Leaf cDNA-AFLP analysis reveals novel mechanisms for boron-induced alleviation of aluminum-toxicity in Citrus grandis seedlings.

Little information is available on the molecular mechanisms of boron (B)-induced alleviation of aluminum (Al)-toxicity. 'Sour pummelo' (Citrus grandis) seedlings were irrigated for 18 weeks with nutrient solution containing different concentrations of B (2.5 or 20µM H3BO3) and Al (0 or 1.2mM AlCl3·6H2O). B alleviated Al-induced inhibition in plant growth accompanied by lower leaf Al. We used cDNA-AFLP to isolate 127 differentially expressed genes from leaves subjected to B and Al interactions. These genes were related to signal transduction, transport, cell wall modification, carbohydrate and energy metabolism, nucleic acid metabolism, amino acid and protein metabolism, lipid metabolism and stress responses. The ameliorative mechanisms of B on Al-toxicity might be related to: (a) triggering multiple signal transduction pathways; (b) improving the expression levels of genes related to transport; (c) activating genes involved in energy production; and (d) increasing amino acid accumulation and protein degradation. Also, genes involved in nucleic acid metabolism, cell wall modification and stress responses might play a role in B-induced alleviation of Al-toxicity. To conclude, our findings reveal some novel mechanisms on B-induced alleviation of Al-toxicity at the transcriptional level in C. grandis leaves.

Leaf cDNA-AFLP analysis of two citrus species differing in manganese tolerance in response to long-term manganese-toxicity.

BACKGROUND: Very little is known about manganese (Mn)-toxicity-responsive genes in citrus plants. Seedlings of 'Xuegan' (Citrus sinensis) and 'Sour pummelo' (Citrus grandis) were irrigated for 17 weeks with nutrient solution containing 2 µM (control) or 600 µM (Mn-toxicity) MnSO4. The objectives of this study were to understand the mechanisms of citrus Mn-tolerance and to identify differentially expressed genes, which might be involved in Mn-tolerance. RESULTS: Under Mn-toxicity, the majority of Mn in seedlings was retained in the roots; C. sinensis seedlings accumulated more Mn in roots and less Mn in shoots (leaves) than C. grandis ones and Mn concentration was lower in Mn-toxicity C. sinensis leaves compared to Mn-toxicity C. grandis ones. Mn-toxicity affected C. grandis seedling growth, leaf CO2 assimilation, total soluble concentration, phosphorus (P) and magenisum (Mg) more than C. sinensis. Using cDNA-AFLP, we isolated 42 up-regulated and 80 down-regulated genes in Mn-toxicity C. grandis leaves. They were grouped into the following functional categories: biological regulation and signal transduction, carbohydrate and energy metabolism, nucleic acid metabolism, protein metabolism, lipid metabolism, cell wall metabolism, stress responses and cell transport. However, only 7 up-regulated and 8 down-regulated genes were identified in Mn-toxicity C. sinensis ones. The responses of C. grandis leaves to Mn-toxicity might include following several aspects: (1) accelerating leaf senescence; (2) activating the metabolic pathway related to ATPase synthesis and reducing power production; (3) decreasing cell transport; (4) inhibiting protein and nucleic acid metabolisms; (5) impairing the formation of cell wall; and (6) triggering multiple signal transduction pathways. We also identified many new Mn-toxicity-responsive genes involved in biological and signal transduction, carbohydrate and protein metabolisms, stress responses and cell transport. CONCLUSIONS: Our results demonstrated that C. sinensis was more tolerant to Mn-toxicity than C. grandis, and that Mn-toxicity affected gene expression far less in C. sinensis leaves. This might be associated with more Mn accumulation in roots and less Mn accumulation in leaves of Mn-toxicity C. sinensis seedlings than those of C. grandis seedlings. Our findings increase our understanding of the molecular mechanisms involved in the responses of plants to Mn-toxicity.

Research on remote sensing classification of fruit trees based on Sentinel-2 multi-temporal imageries.

Accurately obtaining the spatial distribution information of fruit tree planting is of great significance to the development of fruit tree growth monitoring, disease and pest control, and yield estimation. In this study, the Sentenel-2 multispectral remote sensing imageries of different months during the growth period of the fruit trees were used as the data source, and single month vegetation indices, accumulated monthly vegetation indices (∑VIs), and difference vegetation indices between adjacent months (∆VIs) were constructed as input variables. Four conventional vegetation indices of NDVI, PSRI, GNDVI, and RVI and four improved vegetation indices of NDVIre1, NDVIre2, NDVIre3, and NDVIre4 based on the red-edge band were selected to construct a decision tree classification model combined with machine learning technology. Through the analysis of vegetation indices under different treatments and different months, combined with the attribute of Feature_importances_, the vegetation indices of different periods with high contribution were selected as input features, and the Max_depth values of the decision tree model were determined by the hyperparameter learning curve. The results have shown that when the Max_depth value of the decision tree model of the vegetation indices under the three treatments was 6, 8, and 8, the model classification was the best. The accuracy of the three vegetation index processing models on the training set were 0.8936, 0.9153, and 0.8887, and the accuracy on the test set were 0.8355, 0.7611, and 0.7940, respectively. This method could be applied to remote sensing classification of fruit trees in a large area, and could provide effective technical means for monitoring fruit tree planting areas with medium and high resolution remote sensing imageries.

Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law.

Normalized difference vegetation index (NDVI) is one of the most important vegetation indices in crop remote sensing. It features a simple, fast, and non-destructive method and has been widely used in remote monitoring of crop growing status. Beer-Lambert law is widely used in calculating crop leaf area index (LAI), however, it is time-consuming detection and low in output. Our objective was to improve the accuracy of monitoring LAI through remote sensing by integrating NDVI and Beer-Lambert law. In this study, the Beer-Lambert law was firstly modified to construct a monitoring model with NDVI as the independent variable. Secondly, experimental data of wheat from different years and various plant types (erectophile, planophile and middle types) was used to validate the modified model. The results showed that at 130 DAS (days after sowing), the differences in NDVI, leaf area index (LAI) and extinction coefficient (k) of the three plant types with significantly different leaf orientation values (LOVs) reached the maximum. The NDVI of the planophile-type wheat reached saturation earlier than that of the middle and erectophile types. The undetermined parameters of the model (LAI = -ln (a1 × NDVI + b1)/(a2 × NDVI + b2)) were related to the plant type of wheat. For the erectophile-type cultivars (LOV ≥ 60°), the parameters for the modified model were, a1 = 0.306, a2 = -0.534, b1 = -0.065, and b2 = 0.541. For the middle-type cultivars (30° < LOV < 60°), the parameters were, a1 = 0.392, a2 = -0.881, b1 = 0.028, and b2 = 0.845. And for the planophile-type cultivars (LOV ≤ 30°), those parameters were, a1 = 0.596, a2 = -1.306, b1 = 0.014, and b2 = 1.130. Verification proved that the modified model based on integrating NDVI and Beer-Lambert law was better than Beer-Lambert law model only or NDVI-LAI direct model only. It was feasible to quantitatively monitor the LAI of different plant-type wheat by integrating NDVI and Beer-Lambert law, especially for erectophile-type wheat (R2 = 0.905, RMSE = 0.36, RE = 0.10). The monitoring model proposed in this study can accurately reflect the dynamic changes of plant canopy structure parameters, and provides a novel method for determining plant LAI.

Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat.

Remote sensing has been used as an important means of estimating crop production, especially for the estimation of crop yield in the middle and late growth period. In order to further improve the accuracy of estimating winter wheat yield through remote sensing, this study analyzed the quantitative relationship between satellite remote sensing variables obtained from HJ-CCD images and the winter wheat yield, and used the partial least square (PLS) algorithm to construct and validate the multivariate remote sensing models of estimating the yield. The research showed a close relationship between yield and most remote sensing variables. Significant multiple correlations were also recorded between most remote sensing variables. The optimal principal components numbers of PLS models used to estimate yield were 4. Green normalized difference vegetation index (GNDVI), optimized soil-adjusted vegetation index (OSAVI), normalized difference vegetation index (NDVI) and plant senescence reflectance index (PSRI) were sensitive variables for yield remote sensing estimation. Through model development and model validation evaluation, the yield estimation model's coefficients of determination (R2) were 0.81 and 0.74 respectively. The root mean square error (RMSE) were 693.9 kg ha-1 and 786.5 kg ha-1. It showed that the PLS algorithm model estimates the yield better than the linear regression (LR) and principal components analysis (PCA) algorithms. The estimation accuracy was improved by more than 20% than the LR algorithm, and was 13% higher than the PCA algorithm. The results could provide an effective way to improve the estimation accuracy of winter wheat yield by remote sensing, and was conducive to large-area application and promotion.

Mechanisms on boron-induced alleviation of aluminum-toxicity in Citrus grandis seedlings at a transcriptional level revealed by cDNA-AFLP analysis.

The physiological and biochemical mechanisms on boron (B)-induced alleviation of aluminum (B)-toxicity in plants have been examined in some details, but our understanding of the molecular mechanisms underlying these processes is very limited. In this study, we first used the cDNA-AFLP to investigate the gene expression patterns in Citrus grandis roots responsive to B and Al interactions, and isolated 100 differentially expressed genes. Results showed that genes related to detoxification of reactive oxygen species (ROS) and aldehydes (i.e., glutathione S-transferase zeta class-like isoform X1, thioredoxin M-type 4, and 2-alkenal reductase (NADP+-dependent)-like), metabolism (i.e., carboxylesterases and lecithin-cholesterol acyltransferase-like 4-like, nicotianamine aminotransferase A-like isoform X3, thiosulfate sulfurtransferase 18-like isoform X1, and FNR, root isozyme 2), cell transport (i.e., non-specific lipid-transfer protein-like protein At2g13820-like and major facilitator superfamily protein), Ca signal and hormone (i.e., calcium-binding protein CML19-like and IAA-amino acid hydrolase ILR1-like 4-like), gene regulation (i.e., Gag-pol polyprotein) and cell wall modification (i.e., glycosyl hydrolase family 10 protein) might play a role in B-induced alleviation of Al-toxicity. Our results are useful not only for our understanding of molecular processes associated with B-induced alleviation of Al-toxicity, but also for obtaining key molecular genes to enhance Al-tolerance of plants in the future.