Steering ecological-evolutionary dynamics during artificial selection of microbial communities

Abstract

How might we artificially select multi-species microbial communities to improve their functions? In the accompanying article, we have modeled a commensal community where Helper releases Byproduct essential to Manufacturer, and Manufacturer diverts a fraction fP of its growth to make Product. Low-density “Newborn communities” grow and mutate over time T into “Adult communities”, and Adult communities with the highest function (total Product) are randomly partitioned into Newborn communities of the next cycle. Here, to understand selection dynamics, we visualize community function landscape (“landscape”) which relates community phenotype composition (e.g. species ratio; Manufacturer’s fP) to community function. We show that although an interaction can enable species coexistence by driving species ratio toward an "attractor", it can constrain selection if maximal function lies outside of the attractor. Landscape-attractor diagrams allow us to evaluate the effectiveness of different selection regimens, including screening communities comprising one random clone from each species.

Eco-evolutionary dynamics of nested Darwinian populations and the emergence of community-level heredity

“…The growth function G captures the outcome of the ecological dynamics (i.e., the fraction of red particles) after founding populations are allowed to grow for a finite time interval T . We note similarity between the G function and the recently proposed "community-function landscape" (Xie and Shou, 2018). The shape of G depends on the value of particle traits θ (growth rates r 0 and r 1 , and competition parameters a 00 = a intra 0 , a 10 = a inter 0 , a 01 = a inter 1 , a 11 = a intra 1 ), but also on the bottleneck size at dilution B and the collective generation duration T .…”

“…Indeed, simulations of multi-species assemblages have shown that evolution of interaction rates not only improves diversity-dependent fitness, but also increases collective "heritability", defined as the capacity of randomly seeded offspring communities to reach the same dynamical state as their parents (Ikegami and Hashimoto, 2002;Penn, 2003). Further studies have stressed the role of the extracellular environment and of specific interaction networks, pointing out that microscopic constrains can affect the capacity of communities to participate in evolutionary dynamics at the higher level (Williams and Lenton, 2007;Xie and Shou, 2018;Xie et al, 2019).…”

“…Phoretic insect communities are an apparent case in point. In the laboratory, however, the experimenter can readily construct conditions that ensure communities (or any collective of cells) are units of selection in their own right (Johnson and Boerlijst, 2002;Day et al, 2011;Xie and Shou, 2018;Xie et al, 2019). A critical requirement is a birth-death process operating over a time scale longer than the doubling time of individual cells (Hammerschmidt et al, 2014;Rainey et al, 2017;Black et al, 2019).…”

Microbial coexistence through chemical-mediated interactions

“…Microbial communities influence ecosystems by cycling matter (1) and affect human health by facilitating food digestion or causing infections (2)(3)(4)(5). Cohabiting microbes in communities interact and can perform functions that none of the member species can achieve efficiently on their own (6,7).…”