Galleria Infection Synergy Testing

ISEF Category: Biomedical and Health Sciences

Subcategory: Immunology  ·  Difficulty: Intermediate  ·  Setup: School Lab  ·  Time: 1 to 2 Months

The Hook

Two weak antimicrobials can act like a stronger one when you pair them the right way. That matters if you care about antibiotic resistance, because combo treatments can sometimes slow bacteria without needing a high drug dose. You can test that idea in Galleria mellonella, a tiny insect host model that is much cheaper than vertebrate work. Then you can track survival over time and see whether the pair changes the risk of death.

What Is It?

Galleria mellonella are wax moth larvae. You can use them as a tiny living model to ask how an infection changes when two treatments meet. In this setup, honey, garlic allicin, or oregano oil may not kill E. coli K-12 by themselves, but they might help a low antibiotic dose work better. Think of it like two people moving a heavy box. One person cannot lift it alone, but the right pair can.

MIC means minimum inhibitory concentration, the smallest dose that stops visible bacterial growth. A sub-MIC dose sits below that line, so it does not fully block the bacteria on its own. Cox proportional hazards modeling lets you compare how fast different groups lose survival over time, which gives you a cleaner read than only checking who lives at the end.

Why This Is a Good Topic

You can test clear treatment pairs, compare them against single-agent controls, and measure survival as a real outcome. The topic connects to antibiotic resistance and combo therapy, which gives your project a real problem to solve. You will also learn experimental design, basic microbiology, and how to turn time-to-death data into hazard ratios that mean something.

Research Questions

• How does adding honey, garlic allicin, or oregano oil change larval survival compared with antibiotic alone?
• What is the effect of changing the antibiotic dose below the MIC on survival time in infected larvae?
• Does the treatment order, natural antimicrobial first versus antibiotic first, change the survival curve?
• To what extent does one household antimicrobial show stronger synergy than the others at the same sub-MIC antibiotic level?
• Which combination gives the largest Cox hazard reduction after you adjust for larval mass or batch?
• How does the larval survival curve change when you compare infected-only, antimicrobial-only, antibiotic-only, and combination groups?

Basic Materials

• Galleria mellonella larvae from a reputable supplier.
• E. coli K-12 strain approved by your lab or school.
• Honey, garlic allicin source, and oregano oil.
• Sterile saline or PBS.
• Sterile microcentrifuge tubes and disposable transfer pipettes.
• Petri dishes or sterile containers for holding larvae.
• Digital balance with 0.01 g accuracy.
• Labels, marker, and a data sheet.
• Gloves, lab coat, and eye protection.

Advanced Materials

• Biosafety cabinet.
• Shaking incubator.
• Spectrophotometer or microplate reader.
• Micropipettes with sterile filtered tips.
• Autoclave or validated sterilization access.
• Temperature-controlled incubator for larvae.
• Dissecting microscope.
• Colony counter or plate imaging setup.
• Bacterial freezer stocks and approved growth media.
• Reference antibiotic stock solutions prepared by your lab.

Software & Tools

• R: Fits Kaplan-Meier curves, Cox proportional hazards models, and simple effect-size summaries.
• RStudio Desktop: Gives you a clean interface for running R and making plots.
• JASP: Lets you check basic statistical output without a paid license.
• Google Sheets: Keeps treatment labels, observation times, and survival notes organized.
• ImageJ: Measures larval size or image-based quality checks if you document samples with photos.

Experiment Steps

1. Define one main question, one bacterial strain, one antibiotic, and one household antimicrobial pair.
2. Set up control groups that separate infection effects, compound toxicity, and true combination effects.
3. Decide how you will score survival, record observations, and keep handling the same across groups.
4. Build a treatment matrix that lets you compare single agents against combinations at a sub-MIC level.
5. Plan your analysis before you start, including Kaplan-Meier curves, Cox models, and the variables you will adjust for.
6. Write a rule for calling synergy so your conclusion matches your statistics.

Common Pitfalls

• Using larvae that vary a lot in size or age, which makes treatment effects look real when they come from starting health.
• Letting honey, garlic, or oregano oil change across batches, which makes the active dose drift from trial to trial.
• Skipping infected-only and antimicrobial-only controls, which leaves you unable to tell synergy from simple toxicity.
• Scoring larvae under different handling stress or light, which adds noise to the survival curve.
• Fitting a Cox model without checking the proportional hazards assumption, which can make the hazard ratio misleading.

What Makes This Competitive

To move this beyond a class demo, make your controls tight and your analysis sharper than a simple survival count. Compare more than one household antimicrobial, test whether the effect changes across antibiotic classes, and report effect sizes with confidence intervals. A strong project also checks the Cox model assumptions and explains when synergy appears, when it fades, and whether larval size or batch explains part of the signal.

Project Variations

• Test the same synergy question with a different school-approved BSL1 organism instead of E. coli K-12.
• Compare honey types, such as raw versus filtered, to see whether sugar profile changes the interaction signal.
• Swap survival time for bacterial load or turbidity so you can ask whether combinations kill faster or only slow growth.

Learn More

• PubMed: Search review articles on Galleria mellonella infection models, antimicrobial synergy, and survival analysis.
• PubMed Central: Read free full-text papers on insect host models and antibiotic combination studies.
• NCBI Bookshelf: Find background chapters on microbiology, pharmacology, and survival curve interpretation.
• OpenStax Microbiology: Read free textbook chapters on bacterial growth, antibiotic action, and host response on the OpenStax site.
• CDC Antibiotic Resistance Questions and Answers: Get background on why combination therapies matter on the CDC site.
• MIT OpenCourseWare: Use introductory statistics lectures to build intuition for hazard ratios and model assumptions.