Metabolic Pathways

TWiM #240: Aspirin, Colorectal Cancer, and Fusobacterium

Podcast and Annotation Information
  • Annotation by Kristin N. Weninger and Roman L. B. Overturf, Katriana A. Popichak, and Rebecca Seipelt-Thiemann
  • Podcast audio by TWiM: Listen to TWiM #240 Podcast
  • Podcast transcript by Otter. ai and edited by Laurel Thompson, Harshita Sharma, Kristin N. Weninger, and Roman L. B. Overturf: Access Podcast Transcripts
  • Paper Discussed:
    • Brennan CA, Nakatsu G, Gallini Comeau CA, Drew DA,Glickman JN, Schoen RE, Chan AT, Garrett WS. 2021. Aspirin Modulation of the Colorectal Cancer-Associated Microbe Fusobacterium nucleatum. mBio 12(2). https://doi.org/10.1128/mbio.00547-21
    • There was no main paper in this podcast. It was a discussion of vaccines.

1. Paper Abstracts

1.1. Snippet paper; discussion starts at 3:13 minutes

The Most Interesting Things (according to students)

Bacteria in the microbiome can influence risk of cancer, and drugs such as aspirin may modulate bacterial growth and therefore reduce the risk of cancer.

“Aspirin is a chemopreventive agent for colorectal adenoma and cancer (CRC) that, like many drugs inclusive of chemotherapeutics, has been investigated for its effects on bacterial growth and virulence gene expression. Given the evolving recognition of the roles for bacteria in CRC, in this work, we investigate the effects of aspirin with a focus on one oncomicrobe—Fusobacterium nucleatum. We show that aspirin and its primary metabolite salicylic acid alter F. nucleatum strain Fn7-1 growth in culture and that aspirin can effectively kill both actively growing and stationary Fn7-1. We also demonstrate that, at levels that do not inhibit growth, aspirin influences Fn7-1 gene expression. To assess whether aspirin modulation of F. nucleatum may be relevant in vivo, we use the ApcMin/+ mouse intestinal tumor model in which Fn7-1 is orally inoculated daily to reveal that aspirin-supplemented chow is sufficient to inhibit F. nucleatum-potentiated colonic tumorigenesis. We expand our characterization of aspirin sensitivity across other F. nucleatum strains, including those isolated from human CRC tissues, as well as other CRC-associated microbes, enterotoxigenic Bacteroides fragilis, and colibactin-producing Escherichia coli. Finally, we determine that individuals who use aspirin daily have lower fusobacterial abundance in colon adenoma tissues, as determined by quantitative PCR performed on adenoma DNA. Together, our data support that aspirin has direct antibiotic activity against F. nucleatum strains and suggest that consideration of the potential effects of aspirin on the microbiome holds promise in optimizing risk-benefit assessments for use of aspirin in CRC prevention and management.” (Brennan et al. 2021)

1.2. Main discussion (not a paper) starts at 27:05 minutes

The Most Interesting Things (according to students)

Vaccines induce memory immune cells to protect us from very specific proteins on antigens, hence the need for specific vaccines for varying bacteria and viruses.

2. Vision and Change Core Concepts and 2024 ASM Fundamental Statements

Snippet Main (not a paper)
Vision and Change Topics
  • Metabolic Pathways (V&C_MP)
  • Microbial Ecology (V&C_ME)
  • Impact of Microorganisms (V&C_IM)
  • Evolution (V&C_E)
  • Impact of Microorganisms (V&C_IM)
ASM Fundamental Statements
  • Fundamental Statement 14 (ASM_14): Extrinsic factors, such as abiotic and biotic interactions in the environment, can impact survival and growth of microbes.
  • Fundamental Statement 22 (ASM_22):Most microbes interact with hosts in beneficial or neutral ways, with a minority  having a detrimental impact on their host.
  • Fundamental Statement 27 (ASM_27): The extent of microbial diversity is largely unknown, and exploration of this diversity is critical to understanding microbes and their role in the biosphere.
  • Fundamental Statement 2 (ASM_2):. The diversity of microbes has arisen because of processes that include horizontal gene transfer, mutation, reassortment, recombination, and natural selection in varying ecological niches favor the growth and survival of certain variants.
  • Fundamental Statement 26 (ASM_26):. Humans leverage microbes and their products to address problems and improve quality of life.

3.  Potential Learning Objectives for the Podcast

The student will be able to: Paper1 Order2
  • Define microbiome.
 S L
  • Predict how bacterial growth as measured by optical density would be different if an antimicrobial substance is absent versus present in a liquid culture.
  • Design an experiment to examine how microbial growth affects tumor formation if the presence of the bacterial species might be involved in tumor formation.
 S H
  • Define vaccine.
 M L
  • Predict how immunity would be affected if antibody production or function was altered or if a new pathogen was introduced.
 M H

1 Papers: Snippet (S) or Main (M)

2 Learning Objectives: Lower Order or Higher Order (H)

4. Techniques Described (with Time Stamps)

Here is a link to a bio-dictionary that has many, but not all definitions if you need a definition: Explore Biology Bio-Dictionary

4.1. Snippet Paper

  • Growth Curve (5:10):  Growth of a bacterial culture over time can be measured using light spectroscopy and is generally in units of optical density.  The more bacteria that are present, the more light is scattered resulting in a higher OD reading.  When displayed over time, the data are usually a curve with a lag phase, a log phase, and a stationary phase.  Here, the podcasters discussed the growth curve for Fusobacterium in terms of plus or minus aspirin.
  • Murine Cancer Model (11:04): Mice having mutations in the APC gene are a model for colorectal cancer. The researchers used this model to investigate the combined roles of Fusobacterium and aspirin in cancer development.
  • Quantitative Polymerase Chain Reaction (qPCR) (16:01–29:19) This polymerase chain reaction method is used to quantify the amount of DNA or RNA present in a sample.  Here the authors used it to quantify how much Fusobacterium was present in human stool samples from colon cancer patients who were either taking aspirin or not.

4.2. Main Discussion (not a paper)

  • n/a

5. Connections to General Microbiology Processes/Concepts (with Time Stamps)

5.1. Snippet Paper

  • Growth Curve (5:10):  Growth of a bacterial culture over time can be measured using light spectroscopy and is generally in units of optical density.  The more bacteria that are present, the more light is scattered resulting in a higher OD reading.  When displayed over time, the data are usually a curve with a lag phase, a log phase, and a stationary phase.  Here, the podcasters discussed the growth curve for Fusobacterium in terms of plus or minus aspirin.
  • Human Microbiome (11:04–20:19): A microbiome includes both the diversity and ratio/quantity of microbes present in or on an organism.  Altering the breadth of species present or their ratio/number can affect that organism’s health.
  • Resistance (21:25): Microbes can evolve resistance to compounds.  Here the authors mention that some E. coli strains had been evolved to be resistant to benzoate.

5.2. Main Paper

  • Epidemiology and Public Health/Herd Immunity (37:40): Epidemiology is the study of disease spread, while herd immunity is the protection of a whole population (the herd) by having a certain proportion of individuals having true, biological immunity. The podcasters specifically discussed smallpox and how even though a few died from the virus, after immunization, less people were getting ill.
  • Antibody Function (34:51–37:27): Antibodies function in two ways: 1) as receptors and 2) as tags to initiate the immune system or to target destruction of potential pathogens.  The podcasters used the analogy of the lock and key to explain how an antibody binds its target (antigen) and how an antibody is actually composed of several subunit proteins.

6. Podcast Questions

  1. What is a microbiome?
    1. All of the microorganisms that live in the human gut
    2. A small habitat’s population of anaerobic bacterial species
    3. A population of gram positive, aerobic microbes
    4. All microorganisms that live in a particular environment
  2. If an antibiotic-sensitive bacterial colony was grown in a liquid culture and attained an optical density of 1.2 at 12 hours of growth, what would you predict for the optical density at 12 hours if the antimicrobial was included in the medium?
    1. The optical density would increase.
    2. The optical density would decrease.
    3. The optical density would be the same.
    4. The optical density would be negative.
  3. If a fungus is found along with bacterium A on the skin of healthy snakes and snakes with skin tumors were found to have bacterium A and bacterium B on their skin, but no fungus, what would be a good experiment (or experiments) to investigate the relationship of fungus, bacterium B, and snake skin tumor?  Pick all that apply.
    1. Investigate whether the fungus produces a substance that inhibits growth of skin tumors in snakes.
    2. Investigate whether the fungus produces an antimicrobial substance that inhibits growth of bacterium B.
    3. Investigate whether the bacterium B produces a substance that promotes the growth of skin tumors.
    4. Investigate whether bacterium A produces a substance that promotes the growth of bacterium B.
  4. A vaccine is a substance that ________.
    1. stimulates herd immunity in a population
    2. stimulates immunity to a pathogen in an individual
    3. eliminates 99% of pathogenic bacteria species
    4. destabilizes a pathogen in a specific environment
  5. If a person were immunized to a particular pathogen and that person came into contact with a different pathogen, would the person have immunity to the new pathogen? Why?
    1. Yes, because antibodies recognize multiple pathogens in the environment..
    2. Yes, because the antibodies were produced by immunization of the person.
    3. No, because antibodies are very specific for a single or highly related pathogen.
    4. No, because antibodies are degraded quickly and re-vaccination is needed.

7. Figure Reading Exercises

The following are two figure reading exercises, both from the snippet paper (Figures 3 and 4)

7.1. First Figure Reading Exercise

7.1.1. Learning Objectives

Students will be able to:

  • Identify key experimental design features for these experiments including controls, hypotheses, reasoning for the use of particular strains.
  • Evaluate the data to make conclusions about the impact of aspirin and F. nucleatum colonization on tumor count.
  • Predict a disease outcome based on the hypothetical interaction of a compound and bacterium.
Experimental Background (Brennan et al 2021, Figure 3)

There is an expanding interest in the role of the microbiome on health and disease.  In this study, Brennan et al (2021) investigate the effects of aspirin, a known tumor prevention medication, on a bacterium implicated in colorectal cancer, an oncobacterium called Fusobacterium nucleatum.  To test this connection, pathogen-free mice that are genetically predisposed to colorectal tumors (ApcMin/+ mice) were inoculated daily with F. nucleatum (Fn7-1) or a control (sTSB; Sham) and fed chow containing aspirin or no aspirin daily for 14 weeks beginning at age 6 weeks.  The number of colon adenomas per mouse were then quantified.

Adenoma count experiment.
Figure 3: “Colonic adenoma burden in the murine ApcMin/+ model in response to daily Fn7-1 instillation and aspirin supplementation. Conventional specific-pathogen-free ApcMin/+ mice were orally inoculated with Fn7-1 or a sham control (sTSB) daily and simultaneously maintained on either a control chow or a chow supplemented with 200 ppm aspirin, with both treatments beginning at 6 weeks of age and continuing until 14 weeks. Mice were then sacrificed, and the colons were prepared for histological analysis for enumeration of colon adenomas. Data points represent adenoma counts from individual mice, bars indicate the mean ± SEM, and statistical analysis was performed by Kruskal-Wallis test with post hoc Dunn’s test for multiple comparisons. ****, P < 0.0001.” (Brennan et al. 2021, no changes).

 

7.1.2. Questions

  1. What is the hypothesis for this experiment?
    1. Daily aspirin treatment will reduce F. nucleatum-induced adenoma occurrence in mice predisposed to colorectal cancer as measured by histological adenoma counts.
    2. Daily F. nucleatum inoculation treatment will increase adenoma occurrence in mice predisposed to colorectal cancer as measured by histological adenoma counts.
    3. Daily aspirin treatment will increase F. nucleatum-induced adenoma occurrence in mice predisposed to colorectal cancer as measured by histological adenoma counts.
    4. Daily F. nucleatum inoculation treatment will decrease adenoma occurrence in mice predisposed to colorectal cancer as measured by histological adenoma counts.
  2. The mouse strain used in this experiment is not a wild-type strain of mouse.  Why was this mouse used rather than a wild-type strain?
    1. It is a genetic model of colon cancer.
    2. It has a specific microbiome composition.
    3. It has a sensitivity to aspirin treatment.
    4. It is germ-free and has no immune system.
  3. The positive control for this experiment is _______.
    1. Sham
    2. Sham + aspirin
    3. Fn7-1
    4. Fn7-1 + aspirin
  4. The negative controls for this experiment are _________.
    1. Sham and Sham + aspirin
    2. Sham and Fn7-1
    3. Sham + aspirin and Fn7-1 + aspirin
    4. Fn7-1 and Fn7-1 + aspirin
  5. What notation in the strip plot tells you the comparison being made is statistically significant, that is truly different?
    1. dots
    2. asterisks
    3. horizontal bars
    4. whiskers
  6. Which treatment group showed the largest tumor counts?
    1. Sham
    2. Sham + aspirin
    3. Fn7-1
    4. Fn7-1 + aspirin
  7. Was aspirin effective as a preventative treatment for F. nucleatum-induced adenoma occurrence?  What data are your evidence?
    1. Yes, the reduction in tumor count when you compare the Sham to the Fn7-1+ aspirin
    2. Yes, the reduction in tumor count when you compare the Fn7-1 to the Fn7-1+ aspirin
    3. No, the increase in tumor count when you compare the Sham to the Fn7-1
    4. No, the increase in tumor count when you compare the Fn7-1 to the Fn7-1+ aspirin
  8. If another compound (compound X) was replaced with aspirin and the same experiment conducted, but this compound itself caused adenomas, what results would you expect for each treatment condition? Let’s quantify the Sham level in the prior experiment as very low and the Fn7-1 inoculated level as high. (1 =  very low; 2 =  low; 3 =  moderate; 4 = high; 5 =  very high)
    1. ________ Sham
    2. ________ Sham + compound X
    3. ________ Fn7-1
    4. ________ Fn7-1 + compound X

7.2. Second Figure Reading Exercise

7.2.1. Learning Objectives

Students will be able to:

  • Define optical density (OD) and plate count methods (colony forming units) and their uses for microbiologists.
  • Compare the uses of optical density (OD) and colony forming units (CFU) in growth assays.
  • Predict how growth assays results will be different if growth inhibition is occurring.
  • Analyze the optical density and plate count data to make conclusions about the impact of aspirin and aspirin concentration on clinical strain growth.
  • Defend why the researchers examining multiple clinical isolates was a good choice.
Experimental Background (Brennan et al. 2021, Figure 4)

There is an expanding interest in the role of the microbiome on health and disease.  In this study, Brennan et al (2021) investigate the effects of aspirin, a known tumor prevention medication, on a bacterium implicated in colorectal cancer, an oncobacterium called Fusobacterium nucleatum.  First, the researchers confirmed that growth of laboratory strains of F. nucleatum was inhibited by both aspirin and its active component (salicylic acid) in a dose-responsive way using spectroscopy (optical density; OD600).  Since optical density measures do not distinguish living from dead cells, they next quantified cell survival for the laboratory strains as colony forming units per milliliter of culture (CFU/mL) and found identical results.  Since laboratory strains can differ widely in behavior from clinical strains they next wanted to examine whether these results were also true for clinical F. nucelatum strains, bacteria isolated from colorectal cancer (CRC).  To test for growth inhibition, bacterial growth with two different aspirin concentrations was performed with two CRC strains (FnCTI-1 and FnCTI-2) and are reported as growth curves over 36 hours (Panel C).  Cell survival studies in the absence (sTSB) and presence of aspirin (2.5 mM) were also performed with seven CRC strains (FnCTI-1 thru Fn CTI-7) and data are reported as CFU/mL (panel D).

Growth assays for clinical strains.
Figure 4. “Growth inhibition of aspirin on … CRC isolates of F. nucleatum. (C) Growth of the CRC F. nucleatum isolates FnCTI-1 and FnCTI-2 in response to aspirin as determined by optical density. (D) Growth yield of FnCTI-1, -2, -3, -5, -6, and -7 in response to 2.5 mM aspirin after 24 h as determined by CFU per ml. All data represent the mean ± SEM for at least 6 cultures. Growth curves were analyzed by two-way repeated measures ANOVA with post hoc Dunnett’s test. Growth was significantly different from sTSB at P values of <0.05 for each of the following strains at the indicated time points and concentrations: Fn23762 (1 mM and 2.5 mM at 7.5 h), Fn10953 (1 mM at 6.5 h and 2.5 mM at 5.5 h), FnCTI-1 (1 mM at 17.5 h and 2.5 mM at 7 h), and FnCTI-2 (1 mM at 9 h and 2.5 mM at 7 h). Analysis of growth yield data was performed by Mann-Whitney test. **, P < 0.01; ***, P < 0.001.” (Brennan et al 2021, image and text was cropped to Panels C and D).

 

7.2.2. Questions

  1. What is optical density and how do microbiologists use it? 
    1. Optical density measures how much light bacteria absorb and use for cellular respiration.  For microbiologists, it is used to estimate the cell viability in a liquid sample.
    2. Optical density measures how far apart cells are in a sample.  For microbiologists, it can be used to estimate the cell wall components in a liquid sample.
    3. Optical density measures how often cells interact with other substances.  For microbiologists, it can be used to estimate the cell-cell interactions in a liquid sample.
    4. Optical density measures how much light passes through a sample.  For microbiologists, it can be used to estimate the concentration of cells in a liquid sample.
  2. What is a colony forming unit (CFU) and how is that related to bacterial growth and survival?
    1. CFU is a measure of how many cells were killed by a treatment.  The higher the CFU, the fewer bacteria survived.
    2. CFU is a measure of the number of living cells in a culture.  The higher the CFU, the more bacteria survived.
    3. CFU is a measure of how much growth inhibition occurred.  The higher the CFU, the less growth occurred.
    4. CFU is a measure of how well the medium supported cell survival.  The lower the CFU, the more bacteria survived.
  3. Match the following statements with the appropriate technique(s): (C = Colony Forming Units; O = Optical density)
    1. _______ Uses light scattering to determine cell population
    2. _______ Uses growth on plates to determine cell population
    3. _______ Quantifies only living, reproductive cells in a population
    4. _______ Quantifies living and dead cells in a population
    5. _______ Can be used to quantify growth inhibition
    6. _______ Can be used to quantify bacterial killing
  4. What would you expect to see for an optical density (OD) value if growth inhibition occurs when cells are treated with compound A compared to a control solution?
    1. The OD for the solution of cells treated with compound A would be smaller than OD for the solution of cells in the control solution.
    2. The OD for the solution of cells treated with the control solution would be smaller than OD for the solution of cells treated with compound A.
    3. The OD for the solution of cells treated with compound A would be equivalent to the OD for the solution of cells in the control solution.
    4. The OD for the solution of cells treated with compound A would be higher than OD for the solution of cells in the control solution.
  5. Growth inhibition in the absence and presence of aspirin was measured using spectroscopy (OD600) for two clinical bacterial strains (panel C).  Which strain was most sensitive to aspirin and what data support that conclusion?
    1. FnCTI-1 because growth was inhibited at the earliest time in the 36 hour experiment (at 6 hours) for 2.5 mM aspirin (thin gray circles compared to solid black circles)
    2. FnCTI-1 because growth was the least inhibited using 1mM aspirin treatment compared to the control (thick gray circles compared to solid black circles)
    3. FnCTI-2; this strain showed the greatest aspirin-related reduction in growth by comparing each strain at the lowest aspirin concentration to its control (thick shapes to solidshapes)
    4. FnCTI-2  because this strain showed less growth when the two strains were compared with no aspirin in the growth medium (solid black circles and solid teal squares)
  6. To get an idea of how widespread aspirin-sensitivity is in natural, clinical strains, the ability of seven different clinical strains to survive in the presence of 2.5 mM aspirin was measured as growth yield or colony forming units per milliliter (panel D).  Which strains were sensitive to aspirin and what data support that conclusion?
    1. All strains were killed at significant levels by aspirin at 2.5 mM as evidenced by a smaller CFU/mL value for aspirin compared to sTSB.
    2. None of the strains were killed at significant levels by aspirin at 2.5 mM as evidenced by a smaller CFU/mL value for aspirin compared to sTSB.
    3. Only the FnCTI-5 strain was killed at significant levels by aspirin at 2.5 mM as evidenced by a smaller CFU/mL value for aspirin and sTSB.
    4. Only the FnCTI-5 strain was killed at significant levels by aspirin at 2.5 mM as evidenced by a smaller error bar for aspirin and sTSB.
  7. Which of the clinical strains is the most sensitive to aspirin at 2.5 mM and what is the evidence for this?
    1. FnCTI-5, aspirin-treated has the smallest CFU of all the samples.
    2. FnCTI-6, has the largest difference between control and aspirin-treated.
    3. FnCTI-5, aspirin-treated has the smallest error bar of all the samples.
    4. FnCTI-3, has the smallest difference between control and aspirin-treated.
  8. Open response: What data in panel D can you use to make a good argument for the rationale of including a number of clinical strains (not just one) in the study, and not just laboratory strains?

 

8. Paper Information and Licensing

8.1. Snippet paper

8.2. Main paper

  • n/a

License

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Podcast Annotation and Resources in Microbiology Copyright © 2025 by Rebecca Seipelt-Thiemann; Nancy Boury; Gwendowlyn S. Knapp; Amaya Garcia Costas; and Patrick Armstrong is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.

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