Nine simple curves describe the infectious route of all infections.

<p>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 (<i>Rhizobium</i>, <i>Hamiltonella</i>, <i>Wolbachia </i><a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001158#pbio.1001158-Ferrari1" target="_blank">[20]</a>–<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001158#pbio.1001158-Teixeira1" target="_blank">[24]</a>, herpes <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001158#pbio.1001158-Barton1" target="_blank">[26]</a>).</p

The contribution of velocity to disease curves.

<p>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.</p

Plotting data in the phase plane to better monitor infections.

<p>(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.</p

Bifurcation points teach us about defects in the immune response.

<p>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.</p

The double-edged sword of social life.

<p>When a sufficiently high proportion of a population is immune to a pathogen, transmission to non- and poorly immunised individuals (e.g., the young, the immunocompromised, and the weak responders to immunisation) is blocked by the barrier formed by immunised individuals. Conversely, when immunisation rate is insufficient, non-immunised individuals are at greater risk of becoming infected during social contacts.</p

Ets21c phenotypes confirmed by RNAi knockdown.

<p><i>L. monocytogenes</i> or <i>S. pneumoniae</i> were injected into RNAi crosses and control flies. Survival and growth of the bacteria was monitored over the course of infection. (A),(C) L. monocytogenes; (B),(D) S. pneumoniae. Log-rank analysis of the survival curves gives p<0.0001 for all curves (w/o media controls in analysis). The significant sources of variation were assessed by two-way ANOVA and differences in bacterial load between the driven RNAi and the controls at each time point were assessed by the Bonferroni post-test after ANOVA and significantly different values denoted by asterisk (** p<0.01, *** p<0.001).</p

Summary of changes in glycolysis during <i>L. monocytogenes</i> infection.

<p>Metabolite and gene changes within glycolysis during <i>L. monocytogenes</i> infection. Genes selected in Genespring 12.0 using Oneway ANOVA with Welch's correction for unequal variance (p<0.05) are highlighted in blue boxes. Metabolites that are significantly down during infection as determined by a Welch's two-tailed t-test (* p<0.05) are in blue type.</p

<i>L. monocytogenes</i> infection reduces energy stores in <i>D. melanogaster</i>.

<p>Triglyceride (A) and glycogen (B) levels were assayed during later time-points of <i>L. monocytogenes</i> infection. Metabolite levels were normalized to total protein and then represented as percent of the levels in unmanipulated flies. The significant sources of variation were assessed by two-way ANOVA and differences in the metabolite levels at each time point were assessed by the Bonferroni post-test after ANOVA; significantly different values denoted by asterisk (* p<0.05, ** p<0.01, *** p<0.001).</p

Melanization is reduced in ets21c mutants and increased in wntD mutants.

<p>Flies were tested for their ability to melanize during infection by looking for disseminated melanization 4 days post-injection (A) or 3 days post-injection (B) Significance was determined by Fischer's Exact Test (* p<0.05).</p

Select simple and complex sugars decrease during <i>L. monocytogenes</i> infection.

<p>Simple and complex sugar levels as extracted from metabolon data set. (A) Sugar levels in uninfected flies. The Normalized Area was determined by taking the area under each GC/LC peak divided by area under the standard peak, metabolite levels compared by One Way ANOVA and grouped by Tukey Test (q = 0.05). (B) Sugar levels during <i>L. monocytogenes</i> infection, values normalized to zero hour for each compound. Significantly different values determined by Welch's two-tailed t-test (p<0.05; 0 hr vs 48 hr: A, 0 hr vs 24 hr: B, 0 hr vs 6 hr: C).</p