Optimization and control in bacterial Lag phase

The lag phase of bacterial growth is important from a medical and food safety perspective, but difficult to study due to the low density and metabolic rate of cells. A new study by Alon and colleagues reveals that the gene expression program during early lag phase prioritizes carbon source utilization enzymes over genes responsible for biomass accumulation. This cellular strategy ultimately maximizes growth, making the best long-term use of the new nutrient-rich environment. See research article: http://www.biomedcentral.com/1752-0509/7/13

Environmental stresses can alleviate the average deleterious effect of mutations

BACKGROUND: Fundamental questions in evolutionary genetics, including the possible advantage of sexual reproduction, depend critically on the effects of deleterious mutations on fitness. Limited existing experimental evidence suggests that, on average, such effects tend to be aggravated under environmental stresses, consistent with the perception that stress diminishes the organism's ability to tolerate deleterious mutations. Here, we ask whether there are also stresses with the opposite influence, under which the organism becomes more tolerant to mutations. RESULTS: We developed a technique, based on bioluminescence, which allows accurate automated measurements of bacterial growth rates at very low cell densities. Using this system, we measured growth rates of Escherichia coli mutants under a diverse set of environmental stresses. In contrast to the perception that stress always reduces the organism's ability to tolerate mutations, our measurements identified stresses that do the opposite – that is, despite decreasing wild-type growth, they alleviate, on average, the effect of deleterious mutations. CONCLUSIONS: Our results show a qualitative difference between various environmental stresses ranging from alleviation to aggravation of the average effect of mutations. We further show how the existence of stresses that are biased towards alleviation of the effects of mutations may imply the existence of average epistatic interactions between mutations. The results thus offer a connection between the two main factors controlling the effects of deleterious mutations: environmental conditions and epistatic interactions

Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance

This is the final version. Available from Oxford University Press (OUP) via the DOI in this record.Bacteria regulate genes to survive antibiotic stress, but regulation can be far from perfect. When regulation is not optimal, mutations that change gene expression can contribute to antibiotic resistance. It is not systematically understood to what extent natural gene regulation is or is not optimal for distinct antibiotics, and how changes in expression of specific genes quantitatively affect antibiotic resistance. Here we discover a simple quantitative relation between fitness, gene expression, and antibiotic potency, which rationalizes our observation that a multitude of genes and even innate antibiotic defense mechanisms have expression that is critically nonoptimal under antibiotic treatment. First, we developed a pooled-strain drug-diffusion assay and screened Escherichia coli overexpression and knockout libraries, finding that resistance to a range of 31 antibiotics could result from changing expression of a large and functionally diverse set of genes, in a primarily but not exclusively drug-specific manner. Second, by synthetically controlling the expression of single-drug and multidrug resistance genes, we observed that their fitness-expression functions changed dramatically under antibiotic treatment in accordance with a log-sensitivity relation. Thus, because many genes are nonoptimally expressed under antibiotic treatment, many regulatory mutations can contribute to resistance by altering expression and by activating latent defenses.National Institutes of HealthIsraeli Centers of Research Excellence I-CORE ProgramEuropean Research CouncilNational Health and Medical Research Counci

Evolutionary Models for Formation of Network Motifs and Modularity in the Saccharomyces Transcription Factor Network

Many natural and artificial networks contain overrepresented subgraphs, which have been termed network motifs. In this article, we investigate the processes that led to the formation of the two most common network motifs in eukaryote transcription factor networks: the bi-fan motif and the feed-forward loop. Around 100 million y ago, the common ancestor of the Saccharomyces clade underwent a whole-genome duplication event. The simultaneous duplication of the genes created by this event enabled the origin of many network motifs to be established. The data suggest that there are two primary mechanisms that are involved in motif formation. The first mechanism, enabled by the substantial plasticity in promoter regions, is rewiring of connections as a result of positive environmental selection. The second is duplication of transcription factors, which is also shown to be involved in the formation of intermediate-scale network modularity. These two evolutionary processes are complementary, with the pre-existence of network motifs enabling duplicated transcription factors to bind different targets despite structural constraints on their DNA-binding specificities. This process may facilitate the creation of novel expression states and the increases in regulatory complexity associated with higher eukaryotes

Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology

The topology of cellular circuits (the who-interacts-with-whom) is key to understand their robustness to both mutations and noise. The reason is that many biochemical parameters driving circuit behavior vary extensively and are thus not fine-tuned. Existing work in this area asks to what extent the function of any one given circuit is robust. But is high robustness truly remarkable, or would it be expected for many circuits of similar topology? And how can high robustness come about through gradual Darwinian evolution that changes circuit topology gradually, one interaction at a time? We here ask these questions for a model of transcriptional regulation networks, in which we explore millions of different network topologies. Robustness to mutations and noise are correlated in these networks. They show a skewed distribution, with a very small number of networks being vastly more robust than the rest. All networks that attain a given gene expression state can be organized into a graph whose nodes are networks that differ in their topology. Remarkably, this graph is connected and can be easily traversed by gradual changes of network topologies. Thus, robustness is an evolvable property. This connectedness and evolvability of robust networks may be a general organizational principle of biological networks. In addition, it exists also for RNA and protein structures, and may thus be a general organizational principle of all biological systems

Optimal Drug Synergy in Antimicrobial Treatments

The rapid proliferation of antibiotic-resistant pathogens has spurred the use of drug combinations to maintain clinical efficacy and combat the evolution of resistance. Drug pairs can interact synergistically or antagonistically, yielding inhibitory effects larger or smaller than expected from the drugs' individual potencies. Clinical strategies often favor synergistic interactions because they maximize the rate at which the infection is cleared from an individual, but it is unclear how such interactions affect the evolution of multi-drug resistance. We used a mathematical model of in vivo infection dynamics to determine the optimal treatment strategy for preventing the evolution of multi-drug resistance. We found that synergy has two conflicting effects: it clears the infection faster and thereby decreases the time during which resistant mutants can arise, but increases the selective advantage of these mutants over wild-type cells. When competition for resources is weak, the former effect is dominant and greater synergy more effectively prevents multi-drug resistance. However, under conditions of strong resource competition, a tradeoff emerges in which greater synergy increases the rate of infection clearance, but also increases the risk of multi-drug resistance. This tradeoff breaks down at a critical level of drug interaction, above which greater synergy has no effect on infection clearance, but still increases the risk of multi-drug resistance. These results suggest that the optimal strategy for suppressing multi-drug resistance is not always to maximize synergy, and that in some cases drug antagonism, despite its weaker efficacy, may better suppress the evolution of multi-drug resistance.Molecular and Cellular Biolog

Networks from drug–drug surfaces

Molecular and Cellular Biolog

Gauged cooling of topological excitations and emergent fermions on quantum simulators

Simulated cooling is a robust method for preparing low-energy states of many-body Hamiltonians on near-term quantum simulators. In such schemes, a subset of the simulator's spins (or qubits) are treated as a "bath," which extracts energy and entropy from the system of interest. However, such protocols are inefficient when applied to systems whose excitations are highly non-local in terms of the microscopic degrees of freedom, such as topological phases of matter; such excitations are difficult to extract by a local coupling to a bath. We explore a route to overcome this obstacle by encoding of the system's degrees of freedom into those of the quantum simulator in a non-local manner. To illustrate the approach, we show how to efficiently cool the ferromagnetic phase of the quantum Ising model, whose excitations are domain walls, via a "gauged cooling" protocol in which the Ising spins are coupled to a $Z_2$ gauge field that simultaneously acts as a reservoir for removing excitations. We show that our protocol can prepare the ground states of the ferromagnetic and paramagnetic phases equally efficiently. The gauged cooling protocol naturally extends to (interacting) fermionic systems, where it is equivalent to cooling by coupling to a fermionic bath via single-fermion hopping

Universal versus tailored solutions for alleviating disruptive behavior in hospitals

Background: Disruptive behavior among hospital staff can negatively affect quality of care. Motivated by a standard on disruptive behavior issued by The Joint Commission (LD 3.10), as well as the desire to improve patient care, minimize liability, and improve staff retention, hospitals are setting policies to prevent and resolve disruptive behaviors. However, it is unknown whether uniform conflict management tools are equally effective among different hospital settings. Methods: We surveyed residents and nurses to identify similarities and differences among hospital departments in the antecedents, characteristics, and outcomes of disruptive behaviors, and in the effectiveness of conflict management tools. We used a quantitative questionnaire-based assessment to examine conflict perceptions in eight different hospital departments at Rambam Medical Center in Haifa, Israel. Results: Most participants (89 %) reported witnessing disruptive behavior either directly or in other parties; the most significant causes were identified as intense work, miscommunication, and problematic personalities. The forms of these behaviors, however, varied significantly between departments, with some more prone to expressed conflicts, while others were characterized by hidden disruptive behaviors. These outcomes were correlated by the antecedents to disruptive behavior, which in turn affected the effectiveness of alleviating strategies and tools. Some tools, such as processes for evaluating complaints, teamwork and conflict management courses, and introducing a behavioral mission statement, are effective across many antecedents. Other tools, however, are antecedent-specific, falling into two principal categories: tools directly removing a specific problem and tools that offer a way to circumvent the problem. Conclusions: Conflict resolution tools and strategies, based on residents and nurse perceptions, may be more effective if tailored to the specific situation, rather than using a “one-size-fits-all” approach. Electronic supplementary material The online version of this article (doi:10.1186/s13584-015-0018-7) contains supplementary material, which is available to authorized users

Digital Signal Processing Reveals Circadian Baseline Oscillation in Majority of Mammalian Genes

In mammals, circadian periodicity has been described for gene expression in the hypothalamus and multiple peripheral tissues. It is accepted that 10%–15% of all genes oscillate in a daily rhythm, regulated by an intrinsic molecular clock. Statistical analyses of periodicity are limited by the small size of datasets and high levels of stochastic noise. Here, we propose a new approach applying digital signal processing algorithms separately to each group of genes oscillating in the same phase. Combined with the statistical tests for periodicity, this method identifies circadian baseline oscillation in almost 100% of all expressed genes. Consequently, circadian oscillation in gene expression should be evaluated in any study related to biological pathways. Changes in gene expression caused by mutations or regulation of environmental factors (such as photic stimuli or feeding) should be considered in the context of changes in the amplitude and phase of genetic oscillations