Evolution

TWiM #311: Bacteria, Beware of Siderophore Antibiotic Hybrids

Podcast and Annotation Information

  • Annotation by Annotation by Jack Lee, Nathan Krugman, and Blythe Janowiak
  • Podcast audio by TWiM : Listen to TWiM #311 Podcast
  • Podcast transcript by Otter.ai and edited by Marvin Romo, Gracie Helle: Access Podcast Transcripts
  • Papers Discussed:
    • Mahon MB, Sack A, Aleuy OA, Barbera C, Brown E, Buelow H, Civitello DJ, Cohen JM, de Wit LA, Forstchen M, Halliday FW, Heffernan P, Knutie SA, Korotasz A, Larson JG, Rumschlag SL, Selland E, Shepack A, Vincent N, Rohr JR. 2024. A meta-analysis on global change drivers and the risk of infectious disease. Nature. 629(8013):830-836. doi: 10.1038/s41586-024-07380-6
    • Barker KR, Rebick GW, Fakharuddin K, MacDonald C, Mulvey MR, Mataseje LF. 2024. When the Trojan horse is unable to reach inside the city: investigation of the mechanism of resistance behind the first reported cefiderocol-resistant E. coli in Canada. Microbiol Spectr. 12(5):e0322323. doi: 10.1128/spectrum.03223-23

1. Paper Abstracts

1.1. Snippet paper; discussion starts at 2:25 minutes

The Most Interesting Things (according to students)

It is interesting to see another way in which climate change could affect humanity in the future. Most conversations concerning climate change center around weather phenomena, but this article approaches the crisis from a new lens.  Exploration of how biodiversity loss and introduced species significantly increase infectious disease risk was interesting.

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 17:58 minutes

The Most Interesting Things (according to students)

The Trojan horse approach, via the exploitation of iron transports systems to penetrate gram-negative bacteria, was quite creative and highlights the complexity necessary now to produce effective medicine. The discussion on how to improve antibiotics by hybridizing them with other molecules to affect intake,  like the cotransport system was also interesting. The case study, they discuss on resistance emergence, particularly in a patient who had recently traveled from a region with high antibiotic resistance, underscores the global implications of antibiotic resistance and the need for continuous surveillance and research.

“Gram-negative metallo-β-lactamase-producing bacteria can be extremely problematic, especially when found to be extensively drug-resistant (XDR). Cefiderocol is a novel antimicrobial that has been shown to overcome most carbapenemases, with very rare resistance reported to date. Within our institution, two multidrug-resistant and one XDR strains were isolated from a patient who recently emigrated from India. Each isolate underwent whole-genome sequencing to resolve plasmids and determine phylogenetics, strain typing, and mechanisms of resistance. The XDR E. coli was ST167, harbored NDM-5, cirA and PBP3 mutations, consistent with cefiderocol resistance. Our study suggests that the NDM region is required in conjunction with cirA and PBP3 mutations. It is not clear why; however, our study did determine a potential novel iron-transport region unique to the cefiderocol-resistant isolate. This is the first characterized cefiderocol-resistant E.coli reported from Canada. Health centers should be on alert for this clone.” (Barker et al 2024, no changes)

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

Snippet Main
Vision and Change Topics
  • Evolution (V&C_E)
  • Microbial Ecology (V&C_ME)
  • Impact of Microorganisms (V&C_IM)
  • Evolution (V&C_E)
  • Impact of Microorganisms (V&C_IM)
  • Structure and Function (V&C_SF)
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 21 (ASM_21): Microbes and the environment interact with and affect each other.
  • Fundamental Statement 29 (ASM_29): The extent of microbial damage can be minimized by host-derived and external factors, including the microbiome, antibiotics, and immunity.
  • 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 6 (ASM_6): The distinct structures and processes in microbes can be targets for interspecies competition, antimicrobial treatments, and host immunity.
  • Fundamental Statement 29 (ASM_29): The extent of microbial damage can be minimized by host-derived and external factors, including the microbiome, antibiotics, and immunity.

3.  Potential Learning Objectives for the Podcast

The student will be able to: Paper1 Order2
  • Recall how various global environmental changes have impacted infectious diseases.
  • Define meta analysis.
 S L
  • Use the reasoning from the podcast to identify a hypothetical situation affecting infectious disease.
 S H
  • Recall why fetroja is classified as a hybrid antibiotic based on its mechanism of action.
  • Identify how antibiotic hybrids combat bacterial resistance.
 M L
  • Propose an experiment based on the results of this study and additional hypothetical evidence.
 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

  • Meta-analysis (6:46–7:39): Rather than conducting experimental research, the group relied on meta-analysis. This approach uses statistical tools to combine findings from multiple published studies, allowing researchers to assess broader patterns and trends. By aggregating data from diverse studies, meta-analysis can reveal robust associations and trends that may not be evident in individual studies.
  • Geospatial Mapping (7:40–8:15): The study employed a geospatial map to illustrate the global distribution of studies. This mapping technique, with color coding, highlights specific focus areas like habitat loss, climate change, and biodiversity change, showing the spatial and thematic diversity of the studies included.

4.2. Main Paper

  • Whole Genome Sequencing (25:20–30:30): This is a next generation sequencing technology to determine the base sequence of an entire genome.  Here, the researchers sequenced the genomes of the 3 E. coli isolates from a specific patient and compared them to each other to indicate possible sites of resistance mechanisms.
  • Antimicrobial Susceptibility Testing (30:47–38:49): Upon isolating E. coli, the bacteria were tested to determine their susceptibility or resistance to different antibiotics. This technique helps to guide appropriate treatment by identifying which antibiotics will be effective.

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

5.1. Snippet Paper

  • Public Health (5:28–8:16): The podcasters discussed how changing environment/climate affect infectious disease risk.
  • Human Impact on Disease Dynamics (10:50): The hosts discuss how human activities, such as urban development and habitat alteration, influence the risk of infectious diseases. This aligns with the concept of zoonotic diseases and the importance of understanding host-pathogen interactions.

5.2. Main Paper

  • Gram Negative Cell Envelope (22:12–22:49): The podcasters explained how beta lactams struggle to be effective against gram negative microbes because of their complex outer membrane.
  • Iron Acquisition (22:12–22:49): Iron is an essential nutrient for many bacteria, and the ability to acquire it is critical for their growth and survival. Understanding iron transport mechanisms is a fundamental aspect of microbial physiology. The antibiotic being discussed takes advantage of the iron transport system to get into gram negative microbes, making them more effective.
  • Novel Antimicrobials/Antibiotic Resistance (30:47–38:49): The podcasters discussed how E. coli isolates may have developed resistance against a novel antibiotic and this resistance’s relationship to iron.

6. Podcast Questions

  1. The research identified five major drivers of global change related to infectious disease risk. Of these, two had the most significant effect. Which two?
    1. Biodiversity changes
    2. Climate change
    3. Chemical pollution
    4. Habitat change
    5. New species introduction
  2. The snippet study is a meta-analysis. What is meta-analysis?
    1. It is a study that uses artificial intelligence to make new conclusions based on existing data.
    2. It is a study that combines published data and examines all of it to make new, broader conclusions.
    3. It is a study that uses an entire population of strain data to a construct pangenome.
    4. It is a study that is entirely hypothetical and based on correlative findings in the literature.
  3. Based on the podcast discussion, which of the following is a plausible way that environmental change can impact the spread of infectious diseases?
    1. By reducing the genetic diversity, host populations less susceptible.
    2. By eliminating vector habitats, pathogens can evolve less virulent forms.
    3. By eliminating habitats, vectors migrate to more hospitable habitats.
    4. By inducing mutations, pathogens can evolve more virulent forms.
  4. What feature of fetroja’s mechanism of action defines it as a hybrid antibiotic?
    1. It delivers an antibiotic into bacterial cells using an engineered siderophore motif.
    2. It uses two aspects, both ribosomes and tRNAs, to block the protein synthesis pathway.
    3. It combines two different, unrelated antibiotics to create a broader spectrum of action.
    4. It targets viral DNA replication through an integrated siderophore compound.
  5. How do antibiotic hybrids combat bacterial resistance?
    1. They kill sensitive and resistant bacterial strains equally, so resistance doesn’t evolve.
    2. They increase the mutation rate of bacterial genomes, which causes lethal defects.
    3. They exploit normal nutrient uptake pathways to overcome typical bacterial defenses
    4. They increase host immune system activation, so bacteria are killed earlier in infection.
  6. The podcasters note several hypotheses generated by the researchers regarding a fretroja-resistant strain of bacteria that has a unique 7 kilobase segment of DNA encoding several putative proteins.  If they deleted the segment and found the bacterium became sensitive, what would be a reasonable set of experiments to determine which putative proteins contribute?
    1. Perform comparative proteomic studies on resistance and sensitive bacterial strains.
    2. Engineer sensitive bacteria with the 7 kilobase region and look for resistance.
    3. Use fluorescent microscopy studies to observe drug localization in bacteria.
    4. Make a deletion in each gene encoding a putative protein and look for sensitivity.

7. Figure Reading Exercises

The following are two figure reading exercises, both from the snippet paper (Figures 1 and 2).

7.1. First Figure Reading Exercise

7.1.1. Learning Objectives

Students will be able to:

  • Extract numerical data from bar graphs and geospatial schematics.
  • Compare values between different categories within a dataset.
  • Synthesize conclusions across multiple datasets.
  • Evaluate a hypothesis using data in figure.

Experimental Background (Mahon et al. Figure 1)

It is critical to understand how global change drivers, parasite and host characteristics, and study settings influence disease dynamics to predict and mitigate the spread of infectious diseases. To investigate broad impacts, Mahon and colleagues (2024) performed a meta analysis of over 1000 studies to gain broad insight into this large issue.  Here, they provide an overview of the number and distribution of observations (effect sizes) in a comprehensive infectious disease database, categorized across various ecological contexts. The authors aimed to address how ecological factors shape the relationships between environmental changes and infectious disease outcomes to provide guidance for future studies.

7.1.2. Questions

  1. In the infectious disease database, which global change driver has the highest number of observations/has been studied the most?
    1. Chemical Pollution
    2. Habitat Loss or Change
    3. Introduced Species
    4. Climate Change
  2. Approximately how many observations have been conducted on protists?
    1. ~100
    2. ~250
    3. ~500
    4. ~750
  3. If you were to request a random single report from the infectious disease database, which of the following parasite types would this report most likely be?
    1. A terrestrial helminth that infects mammals.
    2. A marine virus that infects amphibians.
    3. A marine arthropod that infects mollusks.
    4. A freshwater bacteria that infects plants.
  4. Can these figures be used to support the hypothesis that habitat loss or change is increasing global disease risk?
    1. Yes, these data demonstrate that habitat loss or change is the most influential global change driver.
    2. Yes, these data indicate human and non-human mammals are infected by parasites more than other taxa.
    3. No, these data demonstrate the prevalence of different events, parasites, and hosts in the literature.
    4. No, these data show habitat loss or change in Europe when Africa has the highest rate of disease.

7.2. Second Figure Reading Exercise

7.2.1. Learning Objectives

Students will be able to:

  • Identify key features and experimental design and the in the plot visualization.
  • Identify and describe the relationship between global change drivers (such as biodiversity change, climate change, chemical pollution, habitat loss, and introduced species) and their impact on disease-related endpoints as shown in the figure.
  • Explain the significance of statistical results, such as p-values and confidence intervals, in determining how strongly each global change driver impacts disease-related harm.

Experimental Background (Mahon et al. Figure 2)

The impact of environmental changes on global health is a major concern.  To quantify how different environmental drivers such as biodiversity change, climate change, chemical pollution, habitat loss, and introduced species have impacted disease-related endpoints, such as infection rates or harm caused by diseases,  Mahon et al, (2024) performed a meta analysis of over 1000 studies. They examined the relationship between these global change drivers and disease-related outcomes using a calculation called hedge’s g*, which tells you how much one group differs from another group.  The measure (g) can have a very large mathematical range, but large effects are typically about 0.8 or above, medium effects are around 0.5, and small effects are around 0.2. The mean effect for each driver on disease endpoints (dot) is presented with 95% confidence intervals (whiskers). Significant differences for pairwise comparisons was assessed through statistical tests, such as t-tests and post hoc comparisons.

*For help with hedge’s g, please see Hedges’ g Definition and Formula

7.2.2. Questions

  1. Match the feature with its description. (1 = 95% confidence interval; 2 = no effect; 3 = notation of statistical significance; 4 = mean; 5 = standard deviation; 6= full data range excluding outliers)
    1. _______ Dot
    2. _______ Dashed line
    3. _______ Whiskers
    4. _______ Asterisks
  2. What does it mean when the confidence intervals for a global change driver do not overlap with zero in the figure?
    1. The effect of the driver is likely insignificant.
    2. The effect of the driver is statistically significant.
    3. The effect of the driver is variable and not reliable.
    4. The driver has no effect on disease-related endpoints.
  3. The letters by each dot are notations of pairwise statistical significance.  When two drivers share a common letter, such as A, what does this indicate?
    1. They are significantly different in the amount of effect they have on disease outcomes.
    2. They are not significantly different in the amount of effect they have on disease outcomes.
    3. They are significantly different in which diseases they effect, such as parasitic or marine.
    4. They are not significantly different in the amount of effect they have on another driver.
  4. If we were to use only the mean effect values, which global change driver has the greatest effect on disease-related harm?
    1. Habitat loss or change
    2. Climate change
    3. Biodiversity change
    4. Introduced species
  5. Taking into account the statistics presented and the mean effects, which global drivers have the greatest effects on disease-related outcomes?
    1. Habitat loss or change
    2. Climate change
    3. Biodiversity change
    4. Introduced species
  6. According to the figure, which global change driver was linked to a decrease in disease-related endpoints?
    1. Biodiversity change
    2. Habitat loss or change
    3. Introduced species
    4. Chemical pollution

8. Paper Information and Licensing

8.1. Snippet paper

  • Mahon MB, Sack A, Aleuy OA, Barbera C, Brown E, Buelow H, Civitello DJ, Cohen JM, de Wit LA, Forstchen M, Halliday FW, Heffernan P, Knutie SA, Korotasz A, Larson JG, Rumschlag SL, Selland E, Shepack A, Vincent N, Rohr JR. 2024. A meta-analysis on global change drivers and the risk of infectious disease. Nature. 629(8013):830-836. doi: 10.1038/s41586-024-07380-6
  • This article is not licensed for Creative Commons use; see the article’s copyright information. Thus, the abstract and figures cannot be copied here. Please see the article on the journal’s website.

8.2. Main paper

  • Barker KR, Rebick GW, Fakharuddin K, MacDonald C, Mulvey MR, Mataseje LF. 2024. When the Trojan horse is unable to reach inside the city: investigation of the mechanism of resistance behind the first reported cefiderocol-resistant E. coli in Canada. Microbiol Spectr. 12(5):e0322323. doi: 10.1128/spectrum.03223-23
  • This article is not licensed for Creative Commons use; see the article’s copyright information. Thus, the abstract and figures cannot be copied here. Please see the article on the journal’s website.

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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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