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.

The ERNCIP survey on COVID-19: Emergency & Business Continuity for fostering resilience in critical infrastructures.

Among the many repercussions of the COVID-19 emergency to be assessed, those on critical infrastructures and the associated businesses and professions are certainly important ones. In this paper, we document the conception, implementation and outcome of a survey organized by European Commission's Joint Research Centre and entitled "COVID-19: Emergency & Business Continuity". This was conducted in April-May 2020 with the participation of critical infrastructure experts (including professionals from the academia and research institutions, infrastructure operators and industry representatives, public authorities and members of security agencies), involved as stakeholders in the European Reference Network for Critical Infrastructure Protection (ERNCIP). Themes explored through this study include an assessment of the business continuity status and the evaluation of emergency management and disaster recovery aspects, as experienced from the perspective of different sectors, organization types and personal perceptions of the respondents.

Linking healthcare and societal resilience during the Covid-19 pandemic.

Coronavirus disease 2019 (Covid-19) has highlighted the link between public healthcare and the broader context of operational response to complex crises. Data are needed to support the work of the emergency services and enhance governance. This study develops a Europe-wide analysis of perceptions, needs and priorities of the public affected by the Covid-19 emergency. An online multilingual survey was conducted from mid-May until mid-July 2020. The questionnaire investigates perceptions of public healthcare, emergency management and societal resilience. In total, N = 3029 valid answers were collected. They were analysed both as a whole and focusing on the most represented countries (Italy, Romania, Spain and the United Kingdom). Our findings highlight some perceived weaknesses in emergency management that are associated with the underlying vulnerability of the global interconnected society and public healthcare systems. The spreading of the epidemic in Italy represented a 'tipping point' for perceiving Covid-19 as an 'emergency' in the surveyed countries. The respondents uniformly suggested a preference for gradually restarting activities. We observed a tendency to ignore the cascading effects of Covid-19 and possible concurrence of threats. Our study highlights the need for practices designed to address the next phases of the Covid-19 crisis and prepare for future systemic shocks. Cascading effects that could compromise operational capacity need to be considered more carefully. We make the case for the reinforcement of cross-border coordination of public health initiatives, for standardization in business continuity management, and for dealing with the recovery at the European level.

Electron Beam Susceptibility of Enteric Viruses and Surrogate Organisms on Fruit, Seed and Spice Matrices.

The objective of this study was to use high-energy electron beam (HEEB) treatments to find surrogate microorganisms for enteric viruses and to use the selected surrogates as proof of concept to investigate low-energy electron beam (LEEB) treatments for enteric virus inactivation at industrial scale on frozen blueberries. Six food matrices inoculated with HAV (hepatitis A virus), MNV S99 (murine norovirus), bacteriophages MS2 and Qß, and Geobacillus stearothermophilus spores were treated with HEEB at 10 MeV using 4, 8 and 16 kGy doses. G. stearothermophilus spores showed the highest inactivation on all matrices except on raisins, with a dose-dependent effect. HAV reached the maximum measurable log10 reduction (> 3.2 log10) when treated at 16 kGy on raisins. MNV showed the highest resistance of all tested microorganisms, independent of the dose, except on frozen blueberries. On frozen blueberries, freeze-dried raspberries, sesame seeds and black peppercorns, HAV showed a mean inactivation level in between those of MS2 and G. stearothermophilus. Based on this, we selected both surrogate organisms as first approximation to estimate HAV inactivation on frozen blueberries during LEEB treatment at 250 keV using 16 kGy. Reductions of 3.1 and 1.3 log10 were measured for G. stearothermophilus spores and MS2, respectively, suggesting that a minimum reduction of 1.4 log10 can be expected for HAV under the same conditions.

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.

Stability and optimal control for some classes of tritrophic systems.

The objective of this paper is to study an optimal resource management problem for some classes of tritrophic systems composed by autotrophic resources (plants), bottom level consumers (herbivores) and top level consumers (humans). The first class of systems we discuss are linear chains, in which biomass flows from plants to herbivores, and from herbivores to humans. In the second class of systems humans are omnivorous and hence compete with herbivores for plant resources. Finally, in the third class of systems humans are omnivorous, but the plant resources are partitioned so that humans and herbivores do not complete for the same ones. The three trophic chains are expressed as Lotka-Volterra models, which seems to be a suitable choice in contexts where there is a shortage of food for the consumers. Our model parameters are taken from the literature on agro-pastoral systems in Sub-Saharan Africa.