Population cycles and species diversity in dynamic Kill-the-Winner model of microbial ecosystems

Abstract

Determinants of species diversity in microbial ecosystems remain poorly understood. Bacteriophages are believed to increase the diversity by the virtue of Kill-the-Winner infection bias preventing the fastest growing organism from taking over the community. Phage-bacterial ecosystems are traditionally described in terms of the static equilibrium state of Lotka-Volterra equations in which bacterial growth is exactly balanced by losses due to phage predation. Here we consider a more dynamic scenario in which phage infections give rise to abrupt and severe collapses of bacterial populations whenever they become sufficiently large. As a consequence, each bacterial population in our model follows cyclic dynamics of exponential growth interrupted by sudden declines. The total population of all species fluctuates around the carrying capacity of the environment, making these cycles cryptic. While a subset of the slowest growing species in our model is always driven towards extinction, in general the overall ecosystem diversity remains high. The number of surviving species is inversely proportional to the variation in their growth rates but increases with the frequency and severity of phage-induced collapses. Thus counter-intuitively we predict that microbial communities exposed to more violent perturbations should have higher diversity.An important and largely unsolved question in microbial ecology is what determines the diversity of microbial ecosystems. Indeed, unbridled competition between microbes sharing common resources would eventually limit species diversity not to exceed the number of different nutrient types 1 . Predation by bacteriophages introduces the negative frequency-dependent selection 2-5 which offers the possibility for a dramatically larger species diversity 5 . In the classical Kill-the-Winner (KtW) model of Thingstad 5 virulent phages reduce populations of their susceptible hosts to a low steady state level, which is independent of hosts' growth rate thus allowing multiple species per nutrient type. The number of co-existing bacterial species in the resulting ecosystem is determined exclusively by the parameters of phage predation 5 , the topology of the phage-bacterial infection network [6][7][8] , and the carrying capacity of the environment 4,[7][8][9] . Microbial populations are routinely exposed to more dynamics than assumed in the traditional steady state KtW model and its variants. Extended Lotka-Volterra equations for two layer ecosystems of phages and bacteria could predict persistently varying populations 10 , even without considering mutations. In addition, microbial systems are typically exposed to changes in interaction rules and new invading species. For example, in the lab experiments 11 E. coli population suffered a dramatic collapse by a factor ~10 4 -10 5 caused by a T7 phage infection. Collapse-driven dynamics is common in both natural 12 and man-made 13-16 ecosystems in which bacteria are engaged in the continuous arms race with phages [17][18][19][20][21] . Here we propose and ...

Review article: bacteriophages in gastroenterology—from biology to clinical applications

“…14 These phages may contribute to higher bacterial diversity following killthe-winner dynamics (Box 1), with cycles of bacterial bloom followed by phage-induced population collapses, preventing fast-growing bacteria from taking over the microbial community. 15 Another interaction observed between phages and bacteria is a stable equilibrium, where neither lysogeny nor lysis takes place, known as a carrier state or pseudolysogeny. 16,17 This carrier state has been described in several bacteria, such as Campylobacter spp., Shigella dysenteriae, Brucella abortus and Proteus mirabilis.…”

“…Targeting thriving bacteria in an ecosystem will prevent the bacterial monopoly, allowing the coexistence of competing bacterial species. 15,111 Piggyback-the-winner dynamic: In ecosystems with high microbial density, temperate phages switch towards lysogeny, thereby reducing phage-induced lysis. Additionally, these phages will prevent infection by other closely related phages (superinfection exclusion), further helping the bacterial clone thrive in the competitive high-density ecosystem.…”

Phage therapy for severe bacterial infections: a narrative review

“…37,38 Usually applied to ecological rather than pharmacological systems, the phage replication cycle is generally held to follow classic Lotka-Volterra dynamics of predator (phage) and prey (bacteria), based on population sizes and interactions between them. 39 Classic pharmacokinetic principles, predator-prey, infectious disease model dynamics, and host immune responses must all be considered, but phage pharmacokinetics (phage distribution and clearance), pharmacodynamics (predator-prey dynamics), and ratio of phages to bacteria (multiplicity of infection [MOI], perhaps better described in terms of initial MOI input ) 40,41 are subject to many, often poorly defined, variables that may influence outcomes. 42 Numerous methods of bacteriophage delivery have been explored, including topical, inhalational, oral and injectable (intravenous, intramuscular, subcutaneous and direct intralesional).…”

Contrasting distributions of bacteriophages and eukaryotic viruses from contaminated coastal sediments

“…This suggests that high bacterial growth rates at sites close to stormwater drains, which might favour the efficiency of prophage induction (Cochran et al, 1998). In addition, abundance and diversity of bacteria might be balanced by phage predation (Maslov and Sneppen, 2017). Although there were differences in both bacterial communities and viral communities between contaminated and reference sites, and a clear relationship between viral and bacterial communities on an OTU level explaining 93.6% of community variation, no overall relationship between phages and their predicted bacterial host could be made.…”