TWiM #239: The Phoenix of Bacteria

1. Paper Abstracts

1.1. Snippet paper; discussion starts at 3:17

The abstract cannot be copied due to licensing restrictions. Please see licensing information and links to the article at the journal’s web page and/or PubMed in Section 8.1.

1.2. Main paper; discussion starts at 32:00 minutes

“The mammalian gut microbiota is a complex community of microorganisms which typically exhibits remarkable stability. As the gut microbiota has been shown to affect many aspects of host health, the molecular keys to developing and maintaining a “healthy” gut microbiota are highly sought after. Yet, the qualities that define a microbiota as healthy remain elusive. We used the ability to resist change in response to antibiotic disruption, a quality we refer to as ecological resistance, as a metric for the health of the bacterial microbiota. Using a mouse model, we found that colonization with the commensal fungus Candida albicans decreased the ecological resistance of the bacterial microbiota in response to the antibiotic clindamycin such that increased microbiota disruption was observed in C. albicans-colonized mice compared to that in uncolonized mice. C. albicans colonization resulted in decreased alpha diversity and small changes in abundance of bacterial genera prior to clindamycin challenge. Strikingly, co-occurrence network analysis demonstrated that C. albicans colonization resulted in sweeping changes to the co-occurrence network structure, including decreased modularity and centrality and increased density. Thus, C. albicans colonization resulted in changes to the bacterial microbiota community and reduced its ecological resistance.” (Markey et al 2021, no changes)

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

3.  Potential Learning Objectives for the Podcast

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

• Kirby-Bauer Susceptibility Test (5:06–9:44): The Kirby-Bauer susceptibility test is utilized to assess sensitivity of microbes to certain antimicrobial substances. A bacterial lawn is inoculated onto an agar plate, then a small paper disk that has been treated with a specific antibiotic is placed onto the agar. As the plate is incubated, the bacteria may form a zone of inhibition depending on if the particular species is susceptible or resistant to a certain antimicrobial. In the context of this paper, this test is performed with a calcium sulfate impregnated bead with tobramycin (used clinically for preventing infections with prosthetic joints) against P. aeruginosa, demonstrating “phoenix colonies.”
• Replica Plating (10:10–19:36): This technique creates several copies of a bacterial plate. This can be done with grid plating (and labeling) to know which bacteria is transferred, or with the formal technique of using a sterile cloth to stamp original colonies onto another plate.
• Growth Curves (13:41): A representation on a graph of increased cell numbers in batch culture, generated by measuring increasing absorbance with a spectrophotometer.

4.2. Main Paper

• Induction of Secondary/Opportunistic Infection (32:00–38:59): The guts of mice are “infected” with Candida albicans yeast species after drinking water laced with the antimicrobial, cefoperazone, for some time. The cefoperazone kills off the natural gut microbiota and allows Candida to proliferate, slowing the “rebound” of the microbiota.
• Bioinformatics (32:00; 36:40): This is the use of computational tools to analyze large amounts of biological data. As Elio says here “no bioinformatics, no microbiology.”
• Co-occurrence Network Analysis (32:00–36:45): Co-occurrence network analysis is a computational technique used to map relationships between microbial species based on their presence, abundance, and interactions within a given environment. Here it was used to examine how Candida albicans alters gut microbial communities following antibiotic treatment. It was found that in the presence of Candida, bacterial populations did not return to their original state as quickly after antibiotic exposure, which suggests that Candida disrupts ecological stability and weakens microbial resilience – specifically, the Bacteroides and Akkermansia relationships were altered.
• Model Organism Experimentation (36:16): This is the use of model organisms to understand complex biological processes without experimenting on humans. In this paper, mice were used as the model organism in this experiment, even though mice are usually not susceptible to C. albicans, this approach provides valuable insight into how the human gut microbiota could possibly react in the experimental circumstances.
• Node Centrality Analysis (41:08): This is a method in graph analytics that allows researchers to identify important nodes in a network, inferring relationships between data sets
• Fast Greedy Algorithm Analysis (45:20): This is a statistical method to detect communities by clustering of data.

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

5.1. Snippet Paper

5.2. Main Paper

4. Which of the following is true about the microbial diversity in the gut?
   1. Higher diversity always improves gut health
   2. Lower diversity can indicate an unhealthy gut
   3. Microbial diversity has no impact on gut stability
   4. Antibiotics always increase gut microbial diversity
5. What is ecological resistance and why is it important for the gut microbiome?
   1. Ecological resistance is the ability of a system to recover after a disturbance, such an antimicrobial treatment.  It is important because the gut microbiome plays a significant role in an organism’s health.
   2. Ecological resistance is the ability of an ecosystem to resist disturbances in homeostatic processes.  It is important because the gut microbiome plays a significant role in an organism’s mental state.
   3. Ecological resistance is the ability of a system distribute negative effects across many individual organisms in the community.  It is important because the gut microbiome is composed of many species.
   4. Ecological resistance is the ability of a system to lose and regain phenotypic and metabolic potential after an environmental stressor.  It is important because the gut microbiome is tressed easily.
6. What role did bioinformatic analyses play in helping researchers determine C. albicans’ influence on antibiotic resistance in gut microbiota?
   1. These analyses helped researchers track the relative proportions of C. albicans to gut microbiota following microbiome depletion by antibiotic exposure.
   2. These analyses helped compare the development and rate of new genetic mutations for resistant gut microbiota in the presence and absence of C. albicans.
   3. These analyses helped researchers track the changes in gut microbiome composition and relationships in the presence of C. albicans following antibiotic exposure.
   4. These analyses helped predict which strains of gut microbiota would become the most resistant following antibiotic exposure in the presence of C. albicans.
7. You are designing an experiment to test the efficacy of ciprofloxacin, an antibacterial gyrase inhibitor, as treatment or prevention for chronic prosthetic joint infections. Which is a good method to test resistance related to an implanted prosthetic joint?
   4. Couple transplant bone and prosthesis material in an open environment to simulate physiological bone and prosthetic joint interactions.
   5. Perform an antimicrobial susceptibility test on nutrient agar incubated anaerobically, utilizing ciprofloxacin against different pathogens.
   6. Add calcium sulfate impregnated beads infused with ciprofloxacin to broth cultures of pathogens and incubate them at body temperature.
   7. Impregnate scaffold materials with bone cells and submerge the structure in an oxygen-rich, low salt medium and swirl every 30 minutes.

7. Figure Reading Exercises

The following are two figure reading exercises, one from the snippet paper (Figure 1) and one from the main paper (Figure 3AB).

7.1. First Figure Reading Exercise

7.1.2. Questions

1. What color represents the highest metabolic activity?
   1. blue
   2. red
   3. green
   4. yellow
2. When did resistant “phoenix” colonies first become visibly apparent within the zone of inhibition?
   1. Day 0
   2. Day 1
   3. Day 3
   4. Day 5
3. Which reason(s) is/are most likely to account for the changes in bacterial growth from day 0 to 4?
   1. The tobramycin is slowly killing the bacteria, resulting in an expanding zone of clearing in the lawn.
   2. The tobramycin slows the bacterial metabolism early, but the bacteria respond by switching to chemotrophy.
   3. The tobramycin enables the bacteria to become motile, so they using their gliding behavior to move.
   4. The tobramycin is slowly moving past the bacteria, so they are then able to grow in a tobramycin-free zone.
4. What is the most likely explanation for the delayed appearance of colonies within the zone of clearance?
   1. The antibiotic takes several days to diffuse across the plate.
   2. The bacteria mutate rapidly and begin dividing immediately.
   3. Some cells survive initial treatment by entering a tolerant state.
   4. The tobramycin becomes less and less potent over time.
5. After performing an antimicrobial susceptibility test, you find growth within the zone of clearance. These colonies might be truly antibiotic-resistant due to mutations, or could be “phoenix colonies” that are simply adjusting their metabolism.  Which experiment would allow you to tell the difference?
   1. Re-perform an antimicrobial susceptibility test using a different antibiotic to test if it is resistant to that class of antibiotics.
   2. Inoculate a new plate with a colony growing within the zone of clearance and re-expose it to the same antibiotic.
   3. Add more disks of the same antibiotic to the current antimicrobial susceptibility test to determine if the growing bacteria is truly resistant.
   4. Add a combination of antibiotics to the current antimicrobial susceptibility test to determine if the growing bacteria is truly resistant.

7.2. Second Figure Reading Exercise

Experimental Background  (Markey et al., Figure 3AB)

The gut microbiome is implicated in overall health, particularly in nutrient uptake and the immune system.  Dysbiosis is the state in which the ratio, species, or number of gut microbiota is compromised.  Markey et al. (2021) used a Candida albicans mouse infection model  to investigate how treatment with antifungal clindamycin affected the gut microbiome, as well as gut microbiome recovery (ecological resistance).  The researchers used next-generation sequencing technologies using 16S rDNA fragments were used in a bioinformatics pipeline to identify and quantify the gut microbiome community in control mice and C. albicans-infected mice exposed to different antibiotic concentrations.  The relative abundances of bacterial community community members are reported as stacked bar charts (panel A).  To quantify the impact on biodiversity, the Simpson’s index was calculated for each condition (panel B).  Simpson’s index, which ranges from 0 to 1, is higher when there are a larger number of species (species richness) and evenness in their abundance.

 

7.2.2. Questions

1. What color represents the abundance of the Bacteroides?
   1. red
   2. yellow
   3. green
   4. blue
2. Which dosage of clindamycin showed the most significant change in microbiota composition compared to the control with no antibiotic in the uncolonized mice?
   1. 1.11 mg/kg
   2. 3.33 mg/kg
   3. 10 mg/kg
   4. No significant change was observed in the uncolonized mice.
3. Which dosage of clindamycin showed the most significant change in microbiota composition compared to the control with no antibiotic in C. albicans-colonized mice?
   1. 1.11 mg/kg
   2. 3.33 mg/kg
   3. 10 mg/kg
   4. No significant change was observed in the colonized mice.
4. The Simpson’s index, a measure of biodiversity, for each treatment group is displayed using a bar graph (panel B). Match the graph feature with its description. (1 = mean; 2 = median; 3 = standard deviation; 4 = standard error of the mean; 5 = full data range excluding outliers; 6 = statistical significance; 7 = individual data points; 8 = type of colonization)
   1. ________ bar height
   2. ________ bar color
   3. ________ symbols
   4. ________ whiskers
   5. ________ asterisks
5. Microbial biodiversity is quantified using the Simpson index and visualized in panel B.  What does this analysis tell you about the difference in microbiome biodiversity between C. albicans-colonized on uncolonized mice at each drug concentration?
   1. There is no statistical difference in microbiome diversity between infected and uninfected mice.
   2. Infected mice have a lower microbiome diversity when treated with 1.11 mg/kg drug than uninfected mice.
   3. Uninfected mice have a higher microbiome diversity when treated with 3.33 mg/kg drug than infected mice.
   4. There is lower microbiome diversity in infected mice compared to uninfected mice at every drug concentration.
6. Using the data presented in panels A and B together, label the conclusion statements below as true (T) or false (F).
   1. ________ Colonization with Candida dramatically alters the mice’s microbiome composition before treatment with antibiotics.
   2. ________Diversity of both uncolonized and Candida-colonized mice did not change in a statistically significant way in the low-dose antibiotic challenge (1.11 mg/kg).
   3. ________The high (10 mg/kg) doses of clindamycin resulted in a decrease in diversity in both uncolonized and Candida-colonized mice.
   4. ________Microbiota composition and diversity are more strongly affected by clindamycin treatment than by Candida colonization.