Impact of Microorganisms

TWiM #318: How to Pick a Winner

Podcast and Annotation Information

1. Paper Abstracts

1.1. Snippet paper; discussion starts at 2:42 minutes

The Most Interesting Things (according to students)

Rare species seen as “insurance” was interesting, particularly the debate among the podcasters (nature is planning the “insurance” versus rare species as part of the community due to natural selection).  Also, RNA viruses do not proofread when replicating so they can change and adapt quickly making them the best microbes to study for evolution because they are the fastest to evolve.

“Using data from a wide range of natural communities including the human microbiome, plants, fish, mushrooms, rodents, beetles, and trees, we show that universally just a few percent of the species account for most of the biomass. This is in line with the classical observation that the vast bulk of biodiversity is very rare. Attempts to find traits allowing the tiny fraction of abundant species to escape rarity have remained unsuccessful. Here, we argue that this might be explained by the fact that hyper-dominance can emerge through stochastic processes. We demonstrate that in neutrally competing groups of species, rarity tends to become a trap if environmental fluctuations result in gains and losses proportional to abundances. This counter-intuitive phenomenon arises because absolute change tends to zero for very small abundances, causing rarity to become a “sticky state”, a pseudoattractor that can be revealed numerically in classical ball-in-cup landscapes. As a result, the vast majority of species spend most of their time in rarity leaving space for just a few others to dominate the neutral community. However, fates remain stochastic. Provided that there is some response diversity, roles occasionally shift as stochastic events or natural enemies bring an abundant species down allowing a rare species to rise to dominance. Microbial time series spanning thousands of generations support this prediction. Our results suggest that near-neutrality within niches may allow numerous rare species to persist in the wings of the dominant ones. Stand-ins may serve as insurance when former key species collapse” (Van Nes et al. 2023)

1.2. Main paper; discussion starts at 25:09 minutes

The Most Interesting Things (according to students)

The gut microbiome has so much impact on the horse’s performance years down the line in as early as 28 days. It makes us wonder why 28 days is the “golden number.”  The samples that had low diversity had up to 40% of their population being made up of the same species which connected to the snippet article on diversity and the importance of rare species.  The complexity and balance of a microbiome will determine how healthy an individual is. For example, periodontal disease occurs when an imbalance of microbes causes gram negative bacteria to form over the normal bacteria.

“Gut bacterial communities have a profound influence on the health of humans and animals. Early-life gut microbial community structure influences the development of immunological competence and susceptibility to disease. For the Thoroughbred racehorse, the significance of early-life microbial colonisation events on subsequent health and athletic performance is unknown. Here we present data from a three-year cohort study of horses bred for racing designed to explore interactions between early-life gut bacterial community structure, health events in later life and athletic performance on the racetrack. Our data show that gut bacterial community structure in the first months of life predicts the risk of specific diseases and athletic performance up to three years old. Foals with lower faecal bacterial diversity at one month old had a significantly increased risk of respiratory disease in later life which was also associated with higher relative abundance of faecal Pseudomonadaceae. Surprisingly, athletic performance up to three years old, measured by three different metrics, was positively associated with higher faecal bacterial diversity at one month old and with the relative abundance of specific bacterial families. We also present data on the impact of antibiotic exposure of foals during the first month of life. This resulted in significantly lower faecal bacterial diversity at 28 days old, a significantly increased risk of respiratory disease in later life and a significant reduction in average prize money earnings, a proxy for athletic performance. Our study reveals associations between early-life bacterial community profiles and health events in later life and it provides evidence of the detrimental impact of antimicrobial treatment in the first month of life on health and performance outcomes in later life. For the first time, this study demonstrates a relationship between early-life gut bacterial communities and subsequent athletic performance that has implications for athletes of all species including humans” (Leng et al. 2024).

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

Snippet Main
Vision and Change Topics
  • Evolution (V&C_E)
  • Impact of Microorganisms (V&C_IM)
  • Microbial Ecology (V&C_ME)
  • Impact of Microorganisms (V&C_IM)
  • Microbial Ecology  (V&C_ME)
ASM Fundamental Statements
  • Fundamental Statement 3 (ASM_3): The evolution of microbes is impacted by their interactions with the environment and a variety of ecological forces, including other microbes, humans, and habitats.
  • 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 3 (ASM_3): The evolution of microbes is impacted by their interactions with the environment and a variety of ecological forces, including other microbes, humans, and habitats.
  • 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.

3.  Potential Learning Objectives for the Podcast

The student will be able to: Paper1 Order2
  • Describe that organisms are found in a diverse community and the members of the community are largely unknown.
  • Explain why it is important that microbes exist in a diverse community using the term response diversity.
 S L
  • Argue why it is hard to study evolution in real time and state the best choice of microbe for these experiments.
  • Predict how a change in the ecosystem, such as temperature change or elimination of a member of the community, would impact the diversity of a community.
 S H
  • Describe how microbial diversity impacts performance.
  • Discuss how different environments and food sources may have altered the horses’ microbiomes.
 M L
  • Predict how gut microbiome diversity might enhance performance.
  • Predict how various treatments affect the Shannon Diversity Index of the system.
  • Identify the implications of the study and suggest further research.
 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

  • Antibiotic Resistance Survey (7:33): A survey performed in order to identify what antibiotics in what doses an organism is resistant to.
  • Genomic Analysis/Sequencing (7:39): Researchers sequenced the genomes of the strains and compared the sequences to identify strains and antibiotic resistances.
  • Clinical Trial (14:40–16:00): These are research studies performed on people that are aimed at evaluating a medical procedure’s efficacy.
  • Quarantine (27:09–28:14): Quarantine is the isolation of an infected individual in order to prevent them from spreading disease, or the isolation of a healthy individual in order to prevent them contracting a disease.
  • Contact Tracing (28:45–29:08): This is an epidemiology method to track disease transmission by identifying where infected people may have contacts others who became infected later.  The researchers collected a large number of samples from the outbreak in Asia and analyzed them in order to identify where each microbe came from and where/when each resistance plasmid was acquired.

4.2. Main Paper

  • Polymerase Chain Reaction (PCR) (42:25): This is a molecular biology technique that makes copies of DNA sections based on complementarity between the template and primers.  Here, it was used to determine the prevalence of Chytrid fungus before and after the epidemic
  • Comparative Morphology (42:37–48:46): This is a method of comparing physical features.  Here, the researchers examined the structures of both the historical isolates and contemporary isolates and found no differences.
  • Whole Genome Sequencing (49:29–49:49): The researchers sequenced the genomes of the fungi, both the HI and the CI,  and examined 25k single nucleotide polymorphisms (SNPs) and found no significant differences.
  • Testing Pathogenicity (44:23–49:30): This is a test of the ability to cause disease.  Here, the researchers compared historical and new BD isolates in two frog species and found no difference in BD’s ability to infect frogs.

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

5.1. Snippet Paper

  • Antibiotic Resistance (8:15–8:42; 17:20–18:31): Salmonella typhi maintains R factor on IncH1 plasmid and encodes resistance to most first-line antimicrobials.  XDR/TDR: XDR is extreme drug resistance; TDR is total drug resistance. Salmonella typhi is now XDR and is developing more resistances -> may soon be TDR
  • Disease Transmission (9:35–10:10): Salmonella typhi is spread within a population via the fecal-oral route
  • Case Fatality Rate (11:30–11:50; 16:04–16:10): Prior to the introduction of antibiotics, typhoid fever had a CFR of 15%, and after the discovery of chloramphenicol, that CFR plummeted to <1%.
  • Disease-induced Diarrhea: (12:35–13:59): There are three distinct types of disease-induced diarrhea: First: ride it out on a toilet, 24-hour bug; Second: More severe, requires antibiotic treatment but recoverable; Third: Infection becomes systemic, leading to bacteremia and sepsis
  • Plasmids (16:50–17:05; 18:20–18:31): Incompatibility group plasmid: plasmid in Salmonella that kept the antibiotic resistance plasmids out. However, that plasmid has been ousted and the resistance plasmids are in.
  • R Factors (17:00–17:32): Resistance traits found on plasmids convey antibiotic resistance to the organism
  • Antibiotic Targets (18:47–20:25): Different antibiotics target different parts of a microbe, which allows a new antibiotic to be utilized against an organism that has become resistant to other treatments
  • Asymptomatic Spread (26:46): Typhoid Mary spread typhoid fever as she was a carrier and shedder of the disease but did not ever present with symptoms

5.2. Main Paper

  • Epidemic/Epizootic Phase (38:30–38:49): The epidemic phase of the fungal infection within the frog population caused widespread amphibian death
  • Endemic/Enzootic Phase: (38:30–38:49): In the post-epidemic phase, the fungal infection was a more common and less devastating disease and the frogs survived the illness

6. Podcast Questions

  1.  Why is it important to consider the ecological context when picking a microorganism for research, as discussed in the podcast? 
    1. It affects the ability of microorganisms to be genetically modified.
    2. It influences the microorganism’s natural behavior and interactions.
    3. It helps determine how long the microorganism can live for.
    4. It provides information on the microorganism’s aesthetic qualities.
  2. Which statement best identifies what response diversity is?
    1. Dominant members of an environment adapt to specific environmental conditions and take over the population.
    2. Rare members of an environment react to small changes in the environment to stabilize long term ecosystems.
    3. Dominant species uptake nutrients from the environment and release waste that can be recycled by the ecosystem.
    4. Organisms modify the environment and the environment modifies organisms so that an equilibrium is achieved.
  3. Why are RNA viruses the best organism to use when studying evolution and adaptation in real time?
    1. They are complex organisms so they can adapt to many environments.
    2. They are the dominant species in most ecosystems so they infect most species.
    3. They do not have proofreading so changes to the genome can occur quickly.
    4. They are a rare species in most ecosystems so they have few hosts available.
  4. Which of these does NOT describe how a change in temperature or the elimination of a member of a microbial community impacts the diversity of that community?
    1. A temperature shift or the loss of a member would likely have a significant effect and decrease diversity by allowing a few resilient species to dominate and exclude less adaptable species.
    2. Elimination of a microbial member or temperature shift would not significantly affect microbial diversity, as the microbial communities can rapidly evolve and recover without losing diversity.
    3. A temperature change or the elimination of a key member would cause a temporary decrease in diversity, but rebound as microbes adapt to the environment and a new dominant species forms.
  5. Why did the researchers use horses from different farms/training centers?
    1. There was not enough space at one location for all of the horses to stay so they had to use horses from multiple locations.
    2. The researchers were interested in determining if weather conditions had an impact on the microbiome.
    3. The researchers wanted to include aspects of feeding and they knew that horse feed was different at the various locations.
    4. To see how eating different grasses and exposure to different soil microbes would impact the horses’ microbiome.
  6.  Which of these does NOT cause a lower Shannon Diversity Index?
    1. Grazing on naturally growing pasture grass.
    2. Being exposed to antimicrobials early in life.
    3. Taking a probiotic of only two microorganisms.
    4. Feeding off breast milk for the horse’s whole life.
  7. What specific characteristic of the gut microbiome was found to correlate with better performance in racehorses?
    1. A high abundance of Firmicutes bacteria and low abundance of Bacteroidetes.
    2. A higher proportion of archaea species compared to bacterial species.
    3. A minimal microbial load with a focus on anti-inflammatory microbes.
    4. Greater microbial diversity and presence of certain beneficial bacteria.
  8. Which of the following research experiments would best further the study of gut microbiomes in animal performance?
    1. Treating with compounds that directly interfere with the synthesis of anabolic steroids.
    2. Increasing oxygen supply to muscle tissue during exercise by engineering blood cells.
    3. Modulating the nutrient and energy availability from food and hence the absorption.
    4. Determining gene expression of biological pathways that involve specific athletic traits.
  9. Which of the following research implications could arise from the findings discussed in the episode regarding microbial communities in athletic animals?
    1. Microbial transplants could be used to enhance the performance of racehorses or athletes.
    2. All microbial species are equally important for improving performance, regardless of diversity.
    3. Only the microbial species with the highest abundance in the gut are beneficial to performance.
    4. The composition of microbial communities cannot be altered to improve athletic outcomes.

7. Figure Reading Exercises

The following are two figure reading exercises, both from the main paper (Figures 1A and 4A-C).

7.1. First Figure Reading Exercise 

7.1.1. Learning Objectives

Students will be able to: 

  • Identify key aspects of a heat map style species abundance chart.
  • Evaluate the data to make conclusions about how the gut microbiome changes over the year.
  • Analyze the data to identify samples with low diversity and samples with high diversity.
  • Predict why the researchers would use samples from day 28 for the rest of the studies.
  • Hypothesize how observed microbiomes might affect future performance.
Experimental Background (Leng et al. 2024, Figure 1A)

The importance of the microbiome, particularly the gut microbiome, to health and performance is an area of great interest.  However, studies that can confirm the relationship of gut microbiome to health and performance are difficult to conduct.  Here, Leng et al. (2024) make careful and controlled examination of this relationship using thoroughbred race horses. In their first experiment, the researchers investigate how the gut microbiome diversity and abundance changes across the first year of each horse’s life by examining the fecal bacterial community because the diversity of the fecal samples correlates with the gut microbiome diversity. They represent the community members by abundance using a heatmap generated using metagenomic sequencing of the 16S rDNA. Abundance of a bacterial family (listed on the left) is quantified using a log base 2 value which is seen in the color key. The SP numbers (top) coordinate with days the fecal samples were collected after birth (01 is 2 days, 02 is 8 days, 03 is 14 days, 04 is 28 days, 05 is 60 days, 06 is 90 days, 07 is 180 days, 08 is 272 days, 09 is 365 days).

Heat map of biodiversity in foals; SP01 is the least abundant, with SP05 through 09 relatively similar in abundance.
Figure 1A “Maturation of the faecal bacterial community during the first year of life in 52 Thoroughbred foals. Data from 16S rRNA sequencing of 430 equine faecal DNA extracts. (A) Heatmap for relative abundance of the 20 most abundant bacterial families. Samples grouped by sample point (SP01, 2 days; SP02, 8 days; SP03, 14 days; SP04, 28 days; SP05, 60 days; SP06, 90 days; SP07, 180 days; SP08, 272 days; SP09, 365 days). Abundance scale corresponds to log2(counts + 1), and counts have been rarefied….” (Leng et al 2024, image cropped and figure legend text limited to panel A only).

7.1.2. Questions

  1. What does the color scale in Figure 1, panel A key represent?
    1. Bacterial species diversity
    2. Statistical significance
    3. Relative abundance (log2(counts+1))
    4. Age of the foals
  2. What color represents low abundance of a bacterial family?
    1. Purple
    2. Teal
    3. Green
    4. Yellow
  3. According to Figure 1A, what is the time point (in days) for sampling point SP04?
    1. 14 days
    2. 28 days
    3. 60 days
    4. 90 days
  4. SP __ is a sample with low biodiversity and SP__ is a sample with high biodiversity.
    1. 07, 03
    2. 01, 04
    3. 06, 09
    4. 01, 02
  5. Which of these answers best explains how the gut microbiome changes over the first year of a race horse’s life?
    1. Diversity is low to start, but gains throughout the first 1-2 months. The diversity decreased a little after the first couple of months but stayed relatively constant for the rest of the year.
    2. Diversity starts high and is lost over the first month of life. The microbiome then gains diversity but never quite reaches as high diversity as was found at the beginning of life.
    3. The gut microbiome started with low diversity and slowly gained diversity throughout the year, having the most diversity towards the last half of the year, which is found in SP07.
    4. The gut microbiome starts with a high microbial diversity and slowly loses the diversity throughout the year, having the least diverse gut microbiome in the last half of the year.
  6. The researchers next focused their study on the relationship of performance and gut microbiome.  Which sample time point can you make a good case for the researchers to use in the follow up experiments and why?
    1. SP01 because it is the least diverse sample so the researchers can find which bacterial family has the biggest impact on the racehorse’s athletic performance.
    2. SP09 because it is the last sample collected so it will give the researchers the most information about the horse’s microbiome and its effect on athletic performance.
    3. SP06 because bacterial diversity starts to become somewhat constant so it will give the best information about the gut microbiome and racehorse athletic performance.
    4. SP04 because it is the most diverse sample so it will help the researchers determine how gut microbiome diversity impacts athletic performance of racehorses.
  7. If Horse A’s microbiome remains constant at SP01’s diversity level and Horse B’s microbiome remains constant at SP07’s diversity level. Which horse, A or B, would you expect to have a better athletic performance and why?
    1. Horse A because it has a less diverse microbiome.
    2. Horse A because it has a more diverse microbiome.
    3. Horse B because it has a less diverse microbiome.
    4. Horse B because it has a more diverse microbiome.

7.2. Second Figure Reading Exercise

7.2.1. Learning Objectives

Students will be able to:

  • Identify key aspects of a box and whisker plot visualization that are relevant for interpreting these data.
  • Analyze the data to make conclusions about the impact of antimicrobial use on bacterial diversity.
  • Evaluate the data to make conclusions about the relationship of early antimicrobial exposure and performance.
  • Defend why a horse with lower bacterial diversity has a decrease in athletic performance.
  • Critique the experiment, providing one example of how they could change the experiment to make it more reliable.
Experimental Background (Leng et al. 2024, Figure 4A-C)

The importance of the microbiome, particularly the gut microbiome, to health and performance is an area of great interest.  Additionally, the role that antibiotic use plays in re-shaping the gut microbiome and affecting performance has been little studied.  To address this, Leng et al. (2024) investigated how antibiotic exposure (which kills bacteria) in the first month of a thoroughbred race horse’s life affected their gut microbiome diversity and adult performance. The researchers first compared the gut microbiome diversity at 28 days old for horses that had antibiotic exposure and did not have antibiotic exposure prior to that point (panel A).  The researchers next examined whether early-life antibiotic exposure (and its subsequent effect on microbiome diversity) impacted the horse’s athletic performance and success of their racing careers as measured by the horse’s official rating at 3 years of age (panel B) and ranked earnings at 3 years of age (panel C). In each panel, data for horses with early-life antibiotic exposure are noted in red and horses without early-life antibiotic exposure are noted in blue.

Three box plots showing bacterial families' (a) diversity, (b) official rating, and (c) ranked earnings with and without antimicrobial use in first month of life. All are much lower with antimicrobial use.
Figure 4A-C. ” Effect of antimicrobial use during first month of life. (A) Comparison of observed diversity of samples taken at SP04 (4 weeks old) from horses that had (Yes) vs did not have (No) antibiotics on or before the sampling point. (B) Comparison of mean Official Rating values of horses stratified by antimicrobial use during first month of life. (C) Comparison of mean ranked earnings of horses stratified by antimicrobial use during first month of life. For panels (A–C) box-and-whisker plots where the lower and upper boundaries of the box represent the 25th and 75th quantiles of the data distribution, respectively, the median is indicated by the horizontal line inside the box and whiskers extending from the box denote the minimum and maximum values excluding outliers. All p values are based on a Mann–Whitney test….” (Leng et al. 2024, image cropped and figure legend text limited to panels A-C only)

7.2.2. Questions

  1. What does the line in the middle of the box and whisker plot represent?
    1. Mean
    2. Median
    3. Mode
    4. Outlier
  2. What do the lower and upper boundaries of the box and whisker plot represent?
    1. The 25th quantile and 75th quantile.
    2. The minimum and maximum values.
    3. The outliers of the data.
    4. A 95% confidence interval of the data.
  3. Using the ranked earnings data broken out by antibiotic use (panel C), how does antimicrobial use during the first month of life appear to affect ranked earnings?
    1. Horses with early-life antimicrobial use had significantly higher ranked earnings.
    2. Horses with early-life antimicrobial use had significantly lower ranked earnings.
    3. There was clearly no significant difference in ranked earnings (medians overlap).
    4. The data are inconclusive because while different, they are not statistically significant.
  4. Why would horses that receive antimicrobial treatment tend to have lower bacterial diversity in their gut microbiome?
    1. Antimicrobials enhance the immune system, which removes excess bacteria from the gut.
    2. Antimicrobials selectively kill harmful bacteria, leaving only beneficial bacterial strains.
    3. Antimicrobials increase the growth of all bacteria, leading to competitive exclusion.
    4. Antimicrobials reduce bacterial species by killing both harmful and beneficial bacteria.
  5. The researchers use ranked earnings as a measure of performance in this study.  This has limitations.  What are some limitations and what other measures might they have used to more directly test performance? Pick all that apply.
    1. Earnings for winning vary by the competitors faced and track;  Compare finishing times against specific competitors and tracks.
    2. Earnings for winning vary by race, so they are not a good indicator for performance; Measure muscle mass per horse weight.
    3. Earnings are not necessarily linear for placing first, second, etc.in a race; Timed trials for each horse over time on a track.
    4. There are no limitations, but the statistical test was not appropriate for the data. The researchers could switch to a different test.
  6. Why might a horse with reduced gut microbiome diversity experience decreased athletic performance?
    1. A decrease in gut microbial diversity weakens the horse’s cardiovascular and respiratory systems, reducing its ability to sustain physical activity.
    2. Reduced bacterial diversity in the gut causes the horse to overeat, making it weigh more than other horses and thereby run slower than the others.
    3. Diverse gut microbiome supports digestion, metabolism, and immune function, so reduced diversity may impair energy availability and recovery.
    4. Reduced gut bacterial diversity leads to dehydration, which directly causes muscle fatigue and poor athletic performance in horses and other animals.
  7. Which statement best fits the conclusions and implications of the series of experiments in panels A–C?
    1. Decreased gut microbiome diversity and ranked earnings was found for early-life antimicrobial use, but did not show a statistically significant difference in official rating. Antimicrobials should be used cautiously.
    2. Statistically significant decreased gut microbiome, ranked earnings, and official rating were found in horses that had early-life antimicrobial exposure.  Antimicrobials should be used cautiously.
    3. Significant differences in gut microbiome diversity and ranked earnings were not observed in horses with early-life antimicrobial use, but were for official rating. Antimicrobials should be used more often.
    4. Antimicrobial use in the first month of life did not cause any significant difference in gut microbiome diversity, official rating, and ranked earnings. Antimicrobials should be used more often.

8. Paper Information and Licensing

8.1. Snippet paper

8.2. Main paper

  • Leng J, Moller-Levet C, Mansergh RI, O’Flaherty R, Cooke R, Sells P, Pinkham C, Pynn O, Smith C, Wise Z, et al. 2024. Early-life gut bacterial community structure predicts disease risk and athletic performance in horses bred for racing. Sci Rep. 14(1):17124. https://www.nature.com/articles/s41598-024-64657-6
  • This article is licensed for Creative Commons use using CC BY 4.0, which allows re-use and adaptation with proper attribution and notation of any changes made. See the article’s copyright information.

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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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