The Escherichia coli RutR transcription factor binds at targets within genes as well as intergenic regions.

The Escherichia coli RutR protein is the master regulator of genes involved in pyrimidine catabolism. Here we have used chromatin immunoprecipitation in combination with DNA microarrays to measure the binding of RutR across the chromosome of exponentially growing E. coli cells. Twenty RutR-binding targets were identified and analysis of these targets generated a DNA consensus logo for RutR binding. Complementary in vitro binding assays showed high-affinity RutR binding to 16 of the 20 targets, with the four low-affinity RutR targets lacking predicted key binding determinants. Surprisingly, most of the DNA targets for RutR are located within coding segments of the genome and appear to have little or no effect on transcript levels in the conditions tested. This contrasts sharply with other E. coli transcription factors whose binding sites are primarily located in intergenic regions. We suggest that either RutR has yet undiscovered function or that evolution has been slow to eliminate non-functional DNA sites for RutR because they do not have an adverse effect on cell fitness

Polymer interfaces and biopharmaceuticals: Chemistry, designs and challenges

The complex interactions between biological components and polymer materials has an extensive technical history. Virtually every surface property has been invoked as being important to biological interfacial response: texture, roughness, topology, porosity, hydrophilic, hydrophobic, polar, apolar, (non)-wettable, non-fouling brushes, surface mobility, rigidity, flexibility, crystalline versus amorphous, aspect ratio. Few surface properties alone, however, provide consistent, global technical solutions to vexing biomedical technology problems, particularly with cell culture, blood, plasma, microbial milieu, and protein solutions. Bio-interface materials performance must therefore be tailored specifically to each application. Short-term contact use (minutes/hours) has different materials interface requirements than long-term (days) use; globular proteins have particularly difficult needs not readily satisfied by any materials solution. Viable biologics interfaces (i.e., fresh blood harvests, cell cultures) must also consider selective gas permeability, leachables, and sterilization issues. Film properties, lamination, cutting, chemical stability, sealing and handling issues are additional considerations for single-use materials. Lastly cost-of-goods and materials economics must be considered, especially for single use technologies. No one-size-fits-all surface solutions currently satisfy all bio-interface materials needs. This talk will review design principles, dogma and actual polymer chemistries to modulate, modify and manipulate polymer surfaces in contact with biological components. Several polymer surface properties will be discussed with regard to their physical chemistry in aqueous media. Traditional and recent developments in non-fouling interfaces and polymer approaches and their hypothesized influences on biophysical interactions with proteins and cells will be presented

Autoregulation of the Escherichia coli melR promoter: repression involves four molecules of MelR

The Escherichia coli MelR protein is a transcription activator that autoregulates its own promoter by repressing transcription initiation. Optimal repression requires MelR binding to a site that overlaps the melR transcription start point and to upstream sites. In this work, we have investigated the different determinants needed for optimal repression and their spatial requirements. We show that repression requires a complex involving four DNA-bound MelR molecules, and that the global CRP regulator plays little or no role

DNA Sampling: a method for probing protein binding at specific loci on bacterial chromosomes

We describe a protocol, DNA sampling, for the rapid isolation of specific segments of DNA, together with bound proteins, from Escherichia coli K-12. The DNA to be sampled is generated as a discrete fragment within cells by the yeast I-SceI meganuclease, and is purified using FLAG-tagged LacI repressor and beads carrying anti-FLAG antibody. We illustrate the method by investigating the proteins bound to the colicin K gene regulatory region, either before or after induction of the colicin K gene promoter

Environmental Virtual Observatories (EVOs): Prospects for knowledge co-creation and resilience in the Information Age

Developments in technologies are shaping information access globally. This presents opportunities and challenges for understanding the role of new technologies in sustainability research. This article focuses on a suite of technologies termed Environmental Virtual Observatories (EVOs) developed for communicating observations and simulation of environmental processes. A strength of EVOs is that they are open and decentralised, thus democratising flow and ownership of information between multiple actors. However, EVOs are discussed rarely beyond their technical aspects. By evaluating the evolution of EVOs, we illustrate why it is timely to engage with policy and societal aspects as well. While first generation EVOs are primed for scientists, second generation EVOs can have broader implications for knowledge co-creation and resilience through their participatory design

Transcription factor distribution in Escherichia coli: studies with FNR protein

Using chromatin immunoprecipitation (ChIP) and high-density microarrays, we have measured the distribution of the global transcription regulator protein, FNR, across the entire Escherichia coli chromosome in exponentially growing cells. Sixty-three binding targets, each located at the 5′ end of a gene, were identified. Some targets are adjacent to poorly transcribed genes where FNR has little impact on transcription. In stationary phase, the distribution of FNR was largely unchanged. Control experiments showed that, like FNR, the distribution of the nucleoid-associated protein, IHF, is little altered when cells enter stationary phase, whilst RNA polymerase undergoes a complete redistribution

Threat history controls flexible escape behavior in mice.

In many instances, external sensory-evoked neuronal activity is used by the brain to select the most appropriate behavioral response. Predator-avoidance behaviors such as freezing and escape1,2 are of particular interest since these stimulus-evoked responses are behavioral manifestations of a decision-making process that is fundamental to survival.3,4 Over the lifespan of an individual, however, the threat value of agents in the environment is believed to undergo constant revision,5 and in some cases, repeated avoidance of certain stimuli may no longer be an optimal behavioral strategy.6 To begin to study this type of adaptive control of decision-making, we devised an experimental paradigm to probe the properties of threat escape in the laboratory mouse Mus musculus. First, we found that while robust escape to visual looming stimuli can be observed after 2 days of social isolation, mice can also rapidly learn that such stimuli are non-threatening. This learned suppression of escape (LSE) is extremely robust and can persist for weeks and is not a generalized adaptation, since flight responses to novel live prey and auditory threat stimuli in the same environmental context were maintained. We also show that LSE cannot be explained by trial number or a simple form of stimulus desensitization since it is dependent on threat-escape history. We propose that the action selection process mediating escape behavior is constantly updated by recent threat history and that LSE can be used as a robust model system to understand the neurophysiological mechanisms underlying experience-dependent decision-making

Accepting higher morbidity in exchange for sacrificing fewer animals in studies developing novel infection-control strategies.

Preventing bacterial infections from becoming the leading cause of death by the year 2050 requires the development of novel, infection-control strategies, building heavily on biomaterials science, including nanotechnology. Pre-clinical (animal) studies are indispensable for this development. Often, animal infection outcomes bear little relation to human clinical outcome. Here, we review conclusions from pathogen-inoculum dose-finding pilot studies for evaluation of novel infection-control strategies in murine models. Pathogen-inoculum doses are generally preferred that produce the largest differences in quantitative infection outcome parameters between a control and an experimental group, without death or termination of animals due to having reached an inhumane end-point during the study. However, animal death may represent a better end-point for evaluation than large differences in outcome parameters or number of days over which infection persists. The clinical relevance of lower pre-clinical outcomes, such as bioluminescence, colony forming units (CFUs) retrieved or more rapid clearance of infection is unknown, as most animals cure infection without intervention, depending on pathogen-species and pathogen-inoculum dose administered. In human clinical practice, patients suffering from infection present to hospital emergency wards, frequently in life-threatening conditions. Animal infection-models should therefore use prevention of death and recurrence of infection as primary efficacy targets to be addressed by novel strategies. To compensate for increased animal morbidity and mortality, animal experiments should solely be conducted for pre-clinical proof of principle and safety. With the advent of sophisticated in vitro models, we advocate limiting use of animal models when exploring pathogenesis or infection mechanisms

Parareal with a Learned Coarse Model for Robotic Manipulation

A key component of many robotics model-based planning and control algorithms is physics predictions, that is, forecasting a sequence of states given an initial state and a sequence of controls. This process is slow and a major computational bottleneck for robotics planning algorithms. Parallel-in-time integration methods can help to leverage parallel computing to accelerate physics predictions and thus planning. The Parareal algorithm iterates between a coarse serial integrator and a fine parallel integrator. A key challenge is to devise a coarse model that is computationally cheap but accurate enough for Parareal to converge quickly. Here, we investigate the use of a deep neural network physics model as a coarse model for Parareal in the context of robotic manipulation. In simulated experiments using the physics engine Mujoco as fine propagator we show that the learned coarse model leads to faster Parareal convergence than a coarse physics-based model. We further show that the learned coarse model allows to apply Parareal to scenarios with multiple objects, where the physics-based coarse model is not applicable. Finally, we conduct experiments on a real robot and show that Parareal predictions are close to real-world physics predictions for robotic pushing of multiple objects. Code (https://doi.org/10.5281/zenodo.3779085) and videos (https://youtu. be/wCh2o1rf-gA) are publicly available