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The dormancy specific regulator, SutA, is an intrinsically-disordered protein that modulates transcription initiation in Pseudomonas aeruginosa

Though bacteria in nature are often nutritionally limited and growing slowly, most of our understanding of core cellular processes such as transcription comes from studies in a handful of model organisms doubling rapidly under nutrient-replete conditions. We previously identified a small protein of unknown function, called SutA, in a global screen of proteins synthesized in Pseudomonas aeruginosa under growth arrest (Babin BM, et al. (2016) SutA is a bacterial transcription factor expressed during slow growth in Pseudomonas aeruginosa. PNAS 113(5):E597-605). SutA binds RNA polymerase (RNAP), causing widespread changes in gene expression, including upregulation of the ribosomal RNA (rRNA) genes. Here, using biochemical and structural methods, we examine how SutA interacts with RNAP and the functional consequences of these interactions. We show that SutA consists of a central -helix with unstructured N- and C-terminal tails, and binds to the {beta}1 domain of RNAP. It activates transcription from the P. aeruginosa rrn promoter by both the housekeeping sigma factor holoenzyme (E{sigma}70) and the general stress response sigma factor holoenzyme (E{sigma}S) in vitro, and its N-terminal tail is required for activation in both holoenzyme contexts. However, we find that the interaction between SutA and each holoenzyme is distinct, with the SutA C-terminal tail and an acidic loop unique to {sigma}70 playing the determining roles in these differences. Our results add SutA to a growing list of transcription regulators that use their intrinsically disordered regions to remodel transcription complexes.\n\nSIGNIFICANCELittle is known about how bacteria regulate their activities during periods of dormancy, yet growth arrest dominates bacterial existence in most environments and is directly relevant to the problem of physiological antibiotic tolerance. Though much is known about transcription in the model organism, Escherichia coli, even there, our understanding of gene expression during dormancy is incomplete. Here we explore how transcription under growth arrest is modulated in Pseudomonas aeruginosa by the small acidic protein, SutA. We show that SutA binds to RNA polymerase and controls transcription by a mechanism that is distinct from other known regulators. Our work underscores the potential for fundamental, mechanistic discovery in this important and understudied realm of bacterial physiology.

molecular biology

Hormonal balance finely tunes dormancy status in sweet cherry flower buds

In temperate trees, optimal timing and quality of flowering directly depend on adequate winter dormancy progression, regulated by a combination of chilling and warm temperatures. Physiological, genetic and functional genomic studies have shown that hormones play a key role in bud dormancy establishment, maintenance and release. We combined physiological, transcriptional analyses, quantification of abscisic acid (ABA) and gibberellins (GAs), and modelling to further investigate how these signaling pathways are associated with dormancy progression in the flower buds of two sweet cherry cultivars. Our results demonstrated that GA-associated pathways have distinct functions and may be differentially related with dormancy. In addition, ABA levels rise at the onset of dormancy, associated with enhanced expression of ABA biosynthesis PavNCED genes, and decreased prior to dormancy release. Following the observations that ABA levels are correlated with dormancy depth, we identified PavUG71B6, a sweet cherry UDP-GLYCOSYLTRANSFERASE gene that up-regulates active catabolism of ABA to ABA-GE and may be associated with low ABA content in the early cultivar. Subsequently, we modelled ABA content and dormancy behavior in three cultivars based on the expression of a small set of genes regulating ABA levels. These results strongly suggest the central role of ABA pathway in the control of dormancy progression and open up new perspectives for the development of molecular-based phenological modelling.

plant biology

Narrow equilibrium window for complex coacervation of tau and RNA under cellular conditions

The conditions that lead to the liquid-liquid phase separation (LLPS) of the tau protein, a microtubule associated protein whose pathological aggregation has been implicated in neurodegenerative disorders, are not well understood. Establishing a phase diagram that delineates the boundaries of phase co-existence is key to understanding its LLPS. Using a combination of EPR, turbidity measurements, and microscopy, we show that tau and RNA form complex coacervates with lower critical solution temperature (LCST) behavior. The coacervates are reversible, and the biopolymers can be driven to the supernatant phase or coacervate phase by varying the experimental conditions (temperature, salt concentration, tau:RNA charge ratio, total polymer concentration and osmotic stress). Furthermore, the coacervates can be driven to a fibrillar state through the addition of heparin. The equilibrium phase diagram of the tau/RNA complex coacervate system can be described by a Flory-Huggins model, augmented by an approximate Voorn Overbeek electrostatic term (FH-VO), after fitting the experimental data to an empirical Flory interaction parameter divided into an entropic and enthalpic term. However, a more advanced model in which tau and RNA are treated as discrete bead-spring chains with a temperature-dependent excluded volume interaction and electrostatic interactions between charged residues, investigated through field theoretic simulations (FTS), provided direct and unique insight into the thermodynamic driving forces of tau/RNA complexation. FTS corroborated the experimental finding that the complex coacervation of tau and RNA is has an entropy-driven contribution, with a transition temperature around the physiological temperature of 37 {degrees}C and salt concentrations around 100-150 mM. Together, experiment and simulation show that LLPS of tau can occur under physiological cellular conditions, but has a narrow equilibrium window over experimentally tunable parameters including temperature, salt and tau concentrations. Guided by our phase diagram, we show that tau can be driven towards LLPS under live cell coculturing conditions with rationally chosen experimental parameters.

biophysics

Visualizing metabolic network dynamics through time-series metabolomics data

New technologies have given rise to an abundance of -omics data, particularly metabolomics data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of new computational visualization methodologies. Here, we present a new method for the visualization of time-course metabolomics data within the context of metabolic network maps. We demonstrate the utility of this method by examining previously published data for two cellular systems--the human platelet and erythrocyte under cold storage for use in transfusion medicine.\n\nThe results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation which mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures.\n\nIn conclusion, this new visualization technique introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types.\n\nAUTHOR SUMMARYProfiling the dynamic state of a metabolic network through the use of time-course metabolomics technologies allows insights into cellular biochemistry. Interpreting these data together at the systems level provides challenges that can be addressed through the development of new visualization approaches. Here, we present a new method for the visualization of time-course metabolomics data that integrates data into an existing metabolic network map. In brief, the metabolomics data are visualized directly on a network map with dynamic elements (nodes that either change size, fill level, or color corresponding with the concentration) while the user controls the time series (i.e., which time point is being displayed) through a graphical interface. We provide short videos that illustrate the utility of this method through its application to existing data sets for the human platelet and erythrocyte. The results presented here give blueprints for the development of visualization methods for other time-course -omics data types that attempt to understand systems-level physiology.

systems biology

Model Classification for Uncertainty Reduction in Biochemical Kinetic Models

A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models. In this study, we use the recently proposed computational approach iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which combines Monte Carlo parameter sampling methods and machine learning techniques, in the context of Bayesian inference. Monte Carlo parameter sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical laws. The machine learning allows us to data-mine the a priori generated kinetic parameters together with the integrated datasets and derive posterior distributions of kinetic parameters consistent with the observed physiology. In this work, we used iSCHRUNK to address a design question: can we identify which are the kinetic parameters and what are their values that give rise to a desired metabolic behavior? Such information is important for a wide variety of studies ranging from biotechnology to medicine. To illustrate the proposed methodology, we performed Metabolic Control Analysis, computed the flux control coefficients of the xylose uptake (XTR), and identified parameters that ensure a rate improvement of XTR in a glucose-xylose co-utilizing S. cerevisiae strain. Our results indicate that only three kinetic parameters need to be accurately characterized to describe the studied physiology, and ultimately to design and control the desired responses of the metabolism. This framework paves the way for a new generation of methods that will systematically integrate the wealth of available omics data and efficiently extract the information necessary for metabolic engineering and synthetic biology decisions.\n\nAuthor SummaryKinetic models are the most promising tool for understanding the complex dynamic behavior of living cells. The primary goal of kinetic models is to capture the properties of the metabolic networks as a whole, and thus we need large-scale models for dependable in silico analyses of metabolism. However, uncertainty in kinetic parameters impedes the development of kinetic models, and uncertainty levels increase with the model size. Tools that will address the issues with parameter uncertainty and that will be able to reduce the uncertainty propagation through the system are therefore needed. In this work, we applied a method called iSCHRUNK that combines parameter sampling and machine learning techniques to characterize the uncertainties and uncover intricate relationships between the parameters of kinetic models and the responses of the metabolic network. The proposed method allowed us to identify a small number of parameters that determine the responses in the network regardless of the values of other parameters. As a consequence, in future studies of metabolism, it will be sufficient to explore a reduced kinetic space, and more comprehensive analyses of large-scale and genome-scale metabolic networks will be computationally tractable.

systems biology

Particle-based simulation reveals macromolecular crowding effects on the Michaelis-Menten mechanism

Many computational models for analyzing and predicting cell physiology rely on in vitro data, collected in dilute and cleanly controlled buffer solutions. However, this can mislead models because about 40% of the intracellular volume is occupied by a dense mixture of proteins, lipids, polysaccharides, RNA, and DNA. These intracellular macromolecules interact with enzymes and their reactants and affect the kinetics of biochemical reactions, making in vivo reactions considerably more complex than the in vitro data indicates. In this work, we present a new type of kinetics that captures and quantifies the effect of volume exclusion and any other spatial phenomena on the kinetics of elementary reactions. We further developed a framework that allows for the efficient parameterization of this type of kinetics using particle simulations. Our formulation, entitled GEneralized Elementary Kinetics (GEEK), can be used to analyze and predict the effect of intracellular crowding on enzymatic reactions and was herein applied to investigate the influence of crowding on phosphoglycerate mutase in Escherichia coli, which exhibits prototypical reversible Michaelis-Menten kinetics. Current research indicates that many enzymes are reaction limited and not diffusion limited, and our results suggest that the influence of fractal diffusion is minimal for these reaction-limited enzymes. Instead, increased association rates and decreased dissociation rates lead to a strong decrease in the effective maximal velocities Vmax and the effective Michaelis-Menten constants KM under physiologically relevant volume occupancies. Finally, the effects of crowding in the context of a linear pathway were explored, with the finding that crowding can have a redistributing effect, relative to ideal conditions, on the effective flux responses in the case of two-fold enzyme overexpression. We suggest that the presented framework in combination with detailed kinetics models will improve our understanding of enzyme reaction networks under non-ideal conditions.

biophysics

Size-based ecological interactions drive food web responses to climate warming

Predicting the impacts of climate change on animal populations and communities requires understanding of feedbacks between direct physiological responses and indirect effects via ecological interactions. Food-dependent body growth and within-species size variation have major effects on dynamics of populations and communities through feedbacks between individual performance and population size structure. Moreover, evidence suggests a link between temperature and population size structure, but we lack an understanding of how this is mediated by species interactions when life history processes are food-dependent. Here, we use a dynamic stage-structured biomass model with food-, size- and temperature-dependent life history processes to assess how temperature affects coexistence, stability and size structure in a tri-trophic food chain. We show that predator biomass densities decline with warming either gradually or in the form of collapses, depending on which consumer life stage they predominantly feed on. Collapses occur when warming destabilizes the community and induces alternative stable states via Allee effects, which emerge when predators promote their own food source through predation. By contrast, warming at low temperatures stabilizes the community as limit cycles turn to fixed point dynamics, unless predators feed only on juveniles. Elevated costs of being large in warmer environments accelerate the decline in predator persistence and mean body size of the community. These results suggest that predator persistence in warmer climates may be lower than previously acknowledged when accounting for size- and food-dependence of life history processes, and that interactions within and between species can mediate the effects of warming on food web stability.\n\nSignificanceClimate warming is altering the dynamics and structure of aquatic ecosystems worldwide. Predicting food web reorganization under rising temperatures requires an understanding of physiological responses and ecological interactions of organisms, both of which depend on body size. We show that size variation within species, food-dependent growth and ecological interactions critically affect how food chains respond to warming. Specifically, warming can stabilize or destabilize food chains and expose predators to increased risk of sudden collapses, resulting in alternative stable food web states. Increasing temperatures can cause abrupt reductions in mean community body size, primarily due to loss of top predators. The potential loss of biodiversity and shifts in ecosystem stability are among the major challenges caused by a warming climate.

ecology

Autoamplification and competition drive symmetry breaking: Initiation of centriole duplication by the PLK4-STIL network

Symmetry breaking, a central principle of physics, has been hailed as the driver of self-organization in biological systems in general and biogenesis of cellular organelles in particular, but the molecular mechanisms of symmetry breaking only begin to become understood. Centrioles, the structural cores of centrosomes and cilia, must duplicate every cell cycle to ensure their faithful inheritance through cellular divisions. Work in model organisms identified conserved proteins required for centriole duplication and found that altering their abundance affects centriole number. However, the biophysical principles that ensure that, under physiological conditions, only a single procentriole is produced on each mother centriole remain enigmatic. Here we propose a mechanistic biophysical model for the initiation of procentriole formation in mammalian cells. We posit that interactions between the master regulatory kinase PLK4 and its activator-substrate STIL form the basis of the procentriole initiation network. The model faithfully recapitulates the experimentally observed transition from PLK4 uniformly distributed around the mother centriole, the \"ring\", to a unique PLK4 focus, the \"spot\", that triggers the assembly of a new procentriole. This symmetry breaking requires a dual positive feedback based on autocatalytic activation of PLK4 and enhanced centriolar anchoring of PLK4-STIL complexes by phosphorylated STIL. We find that, contrary to previous proposals, in situ degradation of active PLK4 is insufficient to break symmetry. Instead, the model predicts that competition between transient PLK4 activity maxima for PLK4-STIL complexes explains both the instability of the PLK4 ring and formation of the unique PLK4 spot. In the model, strong competition at physiologically normal parameters robustly produces a single procentriole, while increasing overexpression of PLK4 and STIL weakens the competition and causes progressive addition of procentrioles in agreement with experimental observations.

cell biology

How to obtain cell volume from dynamic pH, temperature and pressure in plants

We examined the pH/T duality of acidic pH and temperature (T) for the growth of grass shoots in order to determine the phenomenological equation of wall properties ( equation of state, EoS) for living plants. By considering non-meristematic growth as a dynamic series of state transitions (STs) in the extending primary wall, we identified the critical exponents (read: optimum) for this phenomenon, which exhibit a singular behaviour at a critical temperature, critical pH and critical chemical potential () in the form of four power laws: F{pi}({tau}){propto}|{tau}|{beta}-1, F{tau}({tau}){propto}|{pi}|1-, G({tau}){propto}|{tau}|-2-+2{beta} and G{tau}(){propto}||2-. The power-law exponents and {beta} are numbers that are independent of pH (or ) and T, which are known as critical exponents, while {pi} and {tau} represent a reduced pH and reduced temperature, respectively. Various scaling predictions were obtained - the convexity relation + {beta} [≥] 2 for practical pH-based analysis and a {beta} {equiv} 2 identity in a microscopic representation. In the presented scenario, the magnitude that is decisive is the chemical potential of the H+ ions (protons), which force subsequent STs and growth. Furthermore, we observed that the growth rate is generally proportional to the product of the Euler beta functions of T and pH, which are used to determine the hidden content of the Lockhart constant [FE]. It turned out that the evolution equation, when expressed in terms of the same dynamic set of variables, explains either the monotonic growth or periodic extension that is usually observed - like the one detected in pollen tubes - in a unified account. We suggest that cell growth evolves along the path with the least activity, thereby optimising growth under any physiological conditions. The pH dynamics in close-to-natural conditions appears to essentially be responsible for this extreme trajectory, thereby providing a highly nonlinear pH(t), [Formula] transformation. Moreover, the drops in pH that are induced by auxin or fusicoccin, when next converted by the augmented Lockhart equation, are enough to explain a significant fraction of the increase in the growth rate. A self-consistent recurring model is proposed to embrace the inherent complexity of such a biological system, in which several intricate pathways work simultaneously, in order to reconcile the conflicting views of plant cell extension and growth. Eventually, we pose the question: Is the chemical potential of protons a master regulator for tip-growing cells?\n\nAuthor summaryIn plant development, sudden changes such as cell expansion or pollen tube oscillations seem to depend on a correlative group of events rather than on slow shifts in the apex. Hence, in order to understand or to control the processes in the extending cell wall, we need to unravel the general principles and constraints that govern growth. The quest for these principles has primarily focused on the molecular, though merely descriptive, level. Here, we show that it is possible to analyse oscillatory state changes computationally without even requiring knowledge about the exact type of transition. Our results suggest that the cell wall properties and growth of plant cells can be accurately and efficiently predicted by a set of physical and chemical variables such as temperature, pressure and the dynamic pH of the growing plant, which build a scaffold for more specific biochemical predictions. In this context, we observed that cell growth evolves along the path the least action, thereby optimising growth under any physiological conditions. The model equations that we propose span the fields of the biological, physical, chemical and Earth sciences. The common denominator that ties the growth factors together is the chemical potential of protons, which is possibly a central core-controlling mechanism that is able to produce a macroscopic outcome, i.e. structurally and temporally organised apical growth.

biophysics

Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations

Large-scale kinetic models are used for designing, predicting, and understanding the metabolic responses of living cells. Kinetic models are particularly attractive for the biosynthesis of target molecules in cells as they are typically better than other types of models at capturing the complex cellular biochemistry. Using simpler stoichiometric models as scaffolds, kinetic models are built around a steady-state flux profile and a metabolite concentration vector that are typically determined via optimization. However, as the underlying optimization problem is underdetermined, even after incorporating available experimental omics data, one cannot uniquely determine the operational configuration in terms of metabolic fluxes and metabolite concentrations. As a result, some reactions can operate in either the forward or reverse direction while still agreeing with the observed physiology. Here, we analyze how the underlying uncertainty in intracellular fluxes and concentrations affects predictions of constructed kinetic models and their design in metabolic engineering and systems biology studies. To this end, we integrated the omics data of optimally grown Escherichia coli into a stoichiometric model and constructed populations of non-linear large-scale kinetic models of alternative steady-state solutions consistent with the physiology of the E. coli aerobic metabolism. We performed metabolic control analysis (MCA) on these models, highlighting that MCA-based metabolic engineering decisions are strongly affected by the selected steady state and appear to be more sensitive to concentration values rather than flux values. To incorporate this into future studies, we propose a workflow for moving towards more reliable and robust predictions that are consistent with all alternative steady-state solutions. This workflow can be applied to all kinetic models to improve the consistency and accuracy of their predictions. Additionally, we show that, irrespective of the alternative steady-state solution, increased activity of phosphofructokinase and decreased ATP maintenance requirements would improve cellular growth of optimally grown E. coli.

systems biology

Revealing S-layer Functions in the Hyperthermophilic Crenarchaeon Sulfolobus islandicus

The crystalline surface layer (S-layer), consisting of two glycoproteins SlaA and SlaB, is considered to be the exclusive component of the cell envelope outside of the cytoplasmic membrane in Sulfolobus species. Although biochemically and structurally characterized, the S-layer in vivo functions remain largely elusive in Archaea. Here, we investigate how the S-layer genes contribute to the S-layer architecture and affect cellular physiology in a crenarchaeal model, Sulfolobus islandicus M.16.4. Electron micrographs of mutant cells lacking slaA or both slaA and slaB confirm the absence of the outermost layer (SlaA), whereas cells with intact, partially, or completely detached SlaA are observed for the {triangleup}slaB mutant. Importantly, we identify a novel S-layer-associated protein M164_1049, which does not functionally replace its homolog SlaB but likely assists SlaB to stabilize SlaA. Additionally, we find that mutants deficient in SlaA form large cell aggregates and the individual cell size varies significantly. The slaB gene deletion also causes noticeable cellular aggregation, but the size of those aggregates is smaller when compared to {triangleup}slaA and {triangleup}slaAB mutants. We further show the {triangleup}slaA mutant cells exhibit more sensitivity to hyperosmotic stress but are not reduced to wild-type cell size. Finally, we demonstrate that the {triangleup}slaA mutant contains aberrant chromosome copy numbers not seen in wild-type cells where the cell cycle is tightly regulated. Together these data suggest that the lack of slaA results in either cell fusion or irregularities in cell division. Our studies provide novel insights into the physiological and cellular functions of the S-layer in Archaea.\n\nSignificanceRediscovery of the ancient evolutionary relationship between archaea and eukaryotes has revitalized interest in archaeal cell biology. Key to understanding the archaeal cell is the S-layer which is ubiquitous in Archaea but whose in vivo function is unknown. In this study, we genetically dissect how the two well-known S-layer genes as well as a newly identified S-layer-associated-protein-encoding gene contribute to the S-layer architecture in a hyperthermophilic crenarchaeal model S. islandicus. We provide genetic evidence for the first time showing that the slaA gene is a key cell morphology determinant and may play a role in Sulfolobus cell division or cell fusion.

microbiology

Microbiota-dependent elevation of Alcohol Dehydrogenase in Drosophila is associated with changes in alcohol-induced hyperactivity and alcohol preference

The gut microbiota impacts diverse aspects of host biology including metabolism, immunity, and behavior, but the scope of those effects and their underlying molecular mechanisms are poorly understood. To address these gaps, we used Two-dimensional Difference Gel Electrophoresis (2D-DIGE) to identify proteomic differences in male and female Drosophila heads raised with a conventional microbiota and those raised in a sterile environment (axenic). We discovered 22 microbiota-dependent protein differences, and identified a specific elevation in Alcohol Dehydrogenase (ADH) in axenic male flies. Because ADH is a key enzyme in alcohol metabolism, we asked whether physiological and behavioral responses to alcohol were altered in axenic males. Here we show that alcohol induced hyperactivity, the first response to alcohol exposure, is significantly increased in axenic males, requires ADH activity, and is modified by genetic background. While ADH activity is required, we did not detect significant microbe-dependent differences in systemic ADH activity or ethanol level. Like other animals, Drosophila exhibit a preference for ethanol consumption, and here we show significant microbiota-dependent differences in ethanol preference specifically in males. This work demonstrates that male Drosophilas association with their microbiota affects their physiological and behavioral responses to ethanol.

microbiology

Quantitative insights into the cyanobacterial cell economy

Phototrophic microorganisms are promising resources for green biotechnology. Compared to heterotrophic microorganisms, however, the cellular economy of phototrophic growth is still insufficiently understood. We provide a quantitative analysis of light-limited, light-saturated, and light-inhibited growth of the cyanobacterium Synechocystis sp. PCC 6803 using a reproducible cultivation setup. We report key physiological parameters, including growth rate, cell size, and photosynthetic activity over a wide range of light intensities. Intracellular proteins were quantified to monitor proteome allocation as a function of growth rate. Among other physiological adaptations, we identify an upregulation of the translational machinery and downregulation of light harvesting components with increasing light intensity and growth rate. The resulting growth laws are discussed in the context of a coarse-grained model of phototrophic growth and available data obtained by a comprehensive literature search. Our insights into quantitative aspects of cyanobacterial adaptations to different growth rates have implications to understand and optimize photosynthetic productivity.

microbiology

Vigour/tolerance trade-off in cultivated sunflower (Helianthus annuus) response to salinity stress is linked to leaf elemental composition

Developing more stress-tolerant crops will require greater knowledge of the physiological basis of stress tolerance. Here we explore how the variation among twenty cultivated sunflower (Helianthus annuus) genotypes for biomass decline in response to increasing salinity relates to leaf traits and leaf trait adjustments. Genotypes were grown in the greenhouse under five salinity treatments (0, 50, 100, 150, or 200 mM NaCl) for 21 days and assessed for growth, leaf physiological traits, and leaf elemental composition. Results showed that there was a trade-off in performance such that vigorous genotypes, higher biomass at zero mM NaCl, had both a larger absolute decrease and proportional decrease in biomass due to increased salinity. Contrary to expectation, genotypes with a low increase in leaf Na+ and Na+:K+ were no better at maintaining biomass with increasing salinity. Rather, genotypes with a greater reduction in leaf S and K+ content were better at maintaining biomass in the face of increasing salinity. While we found a trade-off between vigour and tolerance, some genotypes were more tolerant than expected. Further analysis of the traits underlying this trade-off will allow us to identify traits/mechanisms that could be bred into high vigour genotypes in order to increase their tolerance.

plant biology

A genetically encoded fluorescent sensor for rapid and specific in vivo detection ofnorepinephrine

Norepinephrine (NE) and epinephrine (Epi), two key biogenic monoamine neurotransmitters, are involved in a wide range of physiological processes. However, their precise dynamics and regulation remain poorly characterized, in part due to limitations of available techniques for measuring these molecules in vivo. Here, we developed a family of GPCR Activation-Based NE/Epi (GRABNE) sensors with a 230% peak {Delta}F/F0 response to NE, good photostability, nanomolar-to-micromolar sensitivities, sub-second rapid kinetics, high specificity to NE vs. dopamine. Viral- or transgenic- mediated expression of GRABNE sensors were able to detect electrical-stimulation evoked NE release in the locus coeruleus (LC) of mouse brain slices, looming-evoked NE release in the midbrain of live zebrafish, as well as optogenetically and behaviorally triggered NE release in the LC and hypothalamus of freely moving mice. Thus, GRABNE sensors are a robust tool for rapid and specific monitoring of in vivo NE/Epi transmission in both physiological and pathological processes.

neuroscience

Transcriptome analysis of the human tibial nerve identifies sexually dimorphic expression of genes involved in pain, inflammation and neuro-immunity

Sex differences in gene expression are important contributors to normal physiology and mechanisms of disease. This is increasingly apparent in understanding and potentially treating chronic pain where molecular mechanisms driving sex differences in neuronal plasticity are giving new insight into why certain chronic pain disorders preferentially affect women versus men. Large transcriptomic resources are increasingly available and can be used to mine for sex differences and molecular insight using donor cohorts. We analyzed more than 250 human tibial nerve (hTN) transcriptomes from the GTex Consortium project to gain insight into sex-dependent gene expression in the peripheral nervous system (PNS). We discover 149 genes with sex differential expression. Many of the genes upregulated in men are associated with inflammation, and appear to be primarily expressed by glia or immune cells. In women, we find the differentially upregulated transcription factor SP4 that drives a regulatory program, and may impact sex differences in PNS physiology. Many of these 149 DE genes have some previous association with chronic pain but few of them have been explored thoroughly. Additionally, using clinical data in the GTex database, we identify a subset of differentially expressed (DE) genes in diseases associated with chronic pain, arthritis and type II diabetes. Our work identifies sexually dimorphic gene expression in the human PNS with implications for discovery of sex-specific pain mechanisms.

neuroscience

Baseline corticosterone does not reflect iridescent plumage traits in female tree swallows

The production of high quality secondary sexual traits can be constrained by trade-offs in the allocation of energy and nutrients with other metabolic activities, and is mediated by physiological processes. In birds, the factors influencing male plumage quality have been well studied; however, factors affecting female plumage quality are poorly understood. Furthermore, it remains uncertain which physiological traits mediate the relationship between body condition and ornaments. In this three-year study of after-second-year female tree swallows (Tachycineta bicolor), we investigated (1) the relationship between baseline corticosterone near the end of the brood-rearing period (CORTBR) and feather colour characteristics (hue, saturation, brightness) the following year, and (2) the relationship between baseline corticosterone measured during incubation (CORTI) and brood rearing (CORTBR), and feather colour in the same year. To control for reproductive effort, we included reproductive parameters as covariates in all analyses. In this first study between CORT and the plumage colour characteristics of a species bearing iridescent feathers, we did not find any relationship between CORTBR and the colour of subsequently-produced feathers, nor did we find any relationship between CORTI and the colour of feathers displayed during that breeding season. If CORT levels at the end of breeding carry over to influence the immediately subsequent moult period as we expect, our results generally indicate that structural plumage quality may not be as sensitive to circulating CORT levels compared to carotenoid-based colouration. Future studies, particularly those employing experimental manipulations of CORT during moult in species with iridescent traits, are necessary to fully determine the role glucocorticoids play in mediating the quality of secondary sexual characteristics.

zoology

Structural basis for prodrug recognition by the SLC15 family of proton coupled peptide transporters

A major challenge in drug development is the optimisation of intestinal absorption and cellular uptake. A successful strategy has been to develop prodrug molecules, which hijack solute carrier (SLC) transporters for active transport into the body. The proton coupled oligopeptide transporters, PepT1 and PepT2, have been successfully targeted using this approach. Peptide transporters display a remarkable capacity to recognise a diverse library of di- and tri-peptides, making them extremely promiscuous and major contributors to the pharmacokinetic profile of several important drug classes, including beta-lactam antibiotics, anti-viral and antineoplastic agents. Of particular interest has been their ability to recognise amino acid and peptide-based prodrug molecules, thereby providing a rational approach to improving drug transport into the body. However, the structural basis for prodrug recognition has remained elusive. Here we present crystal structures of a prokaryotic homologue of the mammalian transporters in complex with the antiviral prodrug valacyclovir and the peptide based photodynamic therapy agent, 5-aminolevulinic acid. The valacyclovir structure reveals that prodrug recognition is mediated through both the amino acid scaffold and the ester bond, which is commonly used to link drug molecules to the carriers physiological ligand, whereas 5-aminolevulinic acid makes far fewer interactions compared to physiological peptides. These structures provide a unique insight into how peptide transporters interact with xenobiotic molecules and provide a template for further prodrug development.

biochemistry