Genome-wide characterization and expression analysis of α-amylase and ß-amylase genes underlying drought tolerance in cassava.

BACKGROUND: Starch hydrolysates are energy sources for plant growth and development, regulate osmotic pressure and transmit signals in response to both biological and abiotic stresses. The α-amylase (AMY) and the ß-amylase (BAM) are important enzymes that catalyze the hydrolysis of plant starch. Cassava (Manihot esculenta Crantz) is treated as one of the most drought-tolerant crops. However, the mechanisms of how AMY and BAM respond to drought in cassava are still unknown. RESULTS: Six MeAMY genes and ten MeBAM genes were identified and characterized in the cassava genome. Both MeAMY and MeBAM gene families contain four genes with alternative splicing. Tandem and fragment replications play important roles in the amplification of MeAMY and MeBAM genes. Both MeBAM5 and MeBAM10 have a BZR1/BES1 domain at the N-terminus, which may have transcription factor functions. The promoter regions of MeAMY and MeBAM genes contain a large number of cis-acting elements related to abiotic stress. MeAMY1, MeAMY2, MeAMY5, and MeBAM3 are proven as critical genes in response to drought stress according to their expression patterns under drought. The starch content, soluble sugar content, and amylase activity were significantly altered in cassava under different levels of drought stress. CONCLUSIONS: These results provide fundamental knowledge for not only further exploring the starch metabolism functions of cassava under drought stress but also offering new perspectives for understanding the mechanism of how cassava survives and develops under drought.

Molecular cloning and expression analysis of sucrose phosphate synthase genes in cassava (Manihot esculenta Crantz).

Sucrose phosphate synthase (SPS), a key rate-limiting enzyme in the sucrose biosynthesis pathway in plants, is encoded by a multi-gene family. Until recently, the identification and characterization of the SPS gene family have been performed for dozens of plant species; however, few studies have involved a comprehensive analysis of the SPS family members in tropical crops, such as cassava (Manihot esculenta Crantz). In the current study, five SPS genes (MeSPS1, MeSPS2, MeSPS3, MeSPS4, and MeSPS5) were isolated from cassava, and their sequence characteristics were comprehensively characterized. These MeSPS genes were found distributed on five chromosomes (Chr2, Chr14, Chr15, Chr16, and Chr18). Phylogenetic analysis showed that the MeSPS protein sequences were clustered into three families, together with other SPS sequences from both dicot and monocot species (families A, B, and C). The spatio-temporal expression pattern analysis of MeSPS genes showed a tissue-specific and partially overlapping expression pattern, with the genes mainly expressed in source tissues during cassava growth and development. Correlation analysis revealed that the expression of MeSPS genes correlated positively with root starch content, indicating that the expression of MeSPS genes might accelerate the rate of starch accumulation in the roots of cassava plants.

Energy-efficient substrate pasteurisation for combined production of shiitake mushroom (Lentinula edodes) and bioethanol.

Hot-air (75-100 °C) pasteurisation (HAP) of birch-wood-based substrate was compared to conventional autoclaving (steam at 121 °C) with regard to shiitake growth and yield, chemical composition of heat-pretreated material and spent mushroom substrate (SMS), enzymatic digestibility of glucan in SMS, and theoretical bioethanol yield. Compared to autoclaving, HAP resulted in faster mycelial growth, earlier fructification, and higher or comparable fruit-body yield. The heat pretreatment methods did not differ regarding the fractions of carbohydrate and lignin in pretreated material and SMS, but HAP typically resulted in lower fractions of extractives. Shiitake cultivation, which reduced the mass fraction of lignin to less than half of the initial without having any major impact on the mass fraction of glucan, enhanced enzymatic hydrolysis of glucan about four-fold. The choice of heating method did not affect enzymatic digestibility. Thus, HAP could substitute autoclaving and facilitate combined shiitake mushroom and bioethanol production.

Physiological and proteomic analysis on long-term drought resistance of cassava (Manihot esculenta Crantz).

Drought stress is one of the potent abiotic stress limiting cassava (Manihot esculenta) yield globally, but studies addressing both physiological and proteomic responses that how cassava crops can adjust their growth and metabolism under drought conditions are lacking. Combining leaf physiological and proteomic characteristics strongly allied with drought tolerance should results in enhanced drought tolerance in cassava crop. Therefore, the aims of this study were to explore the plant physiological and proteomic mechanisms involved in drought adaptation in cassava. Xinxuan 048 (XX048) was exposed to well-watered control (CK, relative soil water content (RSWC) as 80 ± 5%), mild drought stress (LD, RSWC as 65 ± 5%), moderate drought stress (MD, RSWC as 50 ± 5%) and severe drought stress (SD, RSWC as 35 ± 5%) from 30 days after planting. Under drought stress conditions, cassava plant showed a substantial decline in plant height, stem diameter, leaf number, leaf water content, the ratio of free water content to bound water content of leaf (FW/BW), net photosynthetic rate (Pn), intercellular CO2 concentration (Ci), stomatal conductance (Gs) and transpiration rate (Tr) compared with well watered plants. However, compared with control, leaf water content, SPAD value, cell membrane permeability, malondialdehyde (MDA), soluble sugar, protein proline content SOD and CAT activity were at peak under drought stress. The proteomic analysis revealed that among 3 339 identified proteins, drought stress increased and decreased abundance of 262 and 296 proteins, respectively, compared with control condition. These proteins were involved in carbohydrate energy metabolism, protein homeostasis, transcription, cell structure, cell membrane transport, signal transduction, stress and defense responses. These data not only provides a comprehensive dataset on overall proteomic changes in cassava leaves under drought stress, but also highlights the mechanisms by which euphorbiaceae plants can adapt to drought conditions.

NIR hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary study.

Commercial mushroom growth on substrate material produces a heterogeneous waste that can be used for bioenergy purposes. Hyperspectral imaging in the near-infrared (NHI) was used to experimentally study a number of spent mushroom substrate (SMS) packed samples under different conditions (wet vs. dry, open vs. plastic covering, and round or cuboid) and to explore the possibilities of direct characterization of the fresh substrate within a plastic bag. Principal components analysis (PCA) was used to remove the background of images, explore the important studied factors, and identify SMS and mycelia (Myc) based on the pixel clusters within the score plot. Overview PCA modeling indicated high moisture content caused the most significant effects on spectra followed by the uneven distribution of Myc and the plastic cover. There were well-separated pixel clusters for SMS and Myc under different conditions: dry, wet, or wet and plastic covering. The loading peaks of the related component and the second derivative of the mean spectra of pixel clusters of SMS and Myc indicated that there are chemical differences between SMS and Myc. Partial least squares discriminant analysis (PLS-DA) models were calculated and classification of SMS and Myc was successful, whether the materials were dry or wet. Peak shifts because of high moisture content and unexpected peaks from the plastic covering were found. Although the best results were obtained for dried cylinders, it was shown that almost equally good results could be obtained for the wet material and for the wet material covered by plastic. Furthermore, PLS-DA prediction showed that a side face hyperspectral image could represent the information for the entire SMS cylinder when Myc was removed. Thus, the combination of NHI and multivariate image analysis has great potential to develop calibration models to directly predict the contents of water, carbohydrates, lignin, and protein in wet and plastic-covered SMS cylinders.

Multivariate modelling on biomass properties of cassava stems based on an experimental design.

Based on a factorial experimental design (three locations × three cultivars × five harvest times × four replicates) conducted with the objective of investigating variations in fuel characteristics of cassava stem, a multivariate data matrix was formed which was composed of 180 samples and 10 biomass properties for each sample. The properties included as responses were two different calorific values and ash, N, S, Cl, P, K, Ca, and Mg content. Overall principal component analysis (PCA) revealed a strong clustering for the growing locations, but overlapping clusters for the cultivar types and almost no useful information about harvest times. PCA using a partitioned data set (60 × 10) for each location revealed a clustering of cultivars. This was confirmed by soft independent modelling of class analogy (SIMCA) and partial-least-squares discriminant analysis (PLS-DA), and indicated that the locations gave meaningful information about the differences in cultivar, whereas harvest time was not found to be a differentiating factor. Using the PLS technique, it was revealed that ash, K, and Cl content were the most important responses for PLS-DA models. Furthermore, using PLS regression of fuel and soil variables it was also revealed that fuel K and ash content were correlated with the soil P, Si, Ca, and K content, whereas fuel Cl content was correlated with soil pH and content of organic carbon, N, S, and Mg in the soil. Thus, the multivariate modelling used in this study reveals the possibility of performing rigorous analysis of a complex data set when an analysis of variance may not be successful.