Understanding the Adaptive Growth Strategy of Lactobacillus plantarum by In Silico Optimisation

In the study of metabolic networks, optimization techniques are often used to predict flux distributions, and hence, metabolic phenotype. Flux balance analysis in particular has been successful in predicting metabolic phenotypes. However, an inherent limitation of a stoichiometric approach such as flux balance analysis is that it can predict only flux distributions that result in maximal yields. Hence, previous attempts to use FBA to predict metabolic fluxes in Lactobacillus plantarum failed, as this lactic acid bacterium produces lactate, even under glucose-limited chemostat conditions, where FBA predicted mixed acid fermentation as an alternative pathway leading to a higher yield. In this study we tested, however, whether long-term adaptation on an unusual and poor carbon source (for this bacterium) would select for mutants with optimal biomass yields. We have therefore adapted Lactobacillus plantarum to grow well on glycerol as its main growth substrate. After prolonged serial dilutions, the growth yield and corresponding fluxes were compared to in silico predictions. Surprisingly, the organism still produced mainly lactate, which was corroborated by FBA to indeed be optimal. To understand these results, constraint-based elementary flux mode analysis was developed that predicted 3 out of 2669 possible flux modes to be optimal under the experimental conditions. These optimal pathways corresponded very closely to the experimentally observed fluxes and explained lactate formation as the result of competition for oxygen by the other flux modes. Hence, these results provide thorough understanding of adaptive evolution, allowing in silico predictions of the resulting flux states, provided that the selective growth conditions favor yield optimization as the winning strategy

Grid Cells, Place Cells, and Geodesic Generalization for Spatial Reinforcement Learning

Reinforcement learning (RL) provides an influential characterization of the brain's mechanisms for learning to make advantageous choices. An important problem, though, is how complex tasks can be represented in a way that enables efficient learning. We consider this problem through the lens of spatial navigation, examining how two of the brain's location representations—hippocampal place cells and entorhinal grid cells—are adapted to serve as basis functions for approximating value over space for RL. Although much previous work has focused on these systems' roles in combining upstream sensory cues to track location, revisiting these representations with a focus on how they support this downstream decision function offers complementary insights into their characteristics. Rather than localization, the key problem in learning is generalization between past and present situations, which may not match perfectly. Accordingly, although neural populations collectively offer a precise representation of position, our simulations of navigational tasks verify the suggestion that RL gains efficiency from the more diffuse tuning of individual neurons, which allows learning about rewards to generalize over longer distances given fewer training experiences. However, work on generalization in RL suggests the underlying representation should respect the environment's layout. In particular, although it is often assumed that neurons track location in Euclidean coordinates (that a place cell's activity declines “as the crow flies” away from its peak), the relevant metric for value is geodesic: the distance along a path, around any obstacles. We formalize this intuition and present simulations showing how Euclidean, but not geodesic, representations can interfere with RL by generalizing inappropriately across barriers. Our proposal that place and grid responses should be modulated by geodesic distances suggests novel predictions about how obstacles should affect spatial firing fields, which provides a new viewpoint on data concerning both spatial codes

Achieving Optimal Growth through Product Feedback Inhibition in Metabolism

Recent evidence suggests that the metabolism of some organisms, such as Escherichia coli, is remarkably efficient, producing close to the maximum amount of biomass per unit of nutrient consumed. This observation raises the question of what regulatory mechanisms enable such efficiency. Here, we propose that simple product-feedback inhibition by itself is capable of leading to such optimality. We analyze several representative metabolic modules—starting from a linear pathway and advancing to a bidirectional pathway and metabolic cycle, and finally to integration of two different nutrient inputs. In each case, our mathematical analysis shows that product-feedback inhibition is not only homeostatic but also, with appropriate feedback connections, can minimize futile cycling and optimize fluxes. However, the effectiveness of simple product-feedback inhibition comes at the cost of high levels of some metabolite pools, potentially associated with toxicity and osmotic imbalance. These large metabolite pool sizes can be restricted if feedback inhibition is ultrasensitive. Indeed, the multi-layer regulation of metabolism by control of enzyme expression, enzyme covalent modification, and allostery is expected to result in such ultrasensitive feedbacks. To experimentally test whether the qualitative predictions from our analysis of feedback inhibition apply to metabolic modules beyond linear pathways, we examine the case of nitrogen assimilation in E. coli, which involves both nutrient integration and a metabolic cycle. We find that the feedback regulation scheme suggested by our mathematical analysis closely aligns with the actual regulation of the network and is sufficient to explain much of the dynamical behavior of relevant metabolite pool sizes in nutrient-switching experiments

Metabolomic Analysis in Severe Childhood Pneumonia in The Gambia, West Africa: Findings from a Pilot Study

Pneumonia remains the leading cause of death in young children globally and improved diagnostics are needed to better identify cases and reduce case fatality. Metabolomics, a rapidly evolving field aimed at characterizing metabolites in biofluids, has the potential to improve diagnostics in a range of diseases. The objective of this pilot study is to apply metabolomic analysis to childhood pneumonia to explore its potential to improve pneumonia diagnosis in a high-burden setting. and Random Forests (RF). ‘Unsupervised’ (blinded) data were analyzed by Principal Component Analysis (PCA), while ‘supervised’ (unblinded) analysis was by Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structures (OPLS). Potential markers were extracted from S-plots constructed following analysis with OPLS, and markers were chosen based on their contribution to the variation and correlation within the data set. The dataset was additionally analyzed with the machine-learning algorithm RF in order to address issues of model overfitting and markers were selected based on their variable importance ranking. Unsupervised PCA analysis revealed good separation of pneumonia and control groups, with even clearer separation of the groups with PLS-DA and OPLS analysis. Statistically significant differences (p<0.05) between groups were seen with the following metabolites: uric acid, hypoxanthine and glutamic acid were higher in plasma from cases, while L-tryptophan and adenosine-5′-diphosphate (ADP) were lower; uric acid and L-histidine were lower in urine from cases. The key limitation of this study is its small size.Metabolomic analysis clearly distinguished severe pneumonia patients from community controls. The metabolites identified are important for the host response to infection through antioxidant, inflammatory and antimicrobial pathways, and energy metabolism. Larger studies are needed to determine whether these findings are pneumonia-specific and to distinguish organism-specific responses. Metabolomics has considerable potential to improve diagnostics for childhood pneumonia

Induction of Eosinophil Apoptosis by the Cyclin-Dependent Kinase Inhibitor AT7519 Promotes the Resolution of Eosinophil-Dominant Allergic Inflammation

Eosinophils not only defend the body against parasitic infection but are also involved in pathological inflammatory allergic diseases such as asthma, allergic rhinitis and contact dermatitis. Clearance of apoptotic eosinophils by macrophages is a key process responsible for driving the resolution of eosinophilic inflammation and can be defective in allergic diseases. However, enhanced resolution of eosinophilic inflammation by deliberate induction of eosinophil apoptosis using pharmacological agents has not been previously demonstrated. Here we investigated the effect of a novel cyclin-dependent kinase inhibitor drug, AT7519, on human and mouse eosinophil apoptosis and examined whether it could enhance the resolution of a murine model of eosinophil-dominant inflammation in vivo.Eosinophils from blood of healthy donors were treated with AT7519 and apoptosis assessed morphologically and by flow-cytometric detection of annexin-V/propidium iodide staining. AT7519 induced eosinophil apoptosis in a concentration dependent manner. Therapeutic administration of AT7519 in eosinophil-dominant allergic inflammation was investigated using an established ovalbumin-sensitised mouse model of allergic pleurisy. Following ovalbumin challenge AT7519 was administered systemically at the peak of pleural inflammation and inflammatory cell infiltrate, apoptosis and evidence of macrophage phagocytosis of apoptotic eosinophils assessed at appropriate time points. Administration of AT7519 dramatically enhanced the resolution of allergic pleurisy via direct induction of eosinophil apoptosis without detriment to macrophage clearance of these cells. This enhanced resolution of inflammation was shown to be caspase-dependent as the effects of AT7519 were reduced by treatment with a broad spectrum caspase inhibitor (z-vad-fmk).Our data show that AT7519 induces human eosinophil apoptosis and enhances the resolution of a murine model of allergic pleurisy by inducing caspase-dependent eosinophil apoptosis and enhancing macrophage ingestion of apoptotic eosinophils. These findings demonstrate the utility of cyclin-dependent kinase inhibitors such as AT7519 as potential therapeutic agents for the treatment of eosinophil dominant allergic disorders

The unfolded protein response in immunity and inflammation.

The unfolded protein response (UPR) is a highly conserved pathway that allows the cell to manage endoplasmic reticulum (ER) stress that is imposed by the secretory demands associated with environmental forces. In this role, the UPR has increasingly been shown to have crucial functions in immunity and inflammation. In this Review, we discuss the importance of the UPR in the development, differentiation, function and survival of immune cells in meeting the needs of an immune response. In addition, we review current insights into how the UPR is involved in complex chronic inflammatory diseases and, through its role in immune regulation, antitumour responses.This work was supported by the Netherlands Organization for Scientific Research Rubicon grant 825.13.012 (J.G.); US National Institutes of Health (NIH) grants DK044319, DK051362, DK053056 and DK088199, and the Harvard Digestive Diseases Center (HDDC) grant DK034854 (R.S.B.); National Institutes of Health grants DK042394, DK088227, DK103183 and CA128814 (R.J.K.); and European Research Council (ERC) Starting Grant 260961, ERC Consolidator Grant 648889, and the Wellcome Trust Investigator award 106260/Z/14/Z (A.K.).This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nri.2016.6

Neurons Controlling Aplysia Feeding Inhibit Themselves by Continuous NO Production

Neural activity can be affected by nitric oxide (NO) produced by spiking neurons. Can neural activity also be affected by NO produced in neurons in the absence of spiking?Applying an NO scavenger to quiescent Aplysia buccal ganglia initiated fictive feeding, indicating that NO production at rest inhibits feeding. The inhibition is in part via effects on neurons B31/B32, neurons initiating food consumption. Applying NO scavengers or nitric oxide synthase (NOS) blockers to B31/B32 neurons cultured in isolation caused inactive neurons to depolarize and fire, indicating that B31/B32 produce NO tonically without action potentials, and tonic NO production contributes to the B31/B32 resting potentials. Guanylyl cyclase blockers also caused depolarization and firing, indicating that the cGMP second messenger cascade, presumably activated by the tonic presence of NO, contributes to the B31/B32 resting potential. Blocking NO while voltage-clamping revealed an inward leak current, indicating that NO prevents this current from depolarizing the neuron. Blocking nitrergic transmission had no effect on a number of other cultured, isolated neurons. However, treatment with NO blockers did excite cerebral ganglion neuron C-PR, a command-like neuron initiating food-finding behavior, both in situ, and when the neuron was cultured in isolation, indicating that this neuron also inhibits itself by producing NO at rest.Self-inhibitory, tonic NO production is a novel mechanism for the modulation of neural activity. Localization of this mechanism to critical neurons in different ganglia controlling different aspects of a behavior provides a mechanism by which a humeral signal affecting background NO production, such as the NO precursor L-arginine, could control multiple aspects of the behavior

Recruitment and Activation of RSK2 by HIV-1 Tat

The transcriptional activity of the integrated HIV provirus is dependent on the chromatin organization of the viral promoter and the transactivator Tat. Tat recruits the cellular pTEFb complex and interacts with several chromatin-modifying enzymes, including the histone acetyltransferases p300 and PCAF. Here, we examined the interaction of Tat with activation-dependent histone kinases, including the p90 ribosomal S6 kinase 2 (RSK2). Dominant-negative RSK2 and treatment with a small-molecule inhibitor of RSK2 kinase activity inhibited the transcriptional activity of Tat, indicating that RSK2 is important for Tat function. Reconstitution of RSK2 in cells from subjects with a genetic defect in RSK2 expression (Coffin-Lowry syndrome) enhanced Tat transactivation. Tat interacted with RSK2 and activated RSK2 kinase activity in cells. Both properties were lost in a mutant Tat protein (F38A) that is deficient in HIV transactivation. Our data identify a novel reciprocal regulation of Tat and RSK2 function, which might serve to induce early changes in the chromatin organization of the HIV LTR