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Beyond killing: Can we find new ways to manage infection?

Vale PF, McNally L, Doeschl-Wilson A, King KC, Popat R, Domingo-Sananes MR, Allen JE, Soares MP, Kümmerli R - Evol Med Public Health (2016)

Bottom Line: An efficient strategy to stay ahead of rapidly adapting pathogens should include approaches that replace, complement or enhance the effect of both current and novel antimicrobial compounds.We discuss the therapeutic potential of these approaches and examine their possible consequences for pathogen evolution.To guarantee a longer half-life of these alternatives to directly killing pathogens, and to gain a full understanding of their population-level consequences, we encourage future work to incorporate evolutionary perspectives into the development of these treatments.

View Article: PubMed Central - PubMed

Affiliation: Centre for Immunity, Infection and Evolution Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FL, UK pedro.vale@ed.ac.uk.

No MeSH data available.


Related in: MedlinePlus

When comparing the ability of two different groups of hosts to limit damage during infection (e.g. a group with or without a damage control therapy), a common approach is to analyse how host health changes with increasing infection loads for each of the groups of interest. As pathogen loads increase during infection, hosts will lose health, going from a state of no symptoms to illness, and in extreme cases even death. In its simplest form, this relationship may be linear [30, 37], and host groups showing steep negative slopes for this reaction norm suffer a loss in health with increasing loads, while hosts with flat reaction norms are able to maintain health even as pathogen loads increase, and are therefore relatively tolerant. A potentially more realistic outcome is a non-linear relationship between host health and pathogen load. Hosts with more efficient damage prevention or repair mechanisms are able to maintain a higher level of health during infection (blue line) by affecting the sensitivity, slope or severity of the dose-response curve. The aim of therapies that promote tissue damage control is to flatten these relationships (by increasing the period before health plunges and/or lowering the slope)
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eow012-F2: When comparing the ability of two different groups of hosts to limit damage during infection (e.g. a group with or without a damage control therapy), a common approach is to analyse how host health changes with increasing infection loads for each of the groups of interest. As pathogen loads increase during infection, hosts will lose health, going from a state of no symptoms to illness, and in extreme cases even death. In its simplest form, this relationship may be linear [30, 37], and host groups showing steep negative slopes for this reaction norm suffer a loss in health with increasing loads, while hosts with flat reaction norms are able to maintain health even as pathogen loads increase, and are therefore relatively tolerant. A potentially more realistic outcome is a non-linear relationship between host health and pathogen load. Hosts with more efficient damage prevention or repair mechanisms are able to maintain a higher level of health during infection (blue line) by affecting the sensitivity, slope or severity of the dose-response curve. The aim of therapies that promote tissue damage control is to flatten these relationships (by increasing the period before health plunges and/or lowering the slope)

Mentions: One approach to uncover novel therapeutic targets for tissue damage control is to unravel the underlying causes for the enormous variation in disease tolerance that is often observed between species or even sub-species in their response to zoonotic pathogens. For example, bats, mice and humans are susceptible to infection by the Ebola virus, but these species have very different disease outcomes. It has been speculated that bats are especially capable of tolerating many zoonotic viruses through a combination of attenuated immunity—which reduces potential immunopathology—and the ability to minimize oxidative stress—an adaptation to metabolically costly activities like flight [35, 36]. The combined result is incomplete viral clearance and reduced immunopathology, which has been suggested as a plausible explanation for bats being such accomplished viral reservoirs, although concrete data to this effect is currently lacking. One way to compare groups of hosts that may differ in their ability to limit damage during infection is to obtain health read outs (e.g. survival, anaemia, immune markers) for increasing pathogen doses under controlled experimental conditions. These groups of hosts (e.g. different species as in the Ebola example, or human patients receiving damage limitation therapies) may differ in various parameters of this pathogen dose-host health response, including host vigour (the baseline level of health in the absence of infection), sensitivity to increases in pathogen load (the infection dose at which host suffer a severe decline in health) the rate at which host health decreases with increasing pathogen loads (the slope of the decline in health), or the severity of infection, which determines how sick a host can get during infection (Fig. 2). Variation in each of these parameters may reflect distinct underlying mechanisms that either promote greater prevention of damage during infection, or increase damage repair after the damage has been done [29]. If we were able to identify novel mechanisms of disease tolerance, we could then seek to develop therapies that enhance them with drugs that are likely to be more evolution-proof than conventional antibiotics.Figure 2.


Beyond killing: Can we find new ways to manage infection?

Vale PF, McNally L, Doeschl-Wilson A, King KC, Popat R, Domingo-Sananes MR, Allen JE, Soares MP, Kümmerli R - Evol Med Public Health (2016)

When comparing the ability of two different groups of hosts to limit damage during infection (e.g. a group with or without a damage control therapy), a common approach is to analyse how host health changes with increasing infection loads for each of the groups of interest. As pathogen loads increase during infection, hosts will lose health, going from a state of no symptoms to illness, and in extreme cases even death. In its simplest form, this relationship may be linear [30, 37], and host groups showing steep negative slopes for this reaction norm suffer a loss in health with increasing loads, while hosts with flat reaction norms are able to maintain health even as pathogen loads increase, and are therefore relatively tolerant. A potentially more realistic outcome is a non-linear relationship between host health and pathogen load. Hosts with more efficient damage prevention or repair mechanisms are able to maintain a higher level of health during infection (blue line) by affecting the sensitivity, slope or severity of the dose-response curve. The aim of therapies that promote tissue damage control is to flatten these relationships (by increasing the period before health plunges and/or lowering the slope)
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4834974&req=5

eow012-F2: When comparing the ability of two different groups of hosts to limit damage during infection (e.g. a group with or without a damage control therapy), a common approach is to analyse how host health changes with increasing infection loads for each of the groups of interest. As pathogen loads increase during infection, hosts will lose health, going from a state of no symptoms to illness, and in extreme cases even death. In its simplest form, this relationship may be linear [30, 37], and host groups showing steep negative slopes for this reaction norm suffer a loss in health with increasing loads, while hosts with flat reaction norms are able to maintain health even as pathogen loads increase, and are therefore relatively tolerant. A potentially more realistic outcome is a non-linear relationship between host health and pathogen load. Hosts with more efficient damage prevention or repair mechanisms are able to maintain a higher level of health during infection (blue line) by affecting the sensitivity, slope or severity of the dose-response curve. The aim of therapies that promote tissue damage control is to flatten these relationships (by increasing the period before health plunges and/or lowering the slope)
Mentions: One approach to uncover novel therapeutic targets for tissue damage control is to unravel the underlying causes for the enormous variation in disease tolerance that is often observed between species or even sub-species in their response to zoonotic pathogens. For example, bats, mice and humans are susceptible to infection by the Ebola virus, but these species have very different disease outcomes. It has been speculated that bats are especially capable of tolerating many zoonotic viruses through a combination of attenuated immunity—which reduces potential immunopathology—and the ability to minimize oxidative stress—an adaptation to metabolically costly activities like flight [35, 36]. The combined result is incomplete viral clearance and reduced immunopathology, which has been suggested as a plausible explanation for bats being such accomplished viral reservoirs, although concrete data to this effect is currently lacking. One way to compare groups of hosts that may differ in their ability to limit damage during infection is to obtain health read outs (e.g. survival, anaemia, immune markers) for increasing pathogen doses under controlled experimental conditions. These groups of hosts (e.g. different species as in the Ebola example, or human patients receiving damage limitation therapies) may differ in various parameters of this pathogen dose-host health response, including host vigour (the baseline level of health in the absence of infection), sensitivity to increases in pathogen load (the infection dose at which host suffer a severe decline in health) the rate at which host health decreases with increasing pathogen loads (the slope of the decline in health), or the severity of infection, which determines how sick a host can get during infection (Fig. 2). Variation in each of these parameters may reflect distinct underlying mechanisms that either promote greater prevention of damage during infection, or increase damage repair after the damage has been done [29]. If we were able to identify novel mechanisms of disease tolerance, we could then seek to develop therapies that enhance them with drugs that are likely to be more evolution-proof than conventional antibiotics.Figure 2.

Bottom Line: An efficient strategy to stay ahead of rapidly adapting pathogens should include approaches that replace, complement or enhance the effect of both current and novel antimicrobial compounds.We discuss the therapeutic potential of these approaches and examine their possible consequences for pathogen evolution.To guarantee a longer half-life of these alternatives to directly killing pathogens, and to gain a full understanding of their population-level consequences, we encourage future work to incorporate evolutionary perspectives into the development of these treatments.

View Article: PubMed Central - PubMed

Affiliation: Centre for Immunity, Infection and Evolution Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FL, UK pedro.vale@ed.ac.uk.

No MeSH data available.


Related in: MedlinePlus