Single-cell growth inference ofCorynebacterium glutamicumreveals asymptotically linear growth

Abstract

Keywords| cell-size homeostasis | single-cell growth| apical growth | RodA| cell wall SignificanceRegulation of growth and cell size is crucial for the optimization of bacterial cellular function. So far, single bacterial cells have been found to grow exponentially, which implies the need for tight regulation mechanisms to maintain cell size throughout growth and division cycles. Here, we characterize the growth behavior of the apically growing bacterium Corynebacterium glutamicum, by developing a novel and broadly applicable inference method for single-cell growth dynamics. We find that this bacterium grows asymptotically linearly, enabling it to maintain a narrow distribution of cell sizes, despite having a large variability of single-cell growth features. Our results imply a novel interplay between mode of growth and division regulation mechanisms, which may extend to other bacteria with non-exponential growth modes. AbstractIn many bacteria, protein mass production is thought to be rate limiting for growth, implying exponential growth at the single cell level. To maintain cell-size homeostasis in proliferating populations of exponentially growing bacteria, tight growth and division mechanisms are required.However, it remains unclear whether these considerations set universal physical limits to bacterial growth. Here, we characterize the growth dynamics of the actinobacterium Corynebacterium glutamicum -a promising candidate for uncovering novel growth modes. This bacterium exhibits apical cell wall synthesis and division site selection systems appear to be absent, as reflected by a broad distribution of division asymmetries. We develop a novel growth inference method that averages out measurement noise and single-cell variability to obtain elongation rate curves as a function of birth length. Using this approach, we find that C. glutamicum exhibits asymptotically linear single-cell growth. To explain this growth mode, we model elongation as being rate-limited by the apical growth mechanism mediated by cell wall transglycosylases. This model accurately reproduces the observed elongation rate curves, and we further validate the model with growth measurements on a transglycosylase deficient ΔrodA mutant. Finally, with simulations we show that asymptotically linear growth yields a narrower distribution of cell lengths, suggesting that this growth mode can act as a substitute for tight division length and division symmetry regulation.

Distinguishing different modes of growth using single-cell data

“…Qualitatively similar results have been recently obtained for another bacterium, C. glutamicum, in Ref. [31]. These results might point to growth being non-exponential.…”

“…Using a model which includes measurement error as a source of noise, we can guide the binning analysis. Using this methodology, we verified that typical measurement errors (≈ 0.02L b ) [31,46] have negligible effects on the growth rate trends obtained from the experimental data used in our work.…”

“…The authors in Ref. [31] circumvent inspection bias in the elongation speed vs time from birth plots by focusing their analysis on the time period from cell birth to the generation time of the fastest dividing cell.…”

“…Exponentially growing cells cannot employ a mechanism where they control division by timing a constant duration from birth but such a mechanism is possible in case of linear growth ( Amir, 2014 ; Kafri et al, 2016 ; Ho et al, 2018 ). Linear regression performed on vs plot, where is the mean growth rate, was used to infer the mode of growth in the archaeon H. salinarum ( Eun et al, 2018 ), and in the bacteria M. smegmatis ( Logsdon et al, 2017 ) and C. glutamicum ( Messelink et al, 2020 ), for example. If the best linear fit follows the y = x trend, the resulting functional relation might point to growth being exponential.…”

“…If the best linear fit follows the y = x trend, the resulting functional relation might point to growth being exponential. A corollary to this is the rejection of exponential growth when the slope and intercept of the best linear fit deviate from one and zero, respectively ( Messelink et al, 2020 ). Thus, binning and linear regression applied on single-cell data appear to provide information about the underlying biology, in this case, the mode of cellular growth.…”