Microbial life in slow and stopped lanes

Abstract

Microbes in nature often lack nutrients and face extreme or widely fluctuating temperatures, unlike microbes in growth-optimized settings in laboratories that much of the literature examines. Slowed or suspended lives are the norm for microbes. Studying them is important for understanding the consequences of climate change and for addressing fundamental questions about life: are there limits to how slowly a cell's life can progress, and how long cells can remain viable without self-replicating? Recent studies began addressing these questions with single-cell-level measurements and mathematical models. Emerging principles that govern slowed or suspended lives of cells - including lives of dormant spores and microbes at extreme temperatures - are re-defining discrete cellular states as continuums and revealing intracellular dynamics at new timescales. Nearly inactive, lifeless-appearing microbes are transforming our understanding of life.

Keywords: dormancy; quantitative spectrum; quiescence; slow growth; starvation; suspended life.

Figure 1:. Microbes with slowed or suspended lives are abundant in nature, but not in laboratories, and pose questions about fundamental limits to life.

(A) Illustration with the key questions: When a cell’s growth drastically slows down or stops, which factors determine whether the cell remains viable? What determines whether a lifeless-looking cell remains alive or dies? (B) Illustration of cell’s growth rate (purple curve) and frequency of occurrence of each temperature in nature (brown curve), both as a function of temperature. Growth-optimizing temperatures inside laboratories (grey zone). Frigid temperatures (blue zone) and high temperatures (red zone) in nature that slow or stop microbial growth. (C) Illustration showing culture condition in laboratories with abundant nutrients (test tube) and natural habitats for microbes in soil that lack nutrients. (D) Illustration of a fundamental question about life: can a cell’s doubling time (purple curve) increase without an upper bound by decreasing the rates of all intracellular activities to an arbitrarily low value? (E) Illustration showing a challenge to studying slowed growth and the need for single-cell-level measurements. Scenario 1 (top row): all three cells divide to form six cells. Scenario 2 (bottom row): red cells divide with half the doubling time of the red cells in the top row. Green cells do not divide. After the same amount of time as in the top row, three cells become six cells.

Figure 2:. Dormancy as a continuous, quantifiable spectrum.

(A) Dormancy spectrum of S. cerevisiae spores (grey-to-green bar) (2). Dead spores (grey) are at the left end of the spectrum. At the right end of the spectrum are spores (green) that have 100% chance of germinating in the future when nutrients appear. In between the two ends are spores that have between 0 to 100% chance of germinating in the future. A dormant spore’s location on the spectrum is determined by its ability to express genes without nutrients (e.g., in water), as measured by GFP expression in a recent study (2). Multiple factors, including the amount of detectable RNA polymerases I-III, determine the spore’s ability to express genes without nutrients, thereby making the spectrum multi-dimensional. Blue curve relates the spore’s chance of germinating with nutrients to that spore’s gene-expressing ability without nutrients. (B) Dormancy depth of antibiotic-induced, growth-arrested E. coli (40). Cells that are sufficiently deep in dormancy (bottom trough) cannot grow when the antibiotic disappears, and nutrients appear - they are dead. Cells in shallower part of the dormancy (upper troughs) are more likely and take less time to resume growth after the antibiotic disappears. A recent study showed that cells with less ATPs form larger aggregates of proteins - a deeper dormancy - and that when nutrients appear, cells must actively disaggregate the aggregates to resume growth (40). Too large of an aggregate prevents disaggregation, leading to loss of viability.

Figure 3:. Dynamics of germination revealed by continuously monitoring individual spores over time.

(A) Germination without ATP consumption (passive germination) conceptualized as a balloon passively losing its content. A B. subtilis spore is conceptualized as a blue balloon filled with dipicolinic acid and potassium ions (K⁺) that forbid any intracellular activities (e.g., gene-expression, metabolism, etc.). L-alanine, a nutrient, is conceptualized as the hand. Binding of L-alanine to a passive receptor is conceptualized as the hand pressing on the balloon to squeeze K⁺ out of the balloon (spore). The hand does the work and thus the balloon (spore) does not consume any ATPs. After the spore encounters L-alanine for a sufficiently long time - after the hand squeezes the balloon for a sufficiently long time either all at once or in several squeezes - the spore germinates (i.e., balloon has sufficiently deflated). (B) A recent study revealed that if a starving cell takes a longer time to sporulate, the resulting spore is less likely and takes a longer time to germinate than spores that formed earlier (32). Large ellipse is a starving B. subtilis cell and small ellipse is a B. subtilis spore. Black arrows (cell division). Cell becomes paler in orange color with each division. Intensity of orange color represents concentrations of stored molecules inside the cell that are required for germination. Graph (red curves) shows the relationships between chance of germinating, concentrations of germination-aiding molecules stored inside a spore, and time taken to sporulate.

Figure 4.. Non-growing, persister cells in an antibiotic.

(A) Isogenic bacterial population in an antibiotic consists of non-persistent cells (red) and persistent cells (green). As time spent in an antibiotic increase (arrows), both types of cells die (white) but persisters die at a lower rate than non-persisters. (B) Debates exist regarding the various sources for generating persisters. It is clear, however, that persisters arise from some of the isogenic cells stochastically acquiring an elevated, antibiotic-fighting activity that is temporary yet prolonged. The antibiotic-fighting activity, when quantified, can have a wide range of values as shown in the spectrum (bar). A question is how this spectrum yields two distinct subpopulations - non-persisters (red) and persisters (green) - with two distinct, population-level half-lives. (C) Researchers recently used mathematical analyses of population-killing curves, like the one shown here, to reach a consensus on the definition for each form of being susceptible to an antibiotic, including persistence (43). Black shows a biphasic killing curve that defines persistence. Population consisting of both non-persisters and persisters exhibit the black curve, with the faster decay in the early times - extended as red line - representing the deaths of non-persisters and the more slowly decaying curve in the later times representing the deaths of persisters in an antibiotic.

Figure 5:. Emerging principles that govern slowed and suspended lives of microbes.

(A) One of the emerging principles that underlies multiple studies of distinct organisms described in our review: recasting the traditionally defined states of slow or suspended growth as a spectrum (bar). We can deconstruct a state into infinitely many locations - a spectrum - or connect two or more states such as being dead (left end of bar shown in grey) and being definitely viable (right end of bar shown in dark green) by viewing them as merely different locations - each location having a probability between 0 and 1 of being viable while growing slowly or not growing - on a common spectrum. (B) Another of the emerging principles: we can conceptually lump multiple intracellular and extracellular processes into two groups - those promoting viability (blue) and those demoting viability (red) - and use mathematical modeling and analysis to derive a lumped rate for each class as a function of various parameters, as shown here. Thin lines show the two lumped rates for each cell, with no two cells having an identical blue or red curve, due to the stochastic nature of the processes. Solid red and blue curves are the averages of the thin (single-cell) curves shown here, representing population-level rates. Note that in the grey zone (unlivable for a population), some cells have viability-promoting processes occurring more rapidly than viability-demoting processes, thus being viable while the population that it is in faces an extinction.