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Oxygen availability is a key factor in the evolution of multicellularity, as larger and more sophisticated organisms often require mechanisms allowing efficient oxygen delivery to their tissues. One such mechanism is the presence of oxygen-binding proteins, such as globins and hemerythrins, which arose in the ancestor of bilaterian animals. Despite their importance, the precise mechanisms by which oxygen-binding proteins influenced the early stages of multicellular evolution under varying environmental oxygen levels are not yet clear. We address this knowledge gap by heterologously expressing the oxygen-binding proteins myoglobin and myohemerythrin in snowflake yeast, a model system of simple, undifferentiated multicellularity. These proteins increased the depth and rate of oxygen diffusion, increasing the fitness of snowflake yeast growing aerobically. Experiments show that, paradoxically, oxygen-binding proteins confer a greater fitness benefit for larger organisms when O₂is least limiting. We show via biophysical modeling that this is because facilitated diffusion is more efficient when oxygen is abundant, transporting a greater quantity of O₂which can be used for metabolism. By alleviating anatomical diffusion limitations to oxygen consumption, the evolution of oxygen-binding proteins in the oxygen-rich Neoproterozoic may have been a key breakthrough enabling the evolution of increasingly large, complex multicellular metazoan lineages. 

Bacteria sense population density via the cell–cell communication system called quorum sensing (QS). The evolution of QS and its maintenance or loss in mixed bacterial communities is highly relevant to understanding how cell–cell signaling impacts bacterial fitness and competition, particularly under varying environmental conditions such as nutrient availability. We uncovered a phenomenon in whichVibriocells grown in minimal medium optimize expression of the methionine and tetrahydrofolate (THF) synthesis genes via QS. Strains that are genetically “locked” at high cell density grow slowly in minimal glucose media and suppressor mutants accumulate via inactivating mutations inmetF(methylenetetrahydrofolate reductase) andluxR(the master QS transcriptional regulator). In mixed cultures, QS mutant strains initially coexist with wild-type, but as glucose is depleted, wild-type outcompetes the QS mutants. Thus, QS regulation of methionine/THF synthesis is a fitness benefit that links nutrient availability and cell density, preventing accumulation of QS-defective mutants. 

To increase our basic understanding of the ecology and evolution of conjugative plasmids, we need reliable estimates of their rate of transfer between bacterial cells. Current assays to measure transfer rate are based on deterministic modeling frameworks. However, some cell numbers in these assays can be very small, making estimates that rely on these numbers prone to noise. Here, we take a different approach to estimate plasmid transfer rate, which explicitly embraces this noise. Inspired by the classic fluctuation analysis of Luria and Delbrück, our method is grounded in a stochastic modeling framework. In addition to capturing the random nature of plasmid conjugation, our new methodology, the Luria–Delbrück method (“LDM”), can be used on a diverse set of bacterial systems, including cases for which current approaches are inaccurate. A notable example involves plasmid transfer between different strains or species where the rate that one type of cell donates the plasmid is not equal to the rate at which the other cell type donates. Asymmetry in these rates has the potential to bias or constrain current transfer estimates, thereby limiting our capabilities for estimating transfer in microbial communities. In contrast, the LDM overcomes obstacles of traditional methods by avoiding restrictive assumptions about growth and transfer rates for each population within the assay. Using stochastic simulations and experiments, we show that the LDM has high accuracy and precision for estimation of transfer rates compared to the most widely used methods, which can produce estimates that differ from the LDM estimate by orders of magnitude.