T-cell Vaccines and Host Pathology

This is one of several posts that I’ll write about the Ecology and Evolution of Infectious Disease conference (EEID).  I’m starting with the very first talk at the conference, because I’m not feeling particularly creative, and chronological order is my default.  🙂

Let’s talk about vaccines.  The vaccines that you’re familiar with are probably vaccines that cause antibody immunity.  For instance, you might be given a bit of dead pathogen, and your immune system learns how to make antibodies that target the pathogen.  For instance, vaccines for influenza, chickenpox, polio, and hepatitis B are meant to help you develop antibody immunity.

But what if the pathogen mostly hangs out in your cells, and antibodies won’t cut it?  This happens with many pathogens that have persistent infections, like HIV and malaria.  As you know, we don’t have vaccines for malaria and HIV, because these antibody-type vaccines won’t work (yet? ever?).

People are working on a different type of vaccine, called a T-cell vaccine.  T-cells are immune cells that find and kill host cells that are infected with pathogens.  The hope is that T-cell vaccines will better enable T-cells to recognize infected cells.

But here’s the problem.  Some people have found that vaccines for these pathogens that hang out inside of cells can actually cause disease/pathology.  That is, individuals (e.g., mice) have greater pathology when they are exposed to a pathogen after being vaccinated than if they hadn’t been vaccinated.  (Note:  I’m talking about vaccines that are still being developed, not ones that we currently use.)  Obviously, we can’t develop useful vaccines until we figure out why they may be increasing infection and causing pathology.

To work on this, Johnson et al. (2011) made a model (which parallels experimental data) of lymphocytic choriomeningitis virus in mice.  What they found was that at low and high densities of T-cells, pathology was low.  But at intermediate levels of T-cells, pathology was high.  In this case, “pathology” was measured in terms of the number of functional host cells, where few functional host cells means high pathology.


My cartoon of Rustom Antia’s graph.

Why should the number of T-cells matter?  Rustom Antia suggested that at low T-cell numbers/density, the virus kills some host cells and the T-cells don’t kill many host cells.  At high T-cell numbers/density, the virus kills few host cells, but the T-cells kill more host cells.  In both cases, not too many host cells are killed.  At intermediate T-cell numbers/density, both the virus and the T-cells kill many host cells, which ends up being detrimental to the host.  Tada!

So, there you have it.  Now, since this isn’t my area of expertise, I recommend checking out the paper that Rustom based his talk on to find out more.

What do you think?  Will we figure out how to use T-cell vaccines in the near future?


Rustom, Antia. 2013. An immuno-epidemiological approach to understanding vaccine efficacy. EEID.

Johnson et al. 2011. Vaccination Alters the Balance between Protective Immunity, Exhaustion, Escape, and Death in Chronic Infections. Journal of Virology 85(11): 5565–5570.

Microparasite vs. Macroparasite

Edit:  It has come to my attention that this page is one of the top google hits for “microparasite,” “macroparasite,” and “microparasite vs. macroparasite.”  Since dozens of people end up here every day, I’ve remodeled this post to be more informative.  I hope this helps!  If you can think of something that I should add, let me know in the comments!  Thanks for visiting!

Microparasites vs. Macroparasites

The main distinction between microparasites and macroparasites is whether they “multiply” within their definitive host or not.  Microparasites do “multiply” in their definitive host, and macroparasites do not “multiply” in their definitive host.  This distinction is important because it influences the ecology and epidemiology of micro and macroparasitic infections.

“Multiply” vs. “Reproduce”

Why am I saying “multiply” instead of “reproduce?”  Because both microparasites and macroparasites may reproduce in the definitive host.  However, in macroparasites, reproduction usually leads to the production of eggs or larval stages that then leave the definitive host.  That is, reproduction occurs, but the host does not end up with more parasites.  Microparasites use direct reproduction; reproduction leads to an increase in the number of parasites within the host.

Microparasite Infections and Microparasite Examples

Microparasites are tiny!

They tend to have very short generation times in comparison to their hosts.  Compare your life span to that of viruses, bacteria, and fungi, which are the main ‘groups’ of microparasites.

In microparasite infections, the intensity of disease/pathology and infectiousness tend to be determined by whether the host is infected or not.  (At least, they can usually be modeled that way.)  That is, if you are infected, you probably have many, many parasites, because the parasites have high reproductive rates.  In other words, the number of parasites per host is not as important as it is in macroparasite infections.

Hosts tend to develop immunity to microparasites.

Here are some good examples of microparasites:

Macroparasite Infections and Macroparasite Examples

Macroparasites are bigger, and include things like helminths and arthropods.

They have relatively long generation times.

Macroparasite infections tend to be chronic, and they are accumulated relatively slowly.

Hosts don’t usually develop immunity to macroparasites, or else the immunity is short-lived and/or only happens with high parasite burdens.

Here are some good examples of macroparasites:

Modeling Microparasites and Macroparasites:

I originally wrote this post because I was reading Anderson and May’s (1991) “Infectious Diseases of Humans.”  Early on, they defined microparasites and macroparasites and explained why we model them differently.  I’ve basically given you a synopsis of their definitions, and now I’ll briefly touch on modeling.

As with any model, your decisions about what type of model you want to use will depend on a variety of things.  But in general, it makes sense to model microparasite infections with ‘compartmental models’ and presence/absence of infection.  You’re infected, or you aren’t, and if you are infected, then you have the “average” parasite load.  Again, this is because these parasites multiply quickly within the host.

Anderson's compartmental model, taken from here.

Anderson’s compartmental model, taken from here.

If you’re modeling macroparasite infections, the distribution of parasites becomes more important.  In hosts that are infected, most will have very few parasites, and some will have very many parasites.  Transmission and pathology will likely depend on the number of parasites per host.  Therefore, using a “present and average” type of model may not be useful (depending on your question).

Hope this helps!

Force of Infection

I haven’t posted about a paper recently, so here’s a random grab out of my massive folder of “PDFs to Read Someday.”  (I should use differential equations to model how the number of PDFs in that folder changes over time…except that it appears to be exponentially increasing, so maybe I don’t want to know.) 

First, let’s talk about force of infection (FOI).  FOI is the rate at which susceptible individuals become infected per unit time.  It is an important parameter (the most important?) for transmission in epidemiological models.  The problem is that FOI isn’t as easy to measure as you might think.  In order to get good estimates of the rate of infection, you typically need to monitor infection status in many, many individuals.  For instance, even assuming you can identify infected individuals with 100% accuracy, your estimate of FOI is going to be better if you sample 100 individuals (1% resolution) than 20 individuals (5%) resolution. 

Movement rates between classes of the SIR model

Serious lack of FOI-related pictures on the Internet. Diagram from here.

But of course, you can’t identify infected individuals with 100% accuracy.  This is especially apparent when organisms have a period of latent infection, or when infection gets more obvious the longer the individual is infected.  For instance, if a snail has had first intermediate trematode infection for a while, crushing the snail might result in a mini-explosion of rediae/sporocysts all over your dissection surface.  (A brief aside for the curious.)  If it’s a newly developed infection, you might be lucky to spot a single redia/sporocyst.  The same is true when looking for Plasmodium falciparum (malaria parasite) in blood smears; you’re much more likely to identify an infection if parasitemia is very high.

What once were snail gonads are now trematode duplexes. Trematode sprawl. Photo from here.

Finally, if an individual can get infected multiple times, you would underestimate FOI if you could only identify the first infection event.  This is where the Mueller et al. (2012) paper from PNAS comes in.  For P. falciparum in Papua New Guinea, most people have infections with multiple different clones of P. falciparum.  That means that they got multiple clonal infections from one mosquito bite and/or they got subsequent infections with different clones from different mosquito bites. 

To get a super duper accurate FOI measurement with molecular techniques (molFOI), Mueller et al. (2012) took blood samples from ~400 kids aged 0-4 every two months for 69 weeks.  Impressive!  Then they used genotyping of the mps2 gene to determine how many P. falciparum clones each child had at each time point.

Cutting to the chase:  molFOI and the incidence of clinical cases of malaria (i.e., number of reported cases with high parasite loads) had very similar patterns across child age and week/season.  molFOI had a strong relationship with week/season, and this relationship explained ~50% of the relationship between malaria incidence and season.  Furthermore, most of the age*incidence relationship was explained by the age*molFOI relationship.  So, cool new tool for monitoring transmission in malaria control programs!  Neat!

Though they certainly aren’t the first people to demonstrate the efficacy of using insecticide treated bed nets, they also found that ITNs reduced infection risk by ~50%.  Awesome.

Multiple clonal infections are the norm in the P. falciparum system mentioned here.  Do you think that is true in many disease systems? 


Muellera, I., S. Schoepflind, T.A. Smithd, et al. 2012. Force of infection is key to understanding the epidemiology of Plasmodium falciparum malaria in Papua New Guinean children.  PNAS 109:25, 10030-10035.

Disease is Sexy

Let’s stick with my last post’s focus on malaria, but talk about malaria infection in birds (Plasmodium relictum).  This comes from a 2013 Ecology Letters paper entitled “Malaria infection increases bird attractiveness to uninfected mosquitoes.” 


Very complicated diagram of a mosquito. Photo Credit: How Stuff Works.

One of my favorite “topics” in disease ecology is the manipulation of host behavior by parasites.  This is sometimes called the “extended host phenotype.”   And colloquially, manipulated hosts are often called “zombie” hosts (e.g., zombie ants, zombie snails).  As Cornet et al. (2013) pointed out, we usually think about this in terms of a parasite within a host manipulating that host’s behavior.  But here’s a different question:  can a parasite in one host affect a different host’s behavior?  For example, can Plasmodium relictum bacteria in birds affect mosquito behavior? 


“Zombie Snail.” Photo credit: Albus.

Cornet et al. (2013) put infected and uninfected canaries in cages, and let female mosquitoes bite their legs.  (They used some contraption to keep the rest of the bird’s body still, so that any bird defense mechanisms wouldn’t alter vector preference).  Then they used microsatellite genotyping of the blood meals that the mosquitoes took to figure out which mosquitoes fed on which birds.  They did this both during the initial, acute phase of bird infection, and during the later stage, when parasitemia was lower.

The majority of mosquitoes tended to bite one bird in the cage, and they preferred the bird with the most hematocrit.  (Hematocrit is the percent of red blood cells in the blood.)  Before birds were infected, their hematocrit was no different.  During the “acute” phase of infection, when parasitemia was very high, hematocrit was much lower in infected birds than in uninfected birds.  And finally, during the “chronic,” later stage of infection, hematocrit wasn’t different between infected and uninfected birds again.  (If you actually read the paper, you’ll see that hematocrit levels do look different on the graph, but p values were marginal except during the acute phase.)


Cornet et al.’s (2013) results in cartoon format. Malaria infection makes birds more attractive to mosquitoes, but only when infection is chronic and hematocrit isn’t reduced. 

So, mosquitoes preferred infected hosts, but only during the chronic stage of infection.  Cornet et al. (2013) suggest that during the acute phase, when red blood cell counts are lower, blood meals from infected birds wouldn’t be a good source of protein for female mosquitoes.  No matter how sexy the parasites make the birds, the low hematocrit should select for mosquitoes to stay away from infected birds during the acute stage.  But later, when parasitemia is lower and hematocrit increases again, the sexiness of the birds wins out.  Awesome!

Have you ever joked about how you like to go fishing/hiking/whatevering with your “sweet” friend because all of the mosquitoes bite him or her and leave you alone?  I’m going to start assuming all such “sweet” people have chronic malaria.     


Cornet, S., A. Nicot, A. Rivero, and S. Gandon.  2013. Malaria infection increases bird attractiveness to uninfected mosquitoes. Ecology Letters 16: 323-329.

Do parasites like it hot?

Today, I’m writing about a 2013 Ecology Letters paper entitled “Optimal temperature for malaria transmission is dramatically lower than previously predicted.”

Ro is the number of new disease cases that arise when just one infected individual is added to a susceptible population.  This is an important parameter for disease transmission models.  (Check out the wikipedia page for more information.)

Many factors affect Ro.  For malaria (Plasmodium falciparum), most of these factors are related to mosquito and parasite biology.  These are life history traits like the rate at which mosquitos bite people, the competence of the mosquito vector, and the mortality rate of the adult mosquitoes.  Mordecai et al. (2013) knew that these life history traits often don’t have linear relationships with temperature.  Instead, these life history trait rates tend to increase with temperature until some optimum, and then decline.


As it turns out, many biological responses have these nonlinear relationships with temperature because above some optimal temperature, enzymes/proteins become denatured. Photo Credit.


Using nonlinear estimates of the relationships between temperature and life history traits from the literature, Mordecai et al. (2013) mathematically modeled Ro.   They found that Ro peaked at 25°C, which is 6°C less than the peak Ro predicted if temperature is assumed to linearly affect life history traits and Ro.   Neat!


If you assume that temperature has a linear effect on life history traits, peak Ro occurs at 31°C (dotted line). If you allow for nonlinear relationships, peak Ro occurs at 25°C (solid line). Stylized results from Mordecai et al. (2013). 

You might be wondering why this 6°C difference is important.  First of all, the 6°C change equates to going from 77°F to 87.8°F, which is a big change!  Imagine which of those temperatures you’d prefer to work in on a summer day.

Second, temperature is one component of the global climate that is expected to change with climate change.  When people try to model how the range of malaria might shift with climate change, they need to know how mosquitos and the malaria parasite (and thus Ro) respond to various temperatures.  And the answer is:  that response is nonlinear, and the peak Ro is around 25°C! 


Predicted temperature increases around the world for 2070-2100. Photo Credit.

What do you think?  Modeling predicted range shifts is complicated business, and temperature is just one consideration.  Do you think malaria might end up in your continent/country/state/city?


Mordecai, E.A., K.P. Paaijmans, L.R. Johnson, C. Balzer, T. Ben-Horin, E. de Moor, A. McNally, S. Pawar, S.J. Ryan, T.C. Smith, K.D. Lafferty.  2013. Optimal temperature for malaria transmission is dramatically lower than previously predicted.  Ecology Letters 16(1): 22-30.