Antibiotics changed the medical field by enabling doctors to treat severe and life-threatening infections such as bacterial endocarditis, meningitis, pneumococcal pneumonia, tuberculosis, gonorrhea, and syphilis (Kalvaitis, 2008). Antibiotics are defined as a chemical substance that selectively targets microorganisms, usually bacteria (Shlaes, 2011). There are currently over 100 antibiotics, however, most can be divided into fourteen groups, some of which include Fluoroquinolones and Penicillins (Werth, 2022). Antibiotics are categorized by their method of targeting bacteria; some inhibit protein synthesis, cell wall synthesis, or DNA synthesis, which are all processes involved in keeping the bacteria alive and functioning (Britannica, 2021). A less commonly known antibiotic is rifampicin which is essential for treating the deadly disease tuberculosis (World Health Organization, 2021). Rifampicin is classified as a rifamycin, under the larger family of Ansamycins, which blocks the production of RNA ultimately inhibiting protein synthesis. No matter what class the antibiotic is, the goal is to selectively target some essential function of the target bacteria and kill them or inhibit their growth, thus controlling the infection. While antibiotics have saved millions of lives, their success has led to a major problem. Overprescription and casual use of antibiotics that don’t target the correct bacteria have led to increased antibiotic resistance and the rise of “superbugs”. 

Antibiotic resistance can broadly be explained as the rise of bacteria that are less susceptible to the antibiotic due to mutation and natural selection. These less susceptible bacteria go on to reproduce for generations until there arrives a generation that is highly resistant to the antibiotic (Britannica, 2021). Antibiotic resistance is a major threat to public health, and despite all efforts to slow antibiotic resistance, the trend shows no signs of slowing. An antibiotic-resistance-related disease occurs when an individual has a bacterial infection, but the individual is not responsive to the antibiotic treatment due to highly resistant bacteria, thus allowing the infection to progress. According to the CDC, over 2 million people fall ill with antibiotic-resistant-related diseases, while over 23,000 people die from such diseases in the USA alone (Dadgostar, 2019). Antibiotic resistance also has a plethora of financial ramifications; According to the Center for Disease Control and Prevention, approximately 20 billion dollars in US healthcare costs go towards antibiotic resistance-related health issues (Center for Disease Control and Prevention, 2013). Furthermore, antibiotic resistance strikes low-income nations the hardest; This is due to a variety of factors, such as limited diagnoses, overprescription of antibiotics, overcrowded hospitals, sanitation issues, and limited access to “backup” antibiotics (Larsson and Flach, 2021). Researchers are continually working to discover new antibiotics as a means of overcoming antibiotic resistance; but to combat antibiotic resistance and make new and effective antibiotics, we have to improve our understanding of how bacteria adapt and evolve in response to antibiotics. 

Antibiotic resistance develops due to irrational and broad overuse which continually exposes bacteria to these antibiotics and inevitably drives their evolution. There are two main ways bacteria become resistant: acquired antimicrobial resistance and intrinsic resistance (Cloeckaert et. al, 2017). Acquired antimicrobial resistance occurs when microorganisms that were at one point sensitive to an antibiotic adapt through horizontal transfer, which is when bacteria can acquire a foreign gene into their genome, thus acquiring a resistance mutation (Cloeckaert et. al, 2017). Intrinsic resistance is when bacteria have a naturally occurring mutation passed down from the previous generation of bacteria. Evolution occurs as a result of spontaneous but advantageous mutations, which arise from reproduction and selective pressures which are present in the environment. Mutations are spontaneous and occur naturally regardless of whether an antibiotic is present, however, the presence of an antibiotic causes a selective pressure, thus selecting for antibiotic-resistant bacteria (Hawkey, 1998). Since most antibiotics inhibit some essential function of the bacteria, thus either killing them or preventing them from reproducing, only bacteria with either a modified target or the ability to flush out the antibiotic can survive and pass on their genes, creating a next-generation of antibiotic-resistant bacteria (Not-for-Profit MedicineWise, 2019). 

When studying bacterial evolution and genomics, a popular model organism is E. coli. E. coli are rod-shaped cells that are typically 1.1–1.5 μm wide and 2–6 μm long (Desmarchelier and Fegan, 2002). They can be either motile or nonmotile, meaning they can develop flagellum over generations and swim through media (Desmarchelier and Fegan, 2002). E. coli can double every 15 to 20 minutes, which is relatively fast compared to most bacteria (McKernan, 2015). Not only do E. coli reproduce quickly, but they also reproduce asexually. In genomics, asexual reproduction can be beneficial because the offspring inherit a more or less unaltered genome from the parent, thus effects seen in mutants are reproducible (Visser, 2007). E. coli’s ability to double quickly, the large databases detailing their genetic material, and asexual reproduction all make E. coli one of the most popular model organisms for molecular genetics (Yokoyama, 2020). For this paper, the k-12 JW3932 strain of E. coli is used due to its acquired resistance to kanamycin. The strain’s resistance to kanamycin allows kanamycin to be used during culturing to better ensure sterility. 

For rifamycins, antibiotic resistance arises from mutations in the RNA polymerase (RNAP), which is the target enzyme responsible for synthesizing RNA in the cell (Wehrli, 1983); (Dornell, 2021). Rifampicin works by binding to the Beta subunit of RNAP and inhibiting RNA synthesis. More specifically, the rifampicin binds approximately 12 Å away from the RNAP active site (where catalysis occurs), directly blocking the path of the elongating RNA when it becomes 2 to 3 nucleotides in length (Campbell et al., 2001). RNAP is an essential enzyme involved in synthesizing RNA using DNA as a template; Inhibiting RNAP prevents the bacteria from synthesizing RNA, and thus cannot express their genes. The most common mutations linked to rifampicin resistance in Escherichia coli (E. coli) generally occur from amino acids 505 to 537 of the Beta subunit, which forms the channel for the elongating RNA strand. The most common mutations are Aspartic acid 516 to Valine, Asparagine, or Tyrosine, Histidine 526 to Tyrosine, Proline, Arginine, or Glutamine, and Serine 531 to Tyrosine or Phenylalanine. Aspartic Acid 516, Histidine, 526, and Serine 531. These residues interact either directly with the Rifampicin or with the binding pocket, and thus when mutated, prevent binding and lead to rifampicin resistance (Campbell et al., 2001). Often times, however, mutations confer a reproductive fitness cost as the altered enzyme does not work as efficiently.

Bacteria mutate quickly and frequently because they have extremely fast generation times, which is the time it takes to produce a new generation. For example, bacteria like E. coli and Staphylococcus, which are two of the most antibiotic-resistant strains of bacteria, have generation times of around 20 minutes in nutrient-dense media (Missiakas, 2013). Additionally, evolution will select for cells that mutate fast as long as the mutations they produce are beneficial and confer a selective advantage over non-mutated strains. Reproductive fitness can largely be defined as the ability to reproduce in ideal conditions (Frey et al., 2021). One study, conducted by Enne et. al, assessed the reproductive fitness of four rifampicin-resistant E. faecium strains by determining their growth rate in nutrient broth. Enne et al. found that, on average, the range of fitness went from a gain of 2.5% to a cost of 10%, while isolates with a double deleterious mutation were least fit (Enne et al., 2004). The composition of the growing medium, which is essentially the bacteria’s food and what they are grown in, also affects the speed at which bacteria reproduce and adapt. If the growing medium is inoculated with a small concentration of an antibiotic, the bacteria will very likely become resistant, however, if they are exposed to an extremely high dose, most bacteria won’t have a chance to reproduce before they are killed (Baym et. al, 2016).

To properly characterize mutations and antibiotic resistance in any bacteria, the minimum inhibitory concentration (MIC) of an antibiotic must be determined for that particular strain. The MIC is the lowest concentration of an antibiotic that inhibits the growth of a given bacteria. Clinically, the MIC is measured to determine which class of antibiotic is most effective against a given bacteria (Idexx Laboratories, 2019). The higher the MIC is, the more resistant that bacteria is to the antibiotic. The MIC is necessary to characterize the resistance and to properly categorize and assess the bacteria’s fitness. There are two main ways to determine MIC: dilution assays and gradient tests. Dilution methods involve an array of either broth or agar with different concentrations of antibiotic. The goal is to inoculate the medium with bacteria and observe which is the lowest concentration that inhibits growth. The use of broth or agar depends on which antibiotic is being tested, for example, The European Committee on Antimicrobial Susceptibility Testing mainly recommends broth dilutions, with the exception of fosfomycin and mecillinam for which it recommends agar (The European Committee on Antimicrobial Susceptibility Testing, 2020). Gradient assays are significantly less complicated; To complete a gradient assay, a petri dish filled with agar is inoculated with a lawn of bacteria. Immediately after inoculation, a MIC test strip impregnated with up to 29 concentrations of the antibiotic is placed onto the lawn. The MIC is determined by reading the lowest concentration where the lawn of bacteria stops touching the strip, as seen in Image #1. 

Dilution assays are generally more popular than gradient assays as dilution assays are more widely tested and MIC test strips have been shown to produce unreliable results for colistin, vancomycin, and fosfomycin against Staphylococcus spp. strains (Clinical and Laboratory Standards Institute, 2020). A separate paper, however, found no difference in the MIC for Fosfomycin in ESBL E. coli when using gradient tests or dilution assays (Aprile et al., 2020). While it is impossible to draw a conclusion on gradient tests without further assays, current research does not seem to suggest that MIC strips are inaccurate for rifampicin. Furthermore, MIC strips are significantly more efficient and practical for a classroom setting than dilution assays. While the amount of dilutions you complete varies depending on the antibiotic, a standard dilution assay would use at least 10 different concentrations, with a second trial often needed to narrow down the MIC (Tankeshwar, 2022). With the gradient strip, one strip could test up to 29 concentrations in one plate, lowering the likelihood of the need for a second trial with smaller dilutions and taking up less space in an incubator.

Once the MIC has been determined, resistant mutants of the E. coli strain JW3932 are to be selected for via serial culturing. To characterize the mutations that cause resistance to rifampicin, it is necessary to first select for resistance in the bacteria. The idea behind serially culturing is to start with a liquid bacteria stock with a MIC<1 of the given antibiotic, often half the MIC. We then allow this stock to grow to saturation and inoculate a constant amount of bacterial culture from the <1 MIC to a new culture containing a two-fold increased MIC stock. The bacteria that are grown in <1 MIC thus face a moderate selective pressure that will favor bacteria with rifampicin resistance mutations. These mutated bacteria will be inoculated into the two-fold increase MIC, which will select for stronger mutations. Continuing this process with a 2-fold increase in antibiotic concentration each time sequentially selects for even stronger mutations until you have naturally occurring resistance from a variety of mutation events. A study by Weinstein & Zaman used serial culturing as a method to select for rifampin quinone-resistant strains of  E. coli. After only five selection cycles, the researchers acquired bacteria with up to a 64-fold increase in MIC, demonstrating the potential efficacy of serial culturing to mimic the natural selection of antibiotic resistance in a controlled laboratory setting  (Weinstein & Zaman, 2018). The result of this procedure should provide new bacteria strains with an array of rifampicin-resistance mutations, each with their own fitness costs.

As previously mentioned, when bacteria acquire antibiotic resistance, they often suffer a reproductive fitness cost, as in they reproduce slower due to the often deleterious mutations required to achieve antibiotic resistance. The fitness of antibiotic-resistant bacteria plays an important role in understanding the dynamics of resistance and mutations, as the fitness cost of these bacteria imposes a selection against them when they encounter an antibiotic-free environment (Vogwill & MacLean, 2015). An OD600 (optical density at lambda 600 nm) assay is a common method to quantify reproductive fitness. A spectrophotometer, which provides an OD600 value if specified to measure at 600 nm, measures how much light a given substance absorbs over time (Implen, The OD600 Basics). The higher the concentration of bacteria in liquid culture, the higher the optical density of that culture. This is because as the bacteria grow, they cloud up the broth, thus increasing the amount of light absorbed. Often the fitter a mutant is, the faster it will grow, increasing the rate of absorbance over time. However, what properly quantifies fitness is not a single point, but a continuous growth curve, which is the OD600 value measured over time.

Most growth curves for bacteria can be split into four stages: a lag phase where growth is mostly stagnant as bacteria adjust to their environment, a log phase where bacteria begin to grow rapidly at an exponential rate, a stationary phase where the cell density has reached its maximum because the cells run out of broth nutrients, and a death phase where cells begin to die (Tip Biosystems). The generation time is generally the time it takes for the density to double, which is calculated with the equation log(2)/m, with “m” being the slope of the selected log phase on a semi-log axis. In my research conducted in May of 2022, I measured the growth curves of four different E. coli isolates with different MICs of ciprofloxacin, ranging from 0 MIC (wild type), 10 MIC inoculated in 1 MIC, 10 MIC inoculated in 5 MIC, and 1000 MIC plated in 100 MIC (refer to image #2). After plotting the growth curves, the doubling times were calculated using the method described above. The most resistant bacteria, which had a MIC of 1000, had the longest doubling time of 59 minutes, implying the lowest reproductive fitness. The Wild type strain and the 10 MIC plated in 1 MIC strain had the highest reproductive fitness with calculated doubling times of 27 and 25 minutes respectively. The implications of these findings suggest that strains with higher resistance to ciprofloxacin are more likely to suffer a high fitness cost and thus reproduce slower, while susceptible and lower-resistance strains are likely to be generally fitter than their high-resistance counterparts. While the OD600 assay provides a general relationship between MIC and reproductive fitness, this understanding is limited as each mutation associated with a given MIC could confer a different fitness cost and thus requires a deeper genetic understanding for a fuller understanding of this relationship.

A powerful tool for understanding antibiotic resistance on a genetic level is Sanger Sequencing. Sanger sequencing is a method to determine the exact nucleotide sequence of a given portion of DNA (Sigma Aldrich). Determining the exact nucleotide sequence, and in turn, the amino acid sequence is vital for characterizing antibiotic resistance because it reveals the most commonly mutated sites on the gene. Locating these sites provides potentially powerful targets for developing antibiotics as the specific mutations give insight into which parts of the target are affected or easily mutated. Furthermore, researchers can even predict what antibiotics a given bacteria may be resistant to based on Sanger sequencing of selective amplifications of specific regions on the genome. However, this analysis is more accurately achieved through whole genome sequencing, which is a major drawback of Sanger sequencing as it cannot determine if there are mutations outside the selected region (Su & Satola, 2019). Sanger sequencing is much more practical for a classroom setting, however, because it is significantly cheaper, only costing around $10 per colony compared to $80 per colony for whole genome sequencing.  

Through Sanger sequencing, specific mutations acquired can be characterized by MIC and location in the gene. For example, a study conducted by Shea et. al. found that the replacement of Serine 531 to Leucine in the RpoB gene of E coli., which codes for the RNAP beta subunit, confers a MIC of over 16 µg/mL, compared to the 14 Wild Type strains of E. coli whose MICs ranged from 0.5 to 0.12 µg/mL, which is an increase of over 130-fold (Shea et al., 2021). Shea et. al. effectively demonstrated how a certain mutation can correlate to a specific increase in MIC. Shea et al. also found that 9 out of 12 mutations that appeared in their sequencing results occurred within the rifampicin resistance determining region (RRDR) of E. coli, which includes three clusters (I, II, and III) from amino acids 505 to 537, 562 to 575, and 684 to 690, as well an N-terminal from amino acids 143 to 148 (Jin and Gross, 1988). The RRDR is the region of the gene that codes for the amino acids that confer resistance against the antibiotic, which also happens to be composed mainly of amino acids that form the pocket that rifampicin binds to (Campbell et al., 2001).

While the mutations associated with Rifampicin resistance in E. coli and their corresponding MIC increases are widely documented, there appears to be less research assessing the associated doubling times of these documented mutations, suggesting further investigation. This report characterizes common mutations associated with Rifampicin resistance in the RpoB gene of E. coli by not only assessing the fitness of the strains with these mutations but also determining their corresponding MICs. I hypothesize that when exposed to sub-MICs of rifampicin via serial culturing, bacteria will acquire resistance via an array of mutations, mostly in the RRDR. While each mutation will likely have its own fitness cost and MIC, the strains with these mutations will be able to survive and grow in the presence of rifampicin, but not without a large fitness cost that results in a general inverse relationship between doubling time and MIC. In other words, the more resistant a strain becomes due to a given mutation, the lower its reproductive fitness will be. 

To address the question of how reproductive fitness and MIC correlate to certain mutations in RpoB, the MIC must first be determined via a MIC gradient test. To determine the MIC of rifampicin for E. coli strain JW3932, which is the strain used in this research, a culture of JW3932 is grown overnight in liquid culture. Once the culture has grown, the liquid culture of bacteria is spread evenly across the surface of a petri dish filled with agar to create a lawn of bacteria. Lastly, two rifampicin MIC test strips are pressed onto the dish and the culture is left to grow overnight. As previously described, the MIC is determined by reading the lowest concentration where the lawn of bacteria stops touching the strip (image #1). Using this determined MIC, resistant mutants will be selected for via serially culturing. The starting concentration of the dilutions is 0.5 MIC, and a separate culture is grown at 0 MIC as a negative control. This control is necessary because we want to ensure any mutations found in the RpoB gene are due to the difference in MICs of rifampicin and not an outside variable. If no mutations occur in the negative control, or wild-type strain, but do occur in the selected strains grown in MIC, then we can assume any mutation in the RpoB gene is due to the selective pressure of increased Rifampicin concentrations. After growing a culture in 0.5 MIC, a set volume of surviving bacteria are then inoculated into media at 1 MIC. This process is then repeated with a MIC increase of 2-fold each trial until there is no growth in the culture. Based on previous studies, growth will likely stop from 16-fold to 28-fold (Weinstein & Zaman, 2018). 

After the resistant mutants have been selected for, four of the mutants will be saved and plated for sequencing: 0 MIC (as our wild-type negative control), 1 MIC, half of the highest achieved MIC, and the highest achieved MIC. Five colonies from each plate, along with one colony from the negative control wild-type strain, will be sequenced, adding up to a total of 16 colonies for sequencing. Using the “RpoB Forward Primer” and the “RpoB Reverse Primer”, the RpoB gene will be sequenced from Valine 456 to Tyrosine 742, which more than covers the RRDR. The corresponding MICs of the mutants with these mutations are to be determined as well, as the MIC of the culture from the serial culturing is not necessarily the strain’s MIC, as the actual MIC of the strain may be higher. To determine the actual MIC of the mutants, the rifampicin MIC test strip and the dilution assay procedures described earlier will be completed with each mutant for a total of three MIC tests.

Lastly, the reproductive fitness of these mutants and the wild-type will be assessed and quantified. To assess the fitness, the mutants and the wild-type must be grown in a liquid culture with no rifampicin. This is because for higher concentrations of rifampicin, the color of the antibiotic affects the OD value. While lower concentrations do not affect the OD value, it is important to keep all replicates in consistent controlled conditions. A negative control with the wild-type strain is necessary to provide a baseline fitness for the original, unmutated strain to compare against. Once the liquid cultures have grown, the cultures will be sorted into a 96 well-plate, and their ODs are measured at lambda 600 nm automatically every 20 minutes for 24 hours. As previously described, the generation time will be calculated by plotting the OD values on a semi-log axis, finding the slope of the best-fit line of the log-phase, and using the equation log(2)/m . With the fitness quantified and the general MICs of the isolates determined, one can investigate if there is a correlation between high/low MIC, reproductive fitness, and/or a specific RRDR mutation. 

The characterization of these mutations will provide further insight into the dynamics of rifampicin resistance and will address knowledge gaps regarding the prevalence of rifampicin resistance. With this information, further studies could research the stability of the acquired resistance mutations, which is determined by observing if the mutants keep the rifampicin resistance mutations in the absence of the selective pressure, rifampicin. In theory, the fitness cost of the mutation would have an inverse relationship with long-term stability, meaning that, if a mutation confers a large fitness cost in an environment with antibiotics, the mutation will likely revert in an antibiotic-free environment (Dunai et al., 2019). Further investigations of mutant stability in the presence of different antibiotics could help inform the structure and location of more effective targets for developing antibiotics. 

Model Organisms

Gloves and a lab coat should always be worn when handling E. coli. In the case of direct skin contact, the skin should be washed with soap and water. In the case of skin irritation or allergic reactions, see a physician. E. coli should be kept in a tightly closed container in a well-ventilated place (MSDS). When inoculating E. coli, sterile technique should be used. Sterile technique required an open flame to create an updraft. Every time a non-flammable chemical is handled along with the bacteria, the mouth and cap of the bottle is flamed before being used and before being closed. 

The strain used throughout this study was the k-12 JW3932 strain of E. coli (Baba et al., 2006). This strain was selected because it has acquired kanamycin resistance. The kanamycin resistance is important because it allows kanamycin solutions to be added to every trial, preventing contamination by killing all foreign bacteria and leaving the JW292 strain unaffected. The kanamycin solution was consistently diluted to 30 µg/mL in every trial (Baym et al., 2016). When handling kanamycin, gloves, lab coat, and safety goggles should always be worn. Kanamycin should be stored in a -20 ℃ freezer and disposed of according to the given institution’s regulations (MSDS). 

Glycerol stocks are necessary to preserve the bacteria for a prolonged amount of time as the glycerol insulates the bacteria without prohibiting growth, meaning the bacteria are preserved for future inoculation (Addgene, Creating Bacterial Glycerol Stocks for Long-term Storage of Plasmids). When handling glycerol, a lab coat, safety goggles, and gloves should always be worn. In case of eye contact, rinse eyes thoroughly for 15 minutes and seek medical attention (MSDS). To create the glycerol stocks, the E. coli were inoculated into a 20% glycerol and 80% Luria-Broth (LB) solution with a kanamycin concentration of 30 µg/mL. Sterile technique was used during the entire inoculation process. The solutions were then put in a shaking incubator (BioRad Shaking Incubator, 17002946) at 37 ℃ shaking at 150 RPMs overnight. The glycerol stocks were then aliquoted into 1 mL cryo-vials and stored in the -80 ℃ freezer. 

To grow bacteria on a solid agar medium, a full bottle of 1.5% agar is melted down completely to liquid by microwaving in increments of 30 seconds with the lid unscrewed. When handling molten agar, gloves, safety goggles, and a lab coat should always be worn. Heat-resistant gloves should be worn until agar cools to room temperature

Check the agar’s temperature using a KIZEN IR thermometer (KIZEN, Amazon). Once the agar has cooled to 60 ℃, the agar was poured into a 50 mL Falcon tube and the kanamycin solution was added to 30 µg/mL using a P20 micropipette (BTlab, BT1505). To prepare  one petri dish, which requires 20 mL of agar, 30 mL of agar was poured into the Falcon tube. The equation used to calculate how much 6 mg/mL kanamycin stock to add was x µL of  6 mg/mL kan =(30 µg/mL)(y m) agar)/(6000 µg/mL kan). To prevent degradation, the kanamycin must be added once the agar has cooled to 60 ℃ or below. 20 mL of the 1.5% agar with 30 µg/mL kanamycin was poured into a 10 cm petri dish using a 25 mL serological pipette and a Drummond pipette aid (Drummond, 1368110). After pouring and letting it set, the dishes were set upside down to dry overnight. The following day, 100 µL of the JW3932 glycerol stock was inoculated onto the dried petri dish using a P100 micropipette (BTlab, BT1502). 3 mm glass beads or a disposable cell spreader were used to spread the bacteria around evenly. The dish was placed upside down into a static shelf in an incubator (New Brunswick, NB3100) set at 37 ℃ for 16 to 24 hours. To dispose of the dish, use a squeeze bottle to squirt about 5 mL of 30% bleach into the plate and spread the bleach around the surface. When handling bleach, gloves, safety goggles, and a lab coat must always be worn. In case of eye contact, eyes should be thoroughly rinsed and medical attention should be sought. Bleach should be stored away from acids, rust-removers, and ammonia-containing products (MSDS). The agar must be scraped into a bio-waste bag and sealed shut. According to the MSDS, “The agar and its container must be disposed of according to approved disposal technique. Disposal of this product, its solutions, or of any by-products, shall comply with the requirements of all applicable local, regional or national/federal regulations'' (MSDS).

An overnight culture is a culture of bacteria grown in liquid media. 3 mL of Luria Base (LB) broth was used to grow the E. coli in a culture tube with good airflow, along with 30 µg/mL kanamycin to prevent contamination. Using a disposable inoculation loop, dip the loop in the prepared JW3932 glycerol stock and drop the loop in the LB solution. The overnight culture was then put in the shaking incubator at 37 ℃ at 150 rpm for 16 to 24 hours.

Making Antibiotic Stock Solutions + Agar

Nutrient agar was made and stored in eight 500 mL glass pyrex bottles. 10 g of Miller’s Luria Broth base was measured with a 120 g - 0.1 mg Mettler Toledo scale (Mettler Toledo Scale, ME104TE) to create a final concentration of 2.5% LB base. The BD (Becton, Dickinson) Bacto Agar powder was weighed using the same scale and added to each bottle to create a final concentration of 1.5% BD powder. 400 mL of ultrapure water was added to each bottle measured using a 500 mL graduated cylinder. To dissolve the powder, the bottles were heated for 50 seconds in the microwave and then swirled while wearing a heat resistant glove. Along with wearing a lab coat, safety goggles, and gloves, the cap should be held from the top while swirling to prevent bursting agar from reaching the eyes. Each bottle was then autoclaved (Carolina Biological, 701670) at 120 ℃ for 30 minutes after selecting “Liquids”.

0.9 g of kanamycin disulfate powder was weighed out on the Mettler Toledo scale to create a 30 mg/mL kanamycin solution. The powder was then transferred to a separate 50 mL Falcon tube and dissolved in 30 mL of ultrapure water measured using a 50 mL serological pipette and a Drummond pipette aid. The solution was filtered through a 0.2-micron filter and aliquoted into 5 mL Eppendorfs. It is necessary to filter antibiotic stocks because there may be resistant strains growing on the antibiotic or in the water, so filtration is a good precaution to take (Qiagen, Bacteria Cultivation Media and Antibiotics). The kanamycin is then stored in the -20 ℃ freezer.

250 mg of Rifampicin (Rif) powder was weighed out on the 120 g - 0.1 mg Mettler Toledo scale and transferred into a 15 mL Falcon tube. When handling Rifampicin, gloves, lab coat, and safety goggles should always be worn. In case of direct eye contact, the eyes should be rinsed out with plenty of water and contact lenses should be removed.  The product and the empty container should be kept away from heat to prevent degradation (MSDS). Using a 10 mL serological pipette, 8.3 mL of 100% Dimethyl Sulfoxide (DMSO) was added to the powder and vortexed until homogenous to mix to create a final concentration of 30 mg/mL Rif.DMSO. DMSO must never touch exposed skin and should be handled with a gloved hand and a lab coat (MSDS). It is not necessary to filter this solution because bacteria do not grow in DMSO (Singh et al., 2021). In a separate 15 mL Falcon tube, 9 mL of DMSO was added using a 10 mL serological pipette and a Drummond pipette aid along with 1 mL of 30 mg/mL Rif.DMSO using a P1000 micropipette (BioRad, 1660508) to create a final concentration of 3 mg/mL Rif.DMSO. Both solutions were aliquoted separately into 1 mL cryo-vials as DMSO thaws faster in smaller volumes. The solutions were then covered in aluminum foil and stored in the -20 ℃ freezer. The aluminum foil is necessary because DMSO degrades under light (Hawkins, 2016).

Determining the Mean Inhibitory Concentration (MIC)

Determining the Range 

To determine the MIC range, an overnight culture and agar petri dish were prepared the day before. The following day, 1 mL of overnight culture was spun down in a centrifuge (Eppendorf Centrifuge, 542000040) at 5000 rpm for four minutes before the supernatant was removed. Using a P1000 micropipette, the pellet was inoculated and evenly spread across the surface of the petri dish as previously described. This was done to ensure the bacteria were concentrated enough to create a lawn in the center of the dish. Two 0.0016-256 µg/mL RIF MIC strips (Liofilchem Rifampicin MIC Test Strip (0.016-256 µg/mL), 22-777-725) were placed on the agar surface, leaving enough space for results to be read. The plates were then incubated overnight at 37 ℃. To read the results, the ends of the eclipse touching the strip indicate the range (see Image 1). The MIC range indicated was 8 to 14 µg/mL. 

Determining the MIC

Reviews have suggested that MIC test strips are not always precise, therefore it is necessary to narrow down the MIC with a standard broth dilution assay (EUCAST, 2003). To begin the assay, an overnight liquid culture was first prepared. Concentrations were tested in increments of 2 µg/mL from the lowest to the highest indicated concentration. Because the determined MIC range was 8 to 14 µg/mL, the concentrations tested were 8, 10, 12, and 14 µg/mL. As a negative control, one culture had no rifampicin at all and should therefore have abundant growth. If nothing grows in the 0 rifampicin control, this is an indication that an external factor is affecting the growth of the bacteria. As a positive control, one culture had 100 µg/mL rifampicin, and should therefore have no or negligent growth (Rodriguez-Verdugo et al., 2013). If the bacteria grow in 100 µg/mL rifampicin, this is an indication that an external factor is affecting the efficacy of the antibiotic. The OD of the prepared overnight culture was recorded with a Biowave cell density meter (Biowave, 490005-906) to calculate the amount of culture needed to have an initial OD of 0.01 in each liquid culture. The amount of overnight culture inoculated was calculated using the equation: x µL =0.01OD/(3000 µL)y OD , where y OD is the OD of the overnight culture. After the appropriate amounts of rifampicin and inoculum were added to each tube, the cultures were incubated overnight for 24 hours at 37 ℃.  

The MIC was determined by taking the OD of each liquid culture (Biowave cell density meter, 490005-906). To account for the change in color of highly concentrated rifampicin, the spectrophotometer should be re-zeroed for the 100 µg/mL rifampicin culture with an LB blank with a concentration of 100 µg/mL. The other concentrations were blanked with plain LB as the color change from rifampicin at lower concentrations was negligent. For the 0 rifampicin control, the OD should be the highest while the 100 µg/mL control should have an OD of less than 0.05. After taking the OD’s, the MIC was determined by the lowest concentration with an OD of less than 0.1 OD. The determined MIC of the JW3932 strain for rifampicin was 10 µg/mL.

Selecting for Rifampicin Resistance

The procedure to select for rifampicin resistance was modeled after Weinstein and Zaman’s procedure (Weinstein & Zaman, 2018). An overnight liquid culture was prepared the night before. The overnight cultures throughout this procedure were incubated at 30 °C instead of 37 ℃ due to spacial limitations within the laboratory. While Weinstein and Zaman’s procedure started selection at 0.5 MIC, however, the preliminary MIC broth assay showed that the bacteria grow fairly well in 0.8 MIC with an OD of 1.15. This does not cause in issue when selecting for rifampicin as the only thing that changes is the starting MIC-fold when what matters most is that the fold increase in between dilutions is consistent. The amount of inoculum was determined by taking the OD of the prepared overnight culture and calculating the amount needed to achieve a starting OD of 0.01 as previously mentioned. Concentrations of rifampicin were doubled for each trial until there was growth of less than 0.1 OD. Glycerol stocks were made for all liquid cultures to preserve the strains for sequencing. For concentrations over 128 µg/mL, rifampicin was added to the LB blank to match the concentration to account for the color change. The highest concentration achieved was 2048 µg/mL (204.8 MIC) rifampicin with an OD of 0.33.

Sequencing

Designing the Primers

All primers were original and created in SnapGene and ordered from Integrated DNA Technologies. The K-12 E. coli genome was retrieved from the NIH nucleotide library (NIH, Escherichia coli str. K-12 substr. MG1655, complete genome). The region pasted into SnapGene was from nucleotide 4181245 to 4185273 as this region covers the rpoB gene. The forward primer begins at base 1344 and ends at 1365 with the sequence 5’-cggcaaccgtcgtatccgttc-3’ and has a melting temperature of 63 °C. The reverse primer begins at 2227 and ends at 2250 with the sequence 5’-ggcccacttcgtccatagctgtag-3’ and has a melting temperature of 62 °C. Sequences were selected to have at least 50% GC because the primer needs to anneal to the DNA and GC bases have three hydrogen bonds as opposed to ATs which only have two (Giacolone, 2013). The region was selected based on where previous studies have recorded mutations related to rifampicin-resistance, otherwise known as the rifampicin-resistance determining region (RRDR) (Campbell et al., 2001).

Preparing for Sequencing

To prepare for colony sequencing, seven petri dishes were poured with a total of four concentrations: One negative control with no rifampicin, two plates with 1 MIC, and four plates with 51.2 MIC. The plates were poured at 51.2 MIC rather than 102.4 and 204.8 MIC to ensure colonies would be large and healthy enough to select for and PCR. Plates were poured in duplicate to ensure there would be at least five colonies per concentration for sequencing. Bacteria were streaked for single colonies straight from the glycerol stocks, swabbing with an inoculation loop according to Image 2. The 2048 µg/mL strain was streaked onto two 51.2 MIC plates, the 1024 µg/mL strain (102.4 MIC strain) was streaked onto the other two 51.2 MIC plates, the 10 µg/mL strain (1 MIC strain) was streaked onto the two 1 MIC plates, while the 0 µg/mL strain was streaked onto the one control plate. The next day, five of the largest single colonies from each strain were selected as well as one colony from the negative control for a total of 16 colonies. Only one colony from the negative control needed to be selected as it was a baseline for the JW3932 rpoB sequence, therefore there should not be variation. 

Polymerase Chain Reaction (PCR)

The PCR kit used was the Phusion High-Fidelity PCR kit. 100 µM solutions of the reverse and forward primers in TE (Tris Ethylenediaminetetraacetic acid) buffer were created according to the calculation provided by Integrated DNA Technologies. The forward primer required 310 µL of TE buffer to create a 100 µM solution while the reverse primer required 243 µL of TE buffer added with a P1000 micropipette. Both solutions were diluted to 100 µL of two separate 10 µM solutions before being mixed to create 100 µL of a 5 µM forward and reverse solution in TE buffer. 5 µM was created using a P100 to add 50 µL of each 10 µM solution in a separate Eppendorf labeled “Primer Mix”. To create 1.8 mL of the total PCR reaction solution , 900 µL of the PCR master mix provided by Phusion was added with a P1000, 90 µL of the 5 µM primer mix was added with a P100, and 810 µL of RNAse-free water, provided by Phusion, was added with a P1000. This created enough PCR reaction solution  to complete 18 reactions to have extra solution. 100 µL of the PCR reaction solution was pipetted into each of the 16 PCR Eppendorfs using a P100 micropipette. Each colony was inoculated into the corresponding PCR Eppendorf. The original petri dish was then incubated for another 24 hours. The regrown selected colonies were then grown in an overnight culture so glycerol stocks could be made for further analysis. The PCR cycler (Mastercycler PCR Thermal Cycler, 6331000025) was programmed almost exactly as the Phusion kit instructs with the annealing temperature set to 55 ℃ and a heat start step. This step is necessary to release the DNA from the cells when doing colony PCR. The heat start was set to 94 ℃ for 4 minutes, the denaturing step was set to 94 ℃ for 30 seconds, the annealing step was set to 55 ℃ for 1 minute, the elongation phase was set to 72 ℃ for 1 minute, and the last phase was set to 72 ℃ for 10 minutes. The denaturing step through the elongation phase were repeated for 30 cycles.

DNA Gel Electrophoresis Analysis

65 mL of water was measured out in a 100 mL graduated cylinder and added to one 1% gel tablet from Genetics and heated to mix. Two 10-piece combs were placed in an Owl mini casting well (Owl B1A Mini Gel Electrophoresis System from Thermo Fisher, 09-128-510) with the wells being closest to the negative side. After cooling to 55 °C, the gel was poured until halfway up to the comb and set to solidify overnight. The gel was soaked in 1X Tris-acetate-Ethylenediaminetetraacetic acid (TAE) buffer after solidifying. Using a P10 micropipette (Gilson, F144802G), 10 µL of the purple 6X loading buffer (NEB, B7024S) was picked up and spread out on a piece of parafilm in 8 evenly spaced ~1 µL beads. This was done twice for a total of 16 beads. Using a P10, 5 µL of each finished PCR product was mixed with a corresponding bead and immediately loaded into the gel, skipping the first two wells of each row. Two more beads were made using the same ratio but with a 1 kb DNA Ladder (Fisher Scientific 1 kb DNA Ladder, SM0314) instead of the finished PCR product and loaded into the first two wells of each row as a control. If the gel worked, the band should match 600 to 800 base pairs according to the DNA ladder guide. The electrodes were then connected according to charge and set to 100 V. After 30 minutes, the tray was placed into the UV gel imager (ChemiDoc MP Imaging System from BioRad, 12003154). 

Sequencing

The PCR reactions were sent to GENEWIZ for a PCR cleanup and Sanger Sequencing (GENEWIZ Sanger Sequencing).

Fitness Assay

To begin the fitness assay, 16 overnight cultures were prepared: a 0 µg/mL culture, five 10 µg/mL (1 MIC) cultures, five 1,024 µg/mL (102.4 MIC) cultures, and five 2,048 µg/mL (204.8 MIC) cultures inoculated from the corresponding glycerol stocks of the previously selected colonies. After 24 hours of incubation, the OD of each overnight culture was recorded. Each culture was blanked with plain LB. To back dilute to 0.01 OD, 16 more test tubes were set up in 3 mL of LB. The equation  0.01 OD=x OD(V)/(3 mL + V), where x represents the OD of the selected colony and V represents the volume of inoculum, was used to calculate how much inoculum to pipette. Each overnight culture was aliquoted in triplicate into 96-well plates using a P200 multichannel micropipette (Integra Biosciences, 3036) making a total of 48-wells with 100 µL of solution in each. Each colony was tracked with the Tecan Spark machine (Tecan Spark Multiplate Reader) over 24 hours at 37 ℃ in increments of 30 minutes as the standard doubling rate of E. coli is 20 minutes in a laboratory setting (Gibson et al., 2018). The data was exported as an Microsoft Excel sheet where a background subtraction is completed to each time point. The background subtraction subtracts the OD values of the plain LB. The data with the background subtraction is then imported to and analyzed in Prism 9. Outliers are eliminated using the provided standard deviation error bars. The remaining data points are then averaged together based on mutation. To calculate each mutation’s associated doubling time, start by plotting the OD’s on a semi-log graph. In a semi-log graph, there is a “log phase” which almost appears as a straight line and a “lag phase” where the line levels out. The time frames of the log phase were eyeballed and manually selected. Prism 9 then provides a best fit line and the slope of that line is calculated using two points on the line. The doubling time in minutes was calculated with the equation (log(2)/Slope)(60)=Doubling time. The significance of the doubling times is determined using a 2-tailed t-test in google sheets. For example, the doubling times of each S512Y mutation is taken individually for a total of 5-doubling times. Then, the doubling time of each replicate of the WT is taken. A t-test is performed on these two datasets, and if the value is less than 0.05, then their difference is significant. 

Materials List and Procedure

  16 colonies were sequenced in total: one wild-type (WT) with an MIC of 10 µg/mL, five resistant colonies grown in 1 MIC, 5 resistant colonies grown in 102.4 MIC, and five resistant colonies grown in  204.8 MIC. The WT colony was sequenced as a negative control, or in other words, as a baseline genome to compare the other strains against. Because this strain was not resistant, there should be no mutations present in the RRDR, thus any change in the RRDR in the resistant strains indicates a rifampicin-resistance mutation. Sanger Sequencing of the Rifampicin Resistance Determining Region (RRDR) provided four different mutations in three locations: serine 512 to tyrosine (S512Y), proline 564 to leucine (P564L), serine 574 to tyrosine (S574Y), and serine 574 to phenylalanine (S574F). The selected genes were amplified for sequencing using colony PCR and the primers described in the methods section. The finished products were then run through a DNA electrophoresis gel to ensure the PCR effectively amplified the desired genes.  All 15 rifampicin-resistant strains acquired at least one mutation as listed above. Furthermore, all the mutations were present within the I and II clusters of the RRDR. While the N-terminal cluster was not sequenced, there were no mutations present in cluster III (Jin and Gross, 1988). As only the RRDR was sequenced, Figure #1 shows the physical location of all the detected targeted amino acids which is within the rifampicin binding pocket.

Each mutation generally conferred a resistance with a clear split between low MIC (10 µg/mL) and high MIC (1,024/2,048 µg/mL). The MIC-increase is normalized to 1 in terms of the WT which has an MIC of 10 µg/mL, meaning the concentration that harbored low resistant bacteria is defined as 1 MIC, and the high resistance MICs are defined as 102.4 and 204.8 MIC. The five colonies grown in 1 MIC rifampicin with a low resistance all acquired a single unique mutation of S512Y. The five colonies grown in 102.4 MIC had slightly varying results with two of the colonies only having P564L and three of them having the double mutation of P564L/ S574F. Lastly, four of the five colonies grown in 204.8 MIC had the P564L/S574Y double mutation and one had the P564L/ S574F double mutation. The S512Y mutation appeared only in the resistant colonies grown in 1 MIC and was then dropped as the bacteria acquired higher resistance. The presence of P564L alone or in a double mutation guaranteed a high resistance, whether it was grown in 102.4 or 204.8 MIC.

All the mutations acquired converted a smaller amino acid to a larger amino acid (see Figure #2 for R-group changes). The S512Y, S574Y, and S574F mutations all convert serine, a relatively small amino acid with a polar uncharged side chain, to tyrosine and phenylalanine, two large amino acids with hydrophobic side chains. These three mutations affect the shape of the rifampicin binding pocket, making rifampicin binding difficult. The P564L changes a proline, with a small unique structure, to a leucine, a slightly larger amino acid with a hydrophobic side chain. As seen in Figure #3, the P564L mutation interacts directly with the rifampicin, preventing binding in that area of the pocket. 

An OD600 assay was completed on the selected 16 colonies in triplicate to obtain their growth curves. After completing an OD600 assay over 24 hours, the first 8 hours of data were graphed by Prism 9 using a background subtraction in Excel. After 8 hours, the bacteria hit their stationary phase, thus data analysis stopped there. Two colonies, Colony 11 with a single P564L mutation and colony 16 with a double P564L mutation and S574Y mutation, were eliminated from the data set due to their starting ODs being significantly greater than 0.01. This was most likely due to a pipetting or mathematical error made during the dilution procedure while preparing the cultures for the assay. Because only one other colony had a single mutation of P564L, the mutation was not graphed due to a lack of sufficient data. For further analysis of this single mutation, the two strains should be regrown and the assay should be completed again. However, due to time constraints, no further analysis was completed. For the first 2 hours, data was plugged into Prism 9 for every 30 minutes rather than every hour to graph a more defined log phase. 

By setting the y-axis to a semi-log axis, selecting the log phase in hours, determining the slope of the best-fit line, and using the equation (60)log(2)/m where m is the slope, the doubling times in minutes of each mutant were acquired. The log phase for each mutant was from 0.5 hours to 2 hours. Rounded to the nearest minute, the doubling time of the WT was 28 minutes, for S512Y it was 30 minutes, for S574F it was 29 minutes, and for S574Y it was 30 minutes. Figures #4 and 5 indicate that there is a slight difference in the fitness, doubling times, and lag phases, particularly between the WT, S574Y, and the S512Y mutation. Using a two-tailed T-test, all of the doubling times are statistically significant when compared to the WT (Table 2) (p<0.05). However, none of the strains when compared to each other were significantly different (Table 2). While acquiring rifampicin resistance does have a fitness cost, these results also demonstrate that in this experiment, there is no conclusive correlation between MIC and fitness. A limitation of this experiment is that only clusters I, II, and III of the RRDR were sequenced instead of the full-length RpoB or the whole genome. This means there could have been allosteric compensatory mutations affecting the fitnesses and/or MIC of each mutant. 

While further investigation is necessary to find or eliminate allosteric or off-site mutations, these results match a similar 2019 study conducted by Sun et al. where an OD600 fitness assay of both acquired mutants and spontaneous mutants of rifampicin-resistant Riemerella anatipestifer was completed (Sun et al., 2019). While the bacteria species and mutations acquired in this study were all different, the authors concluded that each mutation in the RpoB conferred its own unique reproductive fitness cost, however, there was no correlation between conferred MIC and fitness cost. With further investigation and characterization of mutants in the RpoB, this conclusion opens up discussion for designing new derivatives of rifampicin. Ideally, the antibiotic would target and bind to sites where, if mutated, would severely impede the fitness of the bacteria. This would mean that the resistant bacteria with a mutated site would have a comparatively more difficult time doubling and spreading as opposed to a relatively fit resistant mutant.

The fitness assay and MIC tests provided further insight into the dynamics of common rifampicin resistance mutations and their double mutations. While the double mutations acquired in this study conferred a significantly higher MIC (≥1,024 µg/mL) and impeded the reproductive fitness of the mutant, there was no conclusive correlation between the level of resistance and its fitness cost. More specifically, the P564L/S574F and P564/S574Y mutations both conferred high resistance with P564/S574Y generally resulting in a higher MIC (>2,048 µg/mL). While P564/S574Y conferred the highest fitness cost with a doubling time of 30 minutes, which is a decrease of 7.78% from the WT’s doubling time, it was not significantly higher than the fitness cost of the other two mutations (p>0.05). The mutation that conferred the lowest resistance (10 µg/mL), S512Y, also conferred the second highest reproductive fitness cost with a doubling time decrease of 7.56%. While this difference is significant, it is still not a major fitness cost (Sun et al., 2019). Therefore, this study suggests that the mutation P564L and its double RpoB mutations P564/S574Y and P564L/S574F confer high resistance while the single mutation S512Y confers low resistance. While the mutations all  confer a minor fitness cost (p<0.05), there was no statistically significant correlation between the mutations’ fitness cost and their MIC as none of the strains were significantly different from each other. 

Knowledge gaps that should be addressed are the possibility and characterization of off-site mutations through whole genome sequencing. Furthermore, other forms of fitness, such as competitive fitness, should also be explored. Reproductive fitness is only one aspect of fitness and only informs how fast the strain grows. Doubling times, however, do not inform how well the strain competes with other bacteria over time (Melynk and Wong, 2015). It would also be beneficial to address the same questions with other antibiotics and bacteria species. This information would help inform which antibiotics are most costly for bacteria to acquire resistance to. Understanding the full dynamics of rifampicin resistance in relation to all types of fitness and other forms of antibiotic resistance would result in better-informed prescriptions and aid in developing new effective antibiotics.

Lab Notebook

About the morphology of colonies | Eureka Brewing. (2013, March 24). Eureka Brewing. https://eurekabrewing.wordpress.com/2013/03/24/about-the-morphology-of-colonies/

Addgene. (n.d.). Protocol - How to Create a Bacterial Glycerol Stock. Addgene. https://www.addgene.org/protocols/create-glycerol-stock

Aprile, A., Scalia, G., Stefani, S., & Mezzatesta, M. L. (2020). In vitro fosfomycin study on concordance of susceptibility testing methods against ESBL and carbapenem-resistant Enterobacteriaceae. Journal of Global Antimicrobial Resistance, 23, 286-289. Science Direct. https://doi.org/10.1016/j.jgar.2020.09.022 

Baba, T., Ara, T., Hasegawa, M., Takai, Y., Okumura, Y., Baba, M., Datsenko, K. A., Tomita, M., Wanner, B. L., & Mori, H. (2006). Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Molecular systems biology, 2, 2006.0008. https://doi.org/10.1038/msb4100050 

Baym, M., & Lieberman, T. D. (2016). Spatiotemporal microbial evolution on antibiotic landscapes. Science, 353(6304), 1147-51. PubMed. 10.1126/science.aag0822 

Bioanalyse Limited. (2021). MIC Test Strip. YouTube. https://www.youtube.com/watch?v=1pAFB2tjtmA

BioRad. (n.d.). Precision Plus Protein™ Dual Color Standards from Bio-Rad. Biocompare. https://www.biocompare.com/21740-Prestained-Protein-Peptide-Standards-Multicolor/5608461-Precision-Plus-Protein-Dual-Color-Standards/#ctl06_ctl01_ctl02_contentPane

Britannica, T. Editors of Encyclopaedia (2021, December 14). antibiotic. Encyclopedia Britannica. https://www.britannica.com/science/antibiotic

Campbell, E. A., Korzheva, N., Mustaev, A., Murakami, K., Nair, S., Goldfarb, A., & Darst, S. A. (2001). Structural mechanism for rifampicin inhibition of bacterial RNA polymerase. Cell, 104(6), 901–912. https://doi.org/10.1016/s0092-8674(01)00286-0 

CDC. Antibiotic Resistance Threats in the United States, 2019. Atlanta, GA:

U.S. Department of Health and Human Services, CDC; 2019. https://www.cdc.gov/drugresistance/biggest-threats.html 

Clinical and Laboratory Standards Institute. (2020). Performance Standards for Antimicrobial Susceptibility Testing. M100, 30. https://www.nih.org.pk/wp-content/uploads/2021/02/CLSI-2020.pdf 

Cloeckaert, A., Zygmunt, M. S., & Doublet, B. (2017). Editorial: Genetics of Acquired Antimicrobial Resistance in Animal and Zoonotic Pathogens. Frontiers in microbiology, 8, 2428. https://doi.org/10.3389/fmicb.2017.02428

Dadgostar P. (2019). Antimicrobial Resistance: Implications and Costs. Infection and drug resistance, 12, 3903–3910. https://doi.org/10.2147/IDR.S234610

Desmarchelier, P., & Fegan, N. (2002). Escherichia coli. In Encyclopedia of Dairy Sciences (pp. 948-954). Elsevier. DOI: https://doi.org/10.1016/B0-12-227235-8/00158-9

Dornell, J. (2021, March 25). RNA Polymerase: Function and Definition. Technology Networks. https://www.technologynetworks.com/genomics/articles/rna-polymerase-function-and-definition-346823 

Dunai, A., Spohn, R., Farkas, Z., Lázár, V., Györkei, Á., Apjok, G., Boross, G., Szappanos, B., Grézal, G., Faragó, A., Bodai, L., Papp, B., & Pál, C. (2019). Rapid decline of bacterial drug-resistance in an antibiotic-free environment through phenotypic reversion. eLife, 8, e47088. https://doi.org/10.7554/eLife.47088 

Enne, V. I., Delsol, A. A., Roe, J. M., & Bennett, P. M. (2004). Rifampicin resistance and its fitness cost in Enterococcus faecium. The Journal of antimicrobial chemotherapy, 53(2), 203–207. https://doi.org/10.1093/jac/dkh044 

Escherichia coli str. K-12 substr. MG1655, complete genome. (n.d.). National Library of Medicine. https://www.ncbi.nlm.nih.gov/nuccore/NC_000913.3?report=genbank&from=4181245&to=4185273

European Centre for Disease Prevention and Control. (2018, November 18). Antibiotic resistance – an increasing threat to human health. European Centre for Disease Prevention and Control. https://antibiotic.ecdc.europa.eu/en/publications-data/antibiotic-resistance-increasing-threat-human-health

European Committee for Antimicrobial Susceptibility Testing (EUCAST) of the European Society of Clinical Microbiology and Infectious Diseases (ESCMID). (2003). Determination of minimum inhibitory concentrations (MICs) of antibacterial agents by broth dilution. EUCAST Discussion Document, 9(8), 9-15. Clinical Microbiology and Infection. https://doi.org/10.1046/j.1469-0691.2003.00790.x 

The European Committee on Antimicrobial Susceptibility Testing (2020). Breakpoint tables for interpretation of MICs and zone diameter (v 10.0). https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_10.0_Breakpoint_Tables.pdf

Federal Office of Public Health and Federal Food Safety and Veterinary Office. (2020, November 17). Usage of Antibiotics and Occurrence of Antibiotic Resistance in Switzerland. Federal Office of Public Health. https://www.bag.admin.ch/bag/en/home/krankheiten/infektionskrankheiten-bekaempfen/antibiotikaresistenzen/wie-entwickelt-sich-die-antibiotikaresistenzlage---.html

Frey, P., Baer, J., & Bergada-Pijuan, J. (2021). Quantifying Variation in Bacterial Reproductive Fitness: a High-Throughput Method. ASM Journals, 6(1). https://doi.org/10.1128/mSystems.01323-2 

Giacalone, V. (2013, March 25). Primers, by Design - Tips for Optimal DNA Primer Design. Biocompare. https://www.biocompare.com/Bench-Tips/133581-Primers-by-Design-Tips-for-Optimal-DNA-Primer-Design/

Gibson, B., Wilson, D. J., Feil, E., & Eyre-Walker, A. (2018). The distribution of bacterial doubling times in the wild. Proceedings. Biological sciences, 285(1880), 20180789. https://doi.org/10.1098/rspb.2018.0789

Gilson. (2017). User's Guide. Gilson inc. https://www.gilson.com/pub/media/docs/PIPETMANCLASSIC_UG_LT801120-F.pdf

Hawkey P. M. (1998). The origins and molecular basis of antibiotic resistance. BMJ (Clinical research ed.), 317(7159), 657–660. https://doi.org/10.1136/bmj.317.7159.657

Hawkins Incorporation. (n.d.). Dimethyl Sulfoxide (DMSO), Cell Culture Tested. Hawkins Inc. https://hawkinsinc.com/wp-content/uploads/2016/09/Dimethyl-Sulfoxide-DMSO-Cell-Culture-Tested.pdf

Idexx Laboratories. (2019, March). Microbiology guide to interpreting minimum inhibitory concentration (MIC). Idexx. https://www.idexx.com/files/microbiology-guide-interpreting-mic.pdf 

Implen. (n.d.). The OD600 Basics. Implen. https://www.implen.de/od600-diluphotometer/od600/#top 

Jin, D.J., and Gross, C.A. (1988). Mapping and sequencing of mutations in the Escherichia coli rpoB gene that lead to rifampicin resistance. J. Mol. Biol. 202, 45-58.

Kalvaitis, K. (2008). Penicillin: An accidental discovery changed the course of medicine. Endocrine Today, (August 2008). https://www.healio.com/news/endocrinology/20120325/penicillin-an-accidental-discovery-changed-the-course-of-medicine

Larsson, D.G.J., Flach, CF. Antibiotic resistance in the environment. Nat Rev Microbiol 20, 257–269 (2022). https://doi.org/10.1038/s41579-021-00649-x

Liofilchem. (2019). Drug Specific Supplement for MTS™ Ciprofloxacin. Liofilchem.

McKernan, Lisa. (2015). Using a Simple Escherichia coli Growth Curve Model to Teach the Scientific Method. The American Biology Teacher, 77(5), 357–362. https://doi.org/10.1525/abt.2015.77.5.6 

Melnyk, A. H., Wong, A., & Kassen, R. (2015). The fitness costs of antibiotic resistance mutations. Evolutionary applications, 8(3), 273–283. https://doi.org/10.1111/eva.12196

Missiakas, D. M., & Schneewind, O. (2013). Growth and laboratory maintenance of Staphylococcus aureus. Current protocols in microbiology, Chapter 9, Unit–9C.1. https://doi.org/10.1002/9780471729259.mc09c01s28 

Molodtsov, V., Scharf, N.T., Stefan, M.A., Garcia, G.A. and Murakami, K.S. (2017), Structural basis for rifamycin resistance of bacterial RNA polymerase by the three most clinically important RpoB mutations found in Mycobacterium tuberculosis. Molecular Microbiology, 103: 1034-1045. https://doi.org/10.1111/mmi.13606

Not-for-Profit MedicineWise. (2019, December 18). Antibiotics, explained. NPS MedicineWise. https://www.nps.org.au/consumers/antibiotics-explained 

QIAGEN. (n.d.). Bacterial cultivation media and antibiotics. QIAGEN. https://www.qiagen.com/us/knowledge-and-support/knowledge-hub/bench-guide/plasmid/working-with-plasmids/bacterial-cultivation-media-and-antibiotics

Riley, M., Abe, T., & Arnaud, M. B. (2005). Escherichia coli K-12: a cooperatively developed annotation. National Library of Medicine. https://www.ncbi.nlm.nih.gov/nuccore/NC_000913.3?report=genbank&from=4181245&to=4185273

Rodríguez-Verdugo, A., Gaut, B. S., & Tenaillon, O. (2013). Evolution of Escherichia coli rifampicin resistance in an antibiotic-free environment during thermal stress. BMC evolutionary biology, 13, 50. https://doi.org/10.1186/1471-2148-13-50

Shea, J., Halse, T., & Kohlerschmidt, D. (2021). Low-Level Rifampin Resistance and rpoB Mutations in Mycobacterium tuberculosis: an Analysis of Whole-Genome Sequencing and Drug Susceptibility Test Data in New York. ASM Journals, 59(4). https://doi.org/10.1128/JCM.01885-20 

Sigma Aldrich. (n.d.). Sanger Sequencing Steps & Method. Sigma-Aldrich. https://www.sigmaaldrich.com/US/en/technical-documents/protocol/genomics/sequencing/sanger-sequencing 

Singh, T., Dar, S. A., Das, S., & Haque, S. (2020). Importance of efflux pumps in subjugating antibiotic resistance. Drug Discovery Targeting Drug-Resistant Bacteria, 273-299. https://doi.org/10.1016/B978-0-12-818480-6.00010-

Singh, V. K., Almpani, M., & Romhe, L. G. (2021). The Role of Common Solvents against Pseudomonas aeruginosa-Induced Pathogenicity in a Murine Burn Site Infection Model. ASM Journals, 9(1). Microbiology Spectrum. https://doi.org/10.1128/Spectrum.00233-21 

Song, T., Park, Y., Shamputa, I.C., Seo, S., Lee, S.Y., Jeon, H.-S., Choi, H., Lee, M., Glynne, R.J., Barnes, S.W., Walker, J.R., Batalov, S., Yusim, K., Feng, S., Tung, C.-S., Theiler, J., Via, L.E., Boshoff, H.I.M., Murakami, K.S., Korber, B., Barry, C.E., 3rd and Cho, S.-N. (2014), Compensation of rifampicin-induced fitness defect in TB. Molecular Microbiology, 91: 1106-1119. https://doi.org/10.1111/mmi.12520 

Strong Medicine. (2013, January 19). Mechanisms and Classification of Antibiotics (Antibiotics - Lecture 3) [Video]. YouTube. https://www.youtube.com/watch?v=NGwP471sehI

Su, M., Satola, S. W., & Read, T. D. (2019). Genome-Based Prediction of Bacterial Antibiotic Resistance. Journal of clinical microbiology, 57(3), e01405-18. https://doi.org/10.1128/JCM.01405-18 

Sun, J., Zhu, D., Xu, J. et al. (2019). Rifampin resistance and its fitness cost in Riemerella anatipestifer. BMC Microbiol 19, 107 https://doi.org/10.1186/s12866-019-1478-7 

Tankeshwar, A. (2022, October 4). Broth Dilution Method for MIC Determination – Microbe Online. Microbe Online. https://microbeonline.com/minimum-inhibitory-concentration-mic-broth-dilution-method-procedure-interpretation/

Tip Biosystems. (n.d.). Understanding OD600 and Measuring Cell Growth. Tip Biosystems. https://tipbiosystems.com/blog/understanding-od600-and-measuring-cell-growth/ 

Visser JAGM (2007) Bacterial Solutions to the Problem of Sex. PLoS Biol 5(9): e245. https://doi.org/10.1371/journal.pbio.0050245

Vogwill, T., & MacLean, R. C. (2015). The genetic basis of the fitness costs of antimicrobial resistance: a meta-analysis approach. Evolutionary applications, 8(3), 284–295. https://doi.org/10.1111/eva.12202 

Wehrli W. (1983). Rifampin: mechanisms of action and resistance. Reviews of infectious diseases, 5 Suppl 3, S407–S411. https://doi.org/10.1093/clinids/5.supplement_3.s407 

Weinstein, Z. B., & Zaman, M. H. (2018). Evolution of Rifampin Resistance in Escherichia coli and Mycobacterium smegmatis Due to Substandard Drugs. Antimicrobial agents and chemotherapy, 63(1), e01243-18. https://doi.org/10.1128/AAC.01243-18 

Werth, B. J. (2022, September). Overview of Antibiotics - Infections. Merck Manuals. https://www.merckmanuals.com/home/infections/antibiotics/overview-of-antibiotics 

World Health Organization. (2021, October 14). Tuberculosis. World Health Organization (WHO). https://www.who.int/news-room/fact-sheets/detail/tuberculosis 

Yokoyama, M. (2020, May 28). E. coli as a Model Organism. News Medical Life Sciences. https://www.news-medical.net/life-sciences/E-coli-as-a-Model-Organism.aspx#:~:text=Because%20there%20are%20tools%20available,from%20various%20sources%20during%20growth.