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Scientists watch as bacteria evolve antibiotic resistance.

Growth patterns reveal E. coli’s path to becoming superbugs

PETRI PLATTER  A petri dish more than a meter long helped scientists visualize the evolution of antibiotic resistance in E. coli bacteria. Bacteria placed on the outer edges had to adapt to higher and higher levels of antibiotics as they moved toward the center of the plate.

Harvard Medical School

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By Laurel Hamers

September 8, 2016 at 2:00 pm

For bacteria, practice makes perfect: Adjusting to ever higher levels of antibiotics preps them to morph into super resistant strains, and scientists can now watch it happen. A new device — a huge petri dish coated with different concentrations of antibiotics — makes this normally hidden process visible, microbiologist Michael Baym and colleagues report in the Sept. 9 Science . The setup gives a step-by-step picture of how garden-variety microbes become antibiotic-resistant superbugs .

“As someone who’s studied evolutionary biology for a long time, I think it has a real wow factor,” says Sam Brown, a microbiologist at the Georgia Institute of Technology in Atlanta who wasn’t involved in the study. The bacteria are “climbing this impossible mountain of antibiotics.”

Scientists often study microbial evolution in flasks where everything is mixed together. “Inside that flask, in order for a new strain to evolve, the new mutant has to be more fit than everything around it,” says Baym, of Harvard Medical School. “But in nature, we see a second dynamic: You don’t necessarily need to be more fit than everything around you. You just need to make it into a new environment.”

Baym and colleagues modeled those spatial dynamics using a giant dish more than a meter long instead of a standard palm-sized petri dish. That gave the bacteria more room to diversify and also let the researchers create a gradient of antibiotics on the plate. Low concentrations of trimethoprim or ciprofloxacin antibiotics at the edges ramped up to much higher levels in the middle. Then, the team put Escherichia coli bacteria on each end of the plate and watched the microbes multiply over the next week and a half.  

In general, as the E. coli mutated in ways that let them handle higher and higher levels of antibiotics, their descendants could press into new territory on the plate. The bacteria that made it to the middle could tolerate doses of antibiotics a thousand times higher than what was necessary to kill the original bacteria.

But antibiotic resistance didn’t always make bacteria competitive colonizers. Highly resistant bacteria sometimes spread more slowly. Trapped in the back by faster-moving bacteria at the forefront, the stragglers’ descendants formed pockets of super-resistance at lower antibiotic concentrations.

Baym and his colleagues think the experimental setup could be used to study microbial evolution under other environmental and spatial constraints, like the availability of particular nutrients. 

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A cinematic approach to drug resistance.

Ekaterina Pesheva

HMS Communications

Scientists film bacteria’s maneuvers as they become impervious to drugs

In a creative stroke inspired by Hollywood wizardry, scientists from Harvard Medical School and Technion-Israel Institute of Technology have designed a simple way to observe how bacteria move as they become impervious to drugs.

The experiments, described in the Sept. 9 issue of Science , are thought to provide the first large-scale glimpse of the maneuvers of bacteria as they encounter increasingly higher doses of antibiotics and adapt to survive — and thrive — in them.

To do so, the team constructed a 2-by-4 foot petri dish and filled it with 14 liters of agar, a seaweed-derived jellylike substance commonly used in labs to nourish organisms as they grow.

To observe how the bacterium Escherichia coli adapted to increasingly higher doses of antibiotics, researchers divided the dish into sections and saturated them with various doses of medication. The outermost rims of the dish were free of any drug. The next section contained a small amount of antibiotic — just above the minimum needed to kill the bacteria — and each subsequent section represented a 10-fold increase in dose, with the center of the dish containing 1,000 times as much antibiotic as the area with the lowest dose.

Over two weeks, a camera mounted on the ceiling above the dish took periodic snapshots that the researchers spliced into a time-lapsed montage. The result? A powerful, unvarnished visualization of bacterial movement, death, and survival; evolution at work, visible to the naked eye.

The device, dubbed the Microbial Evolution, and Growth Arena (MEGA) plate, represents a simple, and more realistic, platform to explore the interplay between space and evolutionary challenges that force organisms to change or die, the researchers said.

“We know quite a bit about the internal defense mechanisms bacteria use to evade antibiotics, but we don’t really know much about their physical movements across space as they adapt to survive in different environments,” said study first author Michael Baym, a research fellow in systems biology at HMS.

The researchers caution that their giant petri dish is not intended to perfectly mirror how bacteria adapt and thrive in the real world and in hospital settings, but it does mimic the real-world environments bacteria encounter more closely than traditional lab cultures can. This is because, the researchers say, in bacterial evolution, space, size, and geography matter. Moving across environments with varying antibiotic strengths poses a different challenge for organisms than they face in traditional lab experiments that involve tiny plates with homogeneously mixed doses of drugs.

A cinematic inspiration

The invention was borne out of the pedagogical necessity to teach evolution in a visually captivating way to students in a graduate course at HMS. The researchers adapted an idea from, of all places, Hollywood.

Senior study investigator Roy Kishony, of HMS and Technion, had seen a digital billboard advertising the 2011 film “Contagion,” a grim narrative about a deadly viral pandemic. The marketing tool was built using a giant lab dish to show hordes of painted, glowing microbes creeping slowly across a dark backdrop to spell out the title of the movie.

“This project was fun and joyful throughout,” Kishony said. “Seeing the bacteria spread for the first time was a thrill. Our MEGA plate takes complex, often obscure, concepts in evolution, such as mutation selection, lineages, parallel evolution, and clonal interference, and provides a visual, seeing-is-believing demonstration of these otherwise vague ideas. It’s also a powerful illustration of how easy it is for bacteria to become resistant to antibiotics.”

Co-investigator Tami Lieberman says the images spark the curiosity of lay and professional viewers alike.

“This is a stunning demonstration of how quickly microbes evolve,” said Lieberman, who was a graduate student in the Kishony lab at the time of the research and is now a postdoctoral research fellow at MIT. “When shown the video, evolutionary biologists immediately recognize concepts they’ve thought about in the abstract, while nonspecialists immediately begin to ask really good questions.” Bacteria on the move

Beyond providing a telegenic way to show evolution, the device yielded some key insights about the behavior of bacteria exposed to increasing doses of a drug. Some of them are:

• Bacteria spread until they reached a concentration (antibiotic dose) in which they could no longer grow.
• At each concentration level, a small group of bacteria adapted and survived. Resistance occurred through the successive accumulation of genetic changes. As drug-resistant mutants arose, their descendants migrated to areas of higher antibiotic concentration. Multiple lineages of mutants competed for the same space. The winning strains progressed to the area with the higher drug dose, until they reached a drug concentration at which they could not survive.
• Progressing sequentially through increasingly higher doses of antibiotic, low-resistance mutants gave rise to moderately resistant mutants, eventually spawning highly resistant strains able to fend off the highest doses of antibiotic.
• Ultimately, in a dramatic demonstration of acquired drug resistance, bacteria spread to the highest drug concentration. In the span of 10 days, bacteria produced mutant strains capable of surviving a dose of the antibiotic trimethoprim 1,000 times higher than the one that killed their progenitors. When researchers used another antibiotic — ciprofloxacin — bacteria developed 100,000-fold resistance to the initial dose.
• Initial mutations led to slower growth — a finding that suggests bacteria adapting to the antibiotic aren’t able to grow at optimal speed while developing mutations. Once fully resistant, such bacteria regained normal growth rates.
• The fittest, most resistant mutants were not always the fastest. They sometimes stayed behind weaker strains that braved the frontlines of higher antibiotic doses.
• The classic assumption has been that mutants that survive the highest concentration are the most resistant, but the team’s observations suggest otherwise.

“What we saw suggests that evolution is not always led by the most resistant mutants,” Baym said. “Sometimes it favors the first to get there. The strongest mutants are, in fact, often moving behind more vulnerable strains. Who gets there first may be predicated on proximity rather than mutation strength.”

Co-investigators included Eric Kelsic, Remy Chait, Rotem Gross, and Idan Yelin.

The work was supported by a grant from the National Institutes of Health and by the European Research Council.

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Credit: Johns Hopkins University

Study inspired by curious 15-year-old could advance search for novel antibiotics

New bacteria found in raw honey could benefit the fight against legionnaires' disease and antibiotic resistance, according to new johns hopkins medicine research.

By Alexandria Carolan

Equipped with a suitcase full of honey, high school sophomore Carson Shin contacted university after university, hoping to work with expert biochemists to investigate the sticky substance's antimicrobial properties.

The only problem? Scientists seemed wary of collaborating with a 15-year-old.

Image caption: Carson Shin

Shin couldn't have predicted that, five years later, he would co-author a Johns Hopkins Medicine report showing that dormant and previously undescribed bacteria found in raw honey produce antibiotics that can kill the bacterial pathogen Legionella . The pathogen can be found in potable water and causes Legionnaires' disease, a life-threatening pneumonia that kills one in 10 people infected with it.

The published report not only offers a first step in the development of new antibiotics for Legionella , but has the potential to aid in the fight against antibiotic resistance, says senior author Tamara O'Connor , assistant professor of biological chemistry at Johns Hopkins University School of Medicine.

Shin reached out to O'Connor in the spring of 2019, beginning a summer internship with the professor that he hoped would uncover a novel antimicrobial property of honey.

"Carson showed tremendous initiative and was very inquisitive," O'Connor says. "It's exciting to have any student join the lab who demonstrates this level of intellectual engagement in science."

Initially, Shin and O'Connor exposed Legionella to raw, unpasteurized honey, to test whether the natural substance could kill the bacteria. Surprisingly, honey had little effect on Legionella . However, in the course of these experiments, they identified several different bacteria in the honey that, in response to Legionella , produced and secreted antibiotics that were lethal to the pathogen.

"We found the right conditions for the honey bacteria to thrive, allowing us to tap into a resource we didn't know was there," Shin says.

In nature, O'Connor says, "bacteria figure out ways to outcompete one another, which often involves releasing toxic molecules that kill their competitors." The honey bacteria Shin and O'Connor isolated "recognize Legionella as competition and launch a deadly response."

The honey bacteria were identified as members of the Bacillus and Lysinibacillus genera of bacteria. This is not surprising, O'Connor says, because bacilli produce spores that are protected from the antimicrobial properties of honey. These bacteria are commonly found in raw honey, explaining why it is recommended to eat only pasteurized honey, she says.

Upon sequencing the genomes of two of the bacterial isolates, strain AHB2 and strain AHB11 , the researchers identified them as members of the species Bacillus safensis . Previously, the ability for this group of bacteria to produce antibacterial molecules was not well-documented.

Further experiments revealed how specific the response of honey bacteria to Legionella was.

"Remarkably, the bacteria in honey only produce these antibacterial molecules in response to Legionella species, as none of the other bacterial pathogens we exposed them to elicited this response," O'Connor says.

Image caption: Tamara O'Connor

While other pathogens did not cause honey bacteria to produce these antibiotics, many were susceptible to them, O'Connor says. These results suggest antibacterial molecules produced by honey could target other harmful pathogens and could be used as broad-spectrum antibiotics. While these preliminary findings offer the identification of new antibacterial molecules, more research is needed to determine their potential for developing viable therapeutics, O'Connor says.

Antimicrobial resistance is one of the largest threats to global public health, contributing to nearly 5 million deaths in 2019, according to the World Health Organization—creating a dire need for the development of new antibiotics to treat bacterial infections.

Similar studies of the biowarfare between microorganisms have led scientists to identify many antimicrobial molecules, says O'Connor. The vast majority of antibiotics prescribed by physicians originate from natural products, she says.

"The ability to tap into these resources by identifying new bacteria and the conditions that cause them to produce antibacterial molecules is critical in the fight against antibiotic resistance," she says.

Young scientists like Shin are crucial to combat antibiotic resistance, O'Connor says.

"Carson exemplifies how the curiosity of an aspiring young scientist can lead to exciting new discoveries," she says.

Shin, who is beginning his senior year of college this fall at the University of Pennsylvania, said his experience at Johns Hopkins influenced his decision to study anthropology. Before reaching out to O'Connor, Shin had looked into raw honey's historic and ancient role in traditional medicines of the Egyptians, Greeks, and Islamic countries over thousands of years.

"Our research stems from studying culture. You can learn valuable information about medicine from cultures across the world and across time," Shin says.

The research was supported independently by the Department of Biological Chemistry and the Johns Hopkins University School of Medicine .

Posted in Health

Tagged antibiotics , department of biological chemistry , bacteria

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Science in School

Microbiology: discovering antibacterial agents teach article.

Author(s): Mireia Deumal Fernandez, Mariona Lladonosa Soler, Tamaryin Godinho

What can we do about the antimicrobial resistance crisis? What does it take to develop a new medicine? Can we fight bacteria with everyday substances or even foods? Find out with these engaging microbiology activities.

Introduction

Even though microorganisms can’t be seen with the naked eye, bacteria are the most abundant organisms on the planet. While many of these organisms are harmless to people, and may even be beneficial, such as our gut bacteria, [ 1 ] some cause disease. Since the discovery of penicillin, we have been able to treat bacterial infections with antibiotics. However, antimicrobial resistance has become widespread, and we are once again starting to see life-threatening bacterial infections. [ 2 , 3 ] According to a recent report by the European Medicines Agency (EMA), infections by multidrug-resistant bacteria are estimated to cause 33 000 deaths in the EU every year, with an annual cost due to healthcare expenditures and productivity losses estimated to be approximately €1.5 billion. [ 4 ] In fact, the World Health Organization (WHO) has declared that “ Antibiotic resistance is one of the biggest threats to global health, food security, and development today .”

These activities enable students to explore this serious issue in a hands-on manner. The first activity introduces some of the basic principles and techniques, while, in the second, they get to do their own research by generating and testing hypotheses on potential antimicrobial compounds. The accompanying worksheets provide background information and guided discussions.

Safety note

In addition to following your usual laboratory and biosafety precautions:

Do not incubate plates at human body temperature – this reduces the risk of culturing pathogenic bacteria. [ 5 ]

Dispose properly of leftover antibiotics and bacterial cultures to avoid releasing antibiotics or resistant bacteria into the environment.

After incubation, tape the lids onto the petri dishes to avoid them accidentally opening. Do not tape before incubation to avoid promoting the growth of anaerobic organisms.

Activity 1: Antibiotics and resistance

The aim of this activity is to demonstrate the action of antibiotics and dose–response relationships and to explain the danger posed by antimicrobial resistance. The activity should only be carried out if the antibiotics and bacterial plates can be properly disposed of! If not, use Worksheet 1 before moving to Activity 2. The activity is suitable for students aged 14–19 and will take two lessons to complete (one to set up the incubations; one to analyze and discuss the results).

• Petri dishes with agar growth medium
• Disinfectant, such as 70% ethanol.
• Bacterial cultures (best if at least one Gram-positive and one Gram-negative per student is used)
• Plate spreader or inoculating loop
• Antibiotic tablets
• Thick paper and a paper punch
• Measuring cylinder, beakers/flasks, and pipette for preparing serial dilutions
• Physiological saline solution (0,9% NaCl) or water
• Worksheets 1 and 2 and resources on antimicrobial resistance, such as those from the US Food and Drug Administration ( FDA ) and the WHO [ 6 , 7 ]
• Sterilize your lab bench or surface by spraying it with ethanol and wiping it down with paper. Also disinfect your hands with an alcohol-based hand sanitizer for at least 20 seconds. 
• Using a sterile loop or spreader, touch the bacterial culture and spread it on the plate. It may be better for the teacher or technician to do this step beforehand. See the resource section for detailed protocols.
• Label the bottom of the dish with the name of the group and the antibiotic chosen. Always label the bottom of the dish in case the lids get separated.
• Cut several circles of thick paper per plate with the paper punch. Label them with a pencil (control and antibiotic at different dilutions).
• Dissolve 300 mg of an antibiotic tablet into 200 ml of saline solution or water. Solid tablets should be crushed with a pestle and mortar; gel capsules can be gently twisted apart to pour out the powder inside.
• Make serial 1 in 5 dilutions to get a range of samples of 1/1, 1/5, 1/25, 1/125, and 1/625.
• Soak the thick circle paper in this solution. Use tweezers to place the disk on the plate spread with bacteria.
• Incubate the plates at a constant temperature for 24 hours or until bacterial colonies are visible. If necessary, the supervisor can take them out and keep them in the laboratory fridge until the next lesson. Store the plates upside down to avoid condensation dripping onto the bacteria.
• Hand out Worksheet 1 and the resources on antimicrobial resistance, which can be discussed at this point or at the end of lesson 2.
• Take a picture of the plates and measure the diameter of the inhibition zone with a ruler (without opening the plate).
• Students can research the antibiotic and guess the group of bacteria (gram positive or negative) present in the dish. Later this can be checked with gram staining if feasible.
• Have students fill out Worksheet 2.

This can be done after setting up the plates or just before analyzing them. Go through the provided worksheet to analyze the results and discuss the danger posed by antimicrobial resistance. Discussion questions include the following:

• Aside from resistance, what other consequences are there of taking antibiotics when not necessary?
• In many countries, it is illegal for pharmacies to sell antibiotics without a prescription from a doctor for these reasons. Is this allowed in your country? Do you agree?
• What about antibiotic use in this activity, could that lead to antibiotic resistance? If so, what could we do to prevent this?

Activity 2: Testing substances for antibacterial activity

Having understood the need for the development of new antibiotics, students now get a chance to investigate substances for antibacterial action, followed by a discussion of the key principles of drug development and the difference between any substance that shows activity and a medicine. They will also learn to formulate hypotheses, plan experiments to test them, and analyze the results. Students find it particularly engaging to be able to come up with their own ideas of substances to test.

The activity is suitable for students aged 14–19 and will take three lessons to complete.

• Agar plates seeded with bacteria, prepared as in Activity 1
• Bacterial cultures (e.g., Bacillus cereus )
• Substances to test for antibacterial activity
• Implements for preparing the substances (e.g., chopping board or pestle and mortar)
• Physiological saline solution (0,9% NaCl)
• Disinfectant solution (e.g., 70% ethanol)
• Worksheet 3 (and Worksheet 1 if skipping Activity 1)
• If you haven’t done Activity 1, first discuss the problem of antibiotic resistance with your class and the need for new antibiotics. Worksheet 1 can be used for this purpose.
• Ask the students what substance in their homes they might like to test for antibacterial activity. They might already have some ideas of things they think might be antibacterial and might suggest cleaning products, cosmetics (like mouthwash), or foods.
• Draw up a list of substances to test. Exclude very toxic or corrosive substances like rat poison or toilet cleaner. Suggestions that are nice to include are salt; mouthwash (with alcohol or alcohol-free); an antibacterial soap; a natural soap (some bacteria can feed on the soap!); and various foodstuffs like garlic juice, honey (with sugar solution to compare?), cabbage juice, a ground spice like turmeric or cloves, various essential oils, lemon juice, and apple juice.
• Have students make predictions for each substance regarding whether it will show antibacterial activity. They can do some research first or just guess based on folk wisdom or their own ideas. This is a good opportunity to discuss reliable sources and not believing everything they read on the internet, including ‘published scientific papers’. Not all scientific journals have rigorous quality control! Some good references for antimicrobial activities of foodstuffs include these papers on spices and honey . [ 8 , 9 ]
• Have students start to fill out table 1 of Worksheet 3. Any answers they don’t know can be guessed, researched, or left blank.
• Decide whether to use the substances pure or diluted. Maybe try one of each?
• Students can be assigned to bring in different substances to the next lesson, or the teacher can provide them.
• Using a sterile loop or spreader, spread the bacteria on the plate. It may be better for the teacher or technician to do this step beforehand. See the resource section for detailed protocols.
• Using a marker pen, label the bottom of the dish with the name of the group and the substance chosen. After that, cut four circles of thick paper per plate and label them with a pencil.
• Prepare the substances at the decided dosage and set them aside. See note below.
• Soak each paper circle in the corresponding solution or saline solution as a control. Place on the surface of the agar.

Note on preparation of the substances

• You can try to classify the substances that are going to be tested as oil and water based. The active components of solids, like spice powders, may be water or oil soluble. In this case, prepare water-soluble substances in saline solution and oil-based substances in a neutral oil (for example, sunflower oil, which will also be the control for these substances).
• Fruit/vegetables should be squeezed to extract juice. Harder things like cabbage can be grated first. Extra care should be taken if blades or graters are used.
• For citrus fruits, both the juice and oil from the peel can be tested. Strips of peel can be removed and bent (wear safety glasses to release the oils.
• Take a picture of the plates and measure the diameter of the inhibition zone.
• Have students complete Worksheet 3 with their results.
• Work through the discussion questions on Worksheet 3.

The discussion on the worksheet is intended to help students understand drug development and the wide gap between a substance with a given activity and a useful medicine. The same principles apply to substances with anticancer activity. This knowledge should help them critically evaluate media reports of the amazing disease-fighting properties of various substances.

[1] Stark LA (2010) Beneficial microorganisms: countering microbephobia . CBE Life Sciences Education 9 : 387–389. doi: 10.1187/cbe.10-09-0119.

[2] Chokshi A et al. (2019) Global contributors to antibiotic resistance . Journal of Global Infectious Diseases 11 : 36–42. doi: 10.4103/jgid.jgid_110_18.

[3] Aslam B et al. (2018) Antibiotic resistance: a rundown of a global crisis . Infection and Drug Resistance 11 : 1645–1658. doi: 10.2147/IDR.S173867.

[4] Information on antimicrobial resistance from the EMA: https://www.ema.europa.eu/en/human-regulatory/overview/public-health-threats/antimicrobial-resistance

[5] CLEAPSS safety information on how to handle microorganisms: http://science.cleapss.org.uk/resource/SSS001-Microorganisms.pdf

[6] FDA resources on combating antimicrobial resistance: https://www.fda.gov/consumers/consumer-updates/combating-antibiotic-resistance

[7] A WHO fact sheet on the antibiotic resistance crisis: https://www.who.int/news-room/fact-sheets/detail/antibiotic-resistance

[8] Liu Q et al. (2017) Antibacterial and Antifungal Activities of Spices . International Journal of Molecular Sciences 18 : 1283. doi: 10.3390%2Fijms18061283

[9] Nolan VC, Harrison J, Cox JAG (2019) Dissecting the Antimicrobial Composition of Honey . Antibiotics 8 : 251. doi:10.3390/antibiotics8040251

Experimental procedures

• Read this simple procedure for pouring Agar plates .
• Use this CLEAPSS resource on how to inoculate plates .
• Watch a nice video demonstrating how to set up and perform these experiments using aseptic techniques.
• Use the CLEAPSS safety information on handling microorganisms or a more extensive microbiology safety info from the Association for Science Education.
• Read this extensive microbiology resource from ASSIST.
• Use this protocol for Gram stain

Related articles from Science in School

• Create a living piece of ‘agar art’ with microbes living on our hands: Duarte A, Madureira AM (2019) Painting in a petri dish . Science in School 46 : 48–55.
• Look at real-life applications of school mathematics to understand how diseases spread: Kucharski A et al. (2017) Disease dynamics: understanding the spread of diseases . Science in School 40 : 52–56.
• Explore the antimicrobial properties of some home-made soups: Extance A (2020) Soup – an evidence-based medicine? Science in School 50 .
• Read Understand articles on modern drug design:

– Sucunza D (2014) Inspired by nature: modern drugs . Science in School 28 : 40–45.

– Houlton S (2019) The changing technologies of drug design . Science in School 46 : 25–28.

Classroom resources

• Watch a video about why antibiotic-resistant bacteria are developing.
• Watch a TED-Ed animated video on a possible solution to the antibiotics-resistance problem.
• Find out more about the antibacterial power of honey with this engaging video.
• Watch an animated video by Kurzgesagt explaining how antibiotic-resisting bacteria developed .
• Watch a video on the working mechanism of antibiotics.
• Watch an animation by the FDA on antibiotic resistance .
• Watch an engaging whiteboard video explaining antibiotic resistance.

Further resources

• Take a quiz on antibiotic use.
• Find some information on the history of antimicrobial resistance , with dates for when resistance was first discovered for different antibiotic classes.
• Find details on antimicrobial resistance in Europe .
• Learn about different steps involved in drug development and the importance of pharmacokinetics .

Mireia Deumal Fernández is a biomedical scientist at the University of Barcelona, Spain. She is studying for a master’s degree in clinical trials and medical affairs.

Mariona Lladonosa Soler is a biochemist and is studying for a master’s degree in drug research, development, and control at the University of Barcelona, Spain.

Tamaryin Godinho is the executive editor of Science in School . She did her PhD in the field of molecular medicine and her master’s research on antibiotic development.

This article was inspired by the research of Mireia and Mariona, who developed and presented activities on antimicrobials at the 2019 Hands-On Science conference in Kharkiv, Ukraine.

Supporting materials

Worksheet 1

Worksheet 2

Worksheet 3

Extension activity

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• v.60(10); 2016 Oct

Experimental Induction of Bacterial Resistance to the Antimicrobial Peptide Tachyplesin I and Investigation of the Resistance Mechanisms

Tachyplesin I is a 17-amino-acid cationic antimicrobial peptide (AMP) with a typical cyclic antiparallel β-sheet structure that is a promising therapeutic for infections, tumors, and viruses. To date, no bacterial resistance to tachyplesin I has been reported. To explore the safety of tachyplesin I as an antibacterial drug for wide clinical application, we experimentally induced bacterial resistance to tachyplesin I by using two selection procedures and studied the preliminary resistance mechanisms. Aeromonas hydrophila XS91-4-1, Pseudomonas aeruginosa CGMCC1.2620, and Escherichia coli ATCC 25922 and F41 showed resistance to tachyplesin I under long-term selection pressure with continuously increasing concentrations of tachyplesin I. In addition, P. aeruginosa and E. coli exhibited resistance to tachyplesin I under UV mutagenesis selection conditions. Cell growth and colony morphology were slightly different between control strains and strains with induced resistance. Cross-resistance to tachyplesin I and antimicrobial agents (cefoperazone and amikacin) or other AMPs (pexiganan, tachyplesin III, and polyphemusin I) was observed in some resistant mutants. Previous studies showed that extracellular protease-mediated degradation of AMPs induced bacterial resistance to AMPs. Our results indicated that the resistance mechanism of P. aeruginosa was not entirely dependent on extracellular proteolytic degradation of tachyplesin I; however, tachyplesin I could induce increased proteolytic activity in P. aeruginosa . Most importantly, our findings raise serious concerns about the long-term risks associated with the development and clinical use of tachyplesin I.

INTRODUCTION

Antibiotic resistance in pathogenic bacteria and the emergence of superbacteria have attracted attention from health care workers worldwide. Antimicrobial peptides (AMPs) are promising candidates for development of novel alternative antibiotics. However, studies have indicated that some bacteria (especially human pathogens) are resistant to certain AMPs ( 1 , 2 ), and bacterial resistance to cationic AMPs can arise through long and continual selection in the laboratory ( 3 ). Furthermore, some evidence demonstrates that bacteria can evolve resistance to AMPs after extended clinical application ( 4 ). Bacterial resistance to the AMPs nisin, pexiganan, and colistin has arisen through their clinical use. There is also evidence that pathogen resistance to AMPs may produce resistance to other AMPs with the same source and similar action mechanisms ( 5 ), or even to those with different sources and different action mechanisms ( 6 , 7 ). Habets and Brockhurst previously reported that therapeutic use of pexiganan, representing a promising new class of AMPs, could drive the evolution of pathogens that are resistant to our own immunity peptides ( 6 ). Bacterial evolution of AMP resistance would greatly shorten and restrict the use of AMPs, so elucidation of the potential mechanisms of bacterial resistance to AMPs is required to inform the design of more effective drugs and to reduce the incidence of bacterial drug resistance.

Molecular mechanisms of bacterial resistance to AMPs have been studied mainly by whole-genome sequencing, transcriptome sequencing (RNA-seq), microarray analysis, two-dimensional protein gel electrophoresis, and gene knockout and overexpression studies. Previous studies indicated that Gram-negative pathogens exhibit several different AMP resistance mechanisms, including proteolytic degradation of AMPs, shielding of the bacterial surface, modification of the bacterial outer membrane, pumping of AMPs into or out of the cell, and downregulation of AMP expression ( 8 , 9 ).

Tachyplesin I, a cationic AMP with a disulfide-stabilized β-sheet conformation, was originally isolated from hemocytes of marine horseshoe crabs in 1988 ( 10 ). With potent and broad-spectrum activity against both Gram-positive and Gram-negative bacteria, tachyplesin I was a promising candidate for development of anti-infection, antitumor, and antivirus drugs. Previous studies showed that tachyplesin I can kill bacteria by permeabilizing the bacterial membrane and acting on intracellular targets in bacteria to inhibit DNA, RNA, and protein synthesis and enzyme activity ( 11 , – 13 ). Tachyplesin I acts similarly to magainin-2, MSI-78, and LL-37 by forming a toroidal transmembrane pore ( 14 ). Thus, tachyplesin I exhibits multiple effects and synergistic antibacterial mechanisms, and the development of X33-GAP-TPI yeast strains that express recombinant tachyplesin I at high levels has facilitated its use as a novel alternative antibiotic for wide use in pharmaceuticals and animal feed in the future ( 15 , 16 ).

There are few reports on bacterial resistance to tachyplesin I. We previously reported that neither nonsuccessive induction nor short-term successive induction (fewer than 20 serial passages) could induce resistance to tachyplesin I in Escherichia coli ATCC 25922 and F41 or Staphylococcus aureus ATCC 25923 ( 17 ), but we are unaware of any other studies in this area. Our results indicated that bacterial resistance to tachyplesin I was not easy to induce through short-term exposure, consistent with a previous report examining other AMPs ( 18 ). However, whether bacteria can evolve resistance to tachyplesin I under long-term continuous selection conditions remains unknown. A previous report on the mechanism of bacterial resistance to tachyplesin I indicated only that the Dlt and MprF cell surface proteins on Gram-positive bacteria ( S. aureus ) might mediate tachyplesin I resistance ( 19 ). Although no tachyplesin I-resistant strains have appeared in clinical use, whether bacteria (especially pathogenic bacteria) can evolve resistance to tachyplesin I with long-term clinical use remains unclear. To prevent repetition of the mistakes made with antibiotic use and to avoid or delay the evolution of bacterial resistance to tachyplesin I, it is imperative to understand whether bacteria can produce resistance under long-term selection pressure and to elucidate the underlying resistance mechanisms.

The goal of this study was to determine if tachyplesin I can induce resistance in bacteria after long-term exposure to tachyplesin I or UV mutagenesis. We employed both selection methods for Aeromonas hydrophila XS91-4-1, Pseudomonas aeruginosa CGMCC1.2620, and E. coli ATCC 25922 and F41, monitored bacterial resistance, characterized the stability, cross-resistance, cost of resistance, and ultrastructure of mutant strains, and investigated the potential role of extracellular proteases in the resistance mechanism. Our results provide a theoretical basis for the wide application of tachyplesin I and a preliminary analysis of bacterial resistance mechanisms.

MATERIALS AND METHODS

Microorganisms, media, and growth conditions..

E. coli ATCC 25922 was provided by the Microbial Culture Collection Center of Guangdong (GIMCC), China. P. aeruginosa CGMCC1.2620 was provided by the China General Microbiological Culture Collection Center (CGMCC). A. hydrophila XS91-4-1 was provided by Aihua Li of the Institute of Aquatic Biology, Chinese Academy of Sciences, China. Other strains were from the laboratory of the College of Life Science and Engineering, Henan University of Urban Construction, China. A. hydrophila XS91-4-1 was cultured on soybean-casein digest agar medium (Trypticase soy agar [TSA]) and in Trypticase soy broth (TSB) at 28°C; the other bacteria were cultured in Mueller-Hinton broth (MHB) and on nutrient agar plates at 37°C.

Antibacterial agents.

The AMPs used in this study were tachyplesin I, tachyplesin III, polyphemusin I, and pexiganan. These AMPs (>95% purity) were all synthesized by Gil Biochemical Co., Ltd. (Shanghai, China), and the peptide sequences were as follows: tachyplesin I, NH 2 -K-W-C-F-R-V-C-Y-R-G-I-C-Y-R-R-C-R-CONH 2 , including two disulfide bonds (C-3—C-16 and C-7—C-12) ( 10 ); pexiganan, G-I-G-K-F-L-K-K-A-K-K-F-G-K-A-F-V-K-I-L-K-K-NH 2 ( 20 ); tachyplesin III, K-W-C-F-R-V-C-Y-R-G-I-C-Y-R-K-C-R-NH 2 ( 21 ); and polyphemusin I, R-R-W-C-F-R-V-C-Y-R-G-F-C-Y-R-K-C-R-NH 2 ( 22 ). These AMPs were dissolved in sterile water to yield 10-mg/ml stock solutions, which were filter sterilized before use. Peptide solutions were prepared fresh on the day of the assay or stored at −20°C for a short period.

Antibacterial drug-sensitive papers were provided by Hangzhou Microbial Reagent Co., Ltd. (China).

MIC determination.

The MIC was determined using a standard broth microdilution method for AMPs as described previously by the Clinical and Laboratory Standards Institute (CLSI). The MIC was determined as the lowest concentration for which no visible growth was observed. Briefly, cultured cells in the log phase were diluted to 2 × 10 5 to 4 × 10 5 cells/ml. The inoculum (100 μl) was added to each well of 96-well plates. Peptide samples diluted with fresh broth (100 μl) were added to each well, and the plates were incubated at 37°C or 28°C for 20 h. Experiments were performed in triplicate.

In vitro resistance study. (i) Long-term exposure to tachyplesin I.

Bacteria were cultured in MHB with constant shaking at 160 rpm at 37°C ( E. coli strains and P. aeruginosa ) or 28°C ( A. hydrophila ). We transferred bacteria daily by inoculating 20 μl of stationary-phase culture into 2 ml of MHB. All cells were initially grown in medium without tachyplesin I for 5 transfers. At transfer 6, 20 μl of cell suspension was added to 2 ml of nutrient broth, with or without tachyplesin I at a final concentration of half the MIC, for 20 h with shaking at 160 rpm, for 15 transfers. The regrown bacteria were thereafter transferred to broth containing a double concentration of tachyplesin I every 10 transfers, or more frequently if the selection strains showed weak growth. The experiment was conducted for 69 to 96 serial transfers. Experiments were performed in duplicate.

Every time we increased the concentration of tachyplesin I, a sample of the induction generation was inoculated into 20% (vol/vol) glycerol and stored at −70°C. The MIC value of tachyplesin I for each induced bacterial culture was determined as described above.

(ii) UV mutagenesis selection.

Log-phase cells (10 7 cells/ml) were collected and added to sterile petri dishes with a magnetic stir bar. E. coli ATCC 25922 and P. aeruginosa CGMCC1.2620 cells were mutated for 60 s and 90 s, respectively, by use of a UV lamp (irradiation distance, 28 cm; 18 W). After UV treatment, the bacteria were plated on nutrient agarose plates with high concentrations of tachyplesin I and incubated at 37°C for 20 h. A single colony was selected and cultured in nutrient broth overnight at 37°C. The cultured cells were used to determine the MIC of tachyplesin I for each selection strain.

In vitro susceptibility testing.

We assayed the resistance induced by measuring the MIC of tachyplesin I for each selection strain as described above. Based on a previous report ( 23 ), we identified bacterial resistance to AMPs as a significant increase in the MIC for mutant strains compared to that for control strains.

Assay of the stability of resistance.

To test the stability of resistance, 10 μl of a resistant strain was cultured in 2 ml of nutrient broth medium without tachyplesin I for 5 or 10 continuous passages of 20 h each. The MIC value for each passage strain was determined.

Cross-resistance assay.

The antimicrobial susceptibilities of resistant bacteria to several antimicrobial agents were determined by the K-B disc diffusion method. The inhibitory zone diameter represents the bacterial susceptibility to the antimicrobial agent. Each experiment was performed in triplicate. The K-B disc diffusion method was performed according to the criteria for the sensitivity and drug resistance of antimicrobial agents established by CLSI guidelines.

The susceptibilities of resistant mutants to synthetic tachyplesin III, polyphemusin I, and pexiganan AMPs were determined by MIC assays. The MIC values of these peptides were determined as described above.

Cost of resistance.

To evaluate whether resistance altered the physiology of the isolates, we estimated the maximum absorbance, the maximum growth rate, and the lag phase for all isolates of resistant strains and control strains in unsupplemented MHB and in MHB containing tachyplesin I at half the MIC for control strains. We measured the optical density at 600 nm (OD 600 ) on a Spectramax M 2 model microplate reader (PerkinElmer Instruments Co., Ltd., Shanghai, China) every hour.

Electron microscopy.

Electron microscopy was performed on the evolved and control E. coli ATCC 25922 strains. The samples were prepared for transmission electron microscopy (TEM) as described previously ( 13 ). The bacteria were exposed to 20 μg/ml tachyplesin I for 60 min at 37°C.

Protease assay.

Extracellular protease activity of the bacteria was determined using an agar plate assay as previously described ( 24 ), with minimal modifications. The test agar contained 1% skim milk, 4% glucose, and 3.0% agar powder. Cultures were grown at 37°C for 12 or 24 h, and then 10 ml of culture was filtered through 0.22-μm-pore-size filters. Next, 60 μl filtrate was loaded into holes in the agar plates, and plates were incubated at 37°C for 20 to 24 h.

Zymographic assay.

Zymographic assays were performed as previously described ( 24 ), with minimal modifications. SDS-polyacrylamide gels (12%) were copolymerized with skim milk at a final concentration of 1%. After electrophoresis, SDS was removed by washing the gel twice with 50 mM Tris-HCl (pH 7.8) and 2.5% (vol/vol) Triton X-100 for 30 min, followed by overnight incubation at 37°C in a buffer containing 50 mM Tris-HCl, 5 mM CaCl 2 , pH 7.8, 0.1% (vol/vol) Triton X-100, and 0.02% NaN 3 . Gels were stained for 1 h at room temperature with 0.1% Coomassie brilliant blue R-250 in 10% acetic acid and then destained with 10% acetic acid until clear bands over a dark background were observed.

Degradation assay.

Peptide samples (40 μg/ml) in 20 mM Tris-HCl and 1 mM CaCl 2 (pH 7.8) were mixed with an equal volume of culture filtrate. The culture filtrate samples were prepared using the methods described above. The mixtures were incubated at 37°C for 5 h, and degradation was analyzed as follows. (i) Degradation was analyzed by use of 16.5% Tricine-SDS-polyacrylamide gels. Based on the determined molecular masses of peptide degradation products, the protease cleavage sites in the peptides were determined. (ii) Degradation was analyzed by determining the antibacterial activity of tachyplesin I pretreated with culture filtrates of P. aeruginosa by using the broth microdilution method.

Protease expression assay.

After bacterial cultures ( P. aeruginosa , A. hydrophila , or E. coli ) were grown for 3 h, tachyplesin I was added to a final concentration of 20 μg/ml. After continued growth for 10 h or 15 h, the supernatants were collected and filtered, and proteolytic activity and protease expression were analyzed using the methods described above.

Selection of resistance to tachyplesin I.

To explore the development of resistance to tachyplesin I in vitro , two selection procedures were performed, as follows.

(i) Selection by exposure to increasing tachyplesin I concentrations.

Resistance to tachyplesin I developed after 69 serial transfers only in A. hydrophila XS91-4-1 under long-term selection by exposure to increasing concentrations of tachyplesin I. The other strains were successively exposed to increasing concentrations of tachyplesin I for no fewer than 80 serial transfers under the same conditions. There were marked differences in the MIC values for E. coli F41 and P. aeruginosa between the control induction strains and the corresponding treatment induction strains by transfer 80 (from 10 μg/ml to >160 μg/ml), whereas high resistance to tachyplesin I in E. coli ATCC 25922 was produced only after 85 serial transfers ( Table 1 ). The results showed that tachyplesin I-resistant mutants of A. hydrophila , E. coli F41, E. coli ATCC 25922, and P. aeruginosa were produced after long-term continuous passages.

Induction of resistance to tachyplesin I a

E. coli ATCC 25922 and P. aeruginosa CGMCC1.2620 mutagenized strains grown at high tachyplesin I concentrations were randomly selected to determine the MIC of tachyplesin I. We isolated nine P. aeruginosa strains and one E. coli strain with resistance to tachyplesin I. The MIC values for these mutants were 4-fold higher than those for their original strains ( Table 2 ).

Comparison of tachyplesin I MICs before and after bacterial UV induction

Stability of mutants with induced resistance.

After five serial passages, the MIC values for the resistant strains were unchanged; however, after 10 passages, the MIC values for the resistant strains gradually decreased, except in the case of resistant A. hydrophila strains. The MIC values for the E. coli -96, P. aeruginosa -80, and F41-80 (naming based on the species or strain name and the transfer generation) resistant strains were from >160 µg/ml to 20 µg/ml after six to nine serial passages.

Resistant strains were tested for cross-resistance to several antimicrobial agents by the disc diffusion method ( Table 3 ) and to other AMPs by measurement of the MIC ( Table 4 ). Antimicrobial susceptibilities to several antimicrobial agents differed somewhat based on the selection method. No cross-resistance to cefoperazone or amikacin was observed in the UV-mutagenized P. aeruginosa and E. coli resistant strains, whereas cross-resistance was observed in the corresponding strains with evolution-induced resistance. Cross-resistance to ofloxacin was observed only in the UV-mutagenized E. coli strain. However, compared to that of the P. aeruginosa -80 resistant strain, the antimicrobial susceptibilities of the P. aeruginosa UV-1 and UV-7 (naming based on the induction method and the assigned strain number) resistant strains to polymyxin B were lower.

Antimicrobial susceptibility determination for induction strains by the disc diffusion method a

MICs of other AMPs for induction strains

High-level cross-resistance to pexiganan was observed in the P. aeruginosa -80 and UV-7 strains, the E. coli strains with UV-induced resistance, and the A. hydrophila -69 resistant strain. No cross-resistance to tachyplesin III or polyphemusin I was observed in E. coli ATCC 25922 before or after induction. High-level cross-resistance to tachyplesin III was observed in UV-mutagenized E. coli strains, the E. coli F41 resistant strain, and the P. aeruginosa resistant strains, whereas high-level cross-resistance to polyphemusin I was observed only in UV-mutagenized E. coli resistant strains and the E. coli F41 resistant strain. High-level cross-resistance between tachyplesin I and tachyplesin III was observed in E. coli F41 and P. aeruginosa strains. P. aeruginosa and A. hydrophila strains were insensitive to polyphemusin I before and after the induction.

An important aspect of bacterial resistance to AMPs is whether the acquisition of resistance affects the growth potential of the bacterium. The acquisition of tachyplesin I resistance did not markedly alter either the maximum growth rate or the maximum population density reached in vitro in either the presence or absence of tachyplesin I. However, tachyplesin I resistance in E. coli F41 or P. aeruginosa CGMCC1.2620 was associated with a longer lag phase in the absence of tachyplesin I and a shorter lag phase in the presence of tachyplesin I than those with the corresponding control strains ( Table 5 ). In contrast, the acquisition of tachyplesin I resistance in E. coli ATCC 25922 or A. hydrophila was associated with a shorter lag phase only in the presence of tachyplesin I compared with the corresponding control strains.

Growth of control and resistant strains in unsupplemented medium and in medium containing tachyplesin I a

Ultrastructural changes.

TEM was used to observe morphological and ultrastructural changes in E. coli ATCC 25922 before and after resistance induction. Significant differences in morphology were observed between the E. coli ATCC 25922 original and induction strains. The ultrastructural examination of the original strain revealed intact cell membranes without any noticeable damage and dense cellular cytoplasmic contents ( Fig. 1A ). In contrast, the E. coli ATCC 25922-96 resistant strain exhibited a cytoplasmic membrane structure that was markedly segregated and shrunken, many cytoplasmic vacuoles, and damage throughout the whole cell body ( Fig. 1B ). Thereafter, the morphological structures of both bacteria were observed after treatment with 20 μg/ml tachyplesin I for 60 min. Fibers extending from the cell surface, vacuum formation, cell deformation, and cytoplasmic membrane disruption were clearly observed in both the original strain and the E. coli ATCC 25922-96 resistant strain ( Fig. 1C and ​ andD). D ). The results show that tachyplesin I killed the original strain and the resistant strain of E. coli ATCC 25922 in similar ways.

TEM photographs of the original and induction strains of E. coli ATCC 25922 treated with tachyplesin I. (A) E. coli ATCC 25922 original strain. (B) E. coli ATCC 25922-96 resistant strain. (C) E. coli ATCC 25922 treated with tachyplesin I (20 μg/ml, 60 min). (D) E. coli ATCC 25922-96 resistant strain treated with tachyplesin I (20 μg/ml, 60 min).

Extracellular protease activity and tachyplesin I-induced proteolytic activity in P. aeruginosa .

To explore the action of extracellular proteases on tachyplesin I and the effect on P. aeruginosa resistance to tachyplesin I, extracellular protease activity was determined by a skim milk agar plate assay ( Fig. 2 ). Extracellular protease activity was greater in the P. aeruginosa -80 resistant strain than in the P. aeruginosa -80 control strain. The total extracellular proteolytic activities for the P. aeruginosa -80 strains differed somewhat between 12 h and 24 h. No obvious extracellular protease activity was observed for the E. coli F41-80 and E. coli -96 strains (data not shown) or the P. aeruginosa UV-2, UV-4, and UV-6 strains; only the P. aeruginosa UV-3 strain exhibited protease activity after 24 h.

Extracellular protease activities determined by skim milk agar plate assay of E. coli and P. aeruginosa induction strains. Culture filtrates of 12- or 24-h bacterial cultures were pipetted into holes made in skim milk agar plates. Proteolysis can be seen by dark halos around the holes, which appear black. Hole numbers for P. aeruginosa : 1, blank; 2, P. aeruginosa -80 control strain; 3, P. aeruginosa -80 resistant strain. Hole numbers for UV-induced P. aeruginosa resistant strains: 1, blank; 2 to 5, P. aeruginosa UV-2, -3, -4, and -6 resistant strains.

Thereafter, we observed increased proteolytic activity after addition of tachyplesin I to cultures of the original P. aeruginosa and A. hydrophila strains, which confirmed that upregulation of extracellular proteolytic activity might be an adaptive response of A. hydrophila or P. aeruginosa to the presence of tachyplesin. In contrast, no extracellular proteolytic activity expression was observed for E. coli ( Fig. 3 ), suggesting different mechanisms of tachyplesin I-induced increases in proteolytic activity for different strains.

Tachyplesin I induces increased extracellular proteolytic activity in bacteria. Concentrated culture filtrates of cultures grown with and without 20 μg/ml tachyplesin I for different times were pipetted into holes in skim milk agar plates and incubated. The assays were performed in triplicate. Clearing zone data represent clearing zone diameters minus the 0.2-cm hole diameter. All data represent means and standard deviations.

Zymographic analysis of the activities of extracellular proteases of the P. aeruginosa induction strains indicated that the difference was due mainly to the differential activity of >31-kDa proteases ( Fig. 4A and ​ andB). B ). The nature of these proteases was unknown.

Zymographic analysis of extracellular proteases in P. aeruginosa induction strains. Culture filtrates (20 μl) were analyzed in SDS-PAGE gels copolymerized with skim milk. After electrophoresis, gels were washed, incubated, and counterstained. (A) Culture filtrates of P. aeruginosa -80 resistant and control strains after different times. Lanes: 1, control strain (12 h); 2, resistant strain (12 h); 3, control strain (24 h); 4, resistant strain (24 h). Images for 12 h and 24 h were taken from different areas of the same gel. (B) Culture filtrates of P. aeruginosa resistant strains obtained by UV mutagenesis. Lanes: 1 to 4, P. aeruginosa UV-2, -3, -4, and -6 resistant strains (12 h); 5 to 8, P. aeruginosa UV-2, -3, -4, and -6 resistant strains (24 h).

A P. aeruginosa mutant regulates degradation of tachyplesin I.

To analyze whether the differential activity of extracellular proteases observed in P. aeruginosa induction strains led to differential degradation, we incubated tachyplesin I with concentrated culture filtrates and analyzed degradation in SDS-polyacrylamide gels ( Fig. 5A ). The culture filtrates of the P. aeruginosa -80 resistant and control strains showed limited degradation of tachyplesin I, and there were no differences in tachyplesin I degradation between the P. aeruginosa -80 resistant and control strains. In addition, tachyplesin I was significantly degraded by the P. aeruginosa UV-3 mutant after 12 h and 24 h ( Fig. 5B ) and in the P. aeruginosa UV-4 mutant after 24 h. No significant degradation of tachyplesin I was observed for the P. aeruginosa UV-2 or UV-6 strain.

Resistant strains of P. aeruginosa regulate degradation of tachyplesin I (TacI). Equal amounts of peptide were incubated with culture filtrates of the control strain and the P. aeruginosa -80 and UV-induced resistant strains for 5 h and then analyzed by SDS-PAGE. (A) Culture filtrates of P. aeruginosa -80 resistant and control strains regulate degradation of TacI for different times. Lanes: 1 and 2, control strain and control strain plus TacI (12 h); 3 and 4, resistant strain and resistant strain plus TacI (12 h); 5 and 6, control strain and control strain plus TacI (24 h); 7 and 8, resistant strain and resistant strain plus TacI (24 h). (B) Culture filtrates of UV-induced P. aeruginosa resistant strains regulate degradation of TacI for different times. Lanes: 1 to 4, UV-2 plus TacI, UV-3 plus TacI, UV-4 plus TacI, and UV-6 plus TacI (12 h); 5 to 8, UV-2 plus TacI, UV-3 plus TacI, UV-4 plus TacI, and UV-6 plus TacI (24 h).

Thereafter, we assayed the effect of tachyplesin I pretreatment with culture filtrates on antimicrobial activity. The results showed that the P. aeruginosa -80 resistant and control strains had no observable effect ( Table 6 ), whereas the P. aeruginosa UV-2 to UV-4 and UV-6 strains reduced the antibacterial activity of tachyplesin I to some extent after 12 h ( Table 7 ).

Effects of tachyplesin I treatment with culture filtrates of P. aeruginosa -80 induction strain for 5 h on inhibition rate

Effects of tachyplesin I treatment with culture filtrates of P. aeruginosa UV induction strains for 5 h on inhibition rate

The Gram-negative bacteria P. aeruginosa and E. coli are both important opportunistic pathogens of concern in the health care system, where they are frequent nosocomial pathogenic microorganisms. The pathogenic bacteria E. coli F41 and A. hydrophila XS91-4-1 were isolated from cases of porcine intestinal diarrhea and silver carp hemorrhagic septicemia, respectively. Understanding the different sources of tachyplesin I tolerance in the four Gram-negative bacteria studied herein has important implications for understanding possible AMP resistance mechanisms.

Our results demonstrate that tachyplesin I resistance can be induced in bacteria under conditions of long-term (69 to 85 serial passages) continuous increases in the tachyplesin I concentration. These results are consistent with previous reports that resistance may evolve from consistent long-term exposure to increasing levels of peptides ( 3 ) and synthetic AMP analogues, e.g., α-peptide/β-peptoid peptidomimetics ( 25 ). However, they contradict our previous report indicating no tachyplesin I-resistant mutants in bacteria exposed to fewer than 20 serial passages with higher-concentration tachyplesin I induction ( 17 ), possibly because of differences in induction time and induction method. The resistance observed using the long-term continuous induction method evolves more slowly than that obtained using the UV mutagenesis method. Bacterial resistance to tachyplesin I is slowly inducible and relatively stable.

For the first time, to our knowledge, we provide evidence that evolved resistance to a synthetic tachyplesin I peptide affects bacterial susceptibility to other AMPs and antibacterial agents, producing cross-resistance to the therapeutic peptide pexiganan and the closely related AMPs polyphemusin I and tachyplesin III. This finding raises serious concerns about the long-term risks associated with the development and use of tachyplesin I. However, the mechanism(s) for cross-resistance to the conventional antimicrobial agents cefoperazone and amikacin in evolved strains remains unclear. Based on previous studies, we speculate that tachyplesin I induced changes in aminoglycoside-modifying enzymes and β-lactamase function or regulation in resistant bacteria, which resulted in cross-resistance to amikacin and cefoperazone, respectively.

For resistant strains, we observed different growth features between the induction strains and the corresponding control strains in the same medium. For example, the disappearance of the metallic luster of the E. coli ATCC 25922 resistant strains grown on eosin methylene blue agar medium might be associated with changes in the charge of the cell membrane. The biological characteristics of P. aeruginosa resistant strains were also different, likely due to different metabolic and/or genetic changes in the resistant strains. Moreover, our resistant strains displayed a substantial cost of resistance, mainly in the form of a much longer lag phase in the absence of tachyplesin I for E. coli F41 and P. aeruginosa CGMCC1.2620, whereas no markedly longer lag phase was observed for E. coli ATCC 25922 and A. hydrophila . There were some differences among different resistant strains, which implies that the use of tachyplesin I might result in the spread and medical threat of partially resistant organisms. The results are consistent with previous reports that resistant bacteria took longer to start reproducing than control bacteria in the absence of medium, although their replication rate was unaffected once replication was initiated ( 3 ). However, Hein-Kristensen et al. reported that resistant strains obtained from different lineages have different growth rates (the same or significant decreases) in the absence of peptide compared to wild-type strains ( 25 ). In short, AMP resistance might compromise bacteria in other ways, for example, by reducing their growth rate or causing a longer lag phase.

Some bacteria evade host defense peptides by employing protease-mediated degradation. P. aeruginosa , Enterococcus faecalis , and S. aureus express extracellular proteases capable of degrading and inactivating LL-37 ( 24 , 26 ). Some peptides, such as lactoferrin and HNP-1 and -2, are also degraded and inactivated in the presence of bacterial and host proteases ( 1 ). However, whether the mechanisms of resistance to tachyplesin I are related to degradation and inactivation by extracellular proteases is poorly understood. Our study showed that the tachyplesin I-resistant P. aeruginosa -80 strain exhibited increased levels of extracellular proteolytic activity; however, the increased activity of proteases did not increase tachyplesin I degradation or reduce the antimicrobial inhibition rate relative to that of control strains, which suggests that the P. aeruginosa -80 strain resistance to tachyplesin I is independent of extracellular proteolytic activity. In contrast, protease activity from the UV-3 mutant markedly degraded tachyplesin I and reduced the antimicrobial activity, suggesting that protease activity had a much greater influence on resistance against tachyplesin I than did the P. aeruginosa -80 evolved strain. Furthermore, the UV-2, -4, and -6 resistant mutants also reduce the antimicrobial activity of tachyplesin I relative to that for control strains, which implies that UV-induced mutants might secrete other material that inactivates tachyplesin I. This difference might arise from differential metabolic and/or genetic changes in the evolved-resistance strains and UV-induced mutants. P. aeruginosa resistance to tachyplesin I might involve other resistance mechanisms. Bacterial resistance to AMPs independently of protease production was also reported for Porphyromonas gingivalis . Some reports showed that Porphyromonas gingivalis was resistant to AMPs of human and nonhuman origins. Porphyromonas gingivalis is known to hydrolyze AMPs mainly by expressing robust proteolytic activity ( 27 ). However, Ouhara et al. suggested that Porphyromonas gingivalis resistance to d -enantiomer peptides was independent of its proteolytic capacity ( 27 , 28 ). Thus, the mechanism of bacterial resistance to tachyplesin I requires further research.

In summary, we demonstrate that long-term continuous exposure to high concentrations of tachyplesin I can induce stably resistant Gram-negative bacterial strains with cross-resistance to other antimicrobial peptides and conventional antimicrobial agents. Furthermore, our results suggest the potential involvement of extracellular proteases in mediating this resistance but imply the likely existence of additional resistance mechanisms. We believe that this contribution is theoretically and practically relevant because AMPs are currently being developed for clinical use, and thus investigations of their safety and potential to induce bacterial resistance are necessary. Our results raise serious concerns about the long-term risks associated with the development and clinical use of tachyplesin I. Further experiments are under way to investigate the mechanism of bacterial resistance to tachyplesin I, and these studies may identify possible targets for new antimicrobial agents.

ACKNOWLEDGMENTS

All authors gave approval for the final version of the manuscript.

We declare no competing financial interests.

• Classroom Practicals

Exploring Antibiotic Resistance

AUSTRALIAN CURRICULUM ALIGNMENT: 

Infectious disease differs from other diseases (for example, genetic and lifestyle diseases) in that it is caused by invasion by a pathogen and can be transmitted from one host to another (ACSBL116) 

• Pathogens include prions, viruses, bacteria, fungi, protists and parasites (ACSBL117) 
• Pathogens have adaptations that facilitate their entry into cells and tissues and their transmission between hosts; transmission occurs by various mechanisms including through direct contact, contact with body fluids, and via contaminated food, water or disease­-specific vectors (ACSBL118)  

BACKGROUND: 

The discovery of Penicillin was a major game-changer for the human race in the twentieth century. With the sudden discovery of antibiotics, we gained a weapon against an invisible enemy. Since that first discovery of Penicillin, many other antibiotics have been developed for use against the broad range of pathogenic microbes; each with their strengths and weaknesses. Unfortunately, this weapon has become blunted from overuse in the past decades; leading to antibiotic resistance.

In this practical, students will investigate a selection of antibiotics and observe the efficacy of each against E. coli. Students will gain experience in aseptic technique. Furthermore, students will have the opportunity to understand susceptibility, resistance and inhibition as the words relate to microbes and antimicrobials. They will also gain an understanding that antibiotics are not universal - one that works against one species of bacteria may not work against another. 

PREPARATION- BY LAB TECHNICIAN: 

• Aliquot the bacteria into the appropriate number of sterile vials.
• Using sterile petri dishes or vials, separate the discs by antibiotic type. Label them for easy identification. 
• Distribute the bacteria and antibiotic discs to each student or group to avoid contamination between groups.

METHOD- STUDENT ACTIVITY:

• Use aseptic technique - wipe your bench down with 70% Alcohol and keep your work near the Bunsen burner to take advantage of the updraught the flame will create to waft potential contaminants away from your materials.
• On the bottom of one of your agar plates, use a marker to divide it into four segments. Label the segments A, B, C and P. Remember to put your name and date on the plate.
• The other plate will be your control. Name and date this plate also. 
• Using your sterile pipette, aliquot about two drops of the bacterial broth onto each of your plates and use the spreader to cover the plates evenly. If you are using a glass spreader, pass it through the flame of the Bunsen burner before each use.
• Wait 10-15 minutes before placing the discs on the plate to ensure bacteria has a chance to dry. 
• Flame your forceps and pick up one of the antibiotic discs. 
• Place it in approximately the middle of the appropriate segment of your divided plate and push (very gently) with the forceps to help it stay in place. 
• Repeat for each disc, flaming the forceps between each disc.
• Your plates will be incubated for 24 hours at 37°C, upside down so that the agar is at the top. 
• Measure the diameter of any zones of inhibition.

OBSERVATION AND RESULTS

After a period of 24 hours, the control plate should have an even lawn of bacteria over the surface, while the experiment plate should show a lawn over most of the plate except for rings around the Ampicillin and Chloramphenicol discs (the zones of inhibition). Included below is a guide of the expected results and diameters.

TEACHER'S NOTES

Ensure your students have a good grounding in aseptic technique. If the vial of bacteria is to be shared around the class rather than aliquoted out, it is particularly important that they each use a fresh sterile pipette for each dip into the culture to reduce the chances that the last groups experiment will be contaminated. Once the plates are inoculated, the lids may be taped down as there will be no need for them to be opened after that point until it is time to sterilise everything at the end.

INVESTIGATIONS

• Challenge students to explain the function of the control plate in the experiment. Students may be able to recognise that the control plate would come into play should there be no bacterial growth at all on the experiment plate. A lack of growth on the control plate signals a problem with the bacteria, or an error in inoculation or incubation. No growth on the control plate may indicate the antibiotics were too effective. This can occur when the discs are placed on a very wet plate. In this case, the antibiotics impregnated in the disc may diffuse too far across the plate.
• Ask students to explain why the glass spreaders were passed through the flame of the Bunsen burner before each use. They should be able to identify that the flame should destroy any contamination that may otherwise be transferred to the plate.
• Lead a class discussion to explore the significance of a zone of inhibition and which antibiotic used in the experiment had the greatest zone of inhibition. Students should gain an understanding that the antibiotics diffuse from the discs onto the agar, creating a gradient of concentration highest near the disc and decreasing as the radius expands. The point at which the concentration is too low to inhibit the bacteria will be shown after incubation as the diameter of the zone of inhibition. Where there is no growth in this circle, the bacterium is “susceptible” to the antibiotic, and becomes “resistant” at lower concentrations. If there is no zone of inhibition, then the bacterium is considered resistant to that antibiotic, while a small zone may indicate minimal or moderate susceptibility.

EXTENSION EXERCISE 

Teacher tip.

Swabs can be used instead of pipette and spreader to create the microbial lawn; although it is more difficult to get the plate evenly covered. It can be a great way to make your bacterial culture go further if you are on a budget, as this technique generally uses less of the bacteria.

Time Requirements

• 45 mins  

Material List

• 20 Nutrient Agar Plates
• 20 Sterile Pipettes
• 20 Disposable Spreaders
• 50 Penicillin Discs
• 50 Ampicillin Discs
• 50 Chloramphenicol Discs
• 50 Blank Discs
• 70% Alcohol

 Safety Requirements

• Bacteria from Southern Biological are all risk group 1, so they should pose no threat to the students; however, it is best practice to assume that contamination with a pathogen may occur and take precautions accordingly.
• Sterilise all work surfaces with 70% Alcohol before and after the experiment.  
• Wear appropriate personal protective equipment (PPE). 
• Know and follow all regulatory guidelines for the disposal of laboratory wastes. 
• Work near the Bunsen burner to take advantage of the updraught of the flame to waft potential contaminants away from your materials.

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New Technology Could Lead to Alternative Treatments for Antibiotic-Resistant Bacteria

A team of scientists—including Kate Crawford, seen here—developed a technology that lets them edit the genomes of bacteria-fighting viruses, known as phages, in a streamlined and highly effective way.

As antibiotic resistance becomes an increasingly serious threat to our health, the scientific and medical communities are searching for new medicines to fight infections. Researchers at Gladstone Institutes have just moved closer to that goal with a novel technique for harnessing the power of bacteriophages.

Bacteriophages, or phages for short, are viruses that naturally take over and kill bacteria. Thousands of phages exist, but using them as treatments to fight specific bacteria has so far proven to be challenging. To optimize phage therapy and make it scalable to human disease, scientists need ways to engineer phages into efficient bacteria-killing machines. This would also offer an alternative way to treat bacterial infections that are resistant to standard antibiotics.

Now, Gladstone scientists have developed a technology that lets them edit the genomes of phages in a streamlined and highly effective way, giving them the ability to engineer new phages and study how the viruses can be used to target specific bacteria.

Seth Shipman (right) and members of his lab, including Kate Crawford (left) and Darshini Poola (center), found a way precisely engineer phage genomes. Such engineered phages could be used to kill antibiotic-resistant bacteria, for example.

“Ultimately, if we want to use phages to save the lives of people with infections that are resistant to multiple drugs, we need a way to make and test lots of phage variants to find the best ones,” says Gladstone Associate Investigator Seth Shipman, PhD, the lead author of a study published in Nature Biotechnology . “This new technique lets us successfully and rapidly introduce different edits to the phage genome so we can create numerous variants.”

The new approach relies on molecules called retrons, which originate from bacterial immune systems and act like DNA-production factories inside bacterial cells. Shipman’s team has found ways to program retrons so they make copies of a desired DNA sequence. When phages infect a bacterial colony containing retrons, using the technique described in the team’s new study, the phages integrate the retron-produced DNA sequences into their own genomes.

The Enemy of Your Enemy

Unlike antibiotics, which broadly kill many types of bacteria at once, phages are highly specific for individual strains of bacteria. As rates of antibiotic-resistant bacterial infections rise—with an estimated 2.8 million such infections in the United States each year—researchers are increasingly looking at the potential of phage therapy as an alternative to combat these infections.

“They say that the enemy of your enemy is your friend,” says Shipman, who is also an associate professor in the Department of Bioengineering and Therapeutic Sciences at UCSF, as well as a Chan Zuckerberg Biohub Investigator. “Our enemies are these pathogenic bacteria, and their enemies are phages.”

Already, phages have been successfully used in the clinic to treat a small number of patients with life-threatening antibiotic-resistant infections, but developing the therapies has been complex, time-consuming, and difficult to replicate at scale. Doctors must screen collections of naturally-occurring phages to test whether any could work against the specific bacteria isolated from an individual patient.

“Making multiple edits in phages is something that was previously incredibly hard to do; so much so that, most of the time, scientists simply didn’t do it.”

Seth Shipman, PhD

Shipman’s group wanted to find a way to modify phage genomes to create larger collections of phages that can be screened for therapeutic use, as well as to collect data on what makes some phages more effective or what makes them more or less specific to bacterial targets.

“As the natural predators of bacteria, phages play an important role in shaping microbial communities,” says Chloe Fishman, a former research associate at Gladstone and co-first author of the new study, now pursuing her graduate degree at Rockefeller University. “It’s important to have tools to modify their genomes in order to better study them. It’s also important if we want to engineer them so that we can shape microbial communities to our benefit—to kill antibiotic-resistant bacteria, for example.”

Continuous Phage Editing

To precisely engineer phage genomes, the scientists turned to retrons. In recent years, Shipman and his group pioneered the development and use of retrons to edit the DNA of human cells, yeast, and other organisms.

Shipman and his colleagues began by creating retrons that produce DNA sequences specifically designed to edit invading phages—a system the team dubbed “recombitrons.” Then, they put those retrons into colonies of bacteria. Finally, they let phages infect the bacterial colonies. As the phages infected bacteria after bacteria, they continuously acquired and integrated the new DNA from the recombitrons, editing their own genome as they went along.

The research team showed that the longer they let phages infect a recombitron-containing bacterial colony, the greater the number of phage genomes were edited. Moreover, the researchers could program different bacteria within the colony with different recombitrons, and the phages would acquire multiple edits as they infected the colony.

“As a phage is bouncing from bacterium to bacterium, it picks up different edits,” says Shipman. “Making multiple edits in phages is something that was previously incredibly hard to do; so much so that, most of the time, scientists simply didn’t do it. Now, you basically throw some phages into these cultures, wait a while, and get your multiple-edited phages.”

A Platform to Screen Phages

If scientists already knew exactly what edits they wanted to make to a given phage to optimize its therapeutic potential, the new platform would let them easily and effectively carry out those edits. However, before researchers can predict the consequence of a genetic change, they first need to better understand what makes phages work and how variations to their genomes impact their effectiveness. The recombitron system helps makes progress here, too.

Shipman and his team are working to create large collections of phages that can be screened for therapeutic use and could eventually help predict the effectiveness of a phage at fighting a specific bacterial infection.

If multiple recombitrons are put into a bacterial colony, and phages are allowed to infect the colony for only a short time, different phages will acquire different combinations of edits. Such diverse collections of phages could then be compared.

“Scientists now have a way to edit multiple genes at once if they want to study how these genes interact or introduce modifications that could make the phage a more potent bacterial killer,” says Kate Crawford, a graduate student in the Shipman lab and co-first author of the new study.

Shipman’s team is working on increasing the number of different recombitrons that can be put into a single bacterial colony—and then passed along to phages. They expect that eventually, millions of combinations of edits could be introduced to phages to make huge screening libraries.

“We want to scale this high enough, with enough phage variants, that we can start to predict which phage variants will work against what bacterial infections,” says Shipman.

Julie Langelier Associate Director, Communications 415.734.5000 Email

About the Study

The paper “Continuous Multiplexed Phage Genome Editing Using Recombitrons” was published in the journal Nature Biotechnology on September 5, 2024. The authors are: Chloe B. Fishman, Kate D. Crawford, Santi Bhattarai-Kline, Darshini Poola, Karen Zhang, Alejandro González-Delgado, Matías Rojas-Montero, and Seth Shipman of Gladstone.

The work was supported by the National Science Foundation (MCB 2137692), the National Institute of Biomedical Imaging and Bioengineering (R21EB031393), the Gary and Eileen Morgenthaler Fund, and the National Institute of General Medical Sciences (1DP2GM140917), as well as by the L.K. Whittier Foundation and the Pew Biomedical Scholars Program.

About Gladstone Institutes

Gladstone Institutes is an independent, nonprofit life science research organization that uses visionary science and technology to overcome disease. Established in 1979, it is located in the epicenter of biomedical and technological innovation, in the Mission Bay neighborhood of San Francisco. Gladstone has created a research model that disrupts how science is done, funds big ideas, and attracts the brightest minds.

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How To Grow Bacteria and More

If learning how to grow bacteria in a petri dish interests you, read on.

How Can Bacteria Help Us? How Can Bacteria Harm Us? What Are Antibacterial Agents? Experiment #1: Cheek Cell Swab Experiment #2: Testing Antibacterial Agents Experiment #3: Soap Survey Experiment #4: Bacteria in the Air Experiment #5: Homemade Yogurt More Experiment Ideas

Bacteria Overview

Bacteria are one-celled, or unicellular, microorganisms . They are different from plant and animal cells because they don’t have a distinct, membrane-enclosed nucleus containing genetic material. Instead, their DNA floats in a tangle inside the cell.

Individual bacteria can only be seen with a microscope, but they reproduce so rapidly that they often form colonies that we can see. Bacteria reproduce when one cell splits into two cells through a process called binary fission. Fission occurs rapidly in as little as 20 minutes. Under perfect conditions a single bacterium could grow into over one billion bacteria in only 10 hours! (It’s a good thing natural conditions are rarely perfect, or the earth would be buried in bacteria!)

Growing and testing bacteria is a fun any-time project or a great science fair project. Bacteria are everywhere, and since they reproduce rapidly they are easy to study with just a few simple materials. All you need are some petri dishes , agar, and sterile swabs or an inoculating needle . Agar is a gelatinous medium that provides nutrients and a stable, controlled environment for bacteria growth . Most bacteria will grow well using nutrient agar , but some more fastidious bacteria (those with more complex nutrient requirements like Bacillus stearothermophilus , Branhamella catarrhalis , and Bacillus coagulans ) prefer tryptic soy agar .

You also need a source for bacteria, and this is not hard to find! You can swab your mouth or skin, pets, soil, or household surfaces like the kitchen sink or toilet bowl. If you want to study a particular type of bacteria, you can also purchase live cultures . Keep reading to see four experiments using bacteria, and many more ideas for science projects (also consider this hands-on Bacteria Growing Kit )! Adult supervision is recommended when working with bacteria.

How Can Bacteria Help Us?

Where would we be without bacteria? Well, we might not be getting bacterial diseases, but we would still be a lot worse off! Bacteria perform all sorts of very important functions, both in our bodies and in the world around us. Here are just a few.

Digestion. Our large intestines are full of beneficial bacteria that break down food that our bodies can’t digest on their own. Once the bacteria break it down, our intestines are able to absorb it, giving us more nutrients from our food.

Vitamins. Bacteria in our intestines actually produce and secrete vitamins that are important for our health! For example, E. coli bacteria in our intestines are a major source of vitamin K. (Most E. coli is good for us, but there is a harmful type that causes food poisoning.)

Food. Bacteria are used to turn milk into yogurt, cheese, and other dairy products.

Oxygen. Cyanobacteria (which used to be called blue-green algae) live in water and perform photosynthesis, which results in the production of much of the oxygen we need to breathe.

Cleanup. Oil spills, sewage, industrial waste — bacteria can help us clean all of these up! They ‘eat’ the oil or toxins and convert them into less harmful substances.

Bacteria are amazing creatures, aren’t they? They can be so dangerous and yet so important at the same time. Keep reading to see an experiment that uses good bacteria!

How Can Bacteria Harm Us?

Some types of bacteria cause disease and sickness. These kinds of bacteria are called pathogens. They reproduce very rapidly, like all bacteria. These come in many forms and can cause illnesses from an ear infection to strep throat to cholera. They can get into our bodies via our mouth and nose, or through cuts and scrapes. Some are airborne, others are found in food, resulting in food poisoning. Bacteria are also the cause of plaque buildup on our teeth, which can lead to cavities and gum disease.

Before the discovery of antibiotics, many severe bacterial diseases had no cure and usually resulted in death. Antibiotics work by destroying bacteria or inhibiting their reproduction while leaving the body’s own cells unharmed. After a time, some bacteria develop resistance to an antibiotic, and it will no longer be effective against them. Because of this, scientists are always researching new antibiotics. (Many diseases, such as chicken pox, hepatitis, or polio, are caused by viruses rather than bacteria. Antibiotics have no effect against these diseases.)

Bacterial infections are common, but many of them can be avoided by good cooking, cleaning, and hand-washing practices.

What Are Antibacterial Agents?

How do people stop bacteria from growing and spreading? They control it in two ways: by killing the bacteria cells, and by stopping the bacteria from reproducing. An agent is a solution or method which either kills or stops reproduction. Bactericides are agents that kill bacteria cells. Static agents inhibit cell growth and reproduction.

There are a variety of ways to kill bacteria or keep it from reproducing.

Physical methods:

• Sterilization. The application of heat to kill bacteria. Includes incineration (burning), boiling, and cooking.
• Pasteurization. The use of mild heat to reduce the number of bacteria in a food.
• Cold temperatures. Refrigeration and freezing are two of the most common methods used in homes, for preserving food’s life span.

Chemical methods:

• Antiseptics. These agents can be applied directly to living tissues, including human skin.
• Disinfectants. These agents are not safe for live tissues. Disinfectants are used to clean toilets, sinks, floors, etc.
• Some food preservatives are: sodium benzoate, monosodium glutamate (MSG), sulfur dioxide, salts, sugar, and wood smoke.
• Amoxycillin and Ampicillin—inhibit steps in cell wall synthesis (building)
• Penicillin—inhibits steps in cell wall synthesis
• Erythromycin—inhibits RNA translation for protein synthesis

SAFETY NOTE

While most environmental bacteria are not harmful to healthy individuals, once concentrated in colonies, they can be hazardous.

To minimize risk, wear disposable gloves while handling bacteria, and thoroughly wash your hands before and after. Never eat or drink during bacteria studies, nor inhale or ingest growing cultures. Work in a draft-free room and reduce airflow as much as possible. Keep petri dishes with cultured mediums closed—preferably taped shut—unless sampling or disinfecting. Even then, remove the petri dish only enough to insert your implement or cover medium with bleach or 70% isopropyl alcohol.

When finished experimenting, seal dishes in a plastic bag and dispose. Cover accidental breaks or spills with bleach or alcohol for 10 minutes, then carefully sweep up, seal in a plastic bag, and discard.

Preparing Culture Dishes

Before you can grow bacteria, you’ll need to prepare sterile culture dishes. A 125ml bottle of nutrient agar contains enough to fill about 10 petri dishes.

Water Bath Method – Loosen the agar bottle cap, but do not remove it completely. Place the bottle in hot water at 170-190 °F until all of the agar is liquid. To prevent the bottle from tipping, keep the water level even with the agar level.

• Let the agar cool to 110-120 °F (when the bottle still feels warm but not too hot to touch) before pouring into petri dishes.
• Slide open the cover of the petri dish just enough to pour agar into the dish. Pour enough agar to cover 1/2 to 2/3 of the bottom of the dish (about 10-13ml). Don’t let the bottle mouth touch the dish. Cover the dish immediately to prevent contamination and tilt it back and forth gently until the agar coats the entire bottom of the dish. (Fill as many dishes as you have agar for: you can store extras upside down until you’re ready to use them.)
• Let the petri dishes stand one hour for the agar to solidify before using them.

Experiment #1: Cheek Cell Swab

Make a culture dish using the instructions above. Once the culture dish is prepared, use a sterile cotton swab or inoculating needle and swab the inside of your cheek. Very gently rub the swab over the agar in a few zigzag strokes and replace the lid on the dish. You’ll need to let the dish sit in a warm area for 3-7 days before bacteria growth appears. Record the growth each day with a drawing and a written description. The individual bacteria are too tiny to see without a high-power microscope, but you can see bacteria colonies. Distinguish between different types of bacteria by the color and shape of the colonies.

Experiment #2: Testing Antibacterial Agents

Preparing Sensitivity Squares

One method for testing the antibacterial effectiveness of a substance is to use ‘sensitivity squares.’ Cut small squares of blotter paper (or other absorbent paper) and then soak them in whatever substance you want to test: iodine, ethyl alcohol, antibacterial soap, antiseptics, garlic, etc. Use clean tweezers to handle the squares so you don’t contaminate them. Label them with permanent ink, soak them in the chosen substance, and blot the excess liquid with a paper towel.

Collecting Bacteria

Decide on a source for collecting bacteria. For using sensitivity squares, make sure there is just one source, and keep each dish as consistent as possible. Sources could include a kitchen sink, bathroom counter, cell phone, or another surface you would like to test. Rub a sterile swab across the chosen surface, and then lightly rub it across the prepared agar dish in a zigzag pattern. Rotate the dish and repeat.

Setting Up an Experiment

Each experiment should have a control dish that shows bacteria growth under normal conditions and one or more test dishes in which you change certain variables and examine the results. Examples of variables to test are temperature or the presence of antiseptics. How do these affect bacteria growth?

• Label one dish ‘Control.’ Then in your test dish, use tweezers to add the sensitivity squares that have been soaked in a substance you wish to test for antibacterial properties. It’s a good idea to add a plain square of blotter paper to see if the paper by itself has any effect on bacteria growth. For best results, use multiple test dishes and control the variables so the conditions are identical for each dish: bacteria collected from the same place, exposed to the same amount of antibacterial substance, stored at the same temperature, etc. The more tests you perform, the more data you will collect, and the more confident you can be about your conclusions.
• Place all the dishes in a dark, room-temperature place like a closet.

Wait 3-7 days and examine the bacteria growth in the dishes, without removing the lids. You will see multiple round dots of growth; these are bacteria colonies. Depending on where you collected your bacteria samples, you may have several types of bacteria (and even some mold!) growing in your dishes. Different types of colonies will have different colors and textures. If you have a compound or stereo microscope, try looking at the colonies up close to see more of the differences.

Compare the amount of bacteria in the control dish to the amount in the test dishes. Next, compare the amount of bacteria growth around each paper square. Which one has bacteria growing closest to it? Which one has the least amount of bacteria growing near it? If you did more than one test dish, are the results similar in all the test dishes? If not, what variables do you think might have caused the results to be different? How does this affect your conclusions?

For a variation on this experiment, test the effect of temperature on bacterial growth instead of using sensitivity squares. Put a control dish at room temperature, and place other dishes in dark areas with different temperatures.

Experiment #3: Soap Survey

Every time you touch something you are probably picking up new bacteria and leaving some behind. This is how many infectious diseases spread — we share our bacteria with everyone around us! Even bacteria that lives safely on our skin can make us sick if it gets inside our bodies through our mouths or cuts and scrapes. This is one reason why it is so important that we wash our hands frequently and well.

What kind of soap works best for cutting down on the bacteria on our hands? You can test this by growing some bacteria cultures using agar and petri dishes.

• Two (or more)  petri dishes
• Sterile swabs
• Blotter paper  or other absorbent paper
• Forceps  or tweezers
• Different kinds of hand cleaners: regular soap, antibacterial soap, dish soap, hand sanitizer

1. Prepare the agar according to the directions on the label, then pour enough to cover the bottom of each petri dish. Cover the dishes and let them stand for about an hour until the agar has solidified again. (If you aren’t going to use them right away after they have cooled, store them upside down in the refrigerator.)

2. When your petri dishes are ready, collect some bacteria from your hand or the hand of a volunteer. (Make sure the person hasn’t washed his or her hands too recently!) Do this by rubbing the sterile swab over the palm in a zigzag pattern.

3. Remove the cover from the petri dish and lightly rub the swab back and forth in a zigzag pattern on the agar. Turn the dish a quarter turn and zigzag again. Cover the dish and repeat steps two and three for the other dish, using a new sterile swab. Label the dishes “Test” and “Control.” (You may want to do more than one test dish, so you can compare the results.)

4. Cut the blotter paper into small “sensitivity squares.” Use permanent ink to label the squares for the different types of hand cleaners you are going to test, e.g., “R” for regular soap, “A” for antibacterial soap, and “S” for hand sanitizer. Using tweezers, dip each square into the appropriate cleaner. Blot the excess cleaner on a paper towel and then place the squares on the agar in the “Test” dish. (Spread the squares out so there is distance between them.) Add one square of plain blotter paper to test if blotter paper by itself has any effect. Don’t put any squares in the “Control” dish – this one will show you what the bacterial growth will look like without any soap.

5. Put the dishes in a dark, room-temperature place like a closet and leave them undisturbed for a few days.

What Happened

The rate of bacteria growth in your dishes will depend on temperature and other factors. Check your cultures after a couple of days, but you’ll probably want to wait 5-7 days before recording your data. You will see multiple round dots of growth; these are bacteria colonies. There may be several types of bacteria growing in the dishes. Different types of colonies will have different colors and textures.

For each soap test, count and record the number of bacteria colonies in each dish. To see how effective each soap was, divide the number of colonies in the test dish by the number of colonies in the control dish, then subtract the result from 1 and write the answer as a percentage. For example, if your control dish had 100 colonies and your soap test dish had 30, the soap eliminated 70% of the bacteria: 1 — (30 ÷ 100) = .7 = 70%

According to your results, which type of soap was the most effective at eliminating bacteria?  Does “antibacterial” soap really work better than regular soap? How well did washing hands in water without soap work? What further tests could you do to determine which soaps and hand washing methods are most effective at eliminating bacteria?

Experiment #4: Bacteria in the Air

You need two culture dishes for this experiment, in which you’ll demonstrate how antibacterial agents (such as antibiotics and household cleaners) affect bacteria growth.

Leave the dishes with their lids off in a room-temperature location. Leave the culture dishes exposed for about an hour.

While you wait, cut small squares of paper (blotter paper works well), label them with the names of the antibacterials you’re going to test (e.g. ‘L’ for Lysol, ‘A’ for alcohol, etc.), and soak each in a different household chemical that you wish to test for antibacterial properties. If you have time, you might also experiment with natural antibacterial agents, such as tea tree oil or red pepper. Wipe off any excess liquid and use tweezers to set each of the squares on a different spot in one of the culture dishes. The second culture dish is your ‘control.’ It will show you what an air bacteria culture looks like without any chemical agents.

Store the dishes (with lids on) in a dark place like a closet where they will be undisturbed for a few days. After 3-7 days, take both culture dishes and carefully observe the bacteria growth in each dish, leaving the lids on. The bacteria will be visible in small, colored clusters. Take notes of your observations and make drawings. You could also answer the following questions. In the control culture, How much of the dish is covered with bacteria? In the sensitivity square test culture, Have the bacteria covered this dish to the same extent as the control culture? What effect have each of the chemicals had on the bacteria growth? Did a particular chemical kill the bacteria or just inhibit its growth?

• For further study you could use an  antibiotic disc set  to see what different antibiotics can do against bacteria.
• For a  more advanced project , learn how gram staining relates to the use of antibiotics.

Experiment #5: Homemade Yogurt

Generally when people think of ‘bacteria,’ they think of harmful germs. However, not all forms of bacteria are bad! You can enjoy a tasty product of good bacteria by making a batch of yogurt at home.

You’ll need to use a starter (available at grocery or health food stores), or else one cup of plain, unflavored yogurt that has live cultures in it. (If it contains live cultures, it will say so on the container.)

Slowly heat four cups of milk until it is hot, but not boiling or scalding. The temperature should be around 95-120 degrees to kill some of the harmful bacteria. Cool slightly, until milk is warm, and then add one cup of active yogurt or the starter.

Put the mixture in a large bowl (or glass jars) and cover. Make sure that the bowl or jars are sterilized before using by either running them through the dishwasher or washing them with very hot water.

There are two different methods for culturing the yogurt mixture: You can put the covered bowl or jars into a clean plastic cooler, and fill the cooler with hot water to just below the top of the culture containers. With this method, you will need to occasionally refill the cooler with hot water, so that the temperature of the yogurt stays consistent. The other method is to wrap the containers in a heating pad and towels, setting the heating pad on low to medium heat.

Check the mixture after heating for 3 1/2 to 4 hours. It should be ‘set up,’ having a smooth, creamy consistency similar to store-bought yogurt. If the mixture is not set up yet, heat it for another 1-2 hours. When it is the right consistency, add some flavoring—such as vanilla extract, chocolate syrup, or berries—and store the yogurt in the refrigerator. It should keep for a couple of weeks. For safety, we suggest that you do not eat any yogurt that has separated or has a non-typical consistency.

More Bacteria Experiment Ideas

Here are some other project ideas for you to try on your own or use as a basis for a bacteria science fair project:

• Mouthwash . Swab your teeth and gums and see how well toothpaste or mouthwash work against the plaque-causing bacteria on your teeth.
• Dog’s mouth : Have you heard people say that dogs’ mouths are cleaner than humans’? Design an experiment to test whether this is really true!
• Band-aid . Some band-aids are advertised as being antibacterial. Test to see if they really work better than regular band-aids at inhibiting bacteria.
• Water bottle . Is it safe to keep refilling a water bottle without washing it? Test a sample of water from the bottom of a water bottle that has been used for a couple days and compare it to a sample from a freshly-opened, clean water bottle. You can also test to see if a bottle gets more bacteria in it if you drink with your mouth or with a straw.
• Shoes . Do bacteria grow in your shoes? Is there a difference in bacteria growth between fabric shoes and leather? Do foot powders work to cut down on bacteria?
• Toothbrush . Do bacteria grow on your toothbrush? What are some ways you could try to keep it clean? Mouthwash? Hot water?
• Makeup . Some people recommend getting new mascara every six weeks because bacteria can grow in the tube. Test this by comparing bacteria growth from old mascara and new, unused mascara. You can also test how much bacteria is on other kinds of makeup.

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A bacterial regulatory uORF senses multiple classes of ribosome-targeting antibiotics

• Gabriele Baniulyte
• Joseph T Wade author has email address
• Department of Biomedical Sciences, School of Public Health, University at Albany, SUNY, Rensselaer, USA
• Wadsworth Center, New York State Department of Health, Albany, USA
• RNA Institute, University at Albany, SUNY, Albany, USA
• https://doi.org/ 10.7554/eLife.101217.1
• Open access
• Copyright information

Peer review process

Not revised: This Reviewed Preprint includes the authors’ original preprint (without revision), an eLife assessment, and public reviews.

• Reviewing Editor Alan Hinnebusch Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, United States of America
• Senior Editor David Ron University of Cambridge, Cambridge, United Kingdom

Reviewer #1 (Public Review):

The manuscript reports that expression of the E. coli operon topAI/yjhQ/yjhP is controlled by the translation status of a small open reading frame, that authors have discovered and named toiL, located in the leader region of the operon. The authors propose the following model for topAI activation: Under normal conditions, toiL is translated but topAI is not expressed because of Rho-dependent transcription termination within the topAI ORF and because its ribosome binding site and start codon are trapped in an mRNA hairpin. Ribosome stalling at various codons of the toiL ORF, caused by the presence of some ribosome-targeting antibiotics, triggers an mRNA conformational switch which allows translation of topAI and, in addition, activation of the operon's transcription because the presence of translating ribosomes at the topAI ORF blocks Rho from terminating transcription. Even though the model is appealing and several of the experimental data support some aspects of it, several inconsistencies remain to be solved. In addition, even though TopAI was shown to be an inhibitor of topoisomerase I (Yamaguchi & Inouye, 2015, NAR 43:10387), the authors suggest, without offering any experimental support, that, because ribosome-targeting antibiotics act as inducers, expression of the topAI/yjhQ/yjhP operon may confer resistance to these drugs.

- There is good experimental support of the transcriptional repression/activation switch aspect of the model, derived from well-designed transcriptional reporters and ChIP-qPCR approaches.

- There is a clever use of the topAI-lacZ reporter to find the 23S rRNA mutants where expression topAI was upregulated. This eventually led the authors to identify that translation events occurring at toiL are important to regulate the topAI/yjhQ/yjhP operon. This section can be strengthened if the authors suggest an explanation for how mutant ribosomes translating toiL increased topAI expression. Is there any published evidence that ribosomes with the identified mutations translate slowly (decreased fidelity does not necessarily mean slow translation, does it?)?

- Authors incorporate relevant links to the antibiotic-mediated expression regulation of bacterial resistance genes. Authors can also mention the tryptophan-mediated ribosome stalling at the tnaC leader ORF that activates the expression of tryptophan metabolism genes through blockage of Rho-mediated transcriptional attenuation.

Weaknesses:

The main weaknesses of the work are related to several experimental results that are not consistent with the model, or related to a lack of data that needs to be included to support the model.

The following are a few examples:

- It is surprising that authors do not mention that several published Ribo-seq data from E. coli cells show active translation of toiL (for example Li et al., 2014, Cell 157: 624). Therefore, it is hard to reconcile with the model that starts codon/Shine-Dalgarno mutations in the toiL-lux reporter have no effect on luciferase expression (Figure 2C, bar graphs of the no antibiotic control samples).

- The SHAPE reactivity data shown in Figure 5A are not consistent with the toiL ORF being translated. In addition, it is difficult to visualize the effect of tetracycline on mRNA conformation with the representation used in Figure 5B. It would be better to show SHAPE reactivity without/with Tet (as shown in panel A of the figure).

- The "increased coverage" of topAI/yjhP/yjhQ in the presence of tetracycline from the Ribo-seq data shown in Figure 6A can be due to activation of translation, transcription, or both. For readers to know which of these possibilities apply, authors need to provide RNA-seq data and show the profiles of the topAI/yjhQ/yjhP genes in control/Tet-treated cells.

- Similarly, to support the data of increased ribosomal footprints at the toiL start codon in the presence of Tet (Figure 6B), authors should show the profile of the toiL gene from control and Tet-treated cells.

- Representation of the mRNA structures in the model shown in Figure 5, does not help with visualizing 1) how ribosomes translate toiL since the ORF is trapped in double-stranded mRNA, and 2) how ribosome stalling on toiL would lead to the release of the initiation region of topAI to achieve expression activation.

- The authors speculate that, because ribosome-targeting antibiotics act as expression inducers [by the way, authors should mention and comment that, more than a decade ago, it had been reported that kanamycin (PMID: 12736533) and gentamycin (PMID: 19013277) are inducers of topAI and yjhQ], the genes of the topAI/yjhQ/yjhP operon may confer resistance to these antibiotics. Such a suggestion can be experimentally checked by simply testing whether strains lacking these genes have increased sensitivity to the antibiotic inducers.

• https://doi.org/ 10.7554/eLife.101217.1.sa2

Reviewer #2 (Public Review):

In this important study, Baniulyte and Wade describe how the translation of an 8-codon uORF denoted toiL upstream of the topAI-yjhQP operon is responsive to different ribosome-targeting antibiotics, consequently controlling translation of the TopAI toxin as well as Rho-dependent termination with the gene.

I appreciate that the authors used multiple different approaches such as a genetic screen to identify factors such as 23S rRNA mutations that affect topA1 expression and ribosome profiling to examine the consequences of various antibiotics on toiL-mediated regulation. The results are convincing and clearly described.

I have relatively minor suggestions for improving the manuscript. These mainly relate to the figures.

• https://doi.org/ 10.7554/eLife.101217.1.sa1

Reviewer #3 (Public Review):

The authors nicely show that the translation and ribosome stalling within the ToiL uORF upstream of the co-transcribed topAI-yjhQ toxin-antitoxin genes unmask the topAI translational initiation site, thereby allowing ribosome loading and preventing premature Rho-dependent transcription termination in the topAI region. Although similar translational/transcriptional attenuation has been reported in other systems, the base pairing between the leader sequence and the repressed region by the long RNA looping is somehow unique in toiL-topAI-yjhQP. The experiments are solidly executed, and the manuscript is clear in most parts with areas that could be improved or better explained. The real impact of such a study is not easy to appreciate due to a lack of investigation on the physiological consequences of topAI-yjhQP activation upon antibiotic exposure (see details below).

>Conclusion/model is supported by the integrated approaches consisting of genetics, in vivo SHAPE-seq and Ribo-Seq.

>Provide an elegant example of cis-acting regulatory peptides to a growing list of functional small proteins in bacterial proteomes.

• https://doi.org/ 10.7554/eLife.101217.1.sa0

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Scientists uncover diverse marine microbes with potential for new antibiotics and plastic breakdown

by University of East Anglia

New research shows how oceans can be used to help address major challenges such as the shortage of antimicrobial medicines, solutions for plastic pollution and novel enzymes for genome editing.

In the past 20 years, scientists have greatly increased the number of microbial genomes they have collected from the ocean. However, using this information for biotechnology and medicine has been difficult.

For this new study, led by BGI Research in China in collaboration with the Shandong University, Xiamen University, the Ocean University of China (OUC), the University of Copenhagen (Denmark) and the University of East Anglia (UEA) in the UK, researchers analyzed almost 43,200 genomes of micro-organisms (bacteria, archaea) from marine samples, uncovering a wide range of diversity with 138 distinct groups.

They provide new insights into how genome sizes evolve and, for example, how ocean microbes balance having CRISPR-Cas systems—part of their immune defense—with antibiotic resistance genes. Many of these genes are activated by antibiotics to help the microbes survive.

CRISRP-Cas systems and antibiotic resistance genes are also part of the immune system of bacteria. Using computer-based methods, the team discovered a new CRISPR-Cas9 system and 10 antimicrobial peptides , another important part of the immune system of different organisms.

Antimicrobials—including antibiotics, antivirals, antifungals and antiparasitics—are medicines used to prevent and treat infections in humans, animals and plants. However, according to the World Health Organization, growing resistance caused by overuse of certain drugs threatens the effective prevention and treatment of an increasing range of infections, so there is a need to find new types.

Publishing the results in the journal Nature , in a paper titled "Global marine microbial diversity and its potential in bioprospecting," the team also found three enzymes that can break down a common plastic that pollutes the oceans, polyethylene terephthalate (PET), another major environmental and health issue.

Laboratory experiments confirmed the findings obtained by ocean metagenomics, showing their potential usefulness. Lead UK author Thomas Mock, Professor of Marine Microbiology in UEA's School of Environmental Sciences, said the work takes the field of ocean metagenomics to the "next level."

"This study highlights how large-scale metagenomic sequencing of ocean microbiomes can help us understand marine microbial diversity and how it evolved and find new approaches to use this knowledge in biotechnology and medicine," said Prof Mock.

"The interaction between marine microbes and their environment is underpinning global-scale processes such as carbon fixation and nutrient recycling. Thus, these interactions contribute to the habitability on Earth because the oceans are the largest and most significant ecosystem on the planet.

"Factors such as salinity, temperature changes, light availability, and pressure differences from the surface to the sea floor and the poles to the tropics create unique selection pressures influencing the adaptation and co-evolution of oceanic microbes.

"Building on these insights, our study uses the repository of marine microbial genomes retrieved from metagenomes as a key resource for genome mining and bioprospecting. This method allows us to discover new genetic tools and bioactive compounds."

The data covers a variety of marine environments worldwide, stretching from pole to pole and from the surface to the deepest ocean trenches. The study therefore significantly enhances knowledge of marine microbiomes with the creation of a new publicly available database, which includes around 24,200 species-level genomes.

"While previous studies have provided initial insights into the role of marine systems in maintaining biological diversity , our research not only builds on these findings but also introduces new opportunities for sustainable exploration and utilization of the oceans, which is overdue considering the global challenges human society faces on our planet," said Prof Mock.

"Advancing this work with deep learning-based genome mining of ocean microbiomes, combined with biochemical and biophysical laboratory experiments, shows great potential for tackling global challenges such as antimicrobial shortages and ocean pollution.

"This approach highlights the crucial role of marine microbiomes in improving human well-being and promoting environmental sustainability."

This work is part of the strategic alliance between UEA in Norwich and OUC in Qingdao supporting integrative approaches for advancing science and technology for a sustainable ocean .

Journal information: Nature

Provided by University of East Anglia

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• Open access
• Published: 05 August 2019

Selection for antimicrobial resistance is reduced when embedded in a natural microbial community

• Uli Klümper   ORCID: orcid.org/0000-0002-4169-6548 1 , 2 ,
• Mario Recker   ORCID: orcid.org/0000-0001-9489-1315 3 ,
• Lihong Zhang 2 ,
• Xiaole Yin 4 ,
• Tong Zhang 4 ,
• Angus Buckling 1 &
• William H. Gaze 2  

The ISME Journal volume  13 ,  pages 2927–2937 ( 2019 ) Cite this article

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• Antibiotics
• Bacterial evolution
• Microbial ecology

Antibiotic resistance has emerged as one of the most pressing, global threats to public health. In single-species experiments selection for antibiotic resistance occurs at very low antibiotic concentrations. However, it is unclear how far these findings can be extrapolated to natural environments, where species are embedded within complex communities. We competed isogenic strains of Escherichia coli , differing exclusively in a single chromosomal resistance determinant, in the presence and absence of a pig faecal microbial community across a gradient of antibiotic concentration for two relevant antibiotics: gentamicin and kanamycin. We show that the minimal selective concentration was increased by more than one order of magnitude for both antibiotics when embedded in the community. We identified two general mechanisms were responsible for the increase in minimal selective concentration: an increase in the cost of resistance and a protective effect of the community for the susceptible phenotype. These findings have implications for our understanding of the evolution and selection of antibiotic resistance, and can inform future risk assessment efforts on antibiotic concentrations.

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Reduced selection for antibiotic resistance in community context is maintained despite pressure by additional antibiotics

Antibiotic-degrading resistance changes bacterial community structure via species-specific responses

Ecology and evolution of antimicrobial resistance in bacterial communities

Introduction.

The emergence and spread of antimicrobial resistance (AMR) genes in bacterial pathogens has been identified as one of the major threats to human health by the World Health Organisation [ 1 ]. Whilst AMR genes have been detected in ancient permafrost samples [ 2 ], anthropogenic use of antibiotics has caused a rapid increase in their prevalence [ 3 ]. A large body of theory and in vitro work has identified the role of ecological context, such as treatment regime and environmental heterogeneity, in AMR gene dynamics [ 4 , 5 , 6 , 7 ]. However, the majority of this work has not explicitly considered a crucial feature of microbial ecology: microbes are typically embedded within complex communities of interacting species. This is always the case within human and livestock microbiomes, in which antibiotic-imposed selection is likely to be particularly strong [ 8 ]. Here, we combine experiments and theory to determine how selection for AMR is influenced by the presence of other species derived from a natural gut microbial community. The focus of this study is selection for pre-existing resistance genes within a focal species, rather than selection on de novo variation arising through spontaneous mutations or acquired through horizontal gene transfer from another species.

Recent experimental studies suggest that selection for AMR genes in complex communities is occurring at antibiotic concentrations (the minimum selective concentration; MSC) that are much lower than those that prevent the growth of susceptible bacteria (minimum inhibitory concentration; MIC) [ 9 , 10 ]; as has been previously shown within single species in vitro [ 6 , 7 , 11 ]. However, it is unclear how the presence of other microbial species affects the MSC. While the precise effect of other species is likely context dependent, we hypothesise that the presence of the community will typically increase the MSC. Studies of single species suggest that resistant cells can afford protection to susceptible ones, through both, intracellular and extracellular degradation of antibiotics [ 12 , 13 , 14 ], thus increasing the relative fitness of susceptible strains and hence the MSC. However, excreted metabolites can both potentiate or decrease antibiotic efficacy, thus decreasing or increasing MSCs [ 15 , 16 ]. Further, any costs associated with AMR may be enhanced by increased competition for resources, as, for example, has been observed with respect to resistance in flies to parasitoids [ 17 ] and bacteria to viruses [ 18 ].

To explore the potential effects of community context on AMR selection, we competed isogenic Escherichia coli MG1655 strains, differing exclusively in a single chromosomal resistance determinant, in the presence and absence of a microbial community across a gradient of two different aminoglycoside antibiotics, kanamycin (Kn) and gentamicin (Gm). We embedded the Escherichia coli ( E. coli ), commonly found in the anaerobic digestive tract of warm-blooded mammals [ 19 ], within a pig faecal community in experimental anaerobic digesters in an attempt to partially mimic a gut environment. We additionally employed metagenomic analysis, community typing (16S rRNA gene) and mathematical modelling to provide insights into mechanisms underpinning community effects on AMR selection.

Material and methods

Pig faecal community.

Pig faeces were collected from four Cornish Black pigs without previous exposure to antibiotics in April 2016 on Healey’s Cornish Cyder farm (Penhallow, Cornwall, United Kingdom). Two hundred grams of faeces from each pig were pooled, mixed with 400 mL each of sterile glycerol and 1.8 g/L NaCl solution. The mixture was homogenized for 3 min in a Retsch Knife mill Gm300 (Retsch GmbH, Haan, Germany) at 2000 rotations per minute (rpm), filtered through a sieve (mesh size ~1 mm 2 ), centrifuged at 500 rpm for 60 s at 4 °C and the liquid supernatant fraction was collected and frozen at −80 °C as the inoculum.

Pig faecal extract

Two hundred grams of faeces from each pig were pooled, mixed with 800 mL of sterile 0.9 g/L NaCl solution. The mixture was homogenized for 3 minutes in a Retsch Knife mill Gm300 (Retsch GmbH, Haan, Germany), at 2000 rpm, filtered through a sieve (mesh size ~1 mm 2 ) and the liquid fraction was collected. The extract was then centrifuged (3500 rpm, 20 min, 4 °C), the supernatant collected and autoclaved (121 °C, 20 min). The autoclaved extract was centrifuged again (3500 rpm, 20 min, 4 °C) and the supernatant collected and used as a nutrient supplement.

The focal species, E. coli MG1655, was chromosomally tagged with a Tn 7 gene cassette encoding constitutive red fluorescence, expressed by the mCherry gene [ 20 ] to ensure that E. coli can be detected and distinguished from other community members after competition based on red fluorescence. The Kn resistant, red fluorescent variant containing resistance gene aph(3 ′)-IIb encoding an aminoglycoside 3′-phosphotransferase was created previously [ 21 , 22 ].

To create the Gm resistant mutant the strain was further tagged through electroporation with the pBAM delivery plasmid containing the mini-Tn 5 delivery system [ 23 , 24 ] for Gm resistance gene aacC1 encoding a Gm 3′- N -acetyltransferase [ 25 ]. Successful clones were screened for Gm resistance (30 μg/mL) and for the chosen clone a single strain growth curve in lysogeny broth (LB) was measured to ensure that the cost of the resistance gene was lower than 10% compared with the susceptible strain to ensure competitive ability.

Competition experiments

Competition experiments as well as initial growth of focal species strains were performed in 25 mL serum flasks with butyl rubber stoppers. As growth medium 10 mL of sterile LB medium supplemented with 0.1% pig faecal extract, 50 mg/L Cysteine-HCl as an oxygen scrubber and 1 mg/L resazurin as a redox indicator to ensure anaerobic conditions [ 26 ], was added to each reactor, heated in a water bath to 80 °C and bubbled with oxygen-free N 2 gas until the oxygen indicator resazurin turned colourless. After cooling down to 37 °C the appropriate concentration of antibiotic was added from a 1000× anaerobic stock solution.

Two isogenic pairs of the focal species, the susceptible, red fluorescent E. coli strain with either its Gm or Kn resistant counterpart, were competed across a gradient of six antibiotics concentrations (Gm [μg/mL]: 0, 0.01, 0.1, 1, 10, 100; Kn [μg/mL]: 0, 0.02, 0.2, 2, 20, 200). Strains as well as the community (100 μL of frozen stock) were grown separately under anaerobic conditions in triplicate reactors, replicates were combined, harvested through centrifugation, washed twice in 0.9% anaerobic NaCl solution and finally resuspended in 0.9% NaCl solution, adjusted to OD 600 0.1 (~10 7  bacteria/mL) and subsequently used in competition experiments. While the community was grown as an inoculum from the same frozen, homogenized stock, both subsampling and cultivation bias, inherent when growing an environmental community under laboratory conditions led to differences in original composition of the model community (Figs.  S1 , S2 ). When growing the community in isolation in the absence of antibiotics carrying capacity was reached after 18 h based on OD 600 readings in a spectrophotometer.

Isogenic strains were mixed at 1:1 ratio (community absent treatment), and that mix further added at 10% ratio to 90% of the faecal community (community present treatment). Approximately 10 6 bacteria of either mix were transferred to six replicate reactors of each of the antibiotic concentrations and grown at 37 °C with 120 rpm shaking for 24 h, which allowed growth up to carrying capacity. As a consequence of normalizing the total inoculum size the resulting inoculum size of the focal species in absence (~10 6 bacteria) and presence (~10 5 bacteria = 10% of total inoculum) of the community differed. A volume of 100 μL of each reactor was then transferred to a fresh bioreactor, grown for 24 h, transferred again for a final 24 h growth cycle and finally harvested for subsequent analysis.

Fitness assay

From each reactor after 3 days (T 3 ), as well as the inocula (T 0 ), a dilution series in sterile 0.9% NaCl solution was prepared and plated on LB and LB + AB (30 μg/mL Gm or 75 μg/mL Kn). For appropriate dilutions total and resistant red fluorescent E. coli colonies were counted under the fluorescence microscope. Plating of the susceptible strain on LB + AB plates further did not lead to any growth of spontaneous mutants. The relative fitness ( ρ ) of the resistant ( r ) compared with the susceptible strain ( s ) strain was subsequently calculated based on their individual growth rate ( γ ) throughout the competition experiment:

Statistical significance testing ( n  = 6) was performed using a one-tailed t -test against neutral selection ( ρ  = 1) and ANOVA corrected for multiple testing to compare the relative fitness of different samples.

To assess the MIC of the susceptible and the resistant focal strain individually in the presence and absence of the microbial community reactors were inoculated with 10 5 of the focal bacteria and 10 6 bacteria from the community for the community present treatment. Triplicate reactors were grown overnight across a gradient of antibiotics. Concentrations were increased by 1 μg/mL (Gm susceptible), 2 μg/mL (Km susceptible), 25 μg/mL (Gm resistant) and 50 μg/mL (Km resistant), respectively. Reactors were then harvested and plated out on LB agar. Positive growth was scored as more than fourfold bacterial colonies growing on the plates compared with plating of the inoculum. The MIC was defined as the first concentration at which no positive growth was observed.

DNA extraction and sequencing

Bacteria from each reactor, as well as inoculum and original pig faecal community were harvested through centrifugation of 2 mL of liquid, followed by DNA extraction using the Qiagen PowerSoil kit as per the manufacturer’s instructions. The quality and quantity of the extractions was confirmed by 1% agarose gel electrophoresis and dsDNA BR (Qubit), respectively.

16S rRNA gene libraries were constructed using multiplex primers designed to amplify the V4 region [ 27 ]. Amplicons were generated using a high-fidelity polymerase (Kapa 2G Robust), purified with the Agencourt AMPure XP PCR purification system and quantified using a fluorometer (Qubit, Life Technologies, Carlsbad, CA, USA). The purified amplicons were pooled in equimolar concentrations based on Qubit quantification. The resulting amplicon library pool was diluted to 2 nM with sodium hydroxide and 5 mL were transferred into 995 mL HT1 (Illumina) to give a final concentration of 10 pM. Six hundred millilitres of the diluted library pool was spiked with 10% PhiXControl v3 and placed on ice before loading into Illumina MiSeq cartridge following the manufacturer’s instructions. The sequencing chemistry utilized was MiSeq Reagent Kit v2 (500 cycles) with run metrics of 250 cycles for each paired end read using MiSeq Control Software 2.2.0 and RTA 1.17.28.

Metagenomic libraries were created using the KAPA high throughout Library Prep Kit (Part no: KK8234) optimized for 1 μg of input DNA with a size selection and performed with Beckman Coulter XP beads (Part no: A63880). Samples were sheared with a Covaris S2 sonicator (available from Covaris and Life Technologies) to a size of 350 bp. The ends of the samples were repaired, the 3′–5′ exonuclease activity removed the 3′ overhangs and the polymerase activity filled in the 5′ overhangs creating blunt ends. A single ‘A’ nucleotide was added to the 3′ ends of the blunt fragments to prevent them from ligating to one another during the adapter ligation reaction. A corresponding single ‘T’ nucleotide on the 3′ end of the adapter provided a complementary overhang for ligating the adapter to the fragment ensuring a low rate of chimera formation. Indexing adapters were ligated to the ends of the DNA fragments for hybridisation on a flow cell. The ligated product underwent size selection using the XP beads detailed above, thus removing the majority of un-ligated or hybridized adapters. Prior to hybridisation the samples underwent six cycles of PCR to selectively enrich those DNA fragments with adapter molecules on both ends and to amplify the amount of DNA in the library. The PCR was performed with a PCR primer cocktail that anneals to the ends of the adapter. The insert size of the libraries was verified by running an aliquot of the DNA library on a PerkinElmer GX using the High Sensitivity DNA chip (Part no: 5067–4626) and the concentration was determined by using a High Sensitivity Qubit assay. All raw sequencing data have been submitted to ENA under study accession number PRJEB29924.

16S rRNA gene analysis

Sequence analysis was carried out using mothur v.1.32.1 [ 28 ] and the MiSeq SOP [ 27 ] as accessed on 07.08.2017 on http://www.mothur.org/wiki/MiSeq_SOP . Sequences were classified based on the RDP classifier [ 29 ]. Diversity was assessed based on observed OTUs at 97% sequence similarity. All sequences of the focal species E. coli were removed based on ≥99% sequence similarity. No sequences with this degree of similarity to the focal species were detected in the original faecal community. NMDS plots for the community were created based on the Bray–Curtis dissimilarity metric [ 30 ].

Further sample similarity was tested using analysis of molecular variance (AMOVA) a nonparametric analogue of traditional ANOVA testing. AMOVA is commonly used in population genetics to test the hypothesis that genetic diversity between two or more populations is not significantly different from a community created from stochastically pooling these populations [ 31 , 32 ].

Metagenomic analysis

Metagenomic samples, as well as a reference genome for the focal species E. coli MG1655, were analysed using the ARG-OAP pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured antibiotic resistance gene database [ 33 ]. This resulted in the abundance of different resistance gene classes and subtypes within these groups normalized by 16S rRNA gene copy number. Antibiotics resistance genes detected in the E. coli reference genome were subtracted from the total number of hits per 16S rRNA gene copy based on the abundance of E. coli 16S rRNA gene/total 16S rRNA gene. Further, all antibiotics resistance gene numbers were normalized to the amount of pig faecal community 16S rRNA gene per total 16S rRNA gene copy.

Mathematical model

In order to illustrate possible mechanisms underlying the data for bacterial fitness in the presence/absence of the community for varying concentrations of Gm and Kn, we described our experimental setup mathematically. For this we first developed a discrete-time mathematical model for the growth of the susceptible and drug-resistant bacteria, s and r , respectively, in the presence or absence of the community, c .

Bacterial growth

The discrete-time model describing the growth of the bacteria i , i  =  s, r, c , is governed by the following iterative model

where \(n_i^{t + 1}\) is the size of the population of strain i at time t  + 1, and ϕ i is the maximum growth rate in the absence of competition and drug pressure. The reduction in growth due to density-dependent regulation/resource limitation, given as

with k d as the carrying capacity and e ij being the competition coefficient, describing how much the presence of an allospecific strain j impacts the competitive fitness of strain i . The reduction in bacterial growth due to drug pressure, f i , is governed by a generalised logistic function

where c is the drug concentration (in μg/mL), α i and β i are the parameters describing the dose-response relationship for strain i , and f max  = 0.9 is the maximum growth inhibition.

Model simulation and relative fitness calculation

Starting from an initially small number of bacteria in fresh medium, we ran the model for 30 generations, at which point the bacterial population had reached carrying capacity, and diluted the population accordingly. The bacteria were again allowed to grow for 30 generations before being diluted and grown for a final 30 generations. At this point we calculated the relative fitness of the resistant strain as

Community-dependent change in drug resistance/susceptibility

The Kn data seem to suggest that the benefit of the drug resistant bacteria is reduced in the presence of the community at medium to high drug concentrations pointing towards a decrease in the susceptibility of the susceptible strain in a community context. We captured this scenario by making the dose-response parameters α s,r and β s,r explicitly dependent on the density of the community by increasing the resistance of susceptible strain, s , i.e.

where α i,0 and β i,0 are the time-independent dose-response parameters. The effect of density dependence is further illustrated (Fig.  S3 ).

Parameter estimations

For each drug (Gm and Kn) we obtained a set of parameter values that resulted in a good overall fit between the model simulations and the data, where the data comprised the observed relative fitness for both sets of experiments (i.e. bacteria grown in the presence and absence of the community) for six different drug concentrations. To allow for logarithmic regression the non-antibiotic control was assumed as one order of magnitude lower than the lowest concentration used in the experiment. The parameter values were determined by minimising the root-mean-square error using an optimisation algorithm akin to simulated annealing [ 34 ]. The aim here was not to perform rigorous parameter estimation but rather to find a set of parameters that, given specific model constraints and assumptions, resulted in model behaviours that qualitatively agreed with both the observed dynamics over the repeated growth cycles and the empirically determined fitness values. In fact, our method failed to find a unique set of values that consistently gave the best fitting model, which suggests that the available data was insufficient to determine the global maximum. However, the qualitative relationships between individual parameters and between the parameters comparing the two antimicrobials were fairly consistent between model runs. Tables  1 , 2 list the sets of parameters estimated for the two different antibiotics. These parameters allow changes in community and focal species densities to be estimated throughout the individual competition experiments based on start- and end-point measurements (Fig.  S4 ).

Community context affects selection for gentamicin resistance

Isogenic strains of the focal species E. coli , with and without Gm resistance, were competed in the presence and absence of a pig faecal community across a 5 orders of magnitude gradient of Gm concentrations. Independent of antibiotic concentration the focal species increased in abundance during the 3 day competition experiment from ~10% at inoculation to above 90% relative abundance based on 16S rRNA gene sequencing (Fig.  S5A and B ). Both resistant and sensitive strains, as well as the community, showed positive growth across the whole range of concentrations and both treatments with focal species’ cell counts increasing by 2.25–3.96 orders of magnitude per day (Fig.  1a ).

Malthusian growth parameter per day of the focal species’ isogenic strains for gentamicin. Values are displayed across the antibiotic gradient and in absence and presence of the gut microbial community. a Average (±SD, n  = 6) logarithmic absolute growth per day for the resistant strain, the susceptible strain and the community. A different inoculum size of the focal species in absence (~10 6 bacteria) and presence (~10 5 bacteria = 10% of total inoculum) of the community was used. b Ratio of absolute Malthusian growth parameters (with 95% confidence intervals based on 1000-fold bootstrap analysis) in presence and absence of the microbial community across the gradient of antibiotic concentrations

There was a small competitive fitness cost ( t -test against 1, p  = 0.0005) of Gm resistance in the absence of the community ( ρ r  = 0.955 ± 0.014, mean ± SD), and this cost appeared to be greatly increased when the community was present (Fig.  2 ) ( ρ r  = 0.788 ± 0.016) (ANOVA corrected for multiple testing, p  < 0.01, F  = 360.36). At all concentrations between 0 and 10 μg/mL the susceptible strains relative growth benefited more from presence of the community when compared with the resistant one (ANOVA corrected for multiple testing, p  < 0.05), until at 100 μg/mL Gm, the community had no significant effect on relative growth (ANOVA corrected for multiple testing, p  = 0.259, F  = 1.42) (Fig.  1b ).

Relative fitness of the gentamicin resistant strain. Values (mean ± SD, n  = 6) in presence (black) and absence (red) of the community. Solid lines represent the best fit fitness curve through the mathematical model based on parameter estimates presented in Table  1 . The dashed line indicates neutral selection at a relative fitness of ρ r  = 1, where the intercept with the fitness curve indicates the minimal selective concentration

Community composition is altered across the gentamicin gradient

It is possible that changes in community composition across the antibiotic gradient may have contributed to the observed changes in selection for resistance caused by the community, notably between 10 and 100 μg/mL. The composition of the microbial community changed significantly from the collected faecal sample, to inoculum and further during the duration of the experiment (AMOVA, p  < 0.001, Fig.  S1A ). Above 1 μg/mL Gm the previously dominant Proteobacteria were outcompeted by Firmicutes (Fig.  S1B ) leading to a significant (AMOVA, p  < 0.01) separation of communities below and above this threshold concentration in the NMDS plot (Fig.  S1A ). However, there was no significant change in composition between 10 and 100 μg/mL, suggesting that compositional changes did not play a major role in community-imposed selection.

Community context imposes a cost of resistance

To test the hypotheses derived from the numerical data we used numerical simulations of our experimental set up to determine the likely mechanisms underpinning the observed population dynamics in a common logarithmic growth model. We determined models based on the key empirical findings in the absence of the community (specifically, that there is a cost of resistance in the absence of antibiotics, and that antibiotics inhibit the growth of the sensitive strain in a dose dependent manner), and then determined the most parsimonious way, in which the community could have altered the relative fitness of the resistant and susceptible strains (Table  1 ). We found a good fit to the data simply by assuming that the community imposed a greater competitive effect, constant across the antibiotic gradient, on the resistant rather than the sensitive strain ( e rj  >>  e cj   >  e sj ; where e ij is the competition coefficient imposed on the focal population (resistant r , susceptible s and community c ) by the community). Further, higher concentrations of Gm result in a drastic drop in the community’s growth rate, and hence a reduction of the elevated cost of resistance imposed by the community at these higher levels of antibiotic due to reduced competition. This in combination with very little growth of the susceptible strain explains why the relative fitness between the community present and community absent treatments converged at high antibiotic concentrations (100 μg/mL) with relative fitness primarily determined by the growth of the resistant strain.

The numerical simulation allowed us to estimate the change in MSC from absence to presence of the community by deterministically evaluating the concentration at the intercept with neutral selection at a relative fitness of ρ r  = 1. We estimated a 43-fold increase in MSC in the presence of the community (Fig.  2 ). The MIC for the susceptible strain remained stable at 8–9 μg/mL in both presence and absence of the community (Table  3 ). Consequently, this 43-fold increase in MSC shifts selection from concentrations far below the MIC in absence to concentrations around the MIC in presence of the community.

Community context affects selection for kanamycin resistance

As with Gm, the focal species increased in abundance during the 3 day competition experiment from ~10% at inoculation to above 90% relative abundance (Fig.  S5C and D ). Again, both strains, as well as the community, increased in abundance across both treatments and all concentrations of the 5 orders of magnitude antibiotic gradient with focal species’ cell numbers increasing by 1.45–3.09 orders of magnitude per day (Fig.  3a ). In the absence of this community, Kn resistance also imposed a slight metabolic fitness cost on the resistant strain ( ρ r  = 0.915 ± 0.036) (Fig.  3b ).

Malthusian growth parameter per day of the focal species’ isogenic strains for kanamycin. Values are displayed across the antibiotic gradient and in absence and presence of the gut microbial community. a Average (±SD, n  = 6) logarithmic absolute growth per day for the resistant (red), the susceptible strain and the community. A different inoculum size of the focal species in absence (~10 6 bacteria) and presence (~10 5 bacteria = 10% of total inoculum) of the community was used. b Ratio of absolute Malthusian growth parameters (with 95% confidence intervals based on 1000-fold bootstrap analysis) in the presence and absence of the microbial community across the gradient of antibiotic concentrations

However, unlike Gm, the community did not increase the general cost of resistance. Indeed, the community had no significant effect on the relative fitness of the resistant strain except at a concentration of 20 μg/mL (ANOVA corrected for multiple testing, p  = 0.002, F  = 15.58) (Fig.  4 ). There was a clear fitness advantage for the resistant strain in the absence of the community at this concentration ( ρ r  = 1.288 ± 0.149; t -test against 1, p  = 0.0052), while in the presence of the community, this difference in relative fitness, though still significant ( t -test against 1, p  = 0.0088), was considerably lower ( ρ r  = 1.034 ± 0.020). At 200 μg/mL Kn, close to the susceptible strains MIC, the resistant strain had an equally high relative fitness regardless of the presence of the community (ANOVA corrected for multiple testing, p  = 0.079, F  = 3.84).

Relative fitness of the kanamycin resistant strain. Values (mean ± SD, n  = 6) in the presence (black) and absence (red) of the community. Solid lines represent the best fit fitness curve through the mathematical model based on parameter estimates presented in Table  2 . The dashed line indicates neutral selection at a relative fitness of ρ r  = 1, where the intercept with the fitness curve indicates the minimal selective concentration

Community and antibiotic resistance composition remain stable across the kanamycin gradient

As with the Gm experiment, a significant shift in community composition from collected faecal sample, to inoculum and further during the duration of the Kn experiment (AMOVA, p  < 0.001, Fig.  S2A ) was observed. However, across the whole gradient of antibiotics, Firmicutes (Fig.  S2B ) remained the dominant phylum with no significant changes in community composition as a result. As such, compositional changes again cannot explain the impact of the community on focal strain fitness under selection at 20 μg/mL only. We additionally carried out metagenomic analysis for the 0, 2 and 20 μg/mL Kn treatments to determine whether relative abundance of resistance genes had changed within the community, despite the fact that there were no changes in community composition. Resistance to aminoglycoside (ANOVA, p  = 0.04) and other classes of antibiotics (fosmidomycin, kasugamycin, macrolides, polymyxin and tetracycline (ANOVA, all p  < 0.01)) significantly increased in the community of all reactors compared with the original faecal community independent of antibiotic concentrations (Fig.  5a ). However, there was no significant difference between Kn concentration and the abundance (ANOVA, p  = 0.15) of aminoglycoside resistance in general (Fig.  5a ) or any specific aminoglycoside resistance subtypes (Fig.  5b ) suggesting that relative fitness of the focal species was not influenced by the community resistome. Resistance genes detected by metagenomic analysis were however expressed, since resistant colonies from community members were detected on selective plates after the competition experiment. Unsurprisingly, since no antibiotic concentration-dependent selection for aminoglycoside resistance was observed within the community, no significant co-selection for resistance to any other classes of antibiotics was observed either.

Detected resistance genes. Type ( a ) and aminoglycoside subtype ( b ) relative abundance (resistance gene number normalized with 16S rRNA gene copy number), in original faecal community and in final reactor community at three kanamycin concentrations (mean ± SD; n faeces  = 2, n Kn0  = 6, n Kn2  = 6 n Kn20  = 5). Only genes detected with the ARGs-OAP pipeline are shown. MLS = Macrolides, Lincosamides, Streptogramines

Presence of the community can enhance growth of susceptible E. coli population at intermediate antibiotic concentrations

Numerical simulations showed that, unlike for Gm resistance, a community-imposed increase in the cost of Kn resistance was unable to explain why the benefit to the drug resistant focal E. coli strain was reduced in the presence of the community at intermediate drug concentrations ( e rj  =  e s ). This suggested different interactions between E. coli and the rest of the community, and we speculated that the community might have provided a protective effect against Kn for the susceptible E. coli . Growth data demonstrated this to be the case: in the community treatment the growth rate of both the susceptible and resistant E. coli was altered in a consistent fashion across the lower concentrations. The only exceptions was observed at 20 μg/mL, where the growth rate of the susceptible, but not the resistant strain, was significantly increased by the presence of the community (ANOVA corrected for multiple testing, p  = 0.002, F  = 15.58) (Fig.  3b ). Further, a slight increase in the MIC of the susceptible strain in the presence of the community from 16–18 to 18–20 μg/mL (Table   3 ) supports the notion of a protective effect. We investigated if a protective effect of the community was sufficient to explain the observed data by fitting numerical simulations where the dose-response parameters α s,r and β s,r were explicitly dependent on the time-dependent density of the community (as listed in Table  2 ). The resulting model provided a good fit to the experimental data, suggesting that community protection was driving the observed population dynamics with a 12-fold increase in MSC. Despite this MSC shift, other than for Gm resistance for Kn selection was still observed at concentrations 5–10-fold below the MIC of the susceptible strain.

In this study we investigated how being embedded within a semi-natural community (a pig gut derived community in an anaerobic digester) affects selection for AMR within a focal species ( E. coli ). For two antibiotics (Gm and Kn), we find the presence of the community selects against resistance, resulting in 1–2 orders of magnitude higher minimal selective concentrations for antibiotic resistance and thus achieving selection either closer towards or for Gm even at around the susceptible strains MIC. This suggests that recent in vitro single strain based estimates of MSCs [ 6 , 7 , 11 ] are likely much lower than would be observed in vivo and might explain why in certain ecosystems no selection for antibiotic resistance was observed in focal strains [ 35 ].

The primary mechanisms responsible for this community-imposed reduction in selection for resistance differed for the two tested drugs, yet are likely fairly general based on their ecological origin. For Gm, the community increased the fitness costs reflected by reduced growth rates that are associated with resistance in the absence of antibiotics. These elevated costs were retained at similar levels across the antibiotic gradient, up until doses were so high that only the resistant strain grew (similar behaviour above a certain threshold concentration has previously been described for single strain systems [ 36 , 37 ] and our results show that this holds true in a community context). Resource limitation—directly manipulated or through competition—has been found to increase costs against a range of stressors in a range of organism, from resistance of plasmodium to antimalarial drugs [ 38 ] to phage resistance in bacteria [ 18 ]. This is presumably because resource limitation has a more pronounced effect on resistant genotypes [ 39 ].

For Kn, community-imposed selection against resistance was only apparent at intermediate antibiotic concentrations. The absolute growth rate of the susceptible strain was significantly increased at intermediate concentrations in presence of the community. Our model fitting suggests this is because of a protective effect of the community, further supported by a slight increase in the MIC of the susceptible strain in presence of the community. The protective effect might have only been observed at intermediate concentrations since low concentrations were insufficient to detectably lower the relative fitness of the susceptible strain, while at high concentrations the protective effect was too small to be detectable. Such protective effects have been reported extensively within species [ 13 , 14 ], as well as more recently within more complex communities [ 12 ], either because of extra- or intracellular modification of antibiotics. Other common mechanisms known to increase a strains resistance to antibiotics in communities involve flocculation [ 40 ] or biofilm formation [ 41 , 42 ], but might here only play a minor role due to the shaking conditions.

A main difference between the Kn and Gm competition experiments was observed that could have implications for the likely mechanisms underpinning community-mediated changes in selection for resistance. In the absence of antibiotic selection, a higher community diversity involving a larger proportion of Proteobacteria was detected in the competing community during the Gm experiment compared with the Kn experiment. Since the focal species E. coli also belongs to the Proteobacteria , the difference in community structure and especially proteobacterial abundance could have increased the level of niche overlap between focal strain and community and thus competition [ 43 ], which may explain why Gm resistance had a significantly increased cost while Kn resistance did not.

The two general mechanisms discussed above all underlie selection for standing variation in pre-existing resistance genes, rather than selection on de novo variation arising through spontaneous mutations or horizontal gene transfer from other species. For de novo chromosomal mutations, the community is likely to further limit the spread of resistance, because the reduced population sizes of the focal strains in the presence of the community increase the chance that more costly mutations will be fixed [ 44 ]. In contrast, being embed in a community might enhance the spread of resistance. First, there will be a greater source of resistance genes available to the focal species. Second, selection against resistance acquired through horizontal gene transfer at low antibiotic concentrations might follow different dynamics. While chromosomal resistance might be outcompeted and subsequently lost, resistance genes embedded on conjugative plasmids can persist or even increase in abundance, as a consequence of their sometimes extremely broad host ranges and high transfer frequencies [ 22 , 45 , 46 , 47 , 48 ]. In controlled single strain experiments plasmid born resistance proved more costly than chromosomal resistance [ 7 ]. However, in more complex scenarios selection for mobile genetic element borne resistance usually depends not only on the single acquired resistance gene, but a combination of other linked traits encoded by the MGE as part of the communal gene pool [ 49 ]. Thus, difficulties in making general predictions on the selection dynamics of horizontally acquired resistance in microbial communities arise that merit future research efforts.

In summary, we show that selection for AMR was influenced by being embed in a ‘natural' microbial community, such that the MSC was increased by more than one order of magnitude for two different antibiotics. Further to reducing relative fitness of resistance, being embedded in a community would also reduce absolute fitness, which has been argued to sometimes be the major driver of spread of resistance [ 50 ]. The aim of this study was to identify these general mechanisms underlying decreased selection for AMR in complex community context with a high degree of environmental and ecological realism. More specific individual interactions contributing to these general effects might however depend on the specific community, antibiotic and corresponding resistance genes and should be investigated in the future.

To determine MSCs that are relevant in environmental settings it is thus crucial to test for selection in a complex community context, rather than in single strain systems. Understanding under which concentrations selection for and thus long-term fixation of newly acquired resistance mechanisms is occurring is crucial for future mitigation of the spread of resistance genes as well as their potentially pathogenic hosts [ 51 , 52 ]. Our results further stress the need to preferentially use narrow spectrum antibiotics in clinical therapy to maintain a healthy microbiome within the patient that can more easily recover after antibiotic administration [ 53 ], thus decreasing the likelihood of positive selection for pathogens that might have acquired resistance when embedded in a community.

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Acknowledgements

UK received funding from the European Union’s Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement no. 751699. UK, AB and WG were supported through an MRC/BBSRC grant (MR/N007174/1). XY thanks The University of Hong Kong for a postgraduate studentship.

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Klümper, U., Recker, M., Zhang, L. et al. Selection for antimicrobial resistance is reduced when embedded in a natural microbial community. ISME J 13 , 2927–2937 (2019). https://doi.org/10.1038/s41396-019-0483-z

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Received : 28 February 2019

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DOI : https://doi.org/10.1038/s41396-019-0483-z

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