The emergence of metabolic heterogeneity and diverse growth responses in isogenic bacterial cells

Abstract

Microorganisms adapt to frequent environmental changes through population diversification. Previous studies demonstrated phenotypic diversity in a clonal population and its important effects on microbial ecology. However, the dynamic changes of phenotypic composition have rarely been characterized. Also, cellular variations and environmental factors responsible for phenotypic diversity remain poorly understood. Here, we studied phenotypic diversity driven by metabolic heterogeneity. We characterized metabolic activities and growth kinetics of starved Escherichia coli cells subject to nutrient upshift at single-cell resolution. We observed three subpopulations with distinct metabolic activities and growth phenotypes. One subpopulation was metabolically active and immediately grew upon nutrient upshift. One subpopulation was metabolically inactive and non-viable. The other subpopulation was metabolically partially active, and did not grow upon nutrient upshift. The ratio of these subpopulations changed dynamically during starvation. A long-term observation of cells with partial metabolic activities indicated that their metabolism was later spontaneously restored, leading to growth recovery. Further investigations showed that oxidative stress can induce the emergence of a subpopulation with partial metabolic activities. Our findings reveal the emergence of metabolic heterogeneity and associated dynamic changes in phenotypic composition. In addition, the results shed new light on microbial dormancy, which has important implications in microbial ecology and biomedicine.

Power-law tail in lag time distribution underlies bacterial persistence

“…Within each biological replicate, at least 4 technical replicates were performed, and their average was used for the plot. excellent indicator of dead E. coli cells (40)(41)(42)(43)(44). We additionally confirmed that PI is a good indicator for cell death by ampicillin; when we incubated cells with ampicillin and PI for 80 min and spread them on an LB agar plate containing no ampicillin, none of the PI stained (PI+) cells grew (see SI Appendix, Supplementary Method for detail).…”

“…One issue of PI is the loss of nucleic acids upon lysis by ampicillin. PI stains nucleic acids, but lysis results in the loss of cytoplasmic contents, including nucleic acids (40)(41)(42)(43)(44). Hence, lysed cells are, although they are clearly dead, not stained by PI.…”

“…Hence, lysed cells are, although they are clearly dead, not stained by PI. On the other hand, we can distinguish these lysed cells by their refractivity; when observed via phase-contrast microscopy, a live E. coli cell is normally refractile (i.e., it exhibits a dark area with sharp boundaries), whereas lysed cells exhibit poor phase contrast with diffuse boundaries (44). Therefore, we tracked both PI staining and lysis to evaluate ampicillin killing.…”

Spontaneous mutations in a regulatory gene induce phenotypic heterogeneity and adaptation of Ralstonia solanacearum to changing environments

“…Indeed, efpR mutants always produced two types of colonies arising from a single clone, the type S (‘smooth’, similar to the WT) and the type EV (‘ efpR variant’). Both cell types have distinct phenotypic marks, highlighting that the S/EV switch phenomenon directly affects virulence and also drastically modifies bacterial cell physiology, especially by generating a form of metabolic heterogeneity in the population (Rosenthal et al ., ; Şimşek and Kim, ). The efpR ‐dependent molecular mechanism leading to the emergence of EV type cells remains to be discovered.…”

Single-cell heterogeneity in the chloroplast redox state mediates acclimation to stress in a marine diatom

“…Nevertheless, the difference in cell-cycle phase distribution between the subpopulations supports the latter. The combination of factors such as life history 33,50 , cell cycle phase 35,37 , cell age 51,52 , metabolic activity 53,54 , heterogeneous microenvironment 55 and biological noise 43,56 results in a distribution of different metabolic states within the population 57,58 . The metabolic state of the cell can affect its antioxidant capacity, therefore resulting in variability in sensitivity to oxidative stress as observed here.…”