A dynamic network model predicts the phenotypes of multicellular clusters from cellular properties.

Cell division without cell separation produces multicellular clusters in budding yeast. Two fundamental characteristics of these clusters are their size (the number of cells per cluster) and cellular composition: the fractions of cells with different phenotypes. Using cells as nodes and links between mother and daughter cells as edges, we model cluster growth and breakage by varying three parameters: the cell division rate, the rate at which intercellular connections break, and the kissing number (the maximum number of connections to one cell). We find that the kissing number sets the maximum possible cluster size. Below this limit, the ratio of the cell division rate to the connection breaking rate determines the cluster size. If links have a constant probability of breaking per unit time, the probability that a link survives decreases exponentially with its age. Modeling this behavior recapitulates experimental data. We then use this framework to examine synthetic, differentiating clusters with two cell types, faster-growing germ cells and their somatic derivatives. The fraction of clusters that contain both cell types increases as either of two parameters increase: the kissing number and difference between the growth rate of germ and somatic cells. In a population of clusters, the variation in cellular composition is inversely correlated (r2 = 0.87) with the average fraction of somatic cells in clusters. Our results show how a small number of cellular features can control the phenotypes of multicellular clusters that were potentially the ancestors of more complex forms of multicellular development, organization, and reproduction.

Multicellular growth as a dynamic network of cells.

Cell division without cell separation produces multicellular clusters in budding yeast. Two fundamental characteristics of these clusters are their size (the number of cells per cluster) and cellular composition: the fractions of cells with different phenotypes. However, we do not understand how different cellular features quantitatively influence these two phenotypes. Using cells as nodes and links between mother and daughter cells as edges, we model cluster growth and breakage by varying three parameters: the cell division rate, the rate at which intercellular connections break, and the kissing number (the maximum number of connections to one cell). We find that the kissing number sets the maximum possible cluster size. Below this limit, the ratio of the cell division rate to the connection breaking rate determines the cluster size. If links have a constant probability of breaking per unit time, the probability that a link survives decreases exponentially with its age. Modeling this behavior recapitulates experimental data. We then use this framework to examine synthetic, differentiating clusters with two cell types, faster-growing germ cells and their somatic derivatives. The fraction of clusters that contain both cell types increases as either of two parameters increase: the kissing number and difference between the growth rate of germ and somatic cells. In a population of clusters, the variation in cellular composition is inversely correlated (r2=0.87) with the average fraction of somatic cells in clusters. Our results show how a small number of cellular features can control the phenotypes of multicellular clusters that were potentially the ancestors of more complex forms of multicellular development, organization, and reproduction.

Alternating selection for dispersal and multicellularity favors regulated life cycles.

The evolution of complex multicellularity opened paths to increased morphological diversity and organizational novelty. This transition involved three processes: cells remained attached to one another to form groups, cells within these groups differentiated to perform different tasks, and the groups evolved new reproductive strategies.1,2,3,4,5 Recent experiments identified selective pressures and mutations that can drive the emergence of simple multicellularity and cell differentiation,6,7,8,9,10,11 but the evolution of life cycles, particularly how simple multicellular forms reproduce, has been understudied. The selective pressure and mechanisms that produced a regular alternation between single cells and multicellular collectives are still unclear.12 To probe the factors regulating simple multicellular life cycles, we examined a collection of wild isolates of the budding yeast S. cerevisiae.12,13 We found that all these strains can exist as multicellular clusters, a phenotype that is controlled by the mating-type locus and strongly influenced by the nutritional environment. Inspired by this variation, we engineered inducible dispersal in a multicellular laboratory strain and demonstrated that a regulated life cycle has an advantage over constitutively single-celled or constitutively multicellular life cycles when the environment alternates between favoring intercellular cooperation (a low sucrose concentration) and dispersal (a patchy environment generated by emulsion). Our results suggest that the separation of mother and daughter cells is under selection in wild isolates and is regulated by their genetic composition and the environments they encounter and that alternating patterns of resource availability may have played a role in the evolution of life cycles.

Functional traits and climate drive interspecific differences in disturbance-induced tree mortality.

With climate change, natural disturbances such as storm or fire are reshuffled, inducing pervasive shifts in forest dynamics. To predict how it will impact forest structure and composition, it is crucial to understand how tree species differ in their sensitivity to disturbances. In this study, we investigated how functional traits and species mean climate affect their sensitivity to disturbances while controlling for tree size and stand structure. With data on 130,594 trees located on 7617 plots that were disturbed by storm, fire, snow, biotic or other disturbances from the French, Spanish, and Finnish National Forest Inventory, we modeled annual mortality probability for 40 European tree species as a function of tree size, dominance status, disturbance type, and intensity. We tested the correlation of our estimated species probability of disturbance mortality with their traits and their mean climate niches. We found that different trait combinations controlled species sensitivity to disturbances. Storm-sensitive species had a high height-dbh ratio, low wood density and high maximum growth, while fire-sensitive species had low bark thickness and high P50. Species from warmer and drier climates, where fires are more frequent, were more resistant to fire. The ranking in disturbance sensitivity between species was overall consistent across disturbance types. Productive conifer species were the most disturbance sensitive, while Mediterranean oaks were the least disturbance sensitive. Our study identified key relations between species functional traits and disturbance sensitivity, that allows more reliable predictions of how changing climate and disturbance regimes will impact future forest structure and species composition at large spatial scales.

Strength of species interactions determines biodiversity and stability in microbial communities.

Organisms-especially microbes-tend to live together in ecosystems. While some of these ecosystems are very biodiverse, others are not, and while some are very stable over time, others undergo strong temporal fluctuations. Despite a long history of research and a plethora of data, it is not fully understood what determines the biodiversity and stability of ecosystems. Theory and experiments suggest a connection between species interaction, biodiversity and the stability of ecosystems, where an increase in ecosystem stability with biodiversity could be observed in several cases. However, what causes these connections remains unclear. Here, we show in microbial ecosystems in the laboratory that the concentrations of available nutrients can set the strength of interactions between bacteria. High nutrient concentrations allowed the bacteria to strongly alter the chemical environment, causing on average more negative interactions between species. These stronger interactions excluded more species from the community, resulting in a loss of biodiversity. At the same time, the stronger interactions also decreased the stability of the microbial communities, providing a mechanistic link between species interaction, biodiversity and stability in microbial ecosystems.

Modular Assembly of Polysaccharide-Degrading Marine Microbial Communities.

Understanding the principles that govern the assembly of microbial communities across earth's biomes is a major challenge in modern microbial ecology. This pursuit is complicated by the difficulties of mapping functional roles and interactions onto communities with immense taxonomic diversity and of identifying the scale at which microbes interact [1]. To address this challenge, here, we focused on the bacterial communities that colonize and degrade particulate organic matter in the ocean [2-4]. We show that the assembly of these communities can be simplified as a linear combination of functional modules. Using synthetic polysaccharide particles immersed in natural bacterioplankton assemblages [1, 5], we showed that successional particle colonization dynamics are driven by the interaction of two types of modules: a first type made of narrowly specialized primary degraders, whose dynamics are controlled by particle polysaccharide composition, and a second type containing substrate-independent taxa whose dynamics are controlled by interspecific interactions-in particular, cross-feeding via organic acids, amino acids, and other metabolic byproducts. We show that, as a consequence of this trophic structure, communities can assemble modularly-i.e., by a simple sum of substrate-specific primary degrader modules, one for each complex polysaccharide in the particle, connected to a single broad-niche range consumer module. Consistent with this model, a linear combination of the communities on single-polysaccharide particles accurately predicts community composition on mixed-polysaccharide particles. Our results suggest that the assembly of heterotrophic communities that degrade complex organic materials follows simple design principles that could be exploited to engineer heterotrophic microbiomes.