Theoretical validation of growth curves for quantifying phage-bacteria interactions

Abstract

AbstractBacteria-infecting viruses, bacteriophages, are the most abundant biological entities on the planet, frequently serving as model systems in basic research and increasingly relevant for medical applications such as phage therapy. A common need is to quantify the infectivity of a phage to a given bacterial host (or the resistance of a host to a phage). However, current methods to quantify infectivity suffer from low-throughput or low-precision. One method that has the potential for high-throughput and high-precision quantification of phage-bacteria interactions is growth curves, where bacterial density is measured over time in the presence and absence of phages. Recent work has proposed several approaches to quantify these curves into a metric of phage infectivity. However, little is known about how these metrics relate to one another or to underlying phage and bacterial traits. To address this gap, we apply ecological modeling of phage and bacterial populations to simulate growth curves across a wide range of trait values. Our findings show that many growth curve metrics provide parallel measures of phage infectivity. Informative metrics include the peak and decline portions of bacterial growth curves, are driven by the interactions between underlying phage and bacterial traits, and correlate with conventional measures of phage fitness. Moreover, we show how intrapopulation trait variation can alter growth curve dynamics. Finally, we test the sensitivity of growth curve metrics to inoculum densities, and assess techniques to compare growth curves across different bacterial hosts. In all, our findings support the use of growth curves for precise high-throughput quantification of phage-bacteria interactions across the microbial sciences.SignificanceBacteriophages are viruses that infect bacteria. Phages have long been laboratory models and are increasingly being explored as antimicrobials. Commonly, we need to quantify how well a phage infects a bacterial strain. Unfortunately, current methods are either laborious or imprecise. One method that could be better is growth curves, where bacterial growth is measured over time in the presence or absence of phages. However, it has remained unclear how to use such data to produce a single metric of phage infectivity. Here, we used simulations to show that many different metrics provide parallel measures of phage infectivity that match conventional measures across a range of conditions. Our work suggests that growth curves can provide rapid, precise measurement of phage infectivity.