PT  - JOURNAL ARTICLE
AU  - Erik S. Wright
AU  - Kalin H. Vetsigian
TI  - Stochastic exits from dormancy give rise to heavy-tailed distributions of descendants in bacterial populations
AID  - 10.1101/246629
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 246629
4099  - http://biorxiv.org/content/early/2019/05/15/246629.short
4100  - http://biorxiv.org/content/early/2019/05/15/246629.full
AB  - Variance in reproductive success is a major determinant of the degree of genetic drift in a population. While many plants and animals exhibit high variance in their number of progeny, far less is known about these distributions for microorganisms. Here, we used a strain barcoding approach to quantify variability in offspring number among replicate bacterial populations and developed a Bayesian method to infer the distribution of descendants from this variability. We applied our approach to measure the offspring distributions for 5 strains of bacteria from the genus Streptomyces after germination and growth in a homogenous laboratory environment. The distributions of descendants were heavy-tailed, with a few cells effectively “winning the jackpot” to become a disproportionately large fraction of the population. This extreme variability in reproductive success largely traced back to initial populations of spores stochastically exiting dormancy, which provided early-germinating spores with an exponential advantage. In simulations with multiple dormancy cycles, heavy-tailed distributions of descendants decreased the effective population size by many orders of magnitude and led to allele dynamics differing substantially from classical population genetics models with matching effective population size. Collectively, these results demonstrate that extreme variability in reproductive success can occur even in growth conditions that are far more homogeneous than the natural environment. Thus, extreme variability in reproductive success might be an important factor shaping microbial population dynamics with implications for predicting the fate of beneficial mutations, interpreting sequence variability within populations, and explaining variability in infection outcomes across patients.