Escherichia coli can survive stress by noisy growth modulation

Patange, Om; Schwall, Christian; Jones, Molly Morgan; Villava, Casandra; Griffith, Douglas A.; Phillips, Andrew; Locke, James

doi:10.1038/s41467-018-07702-z

Cited by 102 publications

(110 citation statements)

References 66 publications

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“…This observation is in line with the prediction of Sigma38 playing an important role in shaping genome-wide expression noise, as ppGpp is an 'alarmone' responsible for regulating genes upon entry into stationary phase Hauryliuk et al , 2015 and promoters regulated by Sigma38 require ppGpp for induction Kvint et al , 2000. Moreover, it was recently shown that rpoS activity is heterogeneous among single cells in M9 glucose Patange et al , 2018. Two further significant noise propagators are CRP and PhoB. CRP is a global regulator of genes involved in carbon source catabolism and its activity has been proposed to reflect carbon source influx You et al , 2013.…”

Section: Noise Propagation Explains the Condition-dependent Noise Levsupporting

confidence: 88%

Noise propagation shapes condition-dependent gene expression noise inEscherichia coli

Urchueguía

Galbusera

Bellement-Theroue

et al. 2019

Preprint

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Although it is well appreciated that gene expression is inherently noisy and that transcriptional noise is encoded in a promoter's sequence, little is known about the variation in transcriptional noise across growth conditions. Using flow cytometry we here quantify transcriptional noise in E. coli genome-wide across 8 growth conditions, and find that noise and gene regulation are intimately coupled. Apart from a growth-rate dependent lower bound on noise, we find that individual promoters show highly condition-dependent noise and that condition-dependent expression noise is shaped by noise propagation from regulators to their targets. A simple model of noise propagation identifies TFs that most contribute to both conditionspecific and condition-independent noise propagation. The overall correlation structure of sequence and expression properties of E. coli genes uncovers that genes are organized along two principal axes, with the first axis sorting genes by their mean expression and evolutionary rate of their coding regions, and the second axis sorting genes by their expression noise, the number of regulatory inputs in their promoter, and their expression plasticity.

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Section: Noise Propagation Explains the Condition-dependent Noise Levsupporting

confidence: 88%

Noise propagation shapes condition-dependent gene expression noise inEscherichia coli

Urchueguía

Galbusera

Bellement-Theroue

et al. 2019

Preprint

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“…The network was trained on images from three different multi-position time-lapse movies acquired in our lab, and on movies available from the literature [11,16,17,28,29]; we then evaluated on a third data set acquired in our lab weeks later as well as on data from the literature ( Table 1). To demonstrate generalizability of the approach, and to evaluate performance against other analysis software, we also used the time-lapse image datasets provided with the mother machine analysis software tools Molyso [11], MoMA [16], and BACMMAN [17] to evaluate the performance of DeLTA on completely new data by performing "leave-one-out" training and evaluation pairings.…”

Section: Segmentationmentioning

confidence: 99%

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning

2020

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Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a "mother machine" microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human intervention. Further, the algorithm is fast, with complete analysis of a typical frame containing~150 cells taking <700msec. The framework is not constrained to a particular experimental set up and has the potential to generalize to time-lapse images of other organisms or different experimental configurations. These advances open the door to a myriad of applications including real-time tracking of gene expression and high throughput analysis of strain libraries at single-cell resolution.

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“…In this scenario heterogeneity is no more than an interesting artefact of the regulatory system. The alternative is that heterogeneity is beneficial, perhaps in a bet-hedging sense, as has been indicated before in bacterial stress response systems ( Carey et al, 2018; Patange et al, 2018 ). Actually testing for evidence of bet-hedging is generally challenging ( Simons, 2011; Grimbergen et al, 2015 ), and the strength as a strategy depends both on the specifics of the stress response system and on the typical frequency of environmental changes ( Kussell and Liebler, 2005 ).…”

Section: Discussionmentioning

confidence: 91%

Molecular origins of transcriptional heterogeneity in diazotrophicK. oxytoca

Bashir

Brackston

et al. 2020

Preprint

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Phenotypic heterogeneity in clonal bacterial batch cultures is an important adaptive 10 strategy to changing environments, including in diazotrophs with the unique capacity to convert 11 di-nitrogen into bio-available ammonium. In diazotrophic Klebsiella oxytoca we simultaneously 12 measured mRNA levels of key regulatory (glnK-amtB, nifLA) and structural (nifHDK) operons required 13 for establishing nitrogen fixation, using dual molecule, single cell RNA-FISH. Through stochastic 14 transcription models and mutual information analysis we revealed likely molecular origins for 15 heterogeneity in nitrogenase expression. In wildtype and regulatory variant strains we inferred 16 contributions from intrinsic and extrinsic noise, finding that nifHDK transcription is inherently 17 bursty, but that noise propagation through signalling is also significant. The regulatory gene glnK 18 had the highest discernible effect on nifHDK variance, while noise from factors outside of the 19 regulatory pathway were negligible. Results provide evidence that heterogeneity is a fundamental 20 property of this regulatory system, indicating potential constraints for engineering homogeneous 21 nitrogenase expression. 22 30 Phenotypic heterogeneity may be particularly relevant in costly stress response systems, of 31 which the response to nitrogen starvation is a key example (Schreiber et al., 2016). In organisms 32 such as Klebsiella oxytoca, nitrogen starvation triggers a transition to diazotrophic behaviour in 33 which bacterial cells use atmospheric di-nitrogen as their nitrogen source for growth (Dixon and 34 Kahn, 2004). While this transition, and the associated transcriptional programme, is essential 35 for continued growth under conditions deplete of fixed nitrogen, it is also very costly since the 36 resultant ATP consuming nitrogenase enzyme may ultimately constitute up to 20 % of the proteome 37 (Dixon and Kahn, 2004). As such, if fixed nitrogen sources soon become available again, it is likely 38 advantageous to have not fully undergone the diazotrophic transition. Activation of the nitrogen 39 stress response is potentially somewhat of a gamble in which the payoff depends strongly on future 40 1 of 15conditions. Such a scenario may therefore indicate a requirement for bet-hedging strategies in 41 which heterogeneity proves advantageous at the population level (van Boxtel et al., 2017). 42 Regardless of whether there are advantages to heterogeneity, it is also important to establish 43 the underlying mechanistic causes, especially if there is an aim to ultimately modify cell behaviour. 44 Studies at single-cell resolution are crucial to this, enabling the full distribution of expression levels 45 to be obtained over a population of cells. Such data not only provides qualitative insight into the 46 variability between cells, but can also enable the inference of the underlying dynamical processes 47 and predicts the probability distribution of mRNA copy numbers (Ko, 1991; Peccoud and Ycart, 1995; 87 ., 2006;...

show abstract

Escherichia coli can survive stress by noisy growth modulation

Cited by 102 publications

References 66 publications

Noise propagation shapes condition-dependent gene expression noise inEscherichia coli

Noise propagation shapes condition-dependent gene expression noise inEscherichia coli

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning

Molecular origins of transcriptional heterogeneity in diazotrophicK. oxytoca

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