The Effect of Streptomycin Exposure and Non-exposure on the Evolution of Escherichia coli Populations by the Microevolutionary Process of Natural Selection
By Aurora Lorenz and Sophia Kruger
I. Introduction
Charles Darwin first proposed the concept of evolution as decent with modification. Decent with modification refers to Darwin’s theory that all organisms descended from a common ancestor and, over time, these organisms developed specific modifications, or adaptations, to best accommodate to the unique environment each organism lived (Urry, 2016). This initial theory was the basis of the theory of evolution by natural selection as these proposed adaptations are the result of changes in heritable traits of a population’s organisms. Natural selection favors specific traits within a population that enhances the rate of survival and reproduction amongst the individuals of the population (Than, 2018). The process of natural selection thus increases the frequencies of alleles that allow the population to best adapt to its environment. Natural selection is a microevolutionary process only; it affects individuals in a population but allows evolution to act on the population by influencing the genetic level of the population. Microevolution refers to changes in allele frequencies over generations within a specific population. Given that microevolution concerns evolutionary change at the genetic level, this process explains Darwin’s observation that organisms develop specific adaptations over time as these adaptations result from variation in the phenotypic traits expressed within the given population. Changes in allele frequencies over time account for this phenotypic variation as individual genotypes are affected in the population (Urry, 2016). Such microevolutionary processes—altering the genotypic variation within a population—combine with the process of macroevolution to depict the complete view of evolution as a process that has occurred since the being of time. Macroevolution refers to large-scale evolution that occurs beyond the species level. The process of macroevolution cannot be directly observed within the span of a human’s lifetime and thus requires fossil record (Hlodan, 2007).
Between these two levels of evolution, microevolutionary processes allow evolutionary biologists to best study changes in allele frequencies within a population and determine if these changes result from evolutionary processes. Recently, a popular topic for studying evolution through natural selection is antibiotic resistance amongst strains of bacteria. Upon introduction of antibiotics to bacteria populations natural selection favors phenotypes that resist the fatal effects of such a stressor. Individual bacterium within a given population can develop mutations over time to reduce the effect of antibiotic exposure (Richardson, 2017). Natural selection favors these mutant bacteria as such mutations allow these individuals to not only survive but also reproduce, contributing further generations of mutant bacteria to its population. Antibiotic resistance is not only inherited from each mutant parent generation but also passed on through lateral gene transfer. In lateral gene transfer, a portion of a mutant bacterium’s DNA on its plasmid that codes for a resistant gene, is transferred between unrelated bacterium (Richardson, 2017). Through this process unrelated bacterium develop the same antibiotic resistance and natural selection favors this resistant trait over non-resistant bacteria as that variation is less likely to survive and reproduce in the presence of antibiotic stresses. As a result, natural selection causes bacterial resistance to antibiotics by encouraging the accumulation of antibiotic-resistant bacteria in the population.
Due to continued and widespread misuse and overuse of antibiotics the presence of antibiotic resistant bacteria is increasing at an alarming rate globally. Superbugs, or bacteria strains resistant to numerous antibiotics, are causing such infections and spreading rapidly. As a result, the number of antibiotic resistant infections has increased both nationally and internationally. According to a national survey performed by the IDSA Emerging Infections Network, over 60% of its participants had been exposed to bacterial resistant infections (Ventola, 2015). This bacterial resistant crisis is highly researched and of particular concern for evolutionary biologists focusing on evolutionary medicine. Understanding bacterial resistance, specifically how natural selection encourages such resistance, is of great importance among the scientific community as that shapes how evolutionary medicine can itself evolve to combat this rising issue, whether that be through strategizing how to reduce resistance or how to manufacture and introduce new drugs into the market to fight bacterial infections (Losos, 2013).
The microevolutionary process of natural selection can be observed in populations of E. coli. A given population of E. coli naturally displays phenotypic variation as well as ecological limitations as the survival of the population is limited by its environment. Also, the trait for antibacterial resistance is heritable amongst E. coli and is easily recognized amongst the population due to its impact on the coloration of mutant bacteria. Wildtype, or antibiotic susceptible bacteria, appear white while mutant strains appear purple. When antibiotic resistant and non-resistant populations are exposed to environmental stressors, namely antibiotics, this trait of resistance influences the survival and reproductive rates of given populations due to variance in the individuals’ relative fitness. Bacterium with a greater relative fitness as those that survive and continue to reproduce in the presence of antibiotics while those with lower relative fitness for the trait tend not to. For these reasons, Escherichia coli, are ideal organisms to study.
In this experiment E. coli strains will be introduced to two different environments simulated in LB agar petri dishes. Populations of the E. coli containing both wildtype and mutant individuals, will be placed and observed in dishes containing only LB nutrient broth and other dishes exposed to streptomycin, a common antibiotic. After exposure to both environments, the allele frequencies of both populations will be determined by observing the number of white colonies and purple colonies present in the final population. Because each bacterium is a haploid organism, and each colony resulted from one bacterium, the observed phenotype corresponds to the single allele present in the genotype, either the purple (mutant) or the white (wildtype) allele. The ultimate goal of performing this experiment is to determine if this population of E. coli is undergoing evolutionary processes as it is exposed to changing environments. The tested hypothesis poses that the E. coli population exposed to the streptomycin antibiotic will evolve via natural selection due to the presence of the antibiotic stressor in a way such that increases the frequency of the resistant allele. This will be determined through estimating changes in allele frequencies amongst the E. coli populations and observing if those frequencies are consistent with an evolving or nonevolving population by use of the T-test. It is predicted that the E. coli plates with populations not exposed to streptomycin will have a similar 1:1 ratio of white—wildtype and susceptible—to purple—mutant and resistant—colonies as the original bacteria population had. Oppositely, it is predicted that the E. coli population exposed to streptomycin would have a higher ratio of the resistant colonies to susceptible colonies after exposure than the 1:1 ratio of the original population, consequently raising the allele frequency for the resistant, or purple, allele in this new environment.
II. Methods
To begin this experiment, several LB agar petri plates without the streptomycin antibiotic present, were streaked with a sample of the original population of E. coli that contained a mixture of the wildtype and mutant strains of the bacteria. The following process was followed using sterile procedures. After the span of a week—an adequate amount of time allotted for the bacteria colonies to grow–the resulting colonies in this original population were counted. In order to approximate the number of colonies present, either all colonies were individually counted or, if the population only contained one strain of the bacteria, colonies on one quarter of the plate were counted and this value was multiplied by four to estimate the population on the entire plate. The allele frequencies were determined through counting members of the E. coli population present on the plate. Each white (wildtype) colony and each purple (mutant) colony corresponded to one allele in the population, either the wildtype or mutant allele, therefore the number of colonies counted equaled the total number of alleles present in each population. To determined individual allele frequencies, the number of white or purple colonies was divided by the total number of colonies present in the population. Over the course of this first week, samples of the original population were divided amongst a liquid media containing antibiotic and another liquid media without the antibiotic. From these two populations of bacteria, additional plates were streaked in the second week with either solution to produce plates with E. coli populations exposed or not to the streptomycin antibiotic. These E. coli populations were allowed a week to grow, upon which the number of colonies in each population was counted once again. The allele frequencies were again determined using the values for the number of white (wildtype) and purple (mutant) colonies counted on the plates. The white wildtype allele frequency was named the susceptible allele frequency (S) while the purple mutant allele frequency was called the resistant allele frequency (R). Using the T-test, the evolutionary status for each population from each petri plate was determined. The T-test was performed by calculating the difference between resistant allele frequencies (R) for each individual plate from the original population to the new environment with antibiotic present and again from the original population to the new environment without antibiotic present. These differences were then used in the T-test formula to determine a final value that was compared to a p-value of 0.05 to determine if evolution occurred in the streptomycin absent and streptomycin present population.
III. Results
The results showed a change in allele frequencies from the original population to the populations grown with and without the antibiotic streptomycin. There was a greater average change and a greater standard deviation from the average of the R allele frequencies in the population of E. coli grown in the presence of the antibiotic streptomycin than the population grown without, as seen in both Figure 1 and Table 1. The original R frequency was observed at 0.060313, compared to a frequency of 0.127453276 in the population grown in the antibiotic, and a frequency of 0.043043478 amongst the bacteria grown without an antibiotic present. The results of the T-test performed showed that there was a statistical difference between the change in allele frequency for the bacterial population exposed to antibiotics and the population not exposed to antibiotics, with a p value of 0.00837 which is less than the comparative value of 0.05.
Table 1: Estimated R and S Allele Frequencies With and Without Streptomycin
Allele Population
Original With antibiotic Without antibiotic
R 0.060313 0.127453276 0.043043478
S 0.939687 0.872546724 0.956956522
Figure 1: Average Change in Resistant (R) Allele Frequency With and Without Streptomycin Present
The graph above shows the average change in the R allele frequency and corresponding error bars determined by the standard deviation from the average R allele frequency observed when E. coli was grown in the presence of the antibiotic streptomycin and without the presence of streptomycin. The population observed in the presence of the antibiotic had an overall average R allele frequency of 0.06714 while the population without the antibiotic present observed an average allele frequency of -0.01727. The error bars on the graph indicate the range in which the R allele frequency varied from the average for all individual populations tested per environment in this experiment. The graph shows a greater change in the R allele frequency with the E. coli population that developed in the presence of the antibiotic.
IV. Discussion
After completing this experiment and performing the T-test on bacterial resistance, it can be concluded that the hypothesis, which states that evolution due to natural selection will occur in the population exposed to the streptomycin antibiotic as this microevolutionary process will cause the allele frequency for the allele most likely to survive and reproduce in the presence of this stressor to increase, is supported. Since the T-test resulted in a value of 0.008374 for the set of differences between R allele frequencies of the two populations to the original, which is significantly lower than the comparative p-value of 0.05, the hypothesis must be supported. Since 0.008374<0.05, the T-test dictates that there is a statistical difference in the resistant (R) allele frequency between the streptomycin present population and the original population. The resistant allele frequency for the streptomycin population was significantly higher than the frequency for the original population as noted previously and seen in Table 1. From these T-test results, it can also be concluded that evolution due to natural selection occurred in the E. coli population exposed to the antibiotic as natural selection favored, and thus increased the frequency of, the resistant allele responsible for the increased survival and reproduction of all colonies that contained such allele. The population witnessed a subsequent decrease in the S allele frequency in the streptomycin present population from the S allele frequency in the original population as natural selection did not favor this allele because it did not survive nor reproduce in the same magnitude that the R allele had during antibiotic exposure.
While this experiment performed does not mimic what occurs in human bodies when bacterial resistance prevents an antibiotic from properly treating an infection, it is important to note that the results of this experiment indicate an evolutionary pattern amongst E. coli bacteria, and specifically, resistant bacteria, that may be useful in future studies of bacterial resistance involving different bacteria populations and antibiotics meant to treat bacterial infections. Such a fundamental understanding of bacterial resistance, provided by the results of this experiment, is important for understanding the dangers of antibiotic over-use and misuse. This abuse of antibiotics, and the continuation of antibiotic-resistant bacteria, is often due to an inadequate understanding by the general population of what bacterial resistance is and how it encourages the production of superbugs. A common misconception is that bacterial resistance implies that the body itself is resistant to a given antibiotic, rather than the bacteria itself (Brookes-Howell, 2012). Knowing that the presence of antibiotics within a given system—such as a host’s body or a plate streaked with E. coli populations—allows for bacteria to either mutate in response to the antibiotic, or simply acquire resistance through horizontal gene transfer from an originally resistant bacterium, introduces to individuals that bacterial resistance stems from an overuse and misuse of antibiotics. If one strain of E. coli in this experiment showed signs of evolution due to natural selection for the resistant allele over a span of two weeks, it is appropriate to imagine similar results had one tried to treat a similar bacterial infection with numerous antibiotics without staying to one treatment regime over a longer span of time. Because of such patterns of antibiotic over-use and misuse, recent research in the realm of bacterial resistance studies specifically how to make resistant bacteria sensitive again to antibiotics as opposed to how to combat bacterial resistance with additional drugs—a common process that would affect a host’s immune response to bacteria only rather than impact the bacteria directly. This research focuses on the use of phosphorodiamidate morpholino oligomers (PPMOs) in order to inhibit the function of mRNA sequences that code for resistant genes in the bacteria which would, in turn, revert the bacteria to its original susceptibility to a given antibiotic (Richardson, 2017). Even with these new areas of research concerning antibiotic-resistant bacteria, a basic understanding of the process of bacterial resistance—specifically that bacteria strains under antibiotic stress tend to evolve due to natural selection—and the reasons behind such rampant growth of antibiotic resistance amongst bacteria populations, is crucial to preventing further resistance and the growth of superbugs resistant to numerous antibiotics. Knowing this enables individuals to control their own contribution to the current bacterial resistance crisis by ensuring that they follow any prescribed treatment regime thoroughly and to completion as well as limit their exposure to unnecessary antibiotics.
Literature Cited
Brookes-Howell L. 2012. ‘The Body Gets Used to Them’: Patients’ Interpretations of Antibiotic
Resistance and the Implications of Containment Strategies. J Gen Intern Med, 27(7): 766-772.
Hlodan O. 2007. Macroevolution: Evolution above the Species Level. BioScience, 57(3): 222-
225.
Losos, J.B. 2013. Evolutionary Biology for the 21st Century. PLoS Biology, 11(1): 1-8.
Richardson, L.A. 2017. Understanding and Overcoming Antibiotic Resistance. PLoS Biology,
15(8): 1-5.
Than, K. What is Darwin’s Theory of Evolution? February 26, 2018. https://www.livescience.
com/474-controversy-evolution-works.html. Accessed November 4, 2018.
Urry, L. 2016. Campbell Biology. 11th ed. Hoboken: Pearson Education Inc. 1284p.
Ventola, C.L. 2015. The Antibiotic Resistance Crisis: Part 1: Causes and Threats. P&T, 40(4):
277-283.