RESUMO
Submergence and drought stresses are the main constraints to crop production worldwide. MicroRNAs (miRNAs) are known to play a major role in plant response to various stresses. In this study, we analyzed the expression of maize and teosinte miRNAs by high-throughput sequencing of small RNA libraries in maize and its ancestor teosinte (Zea mays ssp. parviglumis), under submergence, drought, and alternated stress. We found that the expression patterns of 67 miRNA sequences representing 23 miRNA families in maize and other plants were regulated by submergence or drought. miR159a, miR166b, miR167c, and miR169c were downregulated by submergence in both plants but more severely in maize. miR156k and miR164e were upregulated by drought in teosinte but downregulated in maize. Small RNA profiling of teosinte subject to alternate treatments with drought and submergence revealed that submergence as the first stress attenuated the response to drought, while drought being the first stress did not alter the response to submergence. The miRNAs identified herein, and their potential targets, indicate that control of development, growth, and response to oxidative stress could be crucial for adaptation and that there exists evolutionary divergence between these two subspecies in miRNA response to abiotic stresses.
RESUMO
Research on the carbon cycle of coastal marine systems has been of wide concern recently. Accurate knowledge of the temporal and spatial distributions of sea-surface partial pressure (pCO2) can reflect the seasonal and spatial heterogeneity of CO2 flux and is, therefore, essential for quantifying the ocean's role in carbon cycling. However, it is difficult to use one model to estimate pCO2 and determine its controlling variables for an entire region due to the prominent spatiotemporal heterogeneity of pCO2 in coastal areas. Cubist is a commonly-used model for zoning; thus, it can be applied to the estimation and regional analysis of pCO2 in the Gulf of Mexico (GOM). A cubist model integrated with satellite images was used here to estimate pCO2 in the GOM, a river-dominated coastal area, using satellite products, including chlorophyll-a concentration (Chl-a), sea-surface temperature (SST) and salinity (SSS), and the diffuse attenuation coefficient at 490 nm (Kd-490). The model was based on a semi-mechanistic model and integrated the high-accuracy advantages of machine learning methods. The overall performance showed a root mean square error (RMSE) of 8.42 µatm with a coefficient of determination (R2) of 0.87. Based on the heterogeneity of environmental factors, the GOM area was divided into 6 sub-regions, consisting estuaries, near-shores, and open seas, reflecting a gradient distribution of pCO2. Factor importance and correlation analyses showed that salinity, chlorophyll-a, and temperature are the main controlling environmental variables of pCO2, corresponding to both biological and physical effects. Seasonal changes in the GOM region were also analyzed and explained by changes in the environmental variables. Therefore, considering both high accuracy and interpretability, the cubist-based model was an ideal method for pCO2 estimation and spatiotemporal heterogeneity analysis.
RESUMO
MicroRNAs (miRNAs) are short RNAs with essential roles in gene regulation in various organisms including higher plants. In contrast to the vast information on miRNAs from many economically important plants, almost nothing has been reported on the identification or analysis of miRNAs from rubber tree (Hevea brasiliensis L.), the most important natural rubber-producing crop. To identify miRNAs and their target genes in rubber tree, high-throughput sequencing combined with a computational approach was performed. Four small RNA libraries were constructed for deep sequencing from mature and young leaves of two rubber tree clones, PB 260 and PB 217, which provide high and low latex yield, respectively. 115 miRNAs belonging to 56 known miRNA families were identified, and northern hybridization validated miRNA expression and revealed developmental stage-dependent and clone-specific expression for some miRNAs. We took advantage of the newly released rubber tree genome assembly and predicted 20 novel miRNAs. Further, computational analysis uncovered potential targets of the known and novel miRNAs. Predicted target genes included not only transcription factors but also genes involved in various biological processes including stress responses, primary and secondary metabolism, and signal transduction. In particular, genes with roles in rubber biosynthesis are predicted targets of miRNAs. This study provides a basic catalog of miRNAs and their targets in rubber tree to facilitate future improvement and exploitation of rubber tree.