Bacteria grow swiftly and live thriftily.

Bacteria have evolved numerous strategies to use resources efficiently. However, bacterial economies depend on both the physiological context of the organisms as well as their growth state - whether they are growing, non-growing or reinitiating growth. In this essay, we discuss some of the features that make bacteria efficient under these different conditions and during the transitions between them. We also highlight the many outstanding questions regarding the physiology of non-growing bacterial cells. Lastly, we examine how efficiency is apparent in both the mode and tempo of bacterial evolution.

The expression of virulence genes increases membrane permeability and sensitivity to envelope stress in Salmonella Typhimurium.

Virulence gene expression can represent a substantial fitness cost to pathogenic bacteria. In the model entero-pathogen Salmonella Typhimurium (S.Tm), such cost favors emergence of attenuated variants during infections that harbor mutations in transcriptional activators of virulence genes (e.g., hilD and hilC). Therefore, understanding the cost of virulence and how it relates to virulence regulation could allow the identification and modulation of ecological factors to drive the evolution of S.Tm toward attenuation. In this study, investigations of membrane status and stress resistance demonstrate that the wild-type (WT) expression level of virulence factors embedded in the envelope increases membrane permeability and sensitizes S.Tm to membrane stress. This is independent from a previously described growth defect associated with virulence gene expression in S.Tm. Pretreating the bacteria with sublethal stress inhibited virulence expression and increased stress resistance. This trade-off between virulence and stress resistance could explain the repression of virulence expression in response to harsh environments in S.Tm. Moreover, we show that virulence-associated stress sensitivity is a burden during infection in mice, contributing to the inherent instability of S.Tm virulence. As most bacterial pathogens critically rely on deploying virulence factors in their membrane, our findings could have a broad impact toward the development of antivirulence strategies.

Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network.

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 extent to which noise levels of individual promoters vary across growth conditions. Using flow cytometry, we here quantify transcriptional noise in Escherichia coli genome-wide across 8 growth conditions and find that noise levels systematically decrease with growth rate, with a condition-dependent lower bound on noise. Whereas constitutive promoters consistently exhibit low noise in all conditions, regulated promoters are both more noisy on average and more variable in noise across conditions. Moreover, individual promoters show highly distinct variation in noise across conditions. We show that a simple model of noise propagation from regulators to their targets can explain a significant fraction of the variation in relative noise levels and identifies TFs that most contribute to both condition-specific and condition-independent noise propagation. In addition, analysis of the genome-wide correlation structure of various gene properties shows that gene regulation, expression noise, and noise plasticity are all positively correlated genome-wide and vary independently of variations in absolute expression, codon bias, and evolutionary rate. Together, our results show that while absolute expression noise tends to decrease with growth rate, relative noise levels of genes are highly condition-dependent and determined by the propagation of noise through the gene regulatory network.

Subpopulations of sensorless bacteria drive fitness in fluctuating environments.

Populations of bacteria often undergo a lag in growth when switching conditions. Because growth lags can be large compared to typical doubling times, variations in growth lag are an important but often overlooked component of bacterial fitness in fluctuating environments. We here explore how growth lag variation is determined for the archetypical switch from glucose to lactose as a carbon source in Escherichia coli. First, we show that single-cell lags are bimodally distributed and controlled by a single-molecule trigger. That is, gene expression noise causes the population before the switch to divide into subpopulations with zero and nonzero lac operon expression. While "sensorless" cells with zero preexisting lac expression at the switch have long lags because they are unable to sense the lactose signal, any nonzero lac operon expression suffices to ensure a short lag. Second, we show that the growth lag at the population level depends crucially on the fraction of sensorless cells and that this fraction in turn depends sensitively on the growth condition before the switch. Consequently, even small changes in basal expression can significantly affect the fraction of sensorless cells, thereby population lags and fitness under switching conditions, and may thus be subject to significant natural selection. Indeed, we show that condition-dependent population lags vary across wild E. coli isolates. Since many sensory genes are naturally low expressed in conditions where their inducer is not present, bimodal responses due to subpopulations of sensorless cells may be a general mechanism inducing phenotypic heterogeneity and controlling population lags in switching environments. This mechanism also illustrates how gene expression noise can turn even a simple sensory gene circuit into a bet hedging module and underlines the profound role of gene expression noise in regulatory responses.

Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria.

Fluorescence flow cytometry is increasingly being used to quantify single-cell expression distributions in bacteria in high-throughput. However, there has been no systematic investigation into the best practices for quantitative analysis of such data, what systematic biases exist, and what accuracy and sensitivity can be obtained. We investigate these issues by measuring the same E. coli strains carrying fluorescent reporters using both flow cytometry and microscopic setups and systematically comparing the resulting single-cell expression distributions. Using these results, we develop methods for rigorous quantitative inference of single-cell expression distributions from fluorescence flow cytometry data. First, we present a Bayesian mixture model to separate debris from viable cells using all scattering signals. Second, we show that cytometry measurements of fluorescence are substantially affected by autofluorescence and shot noise, which can be mistaken for intrinsic noise in gene expression, and present methods to correct for these using calibration measurements. Finally, we show that because forward- and side-scatter signals scale non-linearly with cell size, and are also affected by a substantial shot noise component that cannot be easily calibrated unless independent measurements of cell size are available, it is not possible to accurately estimate the variability in the sizes of individual cells using flow cytometry measurements alone. To aid other researchers with quantitative analysis of flow cytometry expression data in bacteria, we distribute E-Flow, an open-source R package that implements our methods for filtering debris and for estimating true biological expression means and variances from the fluorescence signal. The package is available at https://github.com/vanNimwegenLab/E-Flow.

Initiation of chromosome replication controls both division and replication cycles in E. coli through a double-adder mechanism.

Living cells proliferate by completing and coordinating two cycles, a division cycle controlling cell size and a DNA replication cycle controlling the number of chromosomal copies. It remains unclear how bacteria such as Escherichia coli tightly coordinate those two cycles across a wide range of growth conditions. Here, we used time-lapse microscopy in combination with microfluidics to measure growth, division and replication in single E. coli cells in both slow and fast growth conditions. To compare different phenomenological cell cycle models, we introduce a statistical framework assessing their ability to capture the correlation structure observed in the data. In combination with stochastic simulations, our data indicate that the cell cycle is driven from one initiation event to the next rather than from birth to division and is controlled by two adder mechanisms: the added volume since the last initiation event determines the timing of both the next division and replication initiation events.

Monitoring single-cell gene regulation under dynamically controllable conditions with integrated microfluidics and software.

Much is still not understood about how gene regulatory interactions control cell fate decisions in single cells, in part due to the difficulty of directly observing gene regulatory processes in vivo. We introduce here a novel integrated setup consisting of a microfluidic chip and accompanying analysis software that enable long-term quantitative tracking of growth and gene expression in single cells. The dual-input Mother Machine (DIMM) chip enables controlled and continuous variation of external conditions, allowing direct observation of gene regulatory responses to changing conditions in single cells. The Mother Machine Analyzer (MoMA) software achieves unprecedented accuracy in segmenting and tracking cells, and streamlines high-throughput curation with a novel leveraged editing procedure. We demonstrate the power of the method by uncovering several novel features of an iconic gene regulatory program: the induction of Escherichia coli's lac operon in response to a switch from glucose to lactose.

Budding yeast as a model organism to study the effects of age.

Although a budding yeast culture can be propagated eternally, individual yeast cells age and eventually die. The detailed knowledge of this unicellular eukaryotic species as well as the powerful tools developed to study its physiology makes budding yeast an ideal model organism to study the mechanisms involved in aging. Considering both detrimental and positive aspects of age, we review changes occurring during aging both at the whole-cell level and at the intracellular level. The possible mechanisms allowing old cells to produce rejuvenated progeny are described in terms of accumulation and inheritance of aging factors. Based on the dynamic changes associated with age, we distinguish different stages of age: early age, during which changes do not impair cell growth; intermediate age, during which aging factors start to accumulate; and late age, which corresponds to the last divisions before death. For each aging factor, we examine its asymmetric segregation and whether it plays a causal role in aging. Using the example of caloric restriction, we describe how the aging process can be modulated at different levels and how changes in different organelles might interplay with each other. Finally, we discuss the beneficial aspects that might be associated with age.

Cell-cell contacts confine public goods diffusion inside Pseudomonas aeruginosa clonal microcolonies.

The maintenance of cooperation in populations where public goods are equally accessible to all but inflict a fitness cost on individual producers is a long-standing puzzle of evolutionary biology. An example of such a scenario is the secretion of siderophores by bacteria into their environment to fetch soluble iron. In a planktonic culture, these molecules diffuse rapidly, such that the same concentration is experienced by all bacteria. However, on solid substrates, bacteria form dense and packed colonies that may alter the diffusion dynamics through cell-cell contact interactions. In Pseudomonas aeruginosa microcolonies growing on solid substrate, we found that the concentration of pyoverdine, a secreted iron chelator, is heterogeneous, with a maximum at the center of the colony. We quantitatively explain the formation of this gradient by local exchange between contacting cells rather than by global diffusion of pyoverdine. In addition, we show that this local trafficking modulates the growth rate of individual cells. Taken together, these data provide a physical basis that explains the stability of public goods production in packed colonies.

Localization of protein aggregation in Escherichia coli is governed by diffusion and nucleoid macromolecular crowding effect.

Aggregates of misfolded proteins are a hallmark of many age-related diseases. Recently, they have been linked to aging of Escherichia coli (E. coli) where protein aggregates accumulate at the old pole region of the aging bacterium. Because of the potential of E. coli as a model organism, elucidating aging and protein aggregation in this bacterium may pave the way to significant advances in our global understanding of aging. A first obstacle along this path is to decipher the mechanisms by which protein aggregates are targeted to specific intercellular locations. Here, using an integrated approach based on individual-based modeling, time-lapse fluorescence microscopy and automated image analysis, we show that the movement of aging-related protein aggregates in E. coli is purely diffusive (Brownian). Using single-particle tracking of protein aggregates in live E. coli cells, we estimated the average size and diffusion constant of the aggregates. Our results provide evidence that the aggregates passively diffuse within the cell, with diffusion constants that depend on their size in agreement with the Stokes-Einstein law. However, the aggregate displacements along the cell long axis are confined to a region that roughly corresponds to the nucleoid-free space in the cell pole, thus confirming the importance of increased macromolecular crowding in the nucleoids. We thus used 3D individual-based modeling to show that these three ingredients (diffusion, aggregation and diffusion hindrance in the nucleoids) are sufficient and necessary to reproduce the available experimental data on aggregate localization in the cells. Taken together, our results strongly support the hypothesis that the localization of aging-related protein aggregates in the poles of E. coli results from the coupling of passive diffusion-aggregation with spatially non-homogeneous macromolecular crowding. They further support the importance of "soft" intracellular structuring (based on macromolecular crowding) in diffusion-based protein localization in E. coli.

Monitoring microbial population dynamics at low densities.

We propose a new and simple method for the measurement of microbial concentrations in highly diluted cultures. This method is based on an analysis of the intensity fluctuations of light scattered by microbial cells under laser illumination. Two possible measurement strategies are identified and compared using simulations and measurements of the concentration of gold nanoparticles. Based on this comparison, we show that the concentration of Escherichia coli and Saccharomyces cerevisiae cultures can be easily measured in situ across a concentration range that spans five orders of magnitude. The lowest measurable concentration is three orders of magnitude (1000×) smaller than in current optical density measurements. We show further that this method can also be used to measure the concentration of fluorescent microbial cells. In practice, this new method is well suited to monitor the dynamics of population growth at early colonization of a liquid culture medium. The dynamic data thus obtained are particularly relevant for microbial ecology studies.

Mixotrophy in orchids: insights from a comparative study of green individuals and nonphotosynthetic individuals of Cephalanthera damasonium.

Some green orchids obtain carbon (C) from their mycorrhizal fungi and photosynthesis. This mixotrophy may represent an evolutionary step towards mycoheterotrophic plants fully feeding on fungal C. Here, we report on nonphotosynthetic individuals (albinos) of the green Cephalanthera damasonium that likely represent another evolutionary step. Albino and green individuals from a French population were compared for morphology and fertility, photosynthetic abilities, fungal partners (using microscopy and molecular tools), and nutrient sources (as characterized by 15N and 13C abundances). Albinos did not differ significantly from green individuals in morphology and fertility, but tended to be smaller. They harboured similar fungi, with Thelephoraceae and Cortinariaceae as mycorrhizal partners and few rhizoctonias. Albinos were nonphotosynthetic, fully mycoheterotrophic. Green individuals carried out photosynthesis at compensation point and received almost 50% of their C from fungi. Orchid fungi also colonized surrounding tree roots, likely to be the ultimate C source. Transition to mycoheterotrophy may require several simultaneous adaptations; albinos, by lacking some of them, may have reduced ecological success. This may limit the appearance of cheaters in mycorrhizal networks.