Causes and consequences of pattern diversification in a spatially self-organizing microbial community

Abstract

Surface-attached microbial communities constitute a vast amount of life on our planet. They contribute to all major biogeochemical cycles, provide essential services to our society and environment, and have important effects on human health and disease. They typically consist of different interacting genotypes that arrange themselves non-randomly across space (referred to hereafter as spatial self-organization). While spatial self-organization is important for the functioning, ecology, and evolution of these communities, the underlying determinants of spatial self-organization remain unclear. Here, we performed a combination of experiments, statistical modeling, and mathematical simulations with a synthetic cross-feeding microbial community consisting of two isogenic strains. We found that two different patterns of spatial self-organization emerged at the same length and time scales, thus demonstrating pattern diversification. This pattern diversification was not caused by initial environmental heterogeneity or by genetic heterogeneity within populations. Instead, it was caused by nongenetic heterogeneity within populations, and we provide evidence that the source of this nongenetic heterogeneity is local differences in the initial spatial positionings of individuals. We further demonstrate that the different patterns exhibit different community-level properties; namely, they have different expansion speeds. Together, our results demonstrate that pattern diversification can emerge in the absence of initial environmental heterogeneity or genetic heterogeneity within populations and can affect community-level properties, thus providing novel insights into the causes and consequences of microbial spatial self-organization.

Leveraging Experimental Strategies to Capture Different Dimensions of Microbial Interactions

“…Briefly, these systems allow microorganisms to be co-cultured on solid plates ( Liu et al, 2017 ), in liquid media ( Kheir et al, 2018 ), or on a submerged surface ( Lapointe et al, 2019 ). Microbes can be perfectly mixed ( Smid and Lacroix, 2013 ) or spatially separated ( Goldschmidt et al, 2021 ), depending on the experimental setup. Another emergent experimental setup for measuring and characterizing microbial interactions is microfluidic cell culture systems (reviewed in Burmeister and Grunberger, 2020 ).…”

Founder cell configuration drives competitive outcome within colony biofilms

“…Hence, it has been implemented widely across many different microorganisms (1,3,12). Increasingly, the colony biofilm model is being exploited to explore competitive dynamics within single and mixed species biofilms (13)(14)(15)(16)(17)(18)(19). Many competitive mechanisms fundamentally depend on spatial co-location of strains, with spatial segregation within multi-strain biofilms offering protection.…”

Type IV pilus shapes a ‘bubble-jet’ pattern opposing spatial intermixing of two interacting bacterial populations

“…These surface-attached communities play important roles in ecosystem processes (3), pollutant removal (4) and human health (5). Biofilms are spatially well-organized, with different populations interacting with each other, arranging themselves non-randomly across space, and ultimately developing well-organized spatial patterns (referred to as ‘spatial self-organization’ hereafter (6, 7)). Spatial patterns of a community reflect the distribution of different populations across the habitat, and this distribution profoundly influences the interactions that occur among these populations.…”

“…On the other hand, spatial demixing (referred to as ‘segregated pattern’ thereafter) stabilizes intransitive interactions among antibiotic-producing, -sensitive and -resistant species (10), and also protects cells from contact-dependent killing by its competitors (11). These outcomes can eventually be magnified to determine community-level properties, such as overall community productivity (8), resistance to invaders (12, 13), and robustness to initial conditions (7, 14).…”