Unlocking conserved and diverged metabolic characteristics in cassava carbon assimilation via comparative genomics approach

Molecular Biology Applications of Psychrophilic Enzymes: Adaptations, Advantages, Expression, and Prospective

Psychrophilic enzymes are primarily produced by microorganisms from extremely low-temperature environments which are known as psychrophiles. Their high efficiency at low temperatures and easy heat inactivation property have attracted extensive attention from various food and industrial bioprocesses. However, the application of these enzymes in molecular biology is still limited. In a previous review, the applications of psychrophilic enzymes in industries such as the detergent additives, the food additives, the bioremediation, and the pharmaceutical medicine, and cosmetics have been discussed. In this review, we discuss the main cold adaptation characteristics of psychrophiles and psychrophilic enzymes, as well as the relevant information on different psychrophilic enzymes in molecular biology. We summarize the mining and screening methods of psychrophilic enzymes. We finally recap the expression of psychrophilic enzymes. We aim to provide a reference process for the exploration and expression of new generation of psychrophilic enzymes.

MRPS36 provides a structural link in the eukaryotic 2-oxoglutarate dehydrogenase complex

The tricarboxylic acid cycle is the central pathway of energy production in eukaryotic cells and plays a key part in aerobic respiration throughout all kingdoms of life. One of the pivotal enzymes in this cycle is 2-oxoglutarate dehydrogenase complex (OGDHC), which generates NADH by oxidative decarboxylation of 2-oxoglutarate to succinyl-CoA. OGDHC is a megadalton protein complex originally thought to be assembled from three catalytically active subunits (E1o, E2o, E3). In fungi and animals, however, the protein MRPS36 has more recently been proposed as a putative additional component. Based on extensive cross-linking mass spectrometry data supported by phylogenetic analyses, we provide evidence that MRPS36 is an important member of the eukaryotic OGDHC, with no prokaryotic orthologues. Comparative sequence analysis and computational structure predictions reveal that, in contrast with bacteria and archaea, eukaryotic E2o does not contain the peripheral subunit-binding domain (PSBD), for which we propose that MRPS36 evolved as an E3 adaptor protein, functionally replacing the PSBD. We further provide a refined structural model of the complete eukaryotic OGDHC of approximately 3.45 MDa with novel mechanistic insights.

Transcriptome integrated metabolic modeling of carbon assimilation underlying storage root development in cassava

The existing genome-scale metabolic model of carbon metabolism in cassava storage roots, rMeCBM, has proven particularly resourceful in exploring the metabolic basis for the phenotypic differences between high and low-yield cassava cultivars. However, experimental validation of predicted metabolic fluxes by carbon labeling is quite challenging. Here, we incorporated gene expression data of developing storage roots into the basic flux-balance model to minimize infeasible metabolic fluxes, denoted as rMeCBMx, thereby improving the plausibility of the simulation and predictive power. Three different conceptual algorithms, GIMME, E-Flux, and HPCOF were evaluated. The rMeCBMx-HPCOF model outperformed others in predicting carbon fluxes in the metabolism of storage roots and, in particular, was highly consistent with transcriptome of high-yield cultivars. The flux prediction was improved through the oxidative pentose phosphate pathway in cytosol, as has been reported in various studies on root metabolism, but hardly captured by simple FBA models. Moreover, the presence of fluxes through cytosolic glycolysis and alanine biosynthesis pathways were predicted with high consistency with gene expression levels. This study sheds light on the importance of prediction power in the modeling of complex plant metabolism. Integration of multi-omics data would further help mitigate the ill-posed problem of constraint-based modeling, allowing more realistic simulation.

Transcriptome analysis of sugar beet (Beta vulgaris L.) in response to alkaline stress

RNA-seq was used to analyze the transcriptional changes in sugar beet (Beta vulgaris L.) triggered by alkaline solution to elucidate the molecular mechanism underlying alkaline tolerance in sugar beet. Several differentially expressed genes related to stress tolerance were identified. Our results provide a valuable resource for the breeding of new germplasms with high alkaline tolerance. Alkalinity is a highly stressful environmental factor that limits plant growth and production. Sugar beet own the ability to acclimate to various abiotic stresses, especially salt and alkaline stress. Although substantial previous studies on response of sugar beet to saline stress has been conducted, the expressions of alkali-responsive genes in sugar beet have not been comprehensively investigated. In this study, we conducted transcriptome analysis of leaves in sugar beet seedlings treated with alkaline solutions for 0 day (control, C), 3 days (short-term alkaline treatment, ST) and 7 days (long-term alkaline treatment, LT). The clean reads were obtained and assembled into 25,507 unigenes. Among them, 975 and 383 differentially expressed genes (DEGs) were identified in the comparison groups ST_vs_C and LT_vs_C, respectively. Gene ontology (GO) analysis revealed that oxidation-reduction process and lipid metabolic process were the most enriched GO term among the DEGs in ST_vs_C and LT_vs_C, respectively. According to Kyoto Encyclopedia of Genes and Genomes pathway, carbon fixation in photosynthetic organisms pathway were significantly enriched under alkaline stress. Besides, expression level of genes encoding D-3-phosphoglycerate dehydrogenase 1, glutamyl-tRNA reductase 1, fatty acid hydroperoxide lyase, ethylene-insensitive protein 2, metal tolerance protein 11 and magnesium-chelatase subunit ChlI, etc., were significantly altered under alkaline stress. Additionally, among the DEGs, 136 were non-annotated genes and 24 occurred with differential alternative splicing. Our results provide a valuable resource on alkali-responsive genes and should benefit the improvement of alkaline stress tolerance in sugar beet.

Understanding carbon utilization routes between high and low starch-producing cultivars of cassava through Flux Balance Analysis

Analysis of metabolic flux was used for system level assessment of carbon partitioning in Kasetsart 50 (KU50) and Hanatee (HN) cassava cultivars to understand the metabolic routes for their distinct phenotypes. First, the constraint-based metabolic model of cassava storage roots, rMeCBM, was developed based on the carbon assimilation pathway of cassava. Following the subcellular compartmentalization and curation to ensure full network connectivity and reflect the complexity of eukaryotic cells, cultivar specific data on sucrose uptake and biomass synthesis were input, and rMeCBM model was used to simulate storage root growth in KU50 and HN. Results showed that rMeCBM-KU50 and rMeCBM-HN models well imitated the storage root growth. The flux-sum analysis revealed that both cultivars utilized different metabolic precursors to produce energy in plastid. More carbon flux was invested in the syntheses of carbohydrates and amino acids in KU50 than in HN. Also, KU50 utilized less flux for respiration and less energy to synthesize one gram of dry storage root. These results may disclose metabolic potential of KU50 underlying its higher storage root and starch yield over HN. Moreover, sensitivity analysis indicated the robustness of rMeCBM model. The knowledge gained might be useful for identifying engineering targets for cassava yield improvement.