A hybrid kinetic and constraint-based model of leaf metabolism allows predictions of metabolic fluxes in different environments.

While flux balance analysis (FBA) provides a framework for predicting steady-state leaf metabolic network fluxes, it does not readily capture the response to environmental variables without being coupled to other modelling formulations. To address this, we coupled an FBA model of 903 reactions of soybean (Glycine max) leaf metabolism with e-photosynthesis, a dynamic model that captures the kinetics of 126 reactions of photosynthesis and associated chloroplast carbon metabolism. Successful coupling was achieved in an iterative formulation in which fluxes from e-photosynthesis were used to constrain the FBA model and then, in turn, fluxes computed from the FBA model used to update parameters in e-photosynthesis. This process was repeated until common fluxes in the two models converged. Coupling did not hamper the ability of the kinetic module to accurately predict the carbon assimilation rate, photosystem II electron flux, and starch accumulation of field-grown soybean at two CO2 concentrations. The coupled model also allowed accurate predictions of additional parameters such as nocturnal respiration, as well as analysis of the effect of light intensity and elevated CO2 on leaf metabolism. Predictions included an unexpected decrease in the rate of export of sucrose from the leaf at high light, due to altered starch-sucrose partitioning, and altered daytime flux modes in the tricarboxylic acid cycle at elevated CO2 . Mitochondrial fluxes were notably different between growing and mature leaves, with greater anaplerotic, tricarboxylic acid cycle and mitochondrial ATP synthase fluxes predicted in the former, primarily to provide carbon skeletons and energy for protein synthesis.

Alternative Crassulacean Acid Metabolism Modes Provide Environment-Specific Water-Saving Benefits in a Leaf Metabolic Model.

Crassulacean acid metabolism (CAM) evolved in arid environments as a water-saving alternative to C3 photosynthesis. There is great interest in engineering more drought-resistant crops by introducing CAM into C3 plants. However, it is unknown whether full CAM or alternative water-saving modes would be more productive in the environments typically experienced by C3 crops. To study the effect of temperature and relative humidity on plant metabolism in the context of water saving, we coupled a time-resolved diel (based on a 24-h day-night cycle) model of leaf metabolism to an environment-dependent gas-exchange model. This combined model allowed us to study the emergence of CAM as a trade-off between leaf productivity and water saving. We show that vacuolar storage capacity in the leaf is a major determinant of the extent of CAM. Moreover, our model identified an alternative CAM cycle involving mitochondrial isocitrate dehydrogenase as a potential contributor to initial carbon fixation at night. Simulations across a range of environmental conditions show that the water-saving potential of CAM strongly depends on the daytime weather conditions and that the additional water-saving effect of carbon fixation by isocitrate dehydrogenase can reach 11% total water saving for the conditions tested.

Flux balance analysis of metabolism during growth by osmotic cell expansion and its application to tomato fruits.

Cell expansion is a significant contributor to organ growth and is driven by the accumulation of osmolytes to increase cell turgor pressure. Metabolic modelling has the potential to provide insights into the processes that underpin osmolyte synthesis and transport, but the main computational approach for predicting metabolic network fluxes, flux balance analysis, often uses biomass composition as the main output constraint and ignores potential changes in cell volume. Here we present growth-by-osmotic-expansion flux balance analysis (GrOE-FBA), a framework that accounts for both the metabolic and ionic contributions to the osmotica that drive cell expansion, as well as the synthesis of protein, cell wall and cell membrane components required for cell enlargement. Using GrOE-FBA, the metabolic fluxes in dividing and expanding cells were analysed, and the energetic costs for metabolite biosynthesis and accumulation in the two scenarios were found to be surprisingly similar. The expansion phase of tomato fruit growth was also modelled using a multiphase single-optimization GrOE-FBA model and this approach gave accurate predictions of the major metabolite levels throughout fruit development, as well as revealing a role for transitory starch accumulation in ensuring optimal fruit development.

Leaf Energy Balance Requires Mitochondrial Respiration and Export of Chloroplast NADPH in the Light.

Key aspects of leaf mitochondrial metabolism in the light remain unresolved. For example, there is debate about the relative importance of exporting reducing equivalents from mitochondria for the peroxisomal steps of photorespiration versus oxidation of NADH to generate ATP by oxidative phosphorylation. Here, we address this and explore energetic coupling between organelles in the light using a diel flux balance analysis model. The model included more than 600 reactions of central metabolism with full stoichiometric accounting of energy production and consumption. Different scenarios of energy availability (light intensity) and demand (source leaf versus a growing leaf) were considered, and the model was constrained by the nonlinear relationship between light and CO2 assimilation rate. The analysis demonstrated that the chloroplast can theoretically generate sufficient ATP to satisfy the energy requirements of the rest of the cell in addition to its own. However, this requires unrealistic high light use efficiency and, in practice, the availability of chloroplast-derived ATP is limited by chloroplast energy dissipation systems, such as nonphotochemical quenching, and the capacity of the chloroplast ATP export shuttles. Given these limitations, substantial mitochondrial ATP synthesis is required to fulfill cytosolic ATP requirements, with only minimal, or zero, export of mitochondrial reducing equivalents. The analysis also revealed the importance of exporting reducing equivalents from chloroplasts to sustain photorespiration. Hence, the chloroplast malate valve and triose phosphate-3-phosphoglycerate shuttle are predicted to have important metabolic roles, in addition to their more commonly discussed contribution to the avoidance of photooxidative stress.

Computational analysis of the productivity potential of CAM.

There is considerable interest in transferring crassulacean acid metabolism (CAM) to C3 crops to improve their water-use efficiency. However, because the CAM biochemical cycle is energetically costly, it is unclear what impact this would have on yield. Using diel flux balance analysis of the CAM and C3 leaf metabolic networks, we show that energy consumption is three-fold higher in CAM at night. However, this additional cost of CAM can be entirely offset by the carbon-concentrating effect of malate decarboxylation behind closed stomata during the day. Depending on the resultant rates of the carboxylase and oxygenase activities of rubisco, the productivity of the PEPCK-CAM subtype is 74-100% of the C3 network. We conclude that CAM does not impose a significant productivity penalty and that engineering CAM into C3 crops is likely to lead to a major increase in water-use efficiency without substantially affecting yield.

High-throughput collagen fingerprinting of intact microfaunal remains; a low-cost method for distinguishing between murine rodent bones.

RATIONALE: Microfaunal skeletal remains can be sensitive indicators of the contemporary ecosystem in which they are sampled and are often recovered in owl pellets in large numbers. Species identification of these remains can be obtained using a range of morphological criteria established for particular skeletal elements, but typically dominated by a reliance on cranial characters. However, this can induce biases under different environmental and taphonomic conditions. The aim of this research was to develop a high-throughput method of objectively identifying rodent remains from archaeological deposits using collagen fingerprinting, most notably the identification of rats from other myomorph rodents as a means to identify disturbances in the archaeofauna through the presence of invasive taxa not contemporary with the archaeological deposits. METHODS: Collagen was extracted from complete microfaunal skeletal remains in such a manner as to leave the bones morphologically intact (i.e., weaker concentration of acid than previously used over shorter length of time). Acid-soluble collagen was then ultrafiltered into ammonium bicarbonate and digested with trypsin prior to dilution in the MALDI matrix and acquisition of peptide mass fingerprints using a matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) mass spectrometer. RESULTS: Collagen fingerprinting was able to distinguish between Rattus, Mus, Apodemus and Micromys at the genus level; at the species level, R. rattus and R. norvegicus could be separated whereas A. flavicollis and A. sylvaticus could not. A total of 12,317 archaeological microvertebrate samples were screened for myomorph signatures but none were found to be invasive rats (Rattus) or mice (Mus). Of the contemporary murine fauna, no harvest mice (Micromys) were identified and only 24 field mouse (Apodemus) discovered. CONCLUSIONS: As a result, no evidence of recent bioturbation could be inferred from the faunal remains of these archaeological deposits. More importantly this work presents a method for high-throughput screening of specific taxa and is the first application of collagen fingerprinting to microfaunal remains of archaeological specimens.

TrypanoCyc: a community-led biochemical pathways database for Trypanosoma brucei.

The metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individual metabolic networks is increasing as we learn more about the enzymes that are active in particular cells under particular conditions and as technologies advance to allow detailed measurements of the cellular metabolome. Metabolic network databases are of increasing importance in allowing us to contextualise data sets emerging from transcriptomic, proteomic and metabolomic experiments. Here we present a dynamic database, TrypanoCyc (http://www.metexplore.fr/trypanocyc/), which describes the generic and condition-specific metabolic network of Trypanosoma brucei, a parasitic protozoan responsible for human and animal African trypanosomiasis. In addition to enabling navigation through the BioCyc-based TrypanoCyc interface, we have also implemented a network-based representation of the information through MetExplore, yielding a novel environment in which to visualise the metabolism of this important parasite.