance acquired between the 1 st and 3 rd bacterial generations.

Statistical analysis of the 3 rd generation minimum inhibitory concentrations between the control and the 6 second CO 2 experimental groups (see appendix I)

Figure 4 - P(T<=t) two-tail = 0.16 (P> 0.05)

The relationship between the average zones of inhibition of the control and 6 second experimental groups in the third generation was not statistically significant as P=0.16, whereby there is more than a 5% chance that the results were due to chance. Therefore, carbon dioxide had no significant effect on the degree of antibiotic resistance acquired by the third generation bacteria.

Statistical analysis of the 4 th generation minimum inhibitory concentrations between the control and the 6 second CO 2 experimental groups

Figure 5 - P(T<=t) two-tail = 0.04 (P< 0.05)

The relationship between the average zones of inhibition of the control and 6 second experimental groups in the fourth generation was statistically significant as P=0.04, thus there is a less than 5% chance that the results were due to chance. Therefore, carbon dioxide affected the degree of antibiotic resistance acquired by the fourth generation, whereby Figure 2a) and 2b) indicate that increased carbon dioxide increased the degree of antibiotic resistance.

Statistical analysis of the rate of resistance between the control and 6 second CO 2 experimental groups acquired between the 1 st and 3 rd bacterial generations

Figure 6 - P(T<=t) two-tail = 0.0079 (P< 0.05)

The relationship between the average rate of resistance of the control and 6 second group acquired between the 1 st and 3 rd generation was statistically significant at P=0.0079, thus there is a less than 1% chance that the results were due to chance. Therefore, increased carbon dioxide significantly increased the rate that antibiotic resistance was acquired between the 1 st and 3 rd bacterial generations.

Statistical analysis of the rate of resistance between the control and 1 second and 2 second CO 2 groups acquired from the 1 st to the 3 rd bacterial generation

Figure 7a) – P(T<=t) two-tail = 0.0005 (P<0.05)

Figure 7b) – P(T<=t) two-tail = 0.0067 (P<0.05)

The rate of resistance between the control and the low concentrations of CO2 were statistically significant, whereby both the 1 and 2 second experimental groups had significantly lower rates of change than control cultures at P=0.0005 and P=0.007 respectively. Thus the rate of antibiotic resistance in these 2 experimental groups was significantly decreased due to low CO2 concentrations compared to the controls.

DISCUSSION

The T-tests conducted between controls and the 6 second CO2 group (see Figure 5) found significantly increased resistance of fourth generation Escherichia coli as P<0.05 and in addition found that the rate of resistance acquired by the 6 second CO2 group between the 1st and 3rd generation was significantly affected by increased CO2 concentrations as P<0.05 (see figure 6). Therefore, the null hypothesis that increasing concentrations of carbon dioxide will have no effect on the extent of antibiotic resistance acquired by E.coli will be rejected in favour of the alternate hypothesis. The P values found suggest that there is less than a 5% chance that the null was incorrectly rejected in a type 1 error.

In both the first and second bacterial generations, zones of inhibition increased as CO2 concentration increased, whereby it was deduced that increased carbon dioxide exposure in the first and second bacterial generations resulted in decreased antibiotic resistance of E.coli in comparison to the control group (see figure 1a and 1b).

Third generation zones of inhibition decreased in increased CO2 concentrations compared to the control, however this was not statistically significant, as P>0.05 for experimental groups in comparison to the control group (see figure 4). Third generation zones of inhibition increased initially in the 1 and 2 second groups in comparison to the control group before decreasing over the 3, 4, 5 and 6 second experimental groups (see figure 1a and 1b). This supports studies relating to the ability for E.coli to become adapted to CO2, initiating CO2 mutations that are dependent on high concentrations of CO2 and cannot be initiated effectively under ambient air (Charles & Roberts, 1968; Ueda et al., 2008). The lower concentrations of CO2 in the 1 and 2 second groups may not be high enough to induce these mutations, however warrants further investigation to investigate the reliability of the results and a link between CO2 mutations and antibiotic resistance. The bacterial cultures exposed to 6 seconds of CO2 expressed the largest degree of resistance in the third generation with 2.375cm diameter zones of inhibition on average compared to 2.625cm of the controls (see figure 1a), which was not statistically significant, however the trend shown in figure 1b) suggests that increased CO2 may result in increased antibiotic resistance.

In the fourth generation, antibiotic resistance was quantified through minimum inhibitory concentrations (MICs). MIC increased as CO2 concentration increased, which validated the generation 3 results (see figure 2a and 2b). The MICs of the 6 second CO2 acterial cultures were significantly higher than the control group, where P=0.04 (see figure 5) thus suggesting that increased CO2 concentration may result in increased antibiotic resistance. Considering that the resistance of the third generation 6 second CO2 bacteria was not statistically significant in comparison to controls at P=0.16, (see figure 4) the significance of the fourth generation suggests that the effect of CO2 on antibiotic resistance may increase with each generation due to the phenotypic changes of biological variation, requiring further investigation with additional bacterial generations.

The contrasting decrease in antibiotic resistance as CO2 concentrations increased in generations 1 and 2 (see figure 1a and 1b) raises questions about whether CO2 initially acts as a negative selection pressure for the bacteria’s survival, enabling a faster rate of resistance in future generations due to adaptation. This is supported by Santillan et al. (2015) in the isolation of a CO2 tolerant Lactobacillus strain, whereby high aqueous CO2 concentrations initially stressed the population before creating a niche for adapted CO2 tolerant bacteria. Further investigation is therefore required pertaining to the nature of the continued rate of survival of Escherichia coli exposed to CO2 without antibiotics.

It’s also worth noting that although the fourth generation bacteria exposed to 6 seconds of CO2 were significantly more resistant in comparison to the fourth generation control group (see figure 5), the MIC of the 6 second group (with the greatest extent of resistance within the study) indicated that the bacteria were not clinically resistant in conjunction with the CLSI guidelines (see appendix J), whereby the MIC must be greater than or equal to 16 μg/mL for the bacteria to be considered resistant (Weinstein et al., 2020). Since the average MIC of the 6 second experimental group was 4.67 μg/mL (see Figure 2a), these bacteria are considered to be between clinically susceptible and intermediate (Weinstein et al., 2020). Thus, in this study, CO2 had a significant impact on the resistance acquired by the bacteria in comparison to the controls, but it would not indicate major problems with the effectiveness of antibiotic treatments. Further research is required to determine if this would change with additional bacterial generations or a larger sample size.

It was also found that increased CO2 concentrations resulted in an increased rate of resistance acquired between the first and third generation of the 6 second experimental group compared to the controls where P=0.08 (see figure 6). This increased rate of resistance (see figure 3a and 3b)may be a result of the increased dissemination of antibiotic resistance genes when exposed to high concentrations of CO2 (Liao et al., 2019). It was also found that bacteria in the 1 and 2 second groups had significant negative rates of change in comparison to the controls where P=0.0005 and P=0.006 respectively, whereby the antibiotic resistance decreased from the 1st to the 3rd generation (see figure 7a and 7b). This may be a result of the CO2 concentrations being too low to induce CO2 mutations of bacteria (Charles & Roberts, 1968), however a larger sample size and further investigation of a link between CO2 mutations and antibiotic resistance is required.

The significant increase in extent of resistance of the 6 second CO2 cultures compared to the controls in the 4th generation suggests that projected increases in atmospheric carbon dioxide concentration may potentially result in widespread increases in antibiotic resistance. Furthermore, the significance of the increased rate of resistance between the control and the 6 second groups supports research by Liao et al. (2019) pertaining to an increase in the dissemination of ARGs when exposed to increased CO2 concentrations that result in faster acquisition of antibiotic resistance. Despite the fact that no bacteria in this study were classified as being clinically resistant by CLSI guidelines, the trends model the potential contribution of CO2 to the development of antibiotic resistance, which may increase burdens on public health as CO2 emissions continue to increase. Replication is required to verify this relationship on a larger scale, with further bacterial generations as well as supporting investigations to determine thresholds for CO2 tolerance, CO2 as a negative selection pressure for bacteria and the occurrence of this phenomenon in other bacteria such as Staphylococcus aureus, which is known to become readily resistant to a wide variety of antibiotics (Chambers & DeLeo, 2009).

LIMITATIONS

The small sample size of each group (n=4 cultures), limits the reliability of the trends relating to the exposure of CO2 and antibiotic resistance. A larger sample size would be beneficial in drawing more reliable relationships between carbon dioxide and antibiotic resistance in each experimental group through the ability to assess consistency of results. The T- tests, although able to compare the average resistance of the controls and each experimental group, was limited by the sample size, so the statistical significance of the data may be less accurate and reliable, therefore the statistical significance in this study may not apply to a larger population. Furthermore, the lack of repetition due to time and resource constraints means that the results of this investigation are not reliable. Future investigations should be repeated to obtain multiple results, to ensure the results are consistent and thus reliable.

The validity of the investigation may have been impacted through the utilisation of e-test strips to obtain MIC values in the fourth generation, as compared to measuring zones of inhibition to quantify antibiotic resistance in the first to third bacterial generations. MIC values were used in the 4th generation to validate the results of the 3rd generation and determine if the bacteria were clinically resistant, however the ability to compare the third and fourth generations is limited due to different measurement methods and further investigation examining the progression of antibiotic resistance through additional bacterial generations is necessary with the use of consistent measurement methods.

The lack of appropriate equipment may have hindered the validity and accuracy of the investigation, particularly the lack of a carbon dioxide incubator and the inability to incubate cultures in known and sustained carbon dioxide environments, whereby a CO2 incubator was used to ensure a constant environment of 5% CO2 in a study conducted by Ueda et al. (2008). Despite constructed airtight seals, it is possible that the environment was not able to be kept constant, limiting the controlled variables and thus the validity and accuracy of the results in each experimental group (see appendix B). Furthermore, the inability to quantify the concentration of CO2 inhibits comparison of results against current and projected atmospheric concentrations.

CONCLUSION

Findings indicate a link between carbon dioxide exposure and antibiotic resistance acquired by Escherichia coli over 4 generations, as it was found that increased carbon dioxide exposure resulted in significantly increased antibiotic resistance of 4th generation

E.coli. Thus the null hypothesis was rejected in favour of the alternate hypothesis, through the significant increase in resistance of the 6 second group compared to the control in the

4th generation (P=0.04), and an increased rate of resistance of this group acquired throughout the 1st to 3rd generations (P=0.008). This suggests that projected increases in global atmospheric carbon dioxide concentrations could potentially contribute to increases in the rate and extent of antibiotic resistance which could have large consequences on public health in the ability to treat infectious disease. Further investigation is required to ensure the reliability of this link through repetition of this experimental method, and could be further extended to test other bacterial species, for example Staphylococcus aureus, which is known to become readily resistant to a wide variety of antibiotics (Chambers & DeLeo, 2009).

ACKNOWLEDGEMENTS

I would like to thank my teacher Ms. Ann Hanna for obtaining equipment, supervising and assisting with logistics, as well as my mentor Amy Sarker from the University of Sydney for helping with alternate solutions for equipment and data collection. I would also like to express my appreciation to my classmate Rebekah Wood for helping with data collection and for suggestions and feedback regarding the methodology of the investigation, and the Industrial Technology faculty for assisting with equipment.

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APPENDICES

APPENDIX A

Aseptic techniques were undertaken throughout the investigation in order to minimise the risk of spreading pathogenic bacteria. This was enabled through the sanitising of all surfaces and equipment before experimentation, between each inoculation, and following completion.

APPENDIX B

Experimental set up as shown with agar plates inside the clear container. This setup was replicated 7 times for each experimental group. A SodaStream with a pressurised CO2 canister was connected to a hose with an on/off tap connected to a hose leading to the airtight container. For each experimental group, agar plates were placed into the corresponding sealed container with the tap on. The SodaStream was turned on for an amount of time denoted by the experimental group, which controlled the amount of CO2 that was pumped into the container.

APPENDIX C

The 7 taps were then turned off and disconnected from the SodaStream at point A. All 7 containers were placed into the same incubator set to 35 degrees Celsius.

A hole was required to be drilled into each container and sealed with silicone. It is possible that this seal did not adequately contain the CO2, limiting the reliability and validity of the data.

Figure B1 – connection point between the SodaStream and the on/off tap. After the gas was pumped into the container the tap was turned off and disconnected at point A

Figure B2 – set up for pumping the CO2 into the container. Agar plates were stacked inside the container.

Figure C1 – experiment outline, detailing the transfer of bacteria between each generation within each group.

APPENDIX D

An initial investigation was undertaken in order to determine the appropriate antibiotic for use throughout the investigation. 6 Mueller Hinton agar plates were inoculated with 100 microlitres of Escherichia coli and spread to create a bacterial lawn. A mastring, containing 5 different antibiotic susceptibility discs (Ampicillin, Chloramphenicol, Penicillin G,

Streptomycin, Sulphatriad and Tetracycline) was placed into the centre of 4 agar plates. The antibiotic disc with the largest average zone of inhibition was used in this experiment in order to model the use of last resort antibiotics that will be the most effective in fighting bacterial growth. Therefore, the results of the complete investigation represent the potential public health problem that will occur if atmospheric CO2 continues to rise with the most effective antibiotics.

Zones of inhibition of E.coli in the presence of differing antibiotics

Table D1 – Preliminary investigation results that were used to determine the most suitable antibiotic

Tetracycline was used as the antibiotic with the largest average zone of inhibition as discussed above.

APPENDIX E

Carbon dioxide concentration in each container was quantified through the amount of carbon dioxide pumped into the container using a gas canister with a valve. This measurement was expressed in seconds. Thus, the CO2 was pumped out at a constant rate for each container and pumping was stopped after reaching specific time intervals. The control group was not exposed to additional CO2, however for the group 2 container, CO2 was pumped in for 1 second. CO2 was pumped in for 2 seconds for the group 3 container, 3 seconds for the group 4 container, 4 seconds for the group 5 container, 5 seconds for the group 6 container and 6 seconds for the group 7 container.

APPENDIX F

An inoculating loop was utilised to swab the bacteria at the edges of the zones of inhibition for each culture for each group. Referring to the right image, the bacteria inside the red circle was swabbed and transferred to the corresponding plate.

For example, an inoculating loop swabbed the bacteria that had grown at the edge of the zone of inhibition of a plate from the control group, and this was inoculated onto a plate that would then be part of the control group for this generation.

Figure F1 – The edges of each zone of inhibition were taken and swabbed onto a new agar plate.

APPENDIX G

E-test strips (pictured to the right) were used to validate the findings from generation 3, and provide a quantitative value recognised by CLSI guidelines. Larger MICs indicate increased antibiotic resistance.

Figure G1 – E-test strips were used in generation 4 to validate generation 3 results and determine the clinical level of resistance through referring to CLSI guidelines

APPENDIX H

T-tests were conducted with an alpha value of 0.05 as a commonly accepted value by scientists to minimise type I and type II errors (Glen, n.d.). Thus obtaining a P value of less than or equal to the alpha is statistically significant.

APPENDIX I

The results of the 3rd generation were statistically analysed due to the validation of their trend in the 4th generation MIC values, and in conjunction with a study conducted by Bidell et al. (2017) that found “two or more prior antibiotic exposures were associated with slightly higher fluoroquinolone nonsusceptibility, multidrug resistance… compared with 0 or 1 exposure”, thus this study required 3 generations at least in order to ensure the bacteria had acquired resistance through 2 previous antibiotic exposures in the 1st and 2nd generations.

APPENDIX J

CLSI guidelines:

Table J1 – MIC (μg/mL) of the 4 th generation were obtained and connected to the CLSI guidelines to determine if the bacteria were clinically resistant.