Bacterial phenotypic heterogeneity through the lens of single-cell RNA sequencing

Bacterial transcription is not monolithic. Microbes exist in a wide variety of cell states that help them adapt to their environment, acquire and produce essential nutrients, and engage in both competition and cooperation with their neighbors. While we typically think of bacterial adaptation as a group behavior, where all cells respond in unison, there is often a mixture of phenotypic responses within a bacterial population, where distinct cell types arise. A primary phenomenon driving these distinct cell states is transcriptional heterogeneity. Given that bacterial mRNA transcripts are extremely short-lived compared to eukaryotes, their transcriptional state is closely associated with their physiology, and thus the transcriptome of a bacterial cell acts as a snapshot of the behavior of that bacterium. Therefore, the application of single-cell transcriptomics to microbial populations will provide novel insight into cellular differentiation and bacterial ecology. In this review, we provide an overview of transcriptional heterogeneity in microbial systems, discuss the findings already provided by single-cell approaches, and plot new avenues of inquiry in transcriptional regulation, cellular biology, and mechanisms of heterogeneity that are made possible when microbial communities are analyzed at single-cell resolution.

Whole-genome sequencing analysis of two heat-evolved Escherichia coli strains

BACKGROUND

High temperatures cause a suite of problems for cells, including protein unfolding and aggregation; increased membrane fluidity; and changes in DNA supercoiling, RNA stability, transcription and translation. Consequently, enhanced thermotolerance can evolve through an unknown number of genetic mechanisms even in the simple model bacterium Escherichia coli. To date, each E. coli study exploring this question resulted in a different set of mutations. To understand the changes that can arise when an organism evolves to grow at higher temperatures, we sequenced and analyzed two previously described E. coli strains, BM28 and BM28 ΔlysU, that have been laboratory adapted to the highest E. coli growth temperature reported to date.

RESULTS

We found three large deletions in the BM28 and BM28 ΔlysU strains of 123, 15 and 8.5 kb in length and an expansion of IS10 elements. We found that BM28 and BM28 ΔlysU have considerably different genomes, suggesting that the BM28 culture that gave rise to BM28 and BM28 ΔlysU was a mixed population of genetically different cells. Consistent with published findings of high GroESL expression in BM28, we found that BM28 inexplicitly carries the groESL bearing plasmid pOF39 that was maintained simply by high-temperature selection pressure. We identified over 200 smaller insertions, deletions, single nucleotide polymorphisms and other mutations, including changes in master regulators such as the RNA polymerase and the transcriptional termination factor Rho. Importantly, this genome analysis demonstrates that the commonly cited findings that LysU plays a crucial role in thermotolerance and that GroESL hyper-expression is brought about by chromosomal mutations are based on a previous misinterpretation of the genotype of BM28.

CONCLUSIONS

This whole-genome sequencing study describes genetically distinct mechanisms of thermotolerance evolution from those found in other heat-evolved E. coli strains. Studying adaptive laboratory evolution to heat in simple model organisms is important in the context of climate change. It is important to better understand genetic mechanisms of enhancing thermotolerance in bacteria and other organisms, both in terms of optimizing laboratory evolution methods for various organisms and in terms of potential genetic engineering of organisms most at risk or most important to our societies and ecosystems.

A Bayesian non-parametric mixed-effects model of microbial growth curves

Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

High Heating Rates Affect Greatly the Inactivation Rate of Escherichia coli

Heat resistance of microorganisms can be affected by different influencing factors. Although, the effect of heating rates has been scarcely explored by the scientific community, recent researches have unraveled its important effect on the thermal resistance of different species of vegetative bacteria. Typically heating rates described in the literature ranged from 1 to 20°C/min but the impact of much higher heating rates is unclear. The aim of this research was to explore the effect of different heating rates, such as those currently achieved in the heat exchangers used in the food industry, on the heat resistance of Escherichia coli. A pilot plant tubular heat exchanger and a thermoresistometer Mastia were used for this purpose. Results showed that fast heating rates had a deep impact on the thermal resistance of E. coli. Heating rates between 20 and 50°C/min were achieved in the heat exchanger, which were much slower than those around 20°C/s achieved in the thermoresistometer. In all cases, these high heating rates led to higher inactivation than expected: in the heat exchanger, for all the experiments performed, when the observed inactivation had reached about seven log cycles, the predictions estimated about 1 log cycle of inactivation; in the thermoresistometer these differences between observed and predicted values were even more than 10 times higher, from 4.07 log cycles observed to 0.34 predicted at a flow rate of 70 mL/min and a maximum heating rate of 14.7°C/s. A quantification of the impact of the heating rates on the level of inactivation achieved was established. These results point out the important effect that the heating rate has on the thermal resistance of E. coli, with high heating rates resulting in an additional sensitization to heat and therefore an effective food safety strategy in terms of food processing.

High pressure homogenization vs heat treatment: safety and functional properties of liquid whole egg

This research investigated the potential of multi-pass homogenization treatment for the inactivation of Salmonella enterica serovar Enteritidis inoculated at different levels in liquid whole egg (LWE) comparing the efficacy of this treatment with a traditional thermal one performed at 65 °C. Moreover, the effects of high pressure treatment (HPH) on structural and functional properties such as viscosity, microstructure and foaming abilities of LWE were investigated. The data obtained suggested that the multi-pass high pressure treatment at 100 MPa of S. enterica serovar Enteritidis inoculated in LWE at 7 and 4 log CFU/ml resulted in a first order inactivation kinetic, while the thermal inactivation curves of S. enterica serovar Enteritidis inoculated at 8 and 4 log CFU/ml presented a non-linear behaviour, with a marked tail after 3 min of treatment at 65 °C. Additionally, HPH treatment caused an increase in foaming capacity of LWE, with respect to the untreated samples, passing from values of 26% of the control to 50% of pressure treated samples.

Evaluation of thermal inactivation of Escherichia coli using microelectrode ion flux measurements with osmotic stress

AIMS

To elucidate the potential use of microelectrode ion flux measurements to evaluate bacterial responses to heat treatment.

METHODS AND RESULTS

Escherichia coli K12 was used as a test bacterium to determine whether various heat treatments (55-70°C for 15 min) affected net ion flux across E. coli cell membranes using the MIFE™ system to measure net K(+) fluxes. No difference in K(+) fluxes was observed before and after heat treatments regardless of the magnitude of the treatment. Applying hyperosmotic stress (3% NaCl w/v) during flux measurement led to a net K(+) loss from the heat-treated E.coli cells below 65°C as well as from nonheated cells. In contrast, with E. coli cells treated at and above 65°C, hyperosmotic stress disrupted the pattern of K(+) flux observed at lower temperatures and resulted in large flux noise with random scatter. This phenomenon was particularly apparent above 70°C. Although E. coli cells lost the potential to recover and grow at and above 62°C, K(+) flux disruption was not clearly observed until 68°C was reached.

CONCLUSIONS

No changes in net K(+) flux from heat-stressed E. coli cells were observed directly as a result of thermal treatments. However, regardless of the magnitude of heat treatment above 55°C, loss of viability indicated by enrichment culture correlated with disrupted K(+) fluxes when previously heated cells were further challenged by imposing hyperosmotic stress during flux measurement. This two-stage process enabled evaluation of the lethality of heat-treated bacterial cells within 2 h and may be an alternative and more rapid method to confirm the lethality of heat treatment.

SIGNIFICANCE AND IMPACT OF THE STUDY

The ability to confirm the lethality of thermal treatments and to specify minimal time/temperature combinations by a nonculture-dependent test offers an alternative system to culture-based methods.

Modeling of Clostridium perfringens vegetative cell inactivation in beef-in-sauce products: a meta-analysis using mixed linear models

The aim of the present study was to predict Clostridium perfringens vegetative cell inactivation during the final reheating step of two beef-in-sauce products prepared and distributed in a French hospital for exposure in risk assessment. In order to account for variability according to experts and international organization recommendations, published data were used to estimate the thermal inactivation parameters of a probabilistic model. Mixed effects models were proposed to describe variability on D(ref) the decimal reduction time at temperature T(ref). Many models differing by their description of variability on D(ref) were tested. Based on goodness-of-fit and parsimony of the model, the one including three random effects was chosen. That model describes random effects of vegetative cell culture conditions, strains and other uncontrolled experimental factors. In order to check the ability of the model to predict inactivation under dynamic thermal conditions, model validation was carried out on published non isothermal data. This model was then used to predict C. perfringens vegetative cell inactivation using temperature profiles inside beef-in-sauce products registered in a French hospital and to explore control measures easier to apply than French regulations.

Escherichia coli population heterogeneity: subpopulation dynamics at super-optimal temperatures

In the past years, we explored the dynamics of Escherichia coli K12 at super-optimal temperatures under static and dynamic temperature conditions (Van Derlinden et al. (2008b, 2009, 2010). Disturbed sigmoid growth curves, i.e., a sequence of growth, inactivation and re-growth, were observed, especially close to the maximum growth temperature. Based on the limited set of experiments (i.e., 2 static temperatures and 2 dynamic temperature profiles), the irregular growth curves were explained by postulating the co-existence of two subpopulations: a more resistant, growing population and a temperature sensitive, inactivating population. In this study, the dynamics of the two subpopulations are studied rigorously at 11 constant temperature levels in the region between 45°C and 46°C, with at least five repetitions per temperature. At all temperatures, the total population follows a sequence of growth, inactivation and re-growth. The sequence of different stages in the growth curves can be explained by the two subpopulations. The first growth phase and the inactivation phase reflect the presence of the sensitive subpopulation. Hereafter, the population's dynamics are dominated by the growth of the resistant subpopulation. Generally, cell counts are characterized by a large variability. The dynamics of the two subpopulations are carefully analyzed using a heterogeneous subpopulation type model to study the relation between the kinetic parameters of the two subpopulations and temperature, and to evaluate if the fraction d of resistant cells varies with temperature. Results indicate that the growth rate of the sensitive subpopulation decreases with increasing temperature within the range of 45-46°C. Furthermore, results point in the direction that the duration of this initial growth phase is approximately constant, i.e., around 2h. Possibly, the stress resistance of the cells decreases after a certain period because the metabolism is fully adapted to exponential growth. Also, the growth rate of the resistant subpopulation decreases with increasing temperature. Due to the extreme variability in the cell density data, derivation of accurate relations was not possible. From the heterogeneous model implementations, given the experimental set-up, both a constant d value and a temperature dependent d value seem plausible.