Memory and Fitness Optimization of Bacteria under Fluctuating Environments

Abstract

Bacteria prudently regulate their metabolic phenotypes by sensing the availability of specific nutrients, expressing the required genes for their metabolism, and repressing them after specific metabolites are depleted. It is unclear, however, how genetic networks maintain and transmit phenotypic states between generations under rapidly fluctuating environments. By subjecting bacteria to fluctuating carbon sources (glucose and lactose) using microfluidics, we discover two types of non-genetic memory in Escherichia coli and analyze their benefits. First, phenotypic memory conferred by transmission of stable intracellular lac proteins dramatically reduces lag phases under cyclical fluctuations with intermediate timescales (1–10 generations). Second, response memory, a hysteretic behavior in which gene expression persists after removal of its external inducer, enhances adaptation when environments fluctuate over short timescales (<1 generation). Using a mathematical model we analyze the benefits of memory across environmental fluctuation timescales. We show that memory mechanisms provide an important class of survival strategies in biology that improve long-term fitness under fluctuating environments. These results can be used to understand how organisms adapt to fluctuating levels of nutrients, antibiotics, and other environmental stresses.

Noise-driven growth rate gain in clonal cellular populations

“…S1, and Movie S1). The dynamics cytometer is similar to the previously reported polydimethylsiloxane (PDMS)-based microfluidics devices designed for long-term single-cell observation (7,9,15,(18)(19)(20)(21). The important differences are (i) the microchannels are directly created on a glass coverslip, not on PDMS; and (ii) the channel region is covered by a semipermeable membrane (SI Appendix, Fig.…”

“…Recently, the techniques of single-cell time-lapse microscopy have advanced to a great extent, revealing the heterogeneous and stochastic nature of single-cell dynamics quantitatively (14). With the aid of microfluidic platforms, tracking single cells over many generations in controlled constant or changing environments has also become feasible, providing insights into the mechanisms of cell size homeostasis and stress responses (3,6,7,9,(15)(16)(17)(18)(19)(20)(21)(22).In this study, through microfluidics time-lapse microscopy and single-cell analysis on large-scale, single-cell lineage trees, we reveal that clonal populations of Escherichia coli indeed grow with a doubling time that is smaller than the mean doubling time of their constituent cells under broad, balanced-growth conditions. We show that the observed growth rate gains and population age structures are predictable from cellular generation time distributions based on a simple age-structured population model.…”

“…Recently, the techniques of single-cell time-lapse microscopy have advanced to a great extent, revealing the heterogeneous and stochastic nature of single-cell dynamics quantitatively (14). With the aid of microfluidic platforms, tracking single cells over many generations in controlled constant or changing environments has also become feasible, providing insights into the mechanisms of cell size homeostasis and stress responses (3,6,7,9,(15)(16)(17)(18)(19)(20)(21)(22).…”

Response of single bacterial cells to stress gives rise to complex history dependence at the population level

“…Similarly, bacterial populations that were exposed to sublethal stress levels showed increased survival of a higher stress level of the same type (4-6). Theoretical and experimental studies indicate that basing cellular decisions on environmental cues perceived in the past can be advantageous in dynamic environments (3,7,8), suggesting that such history-dependent behavior can be the result of adaptive evolution in dynamic environments.In this study we addressed the question of memory on a singlecell level. We asked whether weak stress events provide individual cells with increased tolerance against future stress.…”

Applications of Microfluidics in Quantitative Biology

“…[36] When a similar experiment was repeated by the Kussell group, they discovered two types of non-genetic memory in E. coli. [37] Mathematical modeling The channelbased microfluidics can realize long-term culture and real-time observation for high-resolution data, flexible manipulation of diverse fluids to satisfy the experiment control, and predefined function for special biological requirements. While the droplet-based microfluidics has some other particular characters, like high throughput for sample production, isolated environment to prevent from crosstalk or contamination, and three-dimensional culturing micro-environment for in vivo simulation.…”