Contact Line Catch Up by Growing Ice Crystals.

The effect of freezing on contact line motion is a scientific challenge in the understanding of the solidification of capillary flows. In this Letter, we experimentally investigate the spreading and freezing of a water droplet on a cold substrate. We demonstrate that solidification stops the spreading because the ice crystals catch up with the advancing contact line. Indeed, we observe the formation and growth of ice crystals along the substrate during the drop spreading, and show that their velocity equals the contact line velocity when the drop stops. Modeling the growth of the crystals, we predict the shape of the crystal front and show that the substrate thermal properties play a major role on the frozen drop radius.

Two tomato GDP-D-mannose epimerase isoforms involved in ascorbate biosynthesis play specific roles in cell wall biosynthesis and development.

GDP-D-mannose epimerase (GME, EC 5.1.3.18) converts GDP-D-mannose to GDP-L-galactose, and is considered to be a central enzyme connecting the major ascorbate biosynthesis pathway to primary cell wall metabolism in higher plants. Our previous work demonstrated that GME is crucial for both ascorbate and cell wall biosynthesis in tomato. The aim of the present study was to investigate the respective role in ascorbate and cell wall biosynthesis of the two SlGME genes present in tomato by targeting each of them through an RNAi-silencing approach. Taken individually SlGME1 and SlGME2 allowed normal ascorbate accumulation in the leaf and fruits, thus suggesting the same function regarding ascorbate. However, SlGME1 and SlGME2 were shown to play distinct roles in cell wall biosynthesis, depending on the tissue considered. The RNAi-SlGME1 plants harbored small and poorly seeded fruits resulting from alterations of pollen development and of pollination process. In contrast, the RNAi-SlGME2 plants exhibited vegetative growth delay while fruits remained unaffected. Analysis of SlGME1- and SlGME2-silenced seeds and seedlings further showed that the dimerization state of pectin rhamnogalacturonan-II (RG-II) was altered only in the RNAi-SlGME2 lines. Taken together with the preferential expression of each SlGME gene in different tomato tissues, these results suggest sub-functionalization of SlGME1 and SlGME2 and their specialization for cell wall biosynthesis in specific tomato tissues.

Model-assisted analysis of sugar metabolism throughout tomato fruit development reveals enzyme and carrier properties in relation to vacuole expansion.

A kinetic model combining enzyme activity measurements and subcellular compartmentation was parameterized to fit the sucrose, hexose, and glucose-6-P contents of pericarp throughout tomato (Solanum lycopersicum) fruit development. The model was further validated using independent data obtained from domesticated and wild tomato species and on transgenic lines. A hierarchical clustering analysis of the calculated fluxes and enzyme capacities together revealed stage-dependent features. Cell division was characterized by a high sucrolytic activity of the vacuole, whereas sucrose cleavage during expansion was sustained by both sucrose synthase and neutral invertase, associated with minimal futile cycling. Most importantly, a tight correlation between flux rate and enzyme capacity was found for fructokinase and PPi-dependent phosphofructokinase during cell division and for sucrose synthase, UDP-glucopyrophosphorylase, and phosphoglucomutase during expansion, thus suggesting an adaptation of enzyme abundance to metabolic needs. In contrast, for most enzymes, flux rates varied irrespectively of enzyme capacities, and most enzymes functioned at <5% of their maximal catalytic capacity. One of the major findings with the model was the high accumulation of soluble sugars within the vacuole together with organic acids, thus enabling the osmotic-driven vacuole expansion that was found during cell division.

¹4C pulse labeling to estimate external fluxes and turnovers in primary metabolism.

Steady state (13)C-MFA is classically used to measure fluxes in complex metabolic networks. However, the modeling of steady state labeling allows the quantification of internal fluxes only and requires the estimation, by other methods, of the external fluxes, corresponding to substrate uptake (carbon input into the network) and to the production rate of compounds that accumulate within plant cells (network output). Additionally, it is not always possible to discriminate between different pathways that lead to the same label distribution. Methods to measure fluxes, based on direct measurements of pool size and on (14)C short-time labeling experiments, are described in this chapter. To illustrate this approach, we focus on the quantification of sucrose and starch turnovers.

Comparison between elementary flux modes analysis and 13C-metabolic fluxes measured in bacterial and plant cells.

BACKGROUND: (13)C metabolic flux analysis is one of the pertinent ways to compare two or more physiological states. From a more theoretical standpoint, the structural properties of metabolic networks can be analysed to explore feasible metabolic behaviours and to define the boundaries of steady state flux distributions. Elementary flux mode analysis is one of the most efficient methods for performing this analysis. In this context, recent approaches have tended to compare experimental flux measurements with topological network analysis. RESULTS: Metabolic networks describing the main pathways of central carbon metabolism were set up for a bacteria species (Corynebacterium glutamicum) and a plant species (Brassica napus) for which experimental flux maps were available. The structural properties of each network were then studied using the concept of elementary flux modes. To do this, coefficients of flux efficiency were calculated for each reaction within the networks by using selected sets of elementary flux modes. Then the relative differences - reflecting the change of substrate i.e. a sugar source for C. glutamicum and a nitrogen source for B. napus - of both flux efficiency and flux measured experimentally were compared. For both organisms, there is a clear relationship between these parameters, thus indicating that the network structure described by the elementary flux modes had captured a significant part of the metabolic activity in both biological systems. In B. napus, the extension of the elementary flux mode analysis to an enlarged metabolic network still resulted in a clear relationship between the change in the coefficients and that of the measured fluxes. Nevertheless, the limitations of the method to fit some particular fluxes are discussed. CONCLUSION: This consistency between EFM analysis and experimental flux measurements, validated on two metabolic systems allows us to conclude that elementary flux mode analysis could be a useful tool to complement (13)C metabolic flux analysis, by allowing the prediction of changes in internal fluxes before carbon labelling experiments.