Impact of Microorganisms

TWiM #170: Rats, Lice, and Nanoparticles

Podcast and Annotation Information
  • Annotation by Lauren Ballard, Ben Walsh, Kaitlyn Wesselink, Amaya Garcia Costas, Nancy Boury, and Rebecca Seipelt-Thiemann
  • Podcast audio by TWiM: Listen to TWiM #170 Podcast
  • Podcast transcript by Sarah Morgan: Access TWiM #170 Transcript
  • Papers Discussed:
    • Dean KR, Krauer F, Walløe L, Lingjærde OC, Bramanti B, Stenseth NC, Schmid BV. 2018. Human ectoparasites and the spread of plague in Europe during the Second Pandemic. Proc Natl Acad Sci U S A. 115(6):1304-1309. doi: 10.1073/pnas.1715640115.
    • Deng L, Mohan T, Chang TZ, Gonzalez GX, Wang Y, Kwon YM, Kang SM, Compans RW, Champion JA, Wang BZ. 2018. Double-layered protein nanoparticles induce broad protection against divergent influenza A viruses. Nat Commun. 9(1):359. doi: 10.1038/s41467-017-02725-4.

1. Paper Abstracts

1.1. Snippet paper; discussion starts at 5:45 minutes

The Most Interesting Things (according to students)

  • Rats exonerated! Plague spread by human lice!!!!!!!!!
  • Statistical data modeling predicts that human ectoparasites are responsible for the spread of the plague during the second major pandemic.

“Plague, caused by the bacterium Yersinia pestis, can spread through human populations by multiple transmission pathways. Today, most human plague cases are bubonic, caused by spillover of infected fleas from rodent epizootics, or pneumonic, caused by inhalation of infectious droplets. However, little is known about the historical spread of plague in Europe during the Second Pandemic (14–19th centuries), including the Black Death, which led to high mortality and recurrent epidemics for hundreds of years. Several studies have suggested that human ectoparasite vectors, such as human fleas (Pulex irritans) or body lice (Pediculus humanus humanus), caused the rapidly spreading epidemics. Here, we describe a compartmental model for plague transmission by a human ectoparasite vector. Using Bayesian inference, we found that this model fits mortality curves from nine outbreaks in Europe better than models for pneumonic or rodent transmission. Our results support that human ectoparasites were primary vectors for plague during the Second Pandemic, including the Black Death (1346–1353), ultimately challenging the assumption that plague in Europe was predominantly spread by rats.” (Dean et al. 2018)

1.2. Main paper; discussion starts at 25:45 minutes

The Most Interesting Things (according to students)

The new nanoparticle flu vaccine design has potential to be effective against most, if not all, influenza strains and to provide long-term immunity (durability). So, we wouldn’t need annual flu vaccinations if this works in people!

“Current influenza vaccines provide limited protection against circulating influenza A viruses. A universal influenza vaccine will eliminate the intrinsic limitations of the seasonal flu vaccines. Here we report methodology to generate double-layered protein nanoparticles as a universal influenza vaccine. Layered nanoparticles are fabricated by desolvating tetrameric M2e into protein nanoparticle cores and coating these cores by crosslinking headless HAs. Representative headless HAs of two HA phylogenetic groups are constructed and purified. Vaccinations with the resulting protein nanoparticles in mice induces robust long-lasting immunity, fully protecting the mice against challenges by divergent influenza A viruses of the same group or both groups. The results demonstrate the importance of incorporating both structure-stabilized HA stalk domains and M2e into a universal influenza vaccine to improve its protective potency and breadth. These potent disassemblable protein nanoparticles indicate a wide application in protein drug delivery and controlled release.” (Deng et al. 2018).

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)
  • Structure and Function (V&C_SF)
  • 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 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 28 (ASM_28): A minority of microbes are pathogens that can cause diseases and harm host organisms, society, and ecosystems.
  • Fundamental Statement 9 (ASM_9): The replication of viruses is determined by their unique structures, DNA or RNA genomes, and the cells they infect.
  • Fundamental Statement 26 (ASM_26): Humans leverage microbes and their products to address problems and improve quality of life.
  • Fundamental Statement 28 (ASM_28): A minority of microbes are pathogens that can cause diseases and harm host organisms, society, and ecosystems.

3.  Potential Learning Objectives for the Podcast

The student will be able to: Paper1 Order2
  • Identify differences between bubonic, pneumonic, and septicemic plague.
  • Recall what an SIR model is and identify which three SIR models were fit to the data.
  • Define Ro.
 S L
  • Predict how vaccination rates impact Ro.
 S H
  • Identify the desired properties of a universal influenza vaccine.
  • Identify the viral proteins used to build the vaccine nanoparticle.
 M L
  • Predict how antigenic shift and antigenic drift affect vaccine efficacy
 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

  • Susceptible-Infectious-Recovered Model (SIR Model) (11:40–17:10): Models that use differential equations to estimate the number of people that are susceptible, infectious, and recovered from a particular illness. In this study, the researchers had three models: human ectoparasite vector transmission, pneumonic transmission, and rat-flea transmission.  Each model had a different number of equations because the transmission has different routes.  They were looking for which transmission model best fit the data for nine documented plague outbreaks.
  • Bayesian Inference (13:50–17:00): A method of statistical inference that utilizes Bayes’ theorem to make models when there are unobservable parameters.  This is essentially using how likely an event is to occur in the future based on how often it has occurred in the past.  Michael equates this to looking at baseball or football stats to estimate future performance by a player.

4.2. Main Paper

  • Electron Microscopy (35:12–37:42): This is a high resolution type of microscopy and it was used to examine the structure of nanoparticles synthesized for use in an influenza vaccine.
  • Nanoparticles Vaccines (33:15–end):  This is a vaccine delivered as a nanoparticle.  Here, the addition of ethanol to M2 viral protein tetramers created a protein nanoparticle that was then covered with the stalk portions of the viral protein called hemagglutinin, which were adhered by crosslinking to create the final nanoparticle for testing.
  • Bacculovirus/Insect Cell Expression System (44:00–45:10): Protein expression system where insect cells are infected with a baculovirus designed to produce and secrete specific proteins.  This was the system the researchers used to produce so much of the viral protein for their study.

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

5.1. Snippet Paper

  • Disease Transmission (11:40–17:10): Diseases can be transmitted in a variety of ways.  Here, the researchers were modeling transmission from animal to insect to human, animal to human, or from human to human.
  • R Naught (Ro) (19:50–21:30): This is an infectious disease parameter common in epidemiology.  It is an estimate of transmissability, that is, how many others an infected individual is likely to infect. The plague had a Ro value of 1.5–1.9.  Measles, which is highly infectious, has an Ro value of 12–18.

5.2. Main Paper

  • Vaccines (25:45–end): Development of new influenza vaccine, which includes adding adjuvants in order to induce a stronger immune response and promote long-term protection.
  • Antigenic Drift vs Antigenic Shift (27:00–28:00): Small changes due to mutation (drift) causes epidemics, large changes due to mutation (shift) creates a new strain and pandemics.
  • Viral Proteins (31:45–32:46): The proteins hemagglutinin (glycoprotein) and M2 (membrane protein) were utilized in producing nanoparticles for a possible influenza vaccine.

6. Podcast Questions

  1. What does SIR stand for in the SIR model?
    1. Secondary-Infection-Reservoir
    2. Self-tolerance-Infection-Reservoir
    3. Susceptible-Infectious-Recovered
    4. Sensitive-Infectious-Recovered
  2. Which of the following can be transmitted through aerosols?
    1. Bubonic Plague
    2. Pneumonic Plague
    3. Septicemic Plague
    4. All of the above
  3. What is Ro?
    1. It is a measure of transmissibility for an infectious disease, that is how many people an infected person is likely to infect.
    2. It is a measure of the rate at which an infectious disease causes symptoms to appear in non-vaccinated infected individuals.
    3. It is a measure of ecological immunity, that is the percentage of a population that has immunity to a specific disease.
    4. It is a measure of asymptomatic time, that is the average time it takes for an infected person to recover from an illness.
  4. What were transmission characteristics of the three SIR models the researchers built and used to fit to the data?
    1. human-flea transmission (flea to human)
    2. Flea-septicemic transmission (flea to human)
    3. pneumonic transmission (human to human
    4. rat-flea transmission (rat to flea to human)
  5. How would the calculated Ro value differ in a population that was mostly vaccinated compared to one where only a few were vaccinated?
    1. The calculated Ro would be lower for the highly vaccinated population than the few vaccinated.
    2. The calculated Ro would be higher for the highly vaccinated population than the few vaccinated.
    3. The calculated Ro would be the same low value for both populations regardless of vaccination.
    4. The calculated Ro would be the same high value for both populations regardless of vaccination.
  6. Which two viral proteins were used to make the vaccine nanoparticle?
    1. Neuriminidase and hemagglutinin
    2. Neuriminidase and M2 protein
    3. M2 protein and hemagglutinin
    4. Capsid protein and neuriminidase
  7. An ideal universal influenza vaccine_________.
    1. Generates antibodies to all the virus proteins
    2. Provides immunity to all influenza virus strains
    3. Generates antigenic reactions to all virus proteins
    4. Provides immunity to all virus strains for a lifetime
  8. A single flu strain (original flu strain) was dispersed to three different geographic regions (A, B, and C). In region A the flu strain exhibited antigenic drift. In region B flu strain exhibited antigenic shift. In region C the flu strain exhibited both antigenic shift and antigenic drift.  Which geographic region would likely have more protection from a vaccine that was made to protect against the original flu strain?
    1. Region A
    2. Region B
    3. Region C
    4. All would be protected.

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 2).

7.1. First Figure Reading Exercise

7.1.1. Learning Objectives

Students will be able to: 

  • Identify key features of the models and line graphs.
  • Analyze the data to determine which model is supported in each pandemic.
  • Analyze the data to determine which data features affect outbreak-model accuracy.
  • Predict how reliability in the church record data could affect the model.
Experimental Background (Dean et al., Figure 1)

Bubonic Plague or the Black Death is an infectious disease caused by the bacterium Yersinia pestis. It has caused many pandemics, including one that killed approximately 30% of the world’s population. Transmission has been historically thought to have been rat to flea to human (rat-flea transmission), although aerosol transmission to the lungs from human to human is possible (pneumonic transmission).  Recent studies have suggested a third transmission route, from human commensal insects (human fleas or lice; human ectoparasite transmission).  Identifying the actual transmission route for these historical pandemics can provide better information to prevent future pandemics and limit current pandemics, such as in Madagascar.  To investigate this, Dean et al. (2018) used historical church documents to build three models of transmission dynamics:  rat-flea model (green), pneumonic model (blue), human ectoparasite model (red). Each uses differential equations for known features and Bayesian predictions for unknowable parameters, which are based on prior likelihood events.  They then fit their models to the data to determine which model best explained the death outcomes of nine historical pandemics (panels A-I), and therefore, identify which transmission model is most likely correct.  Confidence intervals are noted by shading above and below the each line in each model.

Nine line charts of observed deaths in specific times and places charted along three models (pneumonic, rat and flea, and human ectoparasite).

Figure 1. “Fit of three models of plague transmission to mortality during Second Pandemic outbreaks. The observed human mortality data (black dots) and the fit (mean and 95% credible interval) of three models for plague transmission [human ectoparasite (red), pneumonic (blue), and rat–flea (green)] for nine plague outbreaks: (A) Givry, France (1348), (B) Florence, Italy (1400), (C) Barcelona, Spain (1490), (D) London, England (1563), (E) Eyam, England (1665), (F) Gdansk, Poland (1709), (G) Stockholm, Sweden (1710), (H) Moscow, Russia (1772), and (I) Island of Malta, Malta (1813).” (Dean et al. 2018)

7.1.2. Questions

  1. Which model’s fit data is shown by the red line and red shading?
    1. observed deaths model
    2. rat-flea model
    3. pneumonic model
    4. human ectoparasite model
  2. A 95% confidence interval is depicted by shading. What does broad shading from the line, as shown in panel E, versus narrow shading from the line, as shown in panel F indicate?
    1. Broad shading indicates stringent parameters were used, so you can be confident in the line being the true value.
    2. Broad shading indicates there is more variability, so you can’t be as confident in the line being the true value.
    3. Broad shading indicates there is less variability, so you can be very confident in the line being the true value.
    4. Broad shading indicates Bayesian parameters were used, so you can be confident in the line being the true value.
  3. Which outbreak shows the lowest number of deaths at the peak of the outbreak?
    1. Florence 1400 (panel B)
    2. London 1563 (panel D)
    3. Evam 1665 (panel E)
    4. Stockholm 1710 (panel G)
  4. Which outbreak lasted the shortest amount of time?
    1. Florence 1400 (panel B)
    2. London 1563 (panel D)
    3. Evam 1665 (panel E)
    4. Stockholm 1710 (panel G)
  5. For the Barcelona 1490 outbreak, given that the black dots indicate actual deaths, which model fits the data best throughout the outbreak (from onset to the end)?  What is your evidence?
    1. Human ectoparasite model; the deaths mirror the red line
    2. Pneumonic model; the blue line has the widest peak
    3. Rat and flea model; the green line has the highest peak
    4. Pneumonic model; the blue line has the flattest curve
  6. For which of the following outbreaks do the confidence intervals make it difficult to elucidate which model fits the data?
    1. Florence 1400 (panel B)
    2. London 1563 (panel D)
    3. Evam 1665 (panel E)
    4. Stockholm 1710 (panel G)
  7. Which of the following outbreak features is common to outbreaks where you can’t confidently pick a model as being the best fit?
    1. Low numbers of deaths
    2. Short outbreak time
    3. High numbers of deaths
    4. Long outbreak time
  8. These models are based on historical church documents.  If the historical data were less accurate, what would be the effect for this study?
    1. The models would be more likely to show unique modes of transmission.
    2. The models would be less likely to show an accurate mode of transmission.
    3. The models would be equally likely to show an accurate mode of transmission.
    4. The models would be more likely to show overlapping modes of transmission.

7.2. Second Figure Reading Exercise

7.2.1. Learning Objectives

Students will be able to:

  • Identify key features of bar charts and radar figure.
  • Identify key experimental features such as controls and variable types.
  • Analyze the data and make conclusions about nanoparticle vaccine success for particular efficacy measurements.
  • Analyze the data and make conclusions about nanoparticle vaccine success for particular vaccine components.
Experimental Background (Deng et al., Figure 2)

Unlike other vaccination protocols where near life-time protection occurs, influenza vaccination does not provide long-lasting protection because the virus changes quickly.  This can make flu vaccination less effective and affect community confidence in vaccination as a whole.  In an effort to move towards the goal of a universal influenza vaccine with high effectiveness and long-term protection, Dean et al. (2018) investigated bioengineering a vaccine nanoparticle.  They identified two very conserved influenza viral proteins: matrix protein 2 ectodomain (M2e) and the stalk domain of the hemagglutinin glycoprotein (head removed hemagglutinin; hrHA). They engineered four copies of the M2e protein (tetramer protein), each being from a different virus host (human, swine, avian, and domestic bird).  They treated the tetamer proteins with ethanol which caused them to form protein nanoparticles which they call Uni4MC.  These particles were then coated with trimeric hemagglutinin stalks from H1-type or H3-type flu viruses (hrH1 and hrH3) to form the full vaccine nanoparticles Uni4C1 and Uni4C3, respectively.  A combination of H1 and H3-type was used to coat the nanoparticle noted at  Uni4C13.  Mice were then injected intramuscularly twice with each nanoparticle or Dulbecco’s phosphate buffered saline (DPBS).  To investigate vaccination efficacy, serum immunoglobulin G (antibody; IgG) specific to each nanoparticle component was quantified using an enzyme-linked immunosorbent (ELISA) assay (panel a; left panel is to M2e, center panel is to H1 type, right panel is to H3 type). The researchers then tested for the ability of the nanoparticle vaccine to protect against other influenza strains.  To do this they tested whether vaccinated mouse serum could bind commonly fatal influenza types H5, H7, and H10, as well as a new bird/swine type, H2, in addition to H1 and H3 (panel b).  These ELISA data are consolidated into a radar plot where the innermost hexagon marks a 1:100 dilution, the center hexagon marks a 1:1000 dilution and the outermost hexagon marks a 1:10,000 dilution.  The lowest serum dilution showing a positive result is identified by a dot, then the dots are connected to form a polygon for each nanoparticle vaccine.  Finally, the researchers wanted to identify whether nanoparticle vaccination was inducing a cellular immune response. To do this, they isolated splenocytes from mice vaccinated with the nanoparticles, challenged them with peptides from the vaccine components (M2e, H1, or H3) or no treatment, and then quantified the number of splenocytes producing the immune factor, interferon gamma (IFNƔ; panel c).

Two bar charts and a radar plot showing immune responses after vaccination.Figure 2. “Humoral and cellular immune responses of vaccinated mice. a Serum IgG endpoint titers against huM2e, formalin-inactivated PR8 H1N1 or formalin-inactivated Aic H3N2. (n = 10) b Radar diagram depicting breadth of immune serum binding to HA subtypes. Because frequent human infection with fatal zoonotic influenza H5N1, H7N9, and H10N8 viruses in recent years represents possible emerging pandemics, the binding activities of immune sera to H5, H7, and H10 were tested. The waning human herd immunity against H2N2 influenza strain and low mutation rate make it likely that a new pandemic could arise from the current circulating H2N2 strain among birds and swine. H2 was included in the serum binding assay. The squares besides HA subtypes indicate HA phylogenetic groups by coloring: blue for group 1 and orange for group 2. c Specific cellular immune responses against M2e, H1, and H3. Peptide pool re-stimulated interferon gamma (IFNγ)-secreting cell clones were determined using ELISpot assay. (n = 4) Data are presented as mean ± SD Statistical significance was analyzed by t-test for a and c. P values shown in bar charts and N.S. indicates no significance between two compared groups. The experiments were repeated twice with similar results.” (Deng et al. 2018)

7.2.2. Questions

  1. The humoral immune response to distinct vaccine nanoparticles was quantified in panel A by using an enzyme-linked immunosorbent (ELISA) assay to detect IgG antibodies to the nanoparticle vaccines.  What sample is the negative control and what does this tell you?
    1. DPBS
    2. Uni4MC
    3. Uni4C1
    4. Uni4C3
    5. Uni4C13
  2. What notation tells you the comparison being made is statistically significant, that is truly different?
    1. N.S.
    2. p<0.01
    3. p<0.05
    4. p<0.005
  3. What can you conclude from the data presented in panel a? [pick all that apply]
    1. All nanoparticles are successful for M2e.
    2. No nanoparticles are successful for M2e.
    3. H1 type nanoparticles are successful for H1 and H3.
    4. H1 type nanoparticles are successful for H1 only.
    5. H1 type nanoparticles are successful for H1 and somewhat for H3.
    6. H3 type nanoparticles are successful for H1 and H3.
    7. H3 type nanoparticles are successful for H3 only.
    8. H3 type nanoparticles are successful for H3 and somewhat for H1.
    9. H1H3 type nanoparticles are successful for M2e, H1 and H3.
    10. H1H3 type nanoparticles are successful for H1 only.
    11. H1H3 type nanoparticles are successful for H3 only.
    12. H1H3 type nanoparticles are successful for H1 and somewhat for H3.
    13. H1H3 type nanoparticles are successful for H3 and somewhat for H1.
  4. In the radar plot, which color polygon shows the data for mice vaccinated with nanoparticle type Uni4C1?
    1. Light blue
    2. Yellow
    3. Dark blue
    4. Pink
  5. In the radar plot, which nanoparticle vaccination serum shows the strongest binding, and to which virus? What is your evidence?
    1. DPBS tested against all; all points are at center
    2. Uni4C1 tested against H10; the point is near 100
    3. Uni4C3 tested against H3; the point is near 10,000
    4. Uni4C13; this shows the largest response to all
  6. In the radar plot, which nanoparticle vaccination serum shows the broadest binding capability across all these known and putatively lethal viruses? What is your evidence?
    1. DPBS; the most successful has all points at center
    2. Uni4C1; the most successful is nearest 100
    3. Uni4C3; the most successful is nearest 10,000
    4. Uni4C13; this shows the largest polygon shape
  7. Which nanoparticle vaccine component is the most successful at generating the cellular immune response (panel c)?
    1. M2e
    2. H1
    3. H3
    4. H1 and H3

8. Paper Information and Licensing

8.1. Snippet paper

  • Dean KR, Krauer F, Walløe L, Lingjærde OC, Bramanti B, Stenseth NC, Schmid BV. 2018. Human ectoparasites and the spread of plague in Europe during the Second Pandemic. Proc Natl Acad Sci U S A. 115(6):1304-1309. doi: 10.1073/pnas.1715640115.
  • This article is licensed for Creative Commons use using CC BY-NC-ND 4.0, which allows re-use and adaptation with proper attribution and notation of any changes. See the article on the journal’s web page.

8.2. Main paper

  • Deng L, Mohan T, Chang TZ, Gonzalez GX, Wang Y, Kwon YM, Kang SM, Compans RW, Champion JA, Wang BZ. 2018. Double-layered protein nanoparticles induce broad protection against divergent influenza A viruses. Nat Commun. 9(1):359. doi: 10.1038/s41467-017-02725-4.
  • 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. 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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