E. coli Resistance Evolution Under Antimicrobial Pressure

ISEF Category: Microbiology

Subcategory: Antimicrobials and Antibiotics  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

Bacteria can adapt faster than you can finish a school semester. Under weak drug pressure, they do not just survive, they can change how they respond next time. That makes this project a real test of evolution in action. You will compare one antimicrobial with a changing drug schedule and watch which strategy pushes resistance farther.

What Is It?

This project looks at how bacteria evolve when they face low, repeated exposure to antimicrobials. The key idea is simple. If a germ keeps meeting a drug, even at a dose that does not kill it, the survivors may be the ones with traits that help them cope better next time. That coping ability often shows up as a higher MIC, which stands for minimum inhibitory concentration, the lowest drug level that stops visible growth.

Think of it like training a lock to resist a key. One drug gives the bacteria one kind of pressure to handle. A rotating drug schedule changes the challenge, so the bacteria may have a harder time adapting to just one solution. You can pair the evolution experiment with public RNA-seq data, which measures which genes are turned up or down, to see whether known resistance genes from CARD, the Comprehensive Antibiotic Resistance Database, line up with the pattern you observe.

Why This Is a Good Topic

This is a strong science fair topic because it asks a clear question, change in resistance over time under different drug pressures, and gives you measurable data through MIC testing and gene expression analysis. It connects to a real problem, antibiotic resistance, which affects medicine, food safety, and public health. You can learn experimental design, controls, dose response thinking, and basic bioinformatics without needing a full medical lab project.

Research Questions

• How does serial passage in a single plant antimicrobial change the MIC of E. coli K-12 over time?
• What is the effect of a rotating two-drug schedule on the rate of MIC increase compared with a single-antimicrobial schedule?
• Does the magnitude of resistance gain differ between wild-type E. coli K-12 and an antibiotic-sensitive control strain?
• To what extent do public RNA-seq datasets show upregulation of CARD-linked efflux or target-protection genes after antimicrobial exposure?
• Which exposure pattern, single-drug or rotating-drug, produces the largest change in growth rate at the end of the experiment?
• How does sub-inhibitory exposure affect the stability of resistance after the pressure is removed?

Basic Materials

• E. coli K-12 strain from a teaching or research supplier.
• Appropriate biosafety approval and adult supervision.
• School or university incubator.
• Sterile petri dishes.
• Agar media suitable for E. coli growth.
• Micropipettes and sterile tips.
• Disposable sterile loops or swabs.
• Petri dish marker.
• Digital caliper or ruler for measuring inhibition zones.
• Spectrophotometer or turbidity meter, if available.
• Notebook or spreadsheet for tracking passages.
• Personal protective equipment, including gloves, lab coat, and eye protection.
• Non-clinical plant antimicrobial source, chosen with supervisor guidance.

Advanced Materials

• Biosafety-approved E. coli K-12 strains, including a reference control.
• Gradient plate setup or equivalent diffusion-based exposure system.
• Microplate reader for growth curves and MIC confirmation.
• Incubator with controlled temperature.
• Autoclave access for sterilization and waste handling.
• DNA or RNA extraction kit, if allowed by the lab.
• qPCR system for targeted validation of resistance genes.
• Access to a next-generation sequencing dataset or library prep workflow.
• Bioinformatics workstation.
• Reference genome and annotation files for E. coli K-12.
• Subscription or institutional access to a wet lab notebook system, if your lab uses one.

Software & Tools

• NCBI GEO: Lets you find public RNA-seq datasets for E. coli exposure studies.
• PubMed: Helps you search review articles and primary papers on plant antimicrobials and resistance genes.
• CARD: Provides reference antimicrobial resistance genes and annotations for comparison.
• R and RStudio: Supports MIC plots, growth curve analysis, and basic statistics.
• ImageJ: Helps measure plate-based inhibition zones from photos when you need image-based comparisons.

Experiment Steps

1. Define one evolution question, one bacterial strain, and one comparison schedule before you collect any data.
2. Choose a primary outcome, such as MIC shift, growth rate, or survival after repeated exposure, so your results stay focused.
3. Plan your control groups, including an untreated line and any single-drug baseline, so you can separate evolution from normal growth changes.
4. Map out how you will sample across generations and how you will confirm that each line really changed over time.
5. Build a data analysis plan for comparing resistance trends and matching them with public RNA-seq gene expression patterns.
6. Decide in advance how you will filter genes, handle replicates, and report uncertainty, so your conclusions stay honest.

Common Pitfalls

• Using too few independent lines, which makes a lucky mutant look like a real trend.
• Changing the exposure strength in an inconsistent way, which prevents fair comparison between the single-drug and rotating-drug groups.
• Mixing up contamination with adaptation, which can happen if plate checks and strain tracking are sloppy.
• Measuring MIC with different endpoints each round, which makes the resistance trend hard to trust.
• Trying to interpret every CARD hit as direct proof of resistance, which can overstate what the RNA-seq data really shows.

What Makes This Competitive

A competitive version of this project does more than report that MIC changed. You need strong replication, a clean control design, and a clear reason why the rotating schedule matters. The best entries connect the evolution result to gene expression evidence, then test whether the same resistance pathways appear across different public datasets. A sharper statistical plan, such as comparing slopes, confidence intervals, or effect sizes, can make your results much stronger.

Project Variations

• Swap E. coli K-12 for another safe teaching strain to see whether the adaptation pattern depends on species background.
• Compare a single plant antimicrobial with a plant extract blend to test whether mixed compounds slow resistance gain.
• Skip the wet lab evolution step and focus on a public RNA-seq meta-analysis of CARD-linked genes under antimicrobial stress.

Learn More

• NCBI Gene Expression Omnibus: Search for public RNA-seq datasets on E. coli, antimicrobial stress, and resistance response studies.
• CARD Database: Use the Comprehensive Antibiotic Resistance Database to identify resistance genes and their annotations.
• PubMed: Search for review articles and primary research on sub-inhibitory antimicrobials and resistance evolution.
• ASM Journals: Read microbiology papers on adaptation, efflux pumps, and antibiotic tolerance in bacteria.
• MIT OpenCourseWare: Look for free microbiology and genetics course materials that explain bacterial growth and gene regulation.