An efficient LC-MS method for isomer separation and detection of sugars, phosphorylated sugars, and organic acids

Advances in Chromatographic and Mass Spectrometric Techniques for Analyzing Reducing Monosaccharides and Their Phosphates in Biological Samples

Reducing monosaccharides and their phosphates are critical metabolites in the central carbon metabolism pathway of living organisms. Variations in their content can indicate abnormalities in metabolic pathways and the onset of certain diseases, necessitating their analysis and detection. Reducing monosaccharides and their phosphates exhibit significant variations in content within biological samples and are present in many isomers, which makes the accurate quantification of reducing monosaccharides and their phosphates in biological samples a challenging task. Various analytical methods such as spectroscopy, fluorescence detection, colorimetry, nuclear magnetic resonance spectroscopy, sensor-based techniques, chromatography, and mass spectrometry are employed to detect monosaccharides and phosphates. In comparison, chromatography and mass spectrometry are highly favored for their ability to simultaneously analyze multiple components and their high sensitivity and selectivity. This review thoroughly evaluates the current chromatographic and mass spectrometric methods used for detecting reducing monosaccharides and their phosphates from 2013 to 2023, highlighting their efficacy and the advancements in these analytical technologies.

Research of saccharides and related biocomplexes: A review with recent techniques and applications

Saccharides and biocompounds as saccharide (sugar) complexes have various roles and biological functions in living organisms due to modifications via nucleophilic substitution, polymerization, and complex formation reactions. Mostly, mono-, di-, oligo-, and polysaccharides are stabilized to inactive glycosides, which are formed in metabolic pathways. Natural saccharides are important in food and environmental monitoring. Glycosides with various functionalities are significant in clinical and medical research. Saccharides are often studied with the chromatographic methods of hydrophilic interaction liquid chromatography and anion exchange chromatograpy, but also with capillary electrophoresis and mass spectrometry with their on-line coupling systems. Sample preparation is important in the identification of saccharide compounds. The cases discussed here focus on bioscience, clinical, and food applications.

The role of metabolomics in informing strategies for improving photosynthesis

Photosynthesis plays a vital role in acclimating to and mitigating climate change, providing food and energy security for a population that is constantly growing, and achieving an economy with zero carbon emissions. A thorough comprehension of the dynamics of photosynthesis, including its molecular regulatory network and limitations, is essential for utilizing it as a tool to boost plant growth, enhance crop yields, and support the production of plant biomass for carbon storage. Photorespiration constrains photosynthetic efficiency and contributes significantly to carbon loss. Therefore, modulating or circumventing photorespiration presents opportunities to enhance photosynthetic efficiency. Over the past eight decades, substantial progress has been made in elucidating the molecular basis of photosynthesis, photorespiration, and the key regulatory mechanisms involved, beginning with the discovery of the canonical Calvin-Benson-Bassham cycle. Advanced chromatographic and mass spectrometric technologies have allowed a comprehensive analysis of the metabolite patterns associated with photosynthesis, contributing to a deeper understanding of its regulation. In this review, we summarize the results of metabolomics studies that shed light on the molecular intricacies of photosynthetic metabolism. We also discuss the methodological requirements essential for effective analysis of photosynthetic metabolism, highlighting the value of this technology in supporting strategies aimed at enhancing photosynthesis.

SIMPEL: using stable isotopes to elucidate dynamics of context specific metabolism

The capacity to leverage high resolution mass spectrometry (HRMS) with transient isotope labeling experiments is an untapped opportunity to derive insights on context-specific metabolism, that is difficult to assess quantitatively. Tools are needed to comprehensively mine isotopologue information in an automated, high-throughput way without errors. We describe a tool, Stable Isotope-assisted Metabolomics for Pathway Elucidation (SIMPEL), to simplify analysis and interpretation of isotope-enriched HRMS datasets. The efficacy of SIMPEL is demonstrated through examples of central carbon and lipid metabolism. In the first description, a dual-isotope labeling experiment is paired with SIMPEL and isotopically nonstationary metabolic flux analysis (INST-MFA) to resolve fluxes in central metabolism that would be otherwise challenging to quantify. In the second example, SIMPEL was paired with HRMS-based lipidomics data to describe lipid metabolism based on a single labeling experiment. Available as an R package, SIMPEL extends metabolomics analyses to include isotopologue signatures necessary to quantify metabolic flux.

Physcomitrium patens response to elevated CO2 is flexible and determined by an interaction between sugar and nitrogen availability

Mosses hold a unique position in plant evolution and are crucial for protecting natural, long-term carbon storage systems such as permafrost and bogs. Due to small stature, mosses grow close to the soil surface and are exposed to high levels of CO2 , produced by soil respiration. However, the impact of elevated CO2 (eCO2 ) levels on mosses remains underexplored. We determined the growth responses of the moss Physcomitrium patens to eCO2 in combination with different nitrogen levels and characterized the underlying physiological and metabolic changes. Three distinct growth characteristics, an early transition to caulonema, the development of longer, highly pigmented rhizoids, and increased biomass, define the phenotypic responses of P. patens to eCO2 . Elevated CO2 impacts growth by enhancing the level of a sugar signaling metabolite, T6P. The quantity and form of nitrogen source influences these metabolic and phenotypic changes. Under eCO2 , P. patens exhibits a diffused growth pattern in the presence of nitrate, but ammonium supplementation results in dense growth with tall gametophores, demonstrating high phenotypic plasticity under different environments. These results provide a framework for comparing the eCO2 responses of P. patens with other plant groups and provide crucial insights into moss growth that may benefit climate change models.

Biochemical and Transcriptome Analyses Reveal a Stronger Capacity for Photosynthate Accumulation in Low-Tillering Rice Varieties

Moderate control of rice tillering and the development of rice varieties with large panicles are important topics for future high-yield rice breeding. Herein, we found that low-tillering rice varieties stopped tillering earlier and had a larger leaf area of the sixth leaf. Notably, at 28 days after sowing, the rice seedlings of the low-tillering group had an average single-culm above-ground biomass of 0.84 g, significantly higher than that of the multi-tillering group by 56.26%, and their NSC (non-structural carbohydrate) and starch contents in sheaths were increased by 43.34% and 97.75%, respectively. These results indicated that the low-tillering group of rice varieties had a stronger ability to store photosynthetic products in the form of starch in their sheaths, which was thus more beneficial for their large panicle development. The results of carbon and nitrogen metabolism analyses showed that the low-tillering group had a relatively strong carbon metabolism activity, which was more favorable for the accumulation of photosynthesis products and the following development of large panicles, while the multi-tillering group showed relatively strong nitrogen metabolism activity, which was more beneficial for the development and formation of new organs, such as tillers. Accordingly, in the low-tillering rice varieties, the up-regulated genes were enriched in the pathways mainly related to the synthesis of carbohydrates, while the down-regulated genes were mainly enriched in the nitrogen metabolism pathways. This study provides new insights into the mechanism of rice tillering regulation and promotes the development of new varieties with ideal plant types.

U-13C-glucose incorporation into source leaves of Brassica napus highlights light-dependent regulations of metabolic fluxes within central carbon metabolism

Plant central carbon metabolism comprises several important metabolic pathways acting together to support plant growth and yield establishment. Despite the emergence of 13C-based dynamic approaches, the regulation of metabolic fluxes between light and dark conditions has not yet received sufficient attention for agronomically relevant plants. Here, we investigated the impact of light/dark conditions on carbon allocation processes within central carbon metabolism of Brassica napus after U-13C-glucose incorporation into leaf discs. Leaf gas-exchanges and metabolite contents were weakly impacted by the leaf disc method and the incorporation of glucose. 13C-analysis by GC-MS showed that U-13C-glucose was converted to fructose for de novo biosynthesis of sucrose at similar rates in both light and dark conditions. However, light conditions led to a reduced commitment of glycolytic carbons towards respiratory substrates (pyruvate, alanine, malate) and TCA cycle intermediates compared to dark conditions. Analysis of 13C-enrichment at the isotopologue level and metabolic pathway isotopic tracing reconstructions identified the contribution of multiple pathways to serine biosynthesis in light and dark conditions. However, the direct contribution of the glucose-6-phosphate shunt to serine biosynthesis was not observed. Our results also provided isotopic evidences for an active metabolic connection between the TCA cycle, glycolysis and photorespiration in light conditions through a rapid reallocation of TCA cycle decarboxylations back to the TCA cycle through photorespiration and glycolysis. Altogether, these results suggest the active coordination of core metabolic pathways across multiple compartments to reorganize C-flux modes.

Inhibitory Effects of Ehretia tinifolia Extract on the Excessive Oxidative and Inflammatory Responses in Lipopolysaccharide-Stimulated Mouse Kupffer Cells

Ehretia tinifolia (E. tinifolia) L., an evergreen tree with substantial biological activity, including antioxidant and anti-inflammatory effects, has been used in many herbal and traditional medicines. To elucidate its antioxidant and anti-inflammatory activity and the underlying mechanisms, we applied a methanol extract of E. tinifolia (ETME) to lipopolysaccharide (LPS)-stimulated mouse immortalized Kupffer cells. ETME suppressed the LPS-induced increase in nitric oxide, a mediator for oxidative stress and inflammation, and restored LPS-mediated depletion of total glutathione level by stabilizing antioxidative nuclear factor erythroid 2-related factor 2 (Nrf2) and the subsequent increase in heme oxygenase-1 levels. Furthermore, ETME inhibited the LPS-induced production of pro-inflammatory cytokines, including tumor necrosis factor-α, interleukin (IL)-1β, and IL-6. The inhibitory effects of ETME on pro-inflammatory responses were regulated by ETME-mediated dephosphorylation of mitogen-activated protein kinases (MAPKs: p38, p44/p42, and stress-associated protein kinase/c-Jun N-terminal kinase) and inhibition of nuclear localization of nuclear factor kappa B (NF-κB). These results suggest that ETME is a possible candidate for protecting Kupffer cells from LPS-mediated oxidative stress and excessive inflammatory responses by activating antioxidant Nrf2/HO-1 and inhibiting pro-inflammatory NF-κB and MAPKs, respectively.

Expression of malic enzyme reveals subcellular carbon partitioning for storage reserve production in soybeans

Central metabolism produces amino and fatty acids for protein and lipids that establish seed value. Biosynthesis of storage reserves occurs in multiple organelles that exchange central intermediates including two essential metabolites, malate, and pyruvate that are linked by malic enzyme. Malic enzyme can be active in multiple subcellular compartments, partitioning carbon and reducing equivalents for anabolic and catabolic requirements. Prior studies based on isotopic labeling and steady-state metabolic flux analyses indicated malic enzyme provides carbon for fatty acid biosynthesis in plants, though genetic evidence confirming this role is lacking. We hypothesized that increasing malic enzyme flux would alter carbon partitioning and result in increased lipid levels in soybeans. Homozygous transgenic soybean plants expressing Arabidopsis malic enzyme alleles, targeting the translational products to plastid or outside the plastid during seed development, were verified by transcript and enzyme activity analyses, organelle proteomics, and transient expression assays. Protein, oil, central metabolites, cofactors, and acyl-acyl carrier protein (ACPs) levels were quantified overdevelopment. Amino and fatty acid levels were altered resulting in an increase in lipids by 0.5-2% of seed biomass (i.e. 2-9% change in oil). Subcellular targeting of a single gene product in central metabolism impacts carbon and reducing equivalent partitioning for seed storage reserves in soybeans.

Metabolic synergy in Camelina reproductive tissues for seed development

Photosynthesis in fruits is well documented, but its contribution to seed development and yield remains largely unquantified. In oilseeds, the pods are green and elevated with direct access to sunlight. With ¹³C labeling in planta and through an intact pod labeling system, a unique multi-tissue comprehensive flux model mechanistically described how pods assimilate up to one-half (33 to 45%) of seed carbon by proximal photosynthesis in Camelina sativa. By capturing integrated tissue metabolism, the studies reveal the contribution of plant architecture beyond leaves, to enable seed filling and maximize the number of viable seeds. The latent capacity of the pod wall in the absence of leaves contributes approximately 79% of seed biomass, supporting greater seed sink capacity and higher theoretical yields that suggest an opportunity for crop productivity gains.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.