Structure and Function

TWiM #182: A Micro Story with Macro Implications

Podcast and Annotation Information

  • Annotation by Leonardo Baumgartner, Martin Leyhe, Triston Walsh, Rebecca Seipelt-Thiemann, and Nancy Boury
  • Podcast audio by TWiM: Listen to TWiM #182 Podcast
  • Podcast transcript by Sarah Morgan: Access TWiM #182 Transcript
  • Papers Discussed:
    • Baltekin Ö, Boucharin A, Tano E, Andersson DI, Elf J. 2017. Antibiotic susceptibility testing in less than 30 min using direct single-cell imaging. Proc Natl Acad Sci USA. 114(34):9170-9175. doi: 10.1073/pnas.1708558114
    • McQuaid JB, Kustka AB, Oborník M, Horák A, McCrow JP, Karas BJ, Zheng H, Kindeberg T, Andersson AJ, Barbeau KA, Allen AE. 2018. Carbonate-sensitive phytotransferrin controls high-affinity iron uptake in diatoms. Nature. 555(7697):534-537. doi: 10.1038/nature25982

1. Paper Abstracts

1.1. Snippet paper; discussion starts at 4:05 minutes

The Most Interesting Things (according to students)

A microfluidic device can use the growth rate of single cells in urine samples to determine their susceptibility to antibiotics.

“The emergence and spread of antibiotic-resistant bacteria are aggravated by incorrect prescription and use of antibiotics. A core problem is that there is no sufficiently fast diagnostic test to guide correct antibiotic prescription at the point of care. Here, we investigate if it is possible to develop a point-of-care susceptibility test for urinary tract infection, a disease that 100 million women suffer from annually and that exhibits widespread antibiotic resistance. We capture bacterial cells directly from samples with low bacterial counts (104 cfu/mL) using a custom-designed microfluidic chip and monitor their individual growth rates using microscopy. By averaging the growth rate response to an antibiotic over many individual cells, we can push the detection time to the biological response time of the bacteria. We find that it is possible to detect changes ingrowth rate in response to each of nine antibiotics that are used to treat urinary tract infections in minutes. In a test of 49 clinical uropathogenic Escherichia coli (UPEC) isolates, all were correctly classified as susceptible or resistant to ciprofloxacin in less than 10 min. The total time for antibiotic susceptibility testing, from loading of sample to diagnostic readout, is less than 30 min, which allows the development of a point-of-care test that can guide correct treatment of urinary tract infection.” (Baltekin et al 2017, no changes)

1.2. Main paper; discussion starts at 26:40 minutes

The Most Interesting Things (according to students)

Diatom growth is dependent on iron, which is captured by the diatom’s phytotransferrin. The phytotransferrin depends on carbonate availability in order to function properly, and it can be affected by lower water pH.

The main paper abstract is not available for re-use in this format. 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.

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

Snippet Main
Vision and Change Topics
  • Structure and Function (V&C_SF)
  • Metabolic Pathways (V&C_MP)
  • Structure and Function (V&C_SF)
  • Metabolic Pathways (V&C_MP)
  • Microbial Ecology (V&C_ME)
  • Impact of Microorganisms (V&C_IM)
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 5 (ASM_5): The structure and function of microbes are revealed by the use of microscopy, culture, and metabolic analyses, molecular methods, and bioinformatic tools.
  • 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 7 (ASM_7): Microbes have evolved structures adapted for specific functions that are often associated with a fitness advantage in a particular environment.
  • 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 21 (ASM_21): Microbes and the environment interact with and affect each other.  
  • Fundamental Statement 24 (ASM_24) :Microbes and their communities are essential for supporting all life as we know it.

3.  Potential Learning Objectives for the Podcast

The student will be able to: Paper1 Order2
  • Identify benefits of a fast antibiotic susceptibility detection system for clinical applications.
  • Identify characteristics that make urinary tract infection microbes a good test for the fast detection system.
 S L
  • Explain how growth rate can be used to indicate susceptibility to antibiotics.
 S H
  • List the features of diatoms.
  • Identify the biological processes that utilize iron.

 

 M L
  • Predict the effect of different environmental or genetic changes on iron capture for transferrin proteins.
 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

  • Complex Media (8:10–8:20): Complex media is one which the exact chemical composition is not known.  Muller-Hinton broth or urine can facilitate growth of UTI-causing organisms in this assay.
  • Microfluidic Devices (9:30–11:00): The “Mother Machine” derivative designed here is a microfluidic device, meaning it works with very small amounts of fluids.  Here, it is a microscopic chip with microscopic lanes filled with fluid that cells can sit in.
  • Phase Contrast Microscopy (13:00): This is a type of microscopy that enhances contract by altering light wave phase.  It was used to visually measure the distance the initial microbes moved through the channels to deduce growth rate.
  • Fast Antibiotic Susceptibility Test (fASTest) (13:00–17:00): This is the test the researchers developed to compare growth rates of microbes under no antibiotics and antibiotics. 

4.2. Main Paper

  • Metagenomic Analysis (33:58–34:52): Metagenomic analysis typically involves isolating DNA from a mixture, such as soil or water, amplifying one or many DNA fragments to identify the species present or sequence unculturable species.  Here, the transferrin-like encoding gene ISIP2 was identified in many metagenomic datasets.
  • By-eye Alignment (34:50–36:05): Visual inspection of sequences to find homology when computers can’t.
  • Computational Alignment (34:50–35:33): This type of alignment was used unsuccessfully to identify homology between phytotransferrin and human transferrin
  • Mutagenesis via Microparticle Bombardment (37:23–37:35): This technique is used to introduce plasmids into diatoms where they replace a target gene by homologous recombination
  • Fluorescence Microscopy (40:45–41:19): This is a type of microscopy that visualizes fluorescent proteins in living or preserved cells or tissues.  Here, the researchers used phytotransferrin fused to a fluorescent-tag (RFP) to show transferrin uptake into cells.  

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

5.1. Snippet Paper

  • Growth Curves (5:25–6:10): Growth curves are approximations of asynchronous growth of microbes in colonies.
  • Urinary Tract Infections (UTI) (6:30–7:00): UTIs affect 100 million people each year; many UTIs have extensive antibiotic resistance and the number keeps growing; UTIs often are single species infections.
  • Growth Rate (8:45–9:20): Individual growth rate correlates with resistance/tolerance to an antibiotic.

5.2. Main Paper

  • Protists (28:00–30:17): Diatoms are photosynthesizing protists with cell walls made of glass. They are the base of the oceanic food web, and are responsible for a large portion of the O2 released in the atmosphere.
  • Iron Acquisition (30:12–30:34): Cells need iron. Diatom Phytotransferrin grabs iron with five-finger domain, and is induced to release iron when pH drops. Transferrin and phytotransferrin ISIP2A independently evolved their five-finger domains (30:34-33:44,46:09-46:33); Phytotransferrins work in a similar way to transferrin (36:15-37:15, 39:09-40:08); carbonate is essential to diatom ISIP2A, and both carbonate and iron need to bind to ISIP2A simultaneously (41:20-42:57).
  • Ecosystem Ecology (42:44–45:12; 46:34–49:26; 51:57–52:45): CO2 levels acidify ocean, carbonate concentrations drop at slight changes, researchers show that ISIP2A drops also, which impacts diatoms populations, and as consequence, it can affect the whole oceanic food chain and O2 production. 

6. Podcast Questions

  1. Which of the following are benefits of this fast system for determining antibiotic resistance? Pick all that apply.
    1. It supports appropriate antibiotic use.
    2. It enables much faster treatment.
    3. It makes testing more expensive.
    4. It enables more effective treatment.
    5. It promotes antibiotic resistance.
    6. It can be used in clinical settings.
    7. It uses single bacterial cells.
  2. Which of the following make the selected test case of urinary tract infections a good choice for the fast system for determining antibiotic resistance? Pick all that apply.
    1. Urinary tract infections are generally caused by small bacteria.
    2. Urinary tract infections are generally a single bacterial species.
    3. Urinary tract infections are most prevalently available in clinics.
    4. Urinary tract infections are always sensitive to several antibiotics.
  3. If a bacterium (bacterium A) is normally sensitive to an antibiotic and it acquires an antibiotic resistance by taking up a plasmid (bacterium B), how would you expect the bacterial growth to be different between the two bacteria? Pick all that could be correct.
    1. Bacterium B would grow larger than Bacterium A.
    2. Bacterium B would divide faster than Bacterium A.
    3. Bacterium B would divide and Bacterium A wouldn’t.
    4. Bacterium B would live and Bacterium A would die.
  4. Which of the following are features or uses of diatoms? Pick all that apply.
    1. Are responsible for ¼ of carbon fixation
    2. Are eukaryotes
    3. Are algae
    4. Cell walls have cholanic acid
    5. Cell walls have silicon dioxide
    6. Photosynthesize
    7. Used in toothpaste
    8. Grow in fresh water
    9. Grow in salt water
    10. Are multicellular
  5. Iron is used in many biological processes as noted by the podcasters. Which of the following does not use iron?
    1. Metabolism
    2. DNA synthesis
    3. Oxygen transport
    4. Respiration
    5. pH maintenance
  6. The podcasters discuss the multi-step process by which transferrin binds and releases iron and carbonate.  What would be the effect of the following?

A = transferrin doesn’t release iron
B = transferrin doesn’t bind iron
C = no effect
X = transferrin doesn’t release carbonate
Y = transferrin doesn’t bind carbonate
Z = transferrin doesn’t enter the cell

    1. _______ Endosomal pH remains alkaline.
    2. _______ Receptor for endocytosis is deleted.
    3. _______ Carbonate binding pocket is deleted.
    4. _______ Iron binding pocket is deleted.

7. Figure Reading Exercises

The following are two figure reading exercises, one from the snippet paper (Figure 3) and one from the main paper (Figure 2a-c).

7.1. First Figure Reading Exercise 

7.1.1. Learning Objectives

Students will be able to: 

  • Identify how growth rate can be used to indicate susceptibility to antibiotics.
  • Compare bacterial growth diagrams to determine changes in growth.
  • Compare bacterial growth diagrams to determine the most effective antibiotic.
  • Describe other applications for a device that captures bacterial cells from samples and monitors the growth rate.

Experimental Background (Baltekin et al 2017, Figure 3)

There is a tremendous need for quick detection of bacterial growth, in particular as it relates to detecting antibiotic resistance.  The authors (Baltekin et al 2017) have developed a method for monitoring bacterial growth in a urine sample that takes less than 30 minutes.  They have been able to observe growth differences when these bacteria are exposed to different antibiotics, such that the bacteria can then be classified as resistant or susceptible to a particular antibiotic and effectively direct patient care.  In this experiment, bacterial cells in suspension are directed to a set of very small channels or tubes that capture them.  Media with and without antibiotic flow into the tubes/channels and the growth is measured as an accumulation of mass that expands using the linearity of the tube/channel.  Growth measures are consolidated across all control (no antibiotic; reference) and experimental (with antibiotic; treatment) channels and normalized to create a graph with time on the x-axis and normalized growth rate on the y-axis.

9-part line charts showing the fast detection of antibiotic resistance.
Figure 3. “Fast detection of response to antibiotic treatment. fASTest experiments testing how fast susceptible E. coli cells respond to (A) ampicillin, (B) AMX-CLA, (C) CIP, (D) DOR, (E) FOS, (F) LEV, (G) MEC, (H) NIT, and (I) TMP-SMX. AMP, ampicillin.” (Beltekin et al 2017, no changes) .Added from Figure 2 legend, panel H: “The overlay of the two population’s normalized growth rate distributions. The time of separation of the treatment population from the reference population based on 99.9% CIs occurs before the dashed magenta line, which indicates the first time point when growth rates are estimated.” (Beltekin et al 2017, no changes)

 

7.1.2. Questions

  1. There is a reference (control, no antibiotic) in each of the nine panels that remains at the level of 1.0 throughout the 30 minute experiment. What does this really indicate?
    1. Bacterial numbers remain constant throughout the experiment for the no antibiotic medium.
    2. All bacterial growth was normalized to the no antibiotic control, so we’d expect to a value of 1.0.
    3. No growth or death occurs for the medium containing no antibiotics, we’d expect a value of 1.0.
    4. All the channels for bacterial growth are completely full throughout the experiment for this.
  2. If we were to examine the microscopic images for a representative pair of reference/treatment channels from panel A, what is the earliest point at which we would likely see a difference?
    1. 5 minutes
    2. 10 minutes
    3. 15 minutes
    4. 20 minutes
  3. What is the quickest-acting antibiotic represented in the set of panels A–E?
    1. Ampicillin
    2. Amoxicillin-clavulanate
    3. Ciproflaxin
    4. Doripenam
    5. Fosfomycin
  4. What is the most effective antibiotic of those represented in panels F–I after 30 minutes?
    1. Lexofloxin
    2. Medcillinam
    3. Nitrofurantoin
    4. Trimethoprim-sulfamethoxazole
  5. Free Response: What is another application for a device that captures bacterial cells from samples and monitors the growth rate?

7.2. Second Figure Reading Exercise

7.2.1. Learning Objectives

Students will be able to:

  • Identify features in a schematic.
  • Define complementation.
  • Analyze functional assay results to determine the effect of deletion on function.
  • Compare functional assay results to determine if complementation had occurred.
  • Design an experiment to test the effect of altering specific amino acids on protein function.

Experimental Background (McQuaid et al. 2018, Figure 2abc)

Iron is scarce in the environment despite being necessary for many important biological processes.  Here the researchers identify an iron transport protein in diatoms, an important member of the food chain and the group responsible for one-quarter of carbon fixation.  The researchers first used oceanic metagenomic data, “by eye” sequence alignment, and bioinformatics tools to identify and characterize a putative iron-binding protein, a phytotransferrin named ISIP2A which is similar to human iron-binding protein, transferrin (panel a).  To confirm its function as an iron uptake protein, they next investigated the effect of deleting ISIP2A on iron uptake in diatoms (panel b).  They also performed complementation studies with the mutant using wild-type ISIP2A, the amino-terminal portion of human transferrin (hTF N lobe), the carboxyl-terminal portion of human transferrin (hTF C lobe), or yellow fluorescent protein (YFP).  To provide additional evidence for or against the relatedness of ISIP2A to human transferrin, the researchers examined the iron uptake effect of mutating the amino acids they had identified as conserved and involved in iron uptake, tyrosine 71 and tyrosine 214 (panel c).

  • The figure and figure legend 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 8.2.

7.2.2. Questions

  1. A structural comparison of protein features between P. tricornatum ISIP2A and human transferrin shows similarities and differences (panel a).  What is the major structural difference that is apparent here?
    1. The amino acids for iron uptake are different.
    2. The amino acids for carbonate coordination are different.
    3. The transmembrane domain (TM) is different.
    4. The signal peptide (SP) is different.
  2. Complementation is when adding a similar gene_______________.
    1. makes the mutant more amenable to cellular fusion.
    2. restores the normal phenotype of a mutant organism.
    3. exacerbates the mutant phenotype of a mutant organism.
    4. delays death of the organism by its slowing metabolism.
  3. Iron uptake was tested for a variety of engineered strains of P. tricornatum, including wild-type and a strain with a deletion in ISIP2A (delta ISIP2A; panel b). What effect does mutating ISIP2A have on iron uptake? What is your evidence?
    1. ISIP2A is necessary for iron uptake because uptake is low in delta ISIP2A and high in wild-type.
    2. ISIP2A is not necessary for  iron uptake because it is low in delta ISIP2A and high in wild-type.
    3. ISIP2A inhibits uptake because uptake is reduced in delta ISIP2A and high in wild-type.
    4. ISIP2A is one of several genes involved in uptake because delta ISIP2A survives with low iron.
  4. Rank the following genes/alleles for their level of complementation of the mutant iron uptake phenotype: ISIP2A complement, hTF N lobe, hTF C lobe, YFP.
    1. ISIP2A complement < hTF N lobe < hTF C lobe < YFP.  
    2. ISIP2A complement > hTF N lobe > hTF C lobe > YFP.  
    3. ISIP2A complement = hTF N lobe > hTF C lobe > YFP.  
    4. ISIP2A complement > hTF N lobe = hTF C lobe > YFP.  
  5. Amino acids tyrosine 71 and tyrosine 214 (panel a) were identified as being involved in iron uptake. To determine if they actually play roles in iron uptake, mutants were constructed and iron uptake tested (panel c). How would you need to change this experiment to test for the function of arginine 183 (R183)?
    1. Construct a triple mutant (tyrosine 71, tyrosine 215, arginine 183) and test for carbonate coordination.
    2. Construct a mutant where amino acid 183 is not arginine and test for survival and iron uptake.
    3. Construct  a mutant protein truncated at amino acid 183 and test for survival and iron uptake.
    4. Construct a mutant where amino acid 183 is not arginine and test for carbonate coordination.

8. Paper Information and Licensing

8.1. Snippet paper

8.2. Main paper

  • McQuaid JB, Kustka AB, Oborník M, Horák A, McCrow JP, Karas BJ, Zheng H, Kindeberg T, Andersson AJ, Barbeau KA, Allen AE. 2018. Carbonate-sensitive phytotransferrin controls high-affinity iron uptake in diatoms. Nature. doi: 10.1038/nature25982
  • This article in not licensed for Creative Commons use, so it cannot be used on any web-based materials. Please see re-use permissions on the journals’s copyright page. Since this paper is not freely accessible, you can request this paper from your library via Interlibrary loan (or a similar process) or contact the corresponding author.

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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