In natural environments, microbes are typically non-dividing and gauge when nutrients permit division. Current models are phenomenological and specific to nutrient-rich, exponentially growing cells, thus cannot predict the first division under limiting nutrient availability. To assess this regime, we supplied starving Escherichia coli with glucose pulses at increasing frequencies. Real-time metabolomics and microfluidic single-cell microscopy revealed unexpected, rapid protein, and nucleic acid synthesis already from minuscule glucose pulses in non-dividing cells. Additionally, the lag time to first division shortened as pulsing frequency increased. We pinpointed division timing and dependence on nutrient frequency to the changing abundance of the division protein FtsZ. A dynamic, mechanistic model quantitatively relates lag time to FtsZ synthesis from nutrient pulses and FtsZ protease-dependent degradation. Lag time changed in model-congruent manners, when we experimentally modulated the synthesis or degradation of FtsZ. Thus, limiting abundance of FtsZ can quantitatively predict timing of the first cell division.

Sinking particulate organic carbon out of the surface ocean sequesters carbon on decadal to millennial timescales. Predicting the particulate carbon flux is therefore critical for understanding both global carbon cycling and the future climate. Microbes play a crucial role in particulate organic carbon degradation, but the impact of depth-dependent microbial dynamics on ocean-scale particulate carbon fluxes is poorly understood. Here we scale-up essential features of particle-associated microbial dynamics to understand the large-scale vertical carbon flux in the ocean. Our model provides mechanistic insight into the microbial contribution to the particulate organic carbon flux profile. We show that the enhanced transfer of carbon to depth can result from populations struggling to establish colonies on sinking particles due to diffusive nutrient loss, cell detachment, and mortality. These dynamics are controlled by the interaction between multiple biotic and abiotic factors. Accurately capturing particle-microbe interactions is essential for predicting variability in large-scale carbon cycling.

Small genes (<150 nucleotides) have been systematically overlooked in phage genomes. We employ a large-scale comparative genomics approach to predict >40,000 small-gene families in ∼2.3 million phage genome contigs. We find that small genes in phage genomes are approximately 3-fold more prevalent than in host prokaryotic genomes. Our approach enriches for small genes that are translated in microbiomes, suggesting the small genes identified are coding. More than 9,000 families encode potentially secreted or transmembrane proteins, more than 5,000 families encode predicted anti-CRISPR proteins, and more than 500 families encode predicted antimicrobial proteins. By combining homology and genomic-neighborhood analyses, we reveal substantial novelty and diversity within phage biology, including small phage genes found in multiple host phyla, small genes encoding proteins that play essential roles in host infection, and small genes that share genomic neighborhoods and whose encoded proteins may share related functions.