Transcriptome Analysis of Sesame (Sesamum indicum L.) Reveals the LncRNA and mRNA Regulatory Network Responding to Low Nitrogen Stress.

Nitrogen is one of the important factors restricting the development of sesame planting and industry in China. Cultivating sesame varieties tolerant to low nitrogen is an effective way to solve the problem of crop nitrogen deficiency. To date, the mechanism of low nitrogen tolerance in sesame has not been elucidated at the transcriptional level. In this study, two sesame varieties Zhengzhi HL05 (ZZ, nitrogen efficient) and Burmese prolific (MD, nitrogen inefficient) in low nitrogen were used for RNA-sequencing. A total of 3964 DEGs (differentially expressed genes) and 221 DELs (differentially expressed lncRNAs) were identified in two sesame varieties at 3d and 9d after low nitrogen stress. Among them, 1227 genes related to low nitrogen tolerance are mainly located in amino acid metabolism, starch and sucrose metabolism and secondary metabolism, and participate in the process of transporter activity and antioxidant activity. In addition, a total of 209 pairs of lncRNA-mRNA were detected, including 21 pairs of trans and 188 cis. WGCNA (weighted gene co-expression network analysis) analysis divided the obtained genes into 29 modules; phenotypic association analysis identified three low-nitrogen response modules; through lncRNA-mRNA co-expression network, a number of hub genes and cis/trans-regulatory factors were identified in response to low-nitrogen stress including GS1-2 (glutamine synthetase 1-2), PAL (phenylalanine ammonia-lyase), CHS (chalcone synthase, CHS), CAB21 (chlorophyll a-b binding protein 21) and transcription factors MYB54, MYB88 and NAC75 and so on. As a trans regulator, lncRNA MSTRG.13854.1 affects the expression of some genes related to low nitrogen response by regulating the expression of MYB54, thus responding to low nitrogen stress. Our research is the first to provide a more comprehensive understanding of DEGs involved in the low nitrogen stress of sesame at the transcriptome level. These results may reveal insights into the molecular mechanisms of low nitrogen tolerance in sesame and provide diverse genetic resources involved in low nitrogen tolerance research.

Comparative physiological and transcriptomic analysis of sesame cultivars with different tolerance responses to heat stress.

High temperature is the main factor affecting plant growth and can cause plant growth inhibition and yield reduction. Here, seedlings of two contrasting sesame varieties, i.e., Zheng Taizhi 3 (heat-tolerant) and SP19 (heat-sensitive), were treated at 43 °C for 10 days. The results showed that the relative electrical conductivity, hydrogen peroxide levels, and superoxide anion radical levels of both varieties increased significantly under high temperature stress. Additionally, dry matter accumulation and chlorophyll content decreased significantly, and the activities of peroxidase (POD), catalase (CAT), and superoxide dismutase (SOD) increased. However, under HT stress, the content of reactive oxygen species in Zheng Taizhi 3 was lower than that in SP19, and the activities of SOD, CAT, and POD as well as the chlorophyll content in Zheng Taizhi 3 were higher than those in SP19. Comparative transcriptome analysis identified 6736 differentially expressed genes (DEGs); 5526 DEGs (2878 up and 2648 down) were identified in Zheng Taizhi 3, and 5186 DEGs (2695 up and 2491 down) were identified in SP19, with 3976 overlapping DEGs. These DEGs included stress tolerance-related heat-shock proteins, as well as genes related to carbohydrate and energy metabolism, signal transduction, endoplasmic reticulum protein processing, amino acid metabolism, and secondary metabolism. Overall, our results showed that the heat tolerance of Zheng Taizhi 3 was attributed to a stronger antioxidant defense system, enabling the variety to avoid oxidative damage compared with the heat-sensitive SP19. Moreover, some specifically expressed and high-abundance genes in Zheng Taizhi 3 were involved in regulatory mechanisms related to heat tolerance, including plant hormone signal transduction and heat shock protein regulation, thereby enhancing heat tolerance. The study contributes to a deeper understanding of the underlying complex molecular mechanisms involved in the responses of sesame seedlings to heat stress and provides a potential strategy for heat-resistant new varieties. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-022-01195-3.

ROBUSTNESS OF MORPHOGEN GRADIENTS WITH "BUCKET BRIGADE" TRANSPORT THROUGH MEMBRANE-ASSOCIATED NON-RECEPTORS.

Robust multiple-fate morphogen gradients are essential for embryo development. Here, we analyze mathematically a model of morphogen gradient (such as Dpp in Drosophila wing imaginal disc) formation in the presence of non-receptors with both diffusion of free morphogens and the movement of morphogens bound to non-receptors. Under the assumption of rapid degradation of unbound morphogen, we introduce a method of functional boundary value problem and prove the existence, uniqueness and linear stability of a biologically acceptable steady-state solution. Next, we investigate the robustness of this steady-state solution with respect to significant changes in the morphogen synthesis rate. We prove that the model is able to produce robust biological morphogen gradients when production and degradation rates of morphogens are large enough and non-receptors are abundant. Our results provide mathematical and biological insight to a mechanism of achieving stable robust long distance morphogen gradients. Key elements of this mechanism are rapid turnover of morphogen to non-receptors of neighoring cells resulting in significant degradation and transport of non-receptor-morphogen complexes, the latter moving downstream through a "bucket brigade" process.

Cytological, genetic, and proteomic analysis of a sesame (Sesamum indicum L.) mutant Siyl-1 with yellow-green leaf color.

BACKGROUND: Both photosynthetic pigments and chloroplasts in plant leaf cells play an important role in deciding on the photosynthetic capacity and efficiency in plants. Systematical investigating the regulatory mechanism of chloroplast development and chlorophyll (Chl) content variation is necessary for clarifying the photosynthesis mechanism for crops. OBJECTIVE: This study aims to explore the critical regulatory mechanism of leaf color mutation in a yellow-green leaf sesame mutant Siyl-1. METHODS: We performed the genetic analysis of the yellow-green leaf color mutation using the F2 population of the mutant Siyl-1. We compared the morphological structure of the chloroplasts, chlorophyll content of the three genotypes of the mutant F2 progeny. We performed the two-dimensional gel electrophoresis (2-DE) and compared the protein expression variation between the mutant progeny and the wild type. RESULTS: Genetic analysis indicated that there were 3 phenotypes of the F2 population of the mutant Siyl-1, i.e., YY type with light-yellow leaf color (lethal); Yy type with yellow-green leaf color, and yy type with normal green leaf color. The yellow-green mutation was controlled by an incompletely dominant nuclear gene, Siyl-1. Compared with the wild genotype, the chloroplast number and the morphological structure in YY and Yy mutant lines varied evidently. The chlorophyll content also significantly decreased (P < 0.05). The 2-DE comparison showed that there were 98 differentially expressed proteins (DEPs) among YY, Yy, and yy lines. All the 98 DEPs were classified into 5 functional groups. Of which 82.7% DEPs proteins belonged to the photosynthesis and energy metabolism group. CONCLUSION: The results revealed the genetic character of yellow-green leaf color mutant Siyl-1. 98 DEPs were found in YY and Yy mutant compared with the wild genotype. The regulation pathway related with the yellow leaf trait mutation in sesame was analyzed for the first time. The findings supplied the basic theoretical and gene basis for leaf color and chloroplast development mechanism in sesame.

V2O5-C-SnO2 Hybrid Nanobelts as High Performance Anodes for Lithium-ion Batteries.

The superior performance of metal oxide nanocomposites has introduced them as excellent candidates for emerging energy sources, and attracted significant attention in recent years. The drawback of these materials is their inherent structural pulverization which adversely impacts their performance and makes the rational design of stable nanocomposites a great challenge. In this work, functional V2O5-C-SnO2 hybrid nanobelts (VCSNs) with a stable structure are introduced where the ultradispersed SnO2 nanocrystals are tightly linked with glucose on the V2O5 surface. The nanostructured V2O5 acts as a supporting matrix as well as an active electrode component. Compared with existing carbon-V2O5 hybrid nanobelts, these hybrid nanobelts exhibit a much higher reversible capacity and architectural stability when used as anode materials for lithium-ion batteries. The superior cyclic performance of VCSNs can be attributed to the synergistic effects of SnO2 and V2O5. However, limited data are available for V2O5-based anodes in lithium-ion battery design.

Semi-implicit Integration Factor Methods on Sparse Grids for High-Dimensional Systems.

Numerical methods for partial differential equations in high-dimensional spaces are often limited by the curse of dimensionality. Though the sparse grid technique, based on a one-dimensional hierarchical basis through tensor products, is popular for handling challenges such as those associated with spatial discretization, the stability conditions on time step size due to temporal discretization, such as those associated with high-order derivatives in space and stiff reactions, remain. Here, we incorporate the sparse grids with the implicit integration factor method (IIF) that is advantageous in terms of stability conditions for systems containing stiff reactions and diffusions. We combine IIF, in which the reaction is treated implicitly and the diffusion is treated explicitly and exactly, with various sparse grid techniques based on the finite element and finite difference methods and a multi-level combination approach. The overall method is found to be efficient in terms of both storage and computational time for solving a wide range of PDEs in high dimensions. In particular, the IIF with the sparse grid combination technique is flexible and effective in solving systems that may include cross-derivatives and non-constant diffusion coefficients. Extensive numerical simulations in both linear and nonlinear systems in high dimensions, along with applications of diffusive logistic equations and Fokker-Planck equations, demonstrate the accuracy, efficiency, and robustness of the new methods, indicating potential broad applications of the sparse grid-based integration factor method.

An Integration Factor Method for Stochastic and Stiff Reaction-Diffusion Systems.

Stochastic effects are often present in the biochemical systems involving reactions and diffusions. When the reactions are stiff, existing numerical methods for stochastic reaction diffusion equations require either very small time steps for any explicit schemes or solving large nonlinear systems at each time step for the implicit schemes. Here we present a class of semi-implicit integration factor methods that treat the diffusion term exactly and reaction implicitly for a system of stochastic reaction-diffusion equations. Our linear stability analysis shows the advantage of such methods for both small and large amplitudes of noise. Direct use of the method to solving several linear and nonlinear stochastic reaction-diffusion equations demonstrates good accuracy, efficiency, and stability properties. This new class of methods, which are easy to implement, will have broader applications in solving stochastic reaction-diffusion equations arising from models in biology and physical sciences.

Array-representation Integration Factor Method for High-dimensional Systems.

High order spatial derivatives and stiff reactions often introduce severe temporal stability constraints on the time step in numerical methods. Implicit integration method (IIF) method, which treats diffusion exactly and reaction implicitly, provides excellent stability properties with good efficiency by decoupling the treatment of reactions and diffusions. One major challenge for IIF is storage and calculation of the potential dense exponential matrices of the sparse discretization matrices resulted from the linear differential operators. Motivated by a compact representation for IIF (cIIF) for Laplacian operators in two and three dimensions, we introduce an array-representation technique for efficient handling of exponential matrices from a general linear differential operator that may include cross-derivatives and non-constant diffusion coefficients. In this approach, exponentials are only needed for matrices of small size that depend only on the order of derivatives and number of discretization points, independent of the size of spatial dimensions. This method is particularly advantageous for high dimensional systems, and it can be easily incorporated with IIF to preserve the excellent stability of IIF. Implementation and direct simulations of the array-representation compact IIF (AcIIF) on systems, such as Fokker-Planck equations in three and four dimensions and chemical master equations, in addition to reaction-diffusion equations, show efficiency, accuracy, and robustness of the new method. Such array-presentation based on methods may have broad applications for simulating other complex systems involving high-dimensional data.