Metabolic modelling revealed source-sink interactions between four segments of Setaria viridis leaves.

As in most plants, during their growth from immature to mature stages, the leaves of Setaria viridis, a model C4 bioenergy plant, have differential growth rates from the base (immature or growing) to the tip (most mature). In this study, we constructed a multi-segment C4 leaf metabolic model of S. viridis with two cell types (bundle sheath and mesophyll cells) across four leaf segments (base to tip). We incorporated differential growth rates for each leaf segment as constraints and integrated transcriptomic data as the objective function for our model simulation using flux balance analysis. The model was able to predict the exchanges of metabolites between immature and mature segments of the leaf and the distribution of the activities of biomass synthesis across those segments. Our model demonstrated the use of a modelling approach in studying the source-sink relationship within an organ and provided insights into the metabolic interactions across different parts of a leaf.

Spatial resolution of an integrated C4+CAM photosynthetic metabolism.

C4 and CAM photosynthesis have repeatedly evolved in plants over the past 30 million years. Because both repurpose the same set of enzymes but differ in their spatial and temporal deployment, they have long been considered as distinct and incompatible adaptations. Portulaca contains multiple C4 species that perform CAM when droughted. Spatially explicit analyses of gene expression reveal that C4 and CAM systems are completely integrated in Portulaca oleracea, with CAM and C4 carbon fixation occurring in the same cells and CAM-generated metabolites likely incorporated directly into the C4 cycle. Flux balance analysis corroborates the gene expression findings and predicts an integrated C4+CAM system under drought. This first spatially explicit description of a C4+CAM photosynthetic metabolism presents a potential new blueprint for crop improvement.

Development of a prediction score (ThyroCOVID) for identifying abnormal thyroid function in COVID-19 patients.

PURPOSE: Thyroid dysfunction in COVID-19 carries clinical and prognostic implications. In this study, we developed a prediction score (ThyroCOVID) for abnormal thyroid function (TFT) on admission amongst COVID-19 patients. METHODS: Consecutive COVID-19 patients admitted to Queen Mary Hospital were prospectively recruited during July 2020-May 2021. Thyroid-stimulating hormone (TSH), free thyroxine (fT4) and free triiodothyronine (fT3) were measured on admission. Multivariable logistic regression analysis was performed to identify independent determinants of abnormal TFTs. ThyroCOVID was developed based on a clinical model with the lowest Akaike information criteria. RESULTS: Five hundred and forty six COVID-19 patients were recruited (median age 50 years, 45.4% men, 72.9% mild disease on admission). 84 patients (15.4%) had abnormal TFTs on admission. Patients with abnormal TFTs were more likely to be older, have more comorbidities, symptomatic, have worse COVID-19 severity, higher SARS-CoV-2 viral loads and more adverse profile of acute-phase reactants, haematological and biochemical parameters. ThyroCOVID consisted of five parameters: symptoms (malaise), comorbidities (ischaemic heart disease/congestive heart failure) and laboratory parameters (lymphocyte count, C-reactive protein, and SARS-CoV-2 cycle threshold values). It was able to identify abnormal TFT on admission with an AUROC of 0.73 (95% CI 0.67-0.79). The optimal cut-off of 0.15 had a sensitivity of 75.0%, specificity of 65.2%, negative predictive value of 93.5% and positive predictive value of 28.1% in identifying abnormal TFTs on admission amongst COVID-19 patients. CONCLUSION: ThyroCOVID, a prediction score to identify COVID-19 patients at risk of having abnormal TFT on admission, was developed based on a cohort of predominantly non-severe COVID-19 patients.

A multiphase flux balance model reveals flexibility of central carbon metabolism in guard cells of C3 plants.

Experimental research into guard cell metabolism has revealed the roles of the accumulation of various metabolites in guard cell function, but a comprehensive understanding of their metabolism over the diel cycle is still incomplete due to the limitations of current experimental methods. In this study we constructed a four-phase flux balance model of guard cell metabolism to investigate the changes in guard cell metabolism over the diel cycle, including the day and night and stomatal opening and closing. Our model predicted metabolic flexibility in guard cells of C3 plants, showing that multiple metabolic processes can contribute to the synthesis and metabolism of malate and sucrose as osmolytes during stomatal opening and closing. Our model showed the possibility of guard cells adapting to varying light availability and sucrose uptake from the apoplast during the day by operating in a mixotrophic mode with a switch between sucrose synthesis via the Calvin-Benson cycle and sucrose degradation via the oxidative pentose phosphate pathway. During stomatal opening, our model predicted an alternative flux mode of the Calvin-Benson cycle with all dephosphorylating steps diverted to diphosphate-fructose-6-phosphate 1-phosphotransferase to produce inorganic pyrophosphate, which is used to pump protons across the tonoplast for the accumulation of osmolytes. An analysis of the energetics of the use of different osmolytes in guard cells showed that malate and Cl- are similarly efficient as the counterion of K+ during stomatal opening.

Metabolic Modeling of the C3-CAM Continuum Revealed the Establishment of a Starch/Sugar-Malate Cycle in CAM Evolution.

The evolution of Crassulacean acid metabolism (CAM) is thought to be along a C3-CAM continuum including multiple variations of CAM such as CAM cycling and CAM idling. Here, we applied large-scale constraint-based modeling to investigate the metabolism and energetics of plants operating in C3, CAM, CAM cycling, and CAM idling. Our modeling results suggested that CAM cycling and CAM idling could be potential evolutionary intermediates in CAM evolution by establishing a starch/sugar-malate cycle. Our model analysis showed that by varying CO2 exchange during the light period, as a proxy of stomatal conductance, there exists a C3-CAM continuum with gradual metabolic changes, supporting the notion that evolution of CAM from C3 could occur solely through incremental changes in metabolic fluxes. Along the C3-CAM continuum, our model predicted changes in metabolic fluxes not only through the starch/sugar-malate cycle that is involved in CAM photosynthetic CO2 fixation but also other metabolic processes including the mitochondrial electron transport chain and the tricarboxylate acid cycle at night. These predictions could guide engineering efforts in introducing CAM into C3 crops for improved water use efficiency.

Multi-tissue to whole plant metabolic modelling.

Genome-scale metabolic models have been successfully applied to study the metabolism of multiple plant species in the past decade. While most existing genome-scale modelling studies have focussed on studying the metabolic behaviour of individual plant metabolic systems, there is an increasing focus on combining models of multiple tissues or organs to produce multi-tissue models that allow the investigation of metabolic interactions between tissues and organs. Multi-tissue metabolic models were constructed for multiple plants including Arabidopsis, barley, soybean and Setaria. These models were applied to study various aspects of plant physiology including the division of labour between organs, source and sink tissue relationship, growth of different tissues and organs and charge and proton balancing. In this review, we outline the process of constructing multi-tissue genome-scale metabolic models, discuss the strengths and challenges in using multi-tissue models, review the current status of plant multi-tissue and whole plant metabolic models and explore the approaches for integrating genome-scale metabolic models into multi-scale plant models.

A mass and charge balanced metabolic model of Setaria viridis revealed mechanisms of proton balancing in C4 plants.

BACKGROUND: C4 photosynthesis is a key domain of plant research with outcomes ranging from crop quality improvement, biofuel production and efficient use of water and nutrients. A metabolic network model of C4 "lab organism" Setaria viridis with extensive gene-reaction associations can accelerate target identification for desired metabolic manipulations and thereafter in vivo validation. Moreover, metabolic reconstructions have also been shown to be a significant tool to investigate fundamental metabolic traits. RESULTS: A mass and charge balance genome-scale metabolic model of Setaria viridis was constructed, which was tested to be able to produce all major biomass components in phototrophic and heterotrophic conditions. Our model predicted an important role of the utilization of NH[Formula: see text] and NO[Formula: see text] ratio in balancing charges in plants. A multi-tissue extension of the model representing C4 photosynthesis was able to utilize NADP-ME subtype of C4 carbon fixation for the production of lignocellulosic biomass in stem, providing a tool for identifying gene associations for cellulose, hemi-cellulose and lignin biosynthesis that could be potential target for improved lignocellulosic biomass production. Besides metabolic engineering, our modeling results uncovered a previously unrecognized role of the 3-PGA/triosephosphate shuttle in proton balancing. CONCLUSIONS: A mass and charge balance model of Setaria viridis, a model C4 plant, provides the possibility of system-level investigation to identify metabolic characteristics based on stoichiometric constraints. This study demonstrated the use of metabolic modeling in identifying genes associated with the synthesis of particular biomass components, and elucidating new role of previously known metabolic processes.

A Dynamic Multi-Tissue Flux Balance Model Captures Carbon and Nitrogen Metabolism and Optimal Resource Partitioning During Arabidopsis Growth.

Plant metabolism is highly adapted in response to its surrounding for acquiring limiting resources. In this study, a dynamic flux balance modeling framework with a multi-tissue (leaf and root) diel genome-scale metabolic model of Arabidopsis thaliana was developed and applied to investigate the reprogramming of plant metabolism through multiple growth stages under different nutrient availability. The framework allowed the modeling of optimal partitioning of resources and biomass in leaf and root over diel phases. A qualitative flux map of carbon and nitrogen metabolism was identified which was consistent across growth phases under both nitrogen rich and limiting conditions. Results from the model simulations suggested distinct metabolic roles in nitrogen metabolism played by enzymes with different cofactor specificities. Moreover, the dynamic model was used to predict the effect of physiological or environmental perturbation on the growth of Arabidopsis leaves and roots.

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.

Modeling Rice Metabolism: From Elucidating Environmental Effects on Cellular Phenotype to Guiding Crop Improvement.

Crop productivity is severely limited by various biotic and abiotic stresses. Thus, it is highly needed to understand the underlying mechanisms of environmental stress response and tolerance in plants, which could be addressed by systems biology approach. To this end, high-throughput omics profiling and in silico modeling can be considered to explore the environmental effects on phenotypic states and metabolic behaviors of rice crops at the systems level. Especially, the advent of constraint-based metabolic reconstruction and analysis paves a way to characterize the plant cellular physiology under various stresses by combining the mathematical network models with multi-omics data. Rice metabolic networks have been reconstructed since 2013 and currently six such networks are available, where five are at genome-scale. Since their publication, these models have been utilized to systematically elucidate the rice abiotic stress responses and identify agronomic traits for crop improvement. In this review, we summarize the current status of the existing rice metabolic networks and models with their applications. Furthermore, we also highlight future directions of rice modeling studies, particularly stressing how these models can be used to contextualize the affluent multi-omics data that are readily available in the public domain. Overall, we envisage a number of studies in the future, exploiting the available metabolic models to enhance the yield and quality of rice and other food crops.

Factors affecting the deceased organ donation rate in the Chinese community: an audit of hospital medical records in Hong Kong.

INTRODUCTION: The number of actual donors per million population is the most commonly used metric to measure organ donation rates worldwide. It is deemed inadequate, however, because it does not take into account the potential donor pool. The aim of this study was to determine the true potential for solid organ donation from deceased brain-dead donors and the reasons for non-donation from potential donors in the Chinese community. METHODS: Medical records of all hospital deaths between 1 January and 31 December 2014 at a large regional hospital in Hong Kong were reviewed. Those who were on mechanical ventilation with documented brain injury and aged ≤75 years were classified as possible organ donors. The reasons why some potential organ donors did not become utilised organ donors were recorded and evaluated. RESULTS: Among 3659 patient deaths, 121 were classified as possible organ donors. The mean age of the possible organ donors was 59.4 years and 72.7% of them were male. The majority (88%) were from non-intensive care units. Of the 121 possible organ donors, 108 were classified as potential organ donors after excluding 13 unlikely to fulfil brain death criteria. Finally 11 patients became actual organ donors with an overall conversion rate of 10%. Reasons for non-donation included medical contra-indication (46%), failure to identify and inform organ donation coordinators (14%), failure of donor maintenance (11%), brain death diagnosis not established (18%), and refusal by relatives (11%). CONCLUSIONS: It is possible to increase the organ donation rate considerably by action at different stages of the donation process. Ongoing accurate audit of current practice is necessary.

Attention during functional tasks is associated with motor performance in children with developmental coordination disorder: A cross-sectional study.

This cross-sectional and exploratory study aimed to compare motor performance and electroencephalographic (EEG) attention levels in children with developmental coordination disorder (DCD) and those with typical development, and determine the relationship between motor performance and the real-time EEG attention level in children with DCD.Eighty-six children with DCD [DCD: n = 57; DCD and attention deficit hyperactivity disorder (ADHD): n = 29] and 99 children with typical development were recruited. Their motor performance was assessed with the Movement Assessment Battery for Children (MABC) and attention during the tasks of the MABC was evaluated by EEG.All children with DCD had higher MABC impairment scores and lower EEG attention scores than their peers (P < 0.05). After accounting for age, sex, body mass index, and physical activity level, the attention index remained significantly associated with the MABC total impairment score and explained 14.1% of the variance in children who had DCD but not ADHD (P = 0.009) and 17.5% of the variance in children with both DCD and ADHD (P = 0.007). Children with DCD had poorer motor performance and were less attentive to movements than their peers. Their poor motor performance may be explained by inattention.

Flux Balance Analysis of Plant Metabolism: The Effect of Biomass Composition and Model Structure on Model Predictions.

The biomass composition represented in constraint-based metabolic models is a key component for predicting cellular metabolism using flux balance analysis (FBA). Despite major advances in analytical technologies, it is often challenging to obtain a detailed composition of all major biomass components experimentally. Studies examining the influence of the biomass composition on the predictions of metabolic models have so far mostly been done on models of microorganisms. Little is known about the impact of varying biomass composition on flux prediction in FBA models of plants, whose metabolism is very versatile and complex because of the presence of multiple subcellular compartments. Also, the published metabolic models of plants differ in size and complexity. In this study, we examined the sensitivity of the predicted fluxes of plant metabolic models to biomass composition and model structure. These questions were addressed by evaluating the sensitivity of predictions of growth rates and central carbon metabolic fluxes to varying biomass compositions in three different genome-/large-scale metabolic models of Arabidopsis thaliana. Our results showed that fluxes through the central carbon metabolism were robust to changes in biomass composition. Nevertheless, comparisons between the predictions from three models using identical modeling constraints and objective function showed that model predictions were sensitive to the structure of the models, highlighting large discrepancies between the published models.

Advances of optical coherence tomography in myopia and pathologic myopia.

The natural course of high-axial myopia is variable and the development of pathologic myopia is not fully understood. Advancements in optical coherence tomography (OCT) technology have revealed peculiar intraocular structures in highly myopic eyes and unprecedented pathologies that cause visual impairment. New OCT findings include posterior precortical vitreous pocket and precursor stages of posterior vitreous detachment; peripapillary intrachoroidal cavitation; morphological patterns of scleral inner curvature and dome-shaped macula. Swept source OCT is capable of imaging deeper layers in the posterior pole for investigation of optic nerve pits, stretched and thinned lamina cribrosa, elongated dural attachment at posterior scleral canal, and enlargement of retrobulbar subarachnoid spaces. This has therefore enabled further evaluation of various visual field defects in high myopia and the pathogenesis of glaucomatous optic neuropathy. OCT has many potential clinical uses in managing visual impairing conditions in pathologic myopia. Understanding how retinal nerve fibers are redistributed in axial elongation will allow the development of auto-segmentation software for diagnosis and monitoring progression of glaucoma. OCT is indispensable in the diagnosis of various conditions associated with myopic traction maculopathy and monitoring of post-surgical outcomes. In addition, OCT is commonly used in the multimodal imaging assessment of myopic choroidal neovascularization. Biometry and topography of the retinal layers and choroid will soon be validated for the classification of myopic maculopathy for utilization in epidemiological studies as well as clinical trials.

Intrarenal abscess caused by community-associated methicillin-resistant Staphylococcus aureus in a transplanted kidney.

Emergence of multidrug-resistant bacteria is important in solid organ transplant recipients, because it can jeopardize patient and graft survival. Methicillin-resistant Staphylococcus aureus (MRSA) infections are not rare in kidney transplant recipients. On the other hand, infections related to community-associated MRSA (CA-MRSA) strains are seldom reported in the literature. Herein, we report the first patient, to our knowledge, with CA-MRSA renal graft abscess who was successfully treated with drainage and parenteral antibiotics.

A genome-scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism.

Tomato (Solanum lycopersicum L.) has been studied extensively due to its high economic value in the market, and high content in health-promoting antioxidant compounds. Tomato is also considered as an excellent model organism for studying the development and metabolism of fleshy fruits. However, the growth, yield and fruit quality of tomatoes can be affected by drought stress, a common abiotic stress for tomato. To investigate the potential metabolic response of tomato plants to drought, we reconstructed iHY3410, a genome-scale metabolic model of tomato leaf, and used this metabolic network to simulate tomato leaf metabolism. The resulting model includes 3410 genes and 2143 biochemical and transport reactions distributed across five intracellular organelles including cytosol, plastid, mitochondrion, peroxisome and vacuole. The model successfully described the known metabolic behaviour of tomato leaf under heterotrophic and phototrophic conditions. The in silico investigation of the metabolic characteristics for photorespiration and other relevant metabolic processes under drought stress suggested that: (i) the flux distributions through the mevalonate (MVA) pathway under drought were distinct from that under normal conditions; and (ii) the changes in fluxes through core metabolic pathways with varying flux ratio of RubisCO carboxylase to oxygenase may contribute to the adaptive stress response of plants. In addition, we improved on previous studies of reaction essentiality analysis for leaf metabolism by including potential alternative routes for compensating reaction knockouts. Altogether, the genome-scale model provides a sound framework for investigating tomato metabolism and gives valuable insights into the functional consequences of abiotic stresses.