Tracing Personalized Health Curves during Infections

Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States of America.
PLoS Biology (Impact Factor: 9.34). 09/2011; 9(9):e1001158. DOI: 10.1371/journal.pbio.1001158
Source: PubMed


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.

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Available from: David Schneider, Jun 28, 2015
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    • "Schneider (2011), after investigating the shapes of “personalized health curves” (equivalent to the pathogen burden–performance trajectories described here) corresponding to various well-studied infections in humans, concluded that most pathogenic and mutualistic host–pathogen interactions can be represented by one of only a few existing archetypical curves. Adapted to the resistance-tolerance context for the majority of livestock diseases, nine major classes of trajectory categories emerge as follows: individual host trajectories may first be classified according to the outcome in terms of infection severity, distinguishing broadly between eventual clearance of pathogens, long-term persistence, and death (illustrated by graphs A, B, C, respectively in Figure 3). "
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    ABSTRACT: We propose two novel approaches for describing and quantifying the response of individual hosts to pathogen challenge in terms of infection severity and impact on host performance. The first approach is a direct extension of the methodology for estimating group tolerance (the change in performance with respect to changes in pathogen burden in a host population) to the level of individuals. The second approach aims to capture the dynamic aspects of individual resistance and tolerance over the entire time course of infections. In contrast to the first approach, which provides a means to disentangle host resistance from tolerance, the second approach focuses on the combined effects of both characteristics. Both approaches provide new individual phenotypes for subsequent genetic analyses and come with specific data requirements. In particular, both approaches rely on the availability of repeated performance and pathogen burden measurements of individuals over the time course of one or several episodes of infection. Consideration of individual tolerance also highlights some of the assumptions hidden within the concept of group tolerance, indicating where care needs to be taken in trait definition and measurement.
    Frontiers in Genetics 12/2012; 3:266. DOI:10.3389/fgene.2012.00266
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    • "Yet for many infections, protective mechanisms involve diverse immune responses that also differ across time (Glass et al., 2012a,b; Leach et al., 2012). Additionally, the relationship between immune measures and protection or indeed, “fitness” is usually not clear (Graham et al., 2010; Schneider, 2011). Cost and logistics generally preclude specific challenge studies using carefully calibrated doses. "
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    ABSTRACT: Breeding livestock that are better able to withstand the onslaught of endemic- and exotic pathogens is high on the wish list of breeders and farmers world-wide. However, the defense systems in both pathogens and their hosts are complex and the degree of genetic variation in resistance and tolerance will depend on the trade-offs that they impose on host fitness as well as their life-histories. The genes and pathways underpinning resistance and tolerance traits may be distinct or intertwined as the outcome of any infection is a result of a balance between collateral damage of host tissues and control of the invading pathogen. Genes and molecular pathways associated with resistance are mainly expressed in the mucosal tract and the innate immune system and control the very early events following pathogen invasion. Resistance genes encode receptors involved in uptake of pathogens, as well as pattern recognition receptors (PRR) such as the toll-like receptor family as well as molecules involved in strong and rapid inflammatory responses which lead to rapid pathogen clearance, yet do not lead to immunopathology. In contrast tolerance genes and pathways play a role in reducing immunopathology or enhancing the host's ability to protect against pathogen associated toxins. Candidate tolerance genes may include cytosolic PRRs and unidentified sensors of pathogen growth, perturbation of host metabolism and intrinsic danger or damage associated molecules. In addition, genes controlling regulatory pathways, tissue repair and resolution are also tolerance candidates. The identities of distinct genetic loci for resistance and tolerance to infectious pathogens in livestock species remain to be determined. A better understanding of the mechanisms involved and phenotypes associated with resistance and tolerance should ultimately help to improve livestock health and welfare.
    Frontiers in Genetics 12/2012; 3:263. DOI:10.3389/fgene.2012.00263
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    Proceedings of the National Academy of Sciences 08/2012; 109(35):13886-7. DOI:10.1073/pnas.1211724109 · 9.67 Impact Factor
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