Impact of Microorganisms

TWiM #298: Impact of Lung Microbiome and Racial Disparities on Asthma

Podcast and Annotation Information
  • Annotation by Vincent Vo, Eden Shimekt, Maggie Ridgway, Anvitha Ananthaneni, and Eileen Hotze, and Rebecca Seipelt-Thiemann
  • Podcast audio by TWiM: Listen to TWiM #298 Podcast
  • Podcast transcript by Otter.ai  and edited by Vincent Vo, Eden Shimekt, Maggie Ridgway, Anvitha Ananthaneni, and Laurel Thompson: Access Podcast Transcripts
  • Papers Discussed:
    • Editorial: 2023. Better training for non-academic careers. Nat Microbiol 8, 1749–1750. doi: 10.1038/s41564-023-01499-4
    • Kozik AJ, Begley LA, Lugogo N, Baptist A, Erb-Downward J, Opron K, Huang YJ. 2023. Airway microbiota and immune mediator relationships differ in obesity and asthma. J Allergy Clin Immunol. 151(4):931-942. doi: 10.1016/j.jaci.2022.11.024

1. Paper Abstracts

1.1. Snippet paper; discussion starts at 3:27 minutes

The Most Interesting Things (according to students)

PhD training needs to be changed.  Also, the degree is erroneously perceived as leading down one career path.

The abstract  from the snippet paper cannot be copied due to licensing restrictions. Please see licensing information and links to the article at the journal’s website.

1.2. Main paper; discussion starts at 23:08 minutes

The Most Interesting Things (according to students)

Many things play a role in microbiome, so we need to include physical environment, economic and psychological stress, and access to quality health care as well as race.  Also, there is a lung microbiome, it isn’t a sterile environment as was once thought.

The abstract from the main paper cannot be copied due to licensing restrictions. Please see licensing information and links to the article at the journal’s website.

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

Snippet Main
Vision and Change Topics
  • The snippet paper is not a scientific research paper, so not applicable
  • Metabolic Pathways (V&C_MP)
  • Microbial Ecology (V&C_ME)
  • Impact of Microbes (V&C_IM)
ASM Fundamental Statements
  • The snippet paper is not a scientific research paper, so not applicable
  • Fundamental Statement 20 (ASM_20): Microbes are ubiquitous, found in diverse and dynamic ecosystems, where they use available resources and often form complex communities.
  • Fundamental Statement 21 (ASM_21): Microbes and the environment interact with and affect each other.
  • 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.

3.  Potential Learning Objectives for the Podcast

The student will be able to: Paper1 Order2
  • The snippet paper is not a scientific research paper, so not applicable.
 S L
  • The snippet paper is not a scientific research paper, so not applicable.
 S H
  • Define microbiome.
  • Identify the patient results for cytokine and immune mediators.
 M L
  • Generalize ways in which a microbiome can be different based on  the podcast discussion.
  • Predict patient type based on bacterial characteristics for the microbiome.
 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

N/A

4.2. Main Paper

  • Biopsychosocial Model (27:03): This is a model that addresses racial and ethnic disparities and asthma outcomes.
  • Multiplex Assays (31:53): These are assays that investigate multiple items using the same sample.  The researchers used a single sample to assess multiple cytokine levels to create cytokine profiles involving antibodies specific for different components of immune system.
  • Computational Analysis (32:00): Computational analyses are those that uses computer methods to help answer biological questions.  Here, the researchers used them to process and sequence DNA from the induced sputum samples.
  • Induced Sputum Sample (32:17): This is a less invasive technique to obtain samples that have similar microbiome composition as brushing the bronchial airway.
  • Bronchoalveolar Lavage (32:50): This is a more invasive technique (compared to induced sputum sampling) involving pouring saline into a subsection of the lung to expel any airway contents.
  • Metagenome Sequence Analysis (35:48; 42:51): Metagenome sequencing involves isolating DNA from a sample and using methods to determine the species present.  Here, the researchers found microbial community composition differed between people who were obese and those who were not obese.
  • Network Analysis (40:56): These are methods that integrate data sets to generate relationship (network) patterns. Microbe and cytokine patterns are specifically mentioned in podcast.

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

5.1. Snippet Paper

N/A

5.2. Main Paper

  • Airway Microbiota & Immune Mediator Relationships (23:40): Michele discusses her work on airway microbiota and immune mediator relationships on obesity and asthma.
  • Statistics on Obesity & Asthma (26:10): Provides national statistics on obesity. Comparing racial disparities in hospitalization with asthma. Discusses stigma of obesity as an obstacle to proper medical care. More highly affected by asthma: children, women, and racial/ethnic groups; small metropolitan areas; black children.
  • Lung Microbiome Research (33:00): Discusses techniques to gain samples for lung (and general) microbiome research.
  • Sleep Apnea (37:55): Discussed diagnosis of obstructive sleep apnea in terms of comparing obese and non-obese patients and their microbiomes. Emphasis was placed on microbes present when aspirating in sleep. 
  • Climate Change on Emerging Diseases (54:53): Florida cases of Malaria seen an increase as well as Texas. Climate change impacting infectious disease with clear data on spread of Malaria.
  • Plant Diseases (58:48): When drought occurs, plants become more sensitive to disease. 

6. Podcast Questions 

  1. What is a microbiome?
    1. All of the microorganisms that live in the human lung
    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. The researchers found that non-obese people had some, but not a great deal of cytokine and immune mediator differences between asthmatic and non-asthmatic.  What did they find for obese individuals with and without asthma?
    1. They also had few differences in either cytokines or immune mediators.
    2. They were quite different in cytokines, but not in immune mediators.
    3. They were quite different in immune mediators, but not in  cytokines.
    4. They found many differences in both cytokines and immune mediators.
  3. The podcasters mention how the microbiome was different between some of the patients.  In general, what ways can a microbiome differ? Pick all that apply.
    1. The species distribution can be altered by motility of the species involved.
    2. The species that are present/absent can differ between individuals.
    3. The distribution of species can differ even if the species are all present in both.
    4. The species can be have switched its cellular respiration based on environment.
  4. Predict the patient group for each bacterial metabolism.
    1. fatty acid synthesis and insulin signaling–obese, asthmatic
    2. porphyrin metabolism and amino acid degradation–non-obese, asthmatic

7. Figure Reading Exercises

The following are two figure reading exercises, both from the main paper (Figures 2b and 3).

7.1. First Figure Reading Exercise

7.1.1. Learning Objectives

Students will be able to:

  • Analyze data in the form of box and whisker plots.
  • Identify whether samples are the same or different given box and whisker plots.
  • Defend the statement that the microbiomes are the same or different.
Experimental Background (Kozik et al. 2023, Figure 2b)

The lung has its own microbiome composed of both bacteria and fungi.  Microbiome studies have shown that differences in total numbers present, which species are present, and the numbers of species present can be indicators of disease.  Kozik et al (2023) investigated the number of species present in asthma patients were not obese or were also obese.  Box and whisker plots were used to represent the number of different fungal species found in the lung microbiome found in each group, as measured by the number of operational taxonomic units (OTU).  

  • The figure from the main paper (figure 2b) cannot be copied due to licensing restrictions. Please see licensing information and links to the article at the journal’s web page https://doi.org/10.1016/j.jaci.2022.11.024

7.1.2. Questions

  1. Based on the data represented in the box and whisker plot, which group has more species of fungi present in the lung?
    1. Non-obese asthma patients
    2. Obese asthma patients
    3. Asthmatic patients
    4. We can’t tell from these data.
  2. Which group shows the least variability among the group for fungal species number?
    1. Non-obese asthma patients
    2. Obese asthma patients
    3. Asthmatic patients
    4. We can’t tell from these data.
  3. Which group has the patient with the largest number of different fungi in their lung microbiome?
    1. A non-obese asthma patient
    2. An obese asthma patient
    3. We can’t tell from these data.
  4. Which statement about the conclusions of this experiment is true and why?
    1. Obese asthma patients have more total fungi than non-obese asthma patients because the box plots are different in size and statistically significant.
    2. Obese asthma patients have fewer fungal species than non-obese asthma patients because the medians of the box plots are different and statistically significant..
    3. Obese asthma patients have more fungal species than non-obese asthma patients because the medians of box plots are different and statistically significant.
    4. Obese asthma patients have far fewer total fungi than non-obese asthma patients range and size of the box plots are different and statistically significant.

7.2. Second Figure Reading Exercise

7.2.1. Learning Objectives

Students will be able to:

  • Interpret correlation values and heat maps to identify relationships between variables.
  • Analyze correlation values and heat maps to identify relationships between variables.
  • Predict relationships between variables when given either correlation values or heat map visualization of a correlation matrix, and vice versa.
Experimental Background (Kozik, et al. Figure 3)

Cytokines are important mediators of the immune response.  Their regulation is tightly controlled and a variety of diseases are possible when they are not, particularly autoimmune diseases and chronic inflammation.  For this experiment, Kozik et al (2023) wanted to examine cytokine similarities and differences in two diseases involving chronic inflammation: asthma and obesity.  To do this, they quantified cytokine levels in the blood and sputum of individuals with and without asthma and obesity.  The mathematical representation of correlation was the Pearson’s correlation, which ranges from highly positively correlated at 1.0 to highly negatively correlation at –1.0.  The Pearson’s correlation value for all cytokines was then visualized as a heat map.   Cytokines measured in sputum are noted with an “s” at the beginning of the name; all others are measured from blood.

There are 4 heat map plots representing the correlation of cytokine levels in a pair-wise fashion among the 4 different groups:

  • Panel A: Individuals without obesity compared to individuals without asthma
  • Panel B: individuals without obesity compared to individuals with asthma
  • Panel C: Individuals with obesity compared to individuals without asthma
  • Panel D: Individuals with obesity compared to individuals with asthma

The figure from the main paper (figure 3) cannot be copied due to licensing restrictions. Please see licensing information and links to the article at the journal’s web page https://doi.org/10.1016/j.jaci.2022.11.024

7.2.2. Questions

  1. Which group shows the most positive correlation of cytokine levels overall?
    1. No asthma, non-obese
    2. No asthma, obese
    3. Asthma, non-obese
    4. Asthma, obese
  2. Which group shows the least correlation of cytokine levels overall? 
    1. No asthma, non-obese
    2. No asthma, obese
    3. Asthma, non-obese
    4. Asthma,  obese
  3. Which group shows the most negative correlation of cytokine levels overall?
    1. No asthma, non-obese
    2. No asthma, obese
    3. Asthma, non-obese
    4. Asthma, obese
  4. If there was a heat map plot of Pearson correlations where the leptin-leptin correlation cell was dark purple, what would you conclude?
    1. The two comparison groups have equally high levels of leptin.
    2. The two comparison groups have equally low levels of leptin.
    3. The two comparison groups have opposite levels of leptin.
    4. The two comparison groups are not correlated at all for leptin levels.
  5. If a Pearson correlation was at the level of 0.9, what would you conclude?
    1. The two comparison groups have equally high or equally low levels of the cytokine.
    2. The two comparison groups have opposite levels of the cytokine, such as one high and one low.
    3. The two comparison groups are not correlated at all for this cytokine.
  6. Based on the heat map of non-obese, no asthma, which cytokine level is most negatively correlated?
    1. PAI1
    2. Resistin
    3. MC1P
    4. Leptin
  7. True/False: Overall, sputum cytokine levels (those noted with an “s” in the name) are more highly correlated to each other than blood cytokine levels are to each other.

 

8. Paper Information and Licensing

8.1. Snippet paper

  • Editorial: 2023. Better training for non-academic careers. Nat Microbiol 8, 1749–1750. doi: 10.1038/s41564-023-01499-4
  • This article is not licensed for Creative Commons use. Thus, the abstract and figures cannot be copied here. Please see the article at the journal’s web page.

8.2. Main paper

  • Kozik AJ, Begley LA, Lugogo N, Baptist A, Erb-Downward J, Opron K, Huang YJ. 2023. Airway microbiota and immune mediator relationships differ in obesity and asthma. J Allergy Clin Immunol. 151(4):931-942. doi: 10.1016/j.jaci.2022.11.024
  • This article is not licensed for Creative Commons use. Thus, the abstract and figures cannot be copied here. Please see the article at the journal’s web page.

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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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