Climate drives rhizosphere microbiome variation and divergent selection between geographically distant Arabidopsis populations

Disentangling the contribution of climatic and edaphic factors to microbiome variation and local adaptation in plants requires an experimental approach to uncouple their effects and test for causality. We used microbial inocula, soil matrices and plant genotypes derived from two natural Arabidopsis thaliana populations in northern and southern Europe in an experiment conducted in climatic chambers mimicking seasonal changes in temperature, day length and light intensity of the home sites of the two genotypes. The southern A. thaliana genotype outperformed the northern genotype in the southern climate chamber, whereas the opposite was true in the northern climate chamber. Recipient soil matrix, but not microbial composition, affected plant fitness, and effects did not differ between genotypes. Differences between chambers significantly affected rhizosphere microbiome assembly, although these effects were small in comparison with the shifts induced by physicochemical differences between soil matrices. The results suggest that differences in seasonal changes in temperature, day length and light intensity between northern and southern Europe have strongly influenced adaptive differentiation between the two A. thaliana populations, whereas effects of differences in soil factors have been weak. By contrast, below-ground differences in soil characteristics were more important than differences in climate for rhizosphere microbiome differentiation

Pollination success in a deceptive orchid is enhanced by co-occurring rewarding magnet plants

It has been debated whether pollination success in nonrewarding plants that flower in association with nectar-producing plants will be diminished by competition for pollinator visits or, alternatively, enhanced through increased local abundance of pollinators (the magnet species effect). We experimentally evaluated these effects using the nonrewarding bumblebee-pollinated orchid Anacamptis morio and associated nectar-producing plants at a site in Sweden. Pollination success (estimated as pollen receipt and pollen removal) in A. morio was significantly greater for individuals translocated to patches of nectar-producing plants (Geum rivale and Allium schoenoprasum) than for individuals placed outside (similar to20 m away) such patches. These results provide support for the existence of a facilitative magnet species effect in the interaction between certain nectar plants and A. morio. To determine the spatial scale of these interactions, we correlated the visitation rate to flowers of A. morio with the density of sympatric nectar plants in 1-m(2) and 100-m(2) plots centered around groups of translocated plants, and at the level of whole meadows (similar to0.5-2 ha). Visitation rate to flowers of A. morio was not correlated with the 1-m(2) patch density of G. rivale and A. schoenoprasum, but showed a significant positive relationship with density of these nectar plants in 100-m(2) plots. In addition, visitation to flowers of A. morio was strongly and positively related to the density of A. schoenoprasum at the level of the meadow. Choice experiments showed that bees foraging on the purple flowers of A. schoenoprasum (a particularly effective magnet species) visit the purple flowers of A. morio more readily (47.6% of choices) than bees foraging on the yellow flowers of Lotus corniculatus (17% of choices). Overall similarity in flower color and shape may increase the probability that a pollinator will temporarily shift from a nectar-producing "magnet" plant to a nonrewarding plant. We discuss the possibility of a mimicry continuum between those orchids that exploit instinctive food-seeking behavior of pollinators and those that show an adaptive resemblance to nectar-producing plants

Vegetation Type and Decomposition Priming Mediate Brackish Marsh Carbon Accumulation Under Interacting Facets of Global Change

Coastal wetland carbon pools are globally important, but their response to interacting facets of global change remain unclear. Numerical models neglect species-specific vegetation responses to sea level rise (SLR) and elevated CO2 (eCO2) that are observed in field experiments, while field experiments cannot address the long-term feedbacks between flooding and soil growth that models show are important. Here, we present a novel numerical model of marsh carbon accumulation parameterized with empirical observations from a long-running eCO2 experiment in an organic rich, brackish marsh. Model results indicate that eCO2 and SLR interact synergistically to increase soil carbon burial, driven by shifts in plant community composition and soil volume expansion. However, newly parameterized interactions between plant biomass and decomposition (i.e. soil priming) reduce the impact of eCO2 on marsh survival, and by inference, the impact of eCO2 on soil carbon accumulation

Escape from a zero current state in a one dimensional array of Josephson junctions

A long one dimensional array of small Josephson junctions exhibits Coulomb blockade of Cooper pair tunneling. This zero current state exists up to a switching voltage, Vsw, where there is a sudden onset of current. In this paper we present histograms showing how Vsw changes with temperature for a long array and calculations of the corresponding escape rates. Our analysis of the problem is based on the existence of a voltage dependent energy barrier and we do not make any assumptions about its shape. The data divides up into two temperature regimes, the higher of which can be explained with Kramers thermal escape model. At low temperatures the escape becomes independent of temperature.Comment: 4 pages 5 figure

Seasonal variations in carbon, nitrogen and phosphorus concentrations and C:N:P stoichiometry in different organs of a Larix principis-rupprechtii Mayr. plantation in the Qinling Mountains, China

Understanding how concentrations of elements and their stoichiometry change with plant growth and age is critical for predicting plant community responses to environmental change. Weusedlong-term field experiments to explore how the leaf, stem and root carbon (C), nitrogen (N) and phosphorous (P) concentrations and their stoichiometry changed with growth and stand age in a L.principis-rupprechtii Mayr. plantation from 2012–2015 in the Qinling Mountains, China. Our results showed that the C, N and P concentrations and stoichiometric ratios in different tissues of larch stands were affected by stand age, organ type andsampling month and displayed multiple correlations with increased stand age in different growing seasons. Generally, leaf C and N concentrations were greatest in the fast-growing season, but leaf P concentrations were greatest in the early growing season. However, no clear seasonal tendencies in the stem and root C, N and P concentrations were observed with growth. In contrast to N and P, few differences were found in organ-specific C concentrations. Leaf N:P was greatest in the fast-growing season, while C:N and C:P were greatest in the late-growing season. No clear variations were observed in stem and root C:N, C:P andN:Pthroughout the entire growing season, but leaf N:P was less than 14, suggesting that the growth of larch stands was limited by N in our study region. Compared to global plant element concentrations and stoichiometry, the leaves of larch stands had higher C, P, C:NandC:PbutlowerNandN:P,andtherootshadgreater PandC:NbutlowerN,C:Pand N:P. Our study provides baseline information for describing the changes in nutritional elements with plant growth, which will facilitates plantation forest management and restoration, and makes avaluable contribution to the global data pool on leaf nutrition and stoichiometry

Scaling Analysis of Magnetic Filed Tuned Phase Transitions in One-Dimensional Josephson Junction Arrays

We have studied experimentally the magnetic field-induced superconductor-insulator quantum phase transition in one-dimensional arrays of small Josephson junctions. The zero bias resistance was found to display a drastic change upon application of a small magnetic field; this result was analyzed in context of the superfluid-insulator transition in one dimension. A scaling analysis suggests a power law dependence of the correlation length instead of an exponential one. The dynamical exponents $z$ were determined to be close to 1, and the correlation length critical exponents were also found to be about 0.3 and 0.6 in the two groups of measured samples.Comment: 4 pages, 4 figure

Advanced engineering of third-generation lysins and formulation strategies for clinical applications

One of the possible solutions for the current antibiotic resistance crisis may be found in (often bacteriophage-derived) peptidoglycan hydrolases. The first clinical trials of these natural enzymes, coined here as first-generation lysins, are currently ongoing. Moving beyond natural endolysins with protein engineering established the second generation of lysins. In second-generation lysins, the focus lies on improving antibacterial and biochemical properties such as antimicrobial activity and stability, as well as expanding their activities towards Gram-negative pathogens. However, solutions to particular key challenges regarding clinical applications are only beginning to emerge in the third generation of lysins, in which protein and biochemical engineering efforts focus on improving properties relevant under clinical conditions. In addition, increasingly advanced formulation strategies are developed to increase the bioavailability, antibacterial activity, and half-life, and to reduce pro-inflammatory responses. This review focuses on third-generation and advanced formulation strategies that are developed to treat infections, ranging from topical to systemic applications. Together, these efforts may fully unlock the potential of lysin therapy and will propel it as a true antibiotic alternative or supplement

RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on <i>Streptomyces coelicolor</i>

RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/RAVEN and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM)