Tracing personalized health curves during infections

Abstract

It is difficult to describe host-microbe interactions in a manner that deals well with both pathogens and mutualists. Perhaps a way can be found using an ecological definition of tolerance, where tolerance is defined as the dose response curve of health versus parasite load. To plot tolerance, individual infections are summarized by reporting the maximum parasite load and the minimum health for a population of infected individuals and the slope of the resulting curve defines the tolerance of the population. We can borrow this method of plotting health versus microbe load in a population and make it apply to individuals; instead of plotting just one point that summarizes an infection in an individual, we can plot the values at many time points over the course of an infection for one individual. This produces curves that trace the course of an infection through phase space rather than over a more typical timeline. These curves highlight relationships like recovery and point out bifurcations that are difficult to visualize with standard plotting techniques. Only nine archetypical curves are needed to describe most pathogenic and mutualistic host-microbe interactions. The technique holds promise as both a qualitative and quantitative approach to dissect host-microbe interactions of all kinds.

Figure 1. Plotting data in the phase plane to better monitor infections.

(A) A sick “patient” is depicted in frames at the top where the red dots indicate parasites and the stature of the “patient” depicts health. In a simple timeline, parasites can be seen to rise and fall and the health falls and returns to its original levels. The relationship between health and parasite levels is visible but not as simple to interpret as shown below in (B). (B) The curves from (A) are replotted in a health by parasite load phase plot. The plot shows three sections: First, the parasites grow but do not affect health (dark blue). The slope here is quite flat. Second, (medium blue) the host begins to clear the pathogens but the health crashes as well in this pathogenesis portion of the plot. Third (light blue), the health recovers while the microbes continue to be cleared.

Figure 2. The contribution of velocity to disease curves.

The cartoons in this article don’t show imaginary data points and thus don’t give an impression of the velocity that a host will pass through health-by-microbe space. Here I’ve used vectors to show velocity. (A) Depicts two curves, one resilient and another leading to parasite growth and host death. Near the origin, both curves traverse the same space and can’t be distinguished on this basis; however, the curves differ in velocity. This highlights the point that it is important to measure velocity when plotting these curves. (B) Depicts a bifurcation point in a curve after an unknown “something changes”. The three following curves differ in their velocity as indicated by the length and direction of the vector arrows. On the right, the vectors are compared next to triangles to make it easier to see the components controlling parasite growth and health. The green curve has exactly the same health to parasite slope as the original, but the velocity of the curve is reduced. Perhaps an antimicrobial has been induced that blocks parasite growth but does not harm the host. The blue curve has the same parasite growth rate but the slope is steeper. In this case an ineffective and host-damaging immune response could have turned on. The red curve shows a reduction in parasite growth and a decrease in slope. Here, an effective but host-damaging antimicrobial may have been produced. This figure highlights the importance of measuring the acceleration of these curves.

Figure 3. Nine simple curves describe the infectious route of all infections.

Curve definitions: Pathogenic: 1. Recovery (uncomplicated flu, measles, gastritis). 2. Permanent and stable disability (lasting meningitis/encephalitis damage). 3. Unstable disability (rheumatic fever sequelae or reactive arthritis). 4. Persistent pathogen infection (tuberculosis, herpes). 5. Death while defeating a microbe (sepsis). 6. Uncontrolled microbial growth and death. Mutualistic: 7. Short-term colonization with a beneficial microbe (transient probiotics). 8. An infection that is cleared but permanently changes the state of the host (live vaccines). 9. Persistent infection with a mutualist (Rhizobium, Hamiltonella, Wolbachia –, herpes [26]).

Figure 4. Bifurcation points teach us about defects in the immune response.

A resilient disease curve is shown in black and four bifurcating disease curves are shown in red. The first bifurcating curve leads to increased death because of a failure to clear microbes. The second bifurcating curve could have a problem both clearing microbes and preventing pathogenesis. The third and fourth bifurcating curves have defects in recovery but are capable of clearing pathogens. Each bifurcating curve or pair of curves defines regions of disease space that suggest different defects in the immune response.