Use of genome-scale metabolic models for plant metabolism

Genome-scale metabolic models (GSMM) have been extensively applied for a wide variety of organisms. However, for plants genome-scale modeling is still in its infancy. It has been successfully developed for Arabidopsis in the last few years. In this study we appraise the existing GSMM for Arabidopsis by computing biomass accumulation using flux balance analysis (FBA) and attempt to outline potential applications for horticultural crops in the future. The predicted growth rates for the Poolman et al. (2009) and Dal’Molin et al. (2010) model were quite similar, despite different carbon sources were provided (glucose and sucrose,100 mmol/g*h DW). The exchange fluxes for respiration in nonphotosynthetic cells confirmed that plants prefer NH3 in the process of nitrogen assimilation. When carbohydrates were used as substrate for respiration the Respiratory quotient (RQ) of Poolman’s model approached 1, which is consistent with experimental data. For the Dal’Molin’s model the RQ is less than 1. H2S is utilized in the Dal’Molin model as sulfur source, whereas sulfate (SO42-) is consumed in the Poolman’s model. The Poolman’s model seems to reflect biological reality more closely than the Dal’Molin model. This could be due to the subcellular compartmentation included in the Dal’Molin model, adding subcellular compartmentation of reactions was done manually. In conclusion, to apply constraints-based reconstruction and analysis (COBRA) methods, including FBA, to plant metabolism requires careful analysis and possibly curation of existing GSMM’s

Dual-mode humidity detection using a lanthanide-based metal-organic framework: towards multifunctional humidity sensors

Combined photoluminescence and impedance spectroscopy studies show that a europium-based metal–organic framework behaves as a highly effective and reliable humidity sensor, enabling dual-mode humidity detection

Excited-state electronic asymmetry prevents photoswitching in terthiophene compounds

The diarylethene moiety is one of the most extensively used switches in the field of molecular electronics. Here we report on spectroscopic and quantum chemical studies of two diarylethene-based compounds with a non-C3-symmetric triethynyl terthiophene core symmetrically substituted with RuCp*(dppe) or trimethylsilyl termini. The ethynyl linkers are strong IR markers that we use in time-resolved vibrational spectroscopic studies to get insight into the character and dynamics of the electronically excited states of these compounds on the picosecond to nanosecond time scale. In combination with electronic transient absorption studies and DFT calculations, our studies show that the conjugation of the non-C3-symmetric triethynyl terthiophene system in the excited state strongly affects one of the thiophene rings involved in the ring closure. As a result, cyclization of the otherwise photochromic 3,3″-dimethyl-2,2′:3′,2″-terthiophene core is inhibited. Instead, the photoexcited compounds undergo intersystem crossing to a long-lived triplet excited state from which they convert back to the ground state

Iminothioindoxyl as a molecular photoswitch with 100 nm band separation in the visible range

Light is an exceptional external stimulus for establishing precise control over the properties and functions of chemical and biological systems, which is enabled through the use of molecular photoswitches. Ideal photoswitches are operated with visible light only, show large separation of absorption bands and are functional in various solvents including water, posing an unmet challenge. Here we show a class of fully-visible-light-operated molecular photoswitches, lminothioindoxyls (ITIs) that meet these requirements. ITIs show a band separation of over 100 nm, isomerize on picosecond time scale and thermally relax on millisecond time scale. Using a combination of advanced spectroscopic and computational techniques, we provide the rationale for the switching behavior of ITIs and the influence of structural modifications and environment, including aqueous solution, on their photochemical properties. This research paves the way for the development of improved photo-controlled systems for a wide variety of applications that require fast responsive functions.</p

Applying habitat and population-density models to land-cover time series to inform IUCN Red List assessments

The IUCN (International Union for Conservation of Nature) Red List categories and criteria are the most widely used framework for assessing the relative extinction risk of species. The criteria are based on quantitative thresholds relating to the size, trends, and structure of species’ distributions and populations. However, data on these parameters are sparse and uncertain for many species and unavailable for others, potentially leading to their misclassification or classification as data deficient. We devised an approach that combines data on land-cover change, species-specific habitat preferences, population abundance, and dispersal distance to estimate key parameters (extent of occurrence, maximum area of occupancy, population size and trend, and degree of fragmentation) and hence predict IUCN Red List categories for species. We applied our approach to nonpelagic birds and terrestrial mammals globally (∼15,000 species). The predicted categories were fairly consistent with published IUCN Red List assessments, but more optimistic overall. We predicted 4.2% of species (467 birds and 143 mammals) to be more threatened than currently assessed and 20.2% of data deficient species (10 birds and 114 mammals) to be at risk of extinction. Incorporating the habitat fragmentation subcriterion reduced these predictions 1.5–2.3% and 6.4–14.9% (depending on the quantitative definition of fragmentation) for threatened and data deficient species, respectively, highlighting the need for improved guidance for IUCN Red List assessors on the application of this aspect of the IUCN Red List criteria. Our approach complements traditional methods of estimating parameters for IUCN Red List assessments. Furthermore, it readily provides an early-warning system to identify species potentially warranting changes in their extinction-risk category based on periodic updates of land-cover information. Given our method relies on optimistic assumptions about species distribution and abundance, all species predicted to be more at risk than currently evaluated should be prioritized for reassessment

Steady-State Properties of Single-File Systems with Conversion

We have used Monte-Carlo methods and analytical techniques to investigate the influence of the characteristic parameters, such as pipe length, diffusion, adsorption, desorption and reaction rate constants on the steady-state properties of Single-File Systems with a reaction. We looked at cases when all the sites are reactive and when only some of them are reactive. Comparisons between Mean-Field predictions and Monte-Carlo simulations for the occupancy profiles and reactivity are made. Substantial differences between Mean-Field and the simulations are found when rates of diffusion are high. Mean-Field results only include Single-File behavior by changing the diffusion rate constant, but it effectively allows passing of particles. Reactivity converges to a limit value if more reactive sites are added: sites in the middle of the system have little or no effect on the kinetics. Occupancy profiles show approximately exponential behavior from the ends to the middle of the system.Comment: 15 pages, 20 figure

Transient behavior in Single-File Systems

We have used Monte-Carlo methods and analytical techniques to investigate the influence of the characteristics, such as pipe length, diffusion, adsorption, desorption and reaction rates on the transient properties of Single-File Systems. The transient or the relaxation regime is the period in which the system is evolving to equilibrium. We have studied the system when all the sites are reactive and when only some of them are reactive. Comparisons between Mean-Field predictions, Cluster Approximation predictions, and Monte Carlo simulations for the relaxation time of the system are shown. We outline the cases where Mean-Field analysis gives good results compared to Dynamic Monte-Carlo results. For some specific cases we can analytically derive the relaxation time. Occupancy profiles for different distribution of the sites both for Mean-Field and simulations are compared. Different results for slow and fast reaction systems and different distribution of reactive sites are discussed.Comment: 18 pages, 19 figure

Tailoring the optical and dynamic properties of iminothioindoxyl photoswitches through acidochromism

Multi-responsive functional molecules are key for obtaining user-defined control of the properties and functions of chemical and biological systems. In this respect, pH-responsive photochromes, whose switching can be directed with light and acid-base equilibria, have emerged as highly attractive molecular units. The challenge in their design comes from the need to accommodate application-defined boundary conditions for both light- and protonation-responsivity. Here we combine time-resolved spectroscopic studies, on time scales ranging from femtoseconds to seconds, with density functional theory (DFT) calculations to elucidate and apply the acidochromism of a recently designed iminothioindoxyl (ITI) photoswitch. We show that protonation of the thermally stable Z isomer leads to a strong batochromically-shifted absorption band, allowing for fast isomerization to the metastable E isomer with light in the 500-600 nm region. Theoretical studies of the reaction mechanism reveal the crucial role of the acid-base equilibrium which controls the populations of the protonated and neutral forms of the E isomer. Since the former is thermally stable, while the latter re-isomerizes on a millisecond time scale, we are able to modulate the half-life of ITIs over three orders of magnitude by shifting this equilibrium. Finally, stable bidirectional switching of protonated ITI with green and red light is demonstrated with a half-life in the range of tens of seconds. Altogether, we designed a new type of multi-responsive molecular switch in which protonation red-shifts the activation wavelength by over 100 nm and enables efficient tuning of the half-life in the millisecond-second range.</p

Mammal assemblage composition predicts global patterns in emerging infectious disease risk

As a source of emerging infectious diseases, wildlife assemblages (and related spatial patterns) must be quantitatively assessed to help identify high-risk locations. Previous assessments have largely focussed on the distributions of individual species; however, transmission dynamics are expected to depend on assemblage composition. Moreover, disease-diversity relationships have mainly been studied in the context of species loss, but assemblage composition and disease risk (e.g. infection prevalence in wildlife assemblages) can change without extinction. Based on the predicted distributions and abundances of 4466 mammal species, we estimated global patterns of disease risk through the calculation of the community-level basic reproductive ratio R0, an index of invasion potential, persistence, and maximum prevalence of a pathogen in a wildlife assemblage. For density-dependent diseases, we found that, in addition to tropical areas which are commonly viewed as infectious disease hotspots, northern temperate latitudes included high-risk areas. We also forecasted the effects of climate change and habitat loss from 2015 to 2035. Over this period, many local assemblages showed no net loss of species richness, but the assemblage composition (i.e. the mix of species and their abundances) changed considerably. Simultaneously, most areas experienced a decreased risk of density-dependent diseases but an increased risk of frequency-dependent diseases. We further explored the factors driving these changes in disease risk. Our results suggest that biodiversity and changes therein jointly influence disease risk. Understanding these changes and their drivers and ultimately identifying emerging infectious disease hotspots can help health officials prioritize resource distribution.Peer reviewe