Emergence of Spatial Structure in Cell Groups and the Evolution of Cooperation

Abstract

On its own, a single cell cannot exert more than a microscopic influence on its immediate surroundings. However, via strength in numbers and the expression of cooperative phenotypes, such cells can enormously impact their environments. Simple cooperative phenotypes appear to abound in the microbial world, but explaining their evolution is challenging because they are often subject to exploitation by rapidly growing, non-cooperative cell lines. Population spatial structure may be critical for this problem because it influences the extent of interaction between cooperative and non-cooperative individuals. It is difficult for cooperative cells to succeed in competition if they become mixed with non-cooperative cells, which can exploit the public good without themselves paying a cost. However, if cooperative cells are segregated in space and preferentially interact with each other, they may prevail. Here we use a multi-agent computational model to study the origin of spatial structure within growing cell groups. Our simulations reveal that the spatial distribution of genetic lineages within these groups is linked to a small number of physical and biological parameters, including cell growth rate, nutrient availability, and nutrient diffusivity. Realistic changes in these parameters qualitatively alter the emergent structure of cell groups, and thereby determine whether cells with cooperative phenotypes can locally and globally outcompete exploitative cells. We argue that cooperative and exploitative cell lineages will spontaneously segregate in space under a wide range of conditions and, therefore, that cellular cooperation may evolve more readily than naively expected.

Cutting through the complexity of cell collectives

“…This result was somewhat counterintuitive, as decreasing d leads to increasing spatial segregation among cell lineages, which in principle could allow for cooperative cells to be favoured even if the benefit of their secreted enzyme is distributed farther away from them (decreasing B L ). However, we see that the conditions favouring cooperation become more stringent as d decreases because nutrient limitation creates a strong advantage for cell lineages that accumulate even marginally greater biovolume at the earliest time points during biofilm growth [42,62,90]. Such cells are able to deny their neighbours access to nutrients and in so doing dramatically reduce their ability to grow.…”

“…Indeed for low d and high B L , cooperative cells outcompete cheater cells 10 times more strongly than they do with the same B L value at high d. Low d leads to more globalized competition for nutrients and increased segregation among cell lineages (i.e. increased relatedness) [42,74], both of which increase the advantage of spatially localized cooperative behaviour [30].…”

“…If G is large, however, cell growth is heterogeneous along the biofilm front, indicating that bacterial growth is limited by nutrient transport. A modification of the G number was recently introduced to shift focus towards the proportion of cells in a biofilm that have sufficient access to substrate for growth, which is accomplished by considering the rate of molecular transport through the boundary layer, rather than the biofilm itself [42]. The modified dimensionless number, d, is…”

“…The d number determines the depth to which substrate diffuses into a biofilm before being depleted, and therefore represents the thickness of the actively growing cell population, in units of h (figure 2a) [42].…”

Biofilm formation and toxin production provide a fitness advantage in mixed colonies of environmental yeast isolates

“…Despite their vulnerability to individual cheaters, biofilms are ubiquitous and stable. The leading hypothesis for the stability of biofilm communities is the spatial structure: Competition, cooperation, and passive processes like clonal growth can generate patches of related cooperative cells able to outcompete unrelated cells (e.g., (Anderson, Garcia, & Cotter, 2014; van Gestel, Weissing, Kuipers, & Kovacs, 2014; Hallatschek, Hersen, Ramanathan, & Nelson, 2007; Millet et al., 2014; Momeni, Brileya, Fields, & Shou, 2013; Müller, Neugeboren, Nelson, & Murray, 2014; Nadell & Bassler, 2011; Nadell, Foster, & Xavier, 2010; Van Dyken, Müller, Mack, & Desai, 2013; Xavier & Foster, 2007), recently reviewed in detail in ref. (Nadell, Drescher, & Foster, 2016)).…”