Bacterial Cooperation Causes Systematic Errors in Pathogen Risk Assessment due to the Failure of the Independent Action Hypothesis

Abstract

The Independent Action Hypothesis (IAH) states that pathogenic individuals (cells, spores, virus particles etc.) behave independently of each other, so that each has an independent probability of causing systemic infection or death. The IAH is not just of basic scientific interest; it forms the basis of our current estimates of infectious disease risk in humans. Despite the important role of the IAH in managing disease interventions for food and water-borne pathogens, experimental support for the IAH in bacterial pathogens is indirect at best. Moreover since the IAH was first proposed, cooperative behaviors have been discovered in a wide range of microorganisms, including many pathogens. A fundamental principle of cooperation is that the fitness of individuals is affected by the presence and behaviors of others, which is contrary to the assumption of independent action. In this paper, we test the IAH in Bacillus thuringiensis (B.t), a widely occurring insect pathogen that releases toxins that benefit others in the inoculum, infecting the diamondback moth, Plutella xylostella. By experimentally separating B.t. spores from their toxins, we demonstrate that the IAH fails because there is an interaction between toxin and spore effects on mortality, where the toxin effect is synergistic and cannot be accommodated by independence assumptions. Finally, we show that applying recommended IAH dose-response models to high dose data leads to systematic overestimation of mortality risks at low doses, due to the presence of synergistic pathogen interactions. Our results show that cooperative secretions can easily invalidate the IAH, and that such mechanistic details should be incorporated into pathogen risk analysis.

Fig 1. The effects of spores and toxin on mortality in Bacillus thuringiensis.

A) The mortality rates for varying quantities of spores of Bacillus thuringiensis a Cry null strain supplemented with 60 pg of toxin (open circles, dashed line) or 180 pg of toxin (solid circles, solid line). A glm of the form y ~ Toxins + log(Spores+1) + Toxins* log(Spores+1) is fit and shown for both toxin doses (see S2 Table in S1 Text for parameter details) with S.E. shown. In both cases spore dose has a positive impact on mortality, and in the 60 pg case, there is a gap between zero spores and the next lowest dose. B) The mortality rates for 900 Bacillus thuringiensis spores supplemented with varying quantities of toxins (+/- S.E.) in two experiments. The fit curves are from a nonlinear regression with an exponential model (Eq 1). This IAH model is not sufficiently threshold-like to describe the data; it overestimates low dose mortality.

Fig 2. The dose-response for wild type Bacillus thuringiensis (+/- S.E.) in two experiments.

The best IAH model (beta-Poisson) fit from all doses over 20% mortality is also shown. Again dashed line corresponds to the first experiment (with open circles), and solid line corresponds to the second experiment (with solid circles).

Fig 3. The distribution of the differences between predicted mortality and ‘actual’ mortality from best-fit models (fit to all doses over 20% mortality) at low doses in simulated experiments (generated from bootstrapping data from each dose 5,000 times).

The difference between predicted and “actual” low-dose risk when the model is chosen between the best fit exponential and beta-Poisson models based on AIC for experiment 1 focusing on the spore dose of mean 150 (A) and experiment 2 focusing on the spore dose of mean 130.59 (B). The difference between predicted and “actual” low-dose risk when the less commonly used, exact (confluent hypergeometric) form is shown for data in experiment 1 (C) and experiment 2 (D).