Leucine-rich repeat receptor-like protein kinase AtORPK1 promotes oxidative stress resistance in an AtORPK1-AtKAPP mediated module in Arabidopsis

Integrated Transcriptomic and Proteomic Analyses of Low-Nitrogen-Stress Tolerance and Function Analysis of ZmGST42 Gene in Maize

Maize (Zea mays L.) is one of the major staple crops providing human food, animal feed, and raw material support for biofuel production. For its growth and development, maize requires essential macronutrients. In particular, nitrogen (N) plays an important role in determining the final yield and quality of a maize crop. However, the excessive application of N fertilizer is causing serious pollution of land area and water bodies. Therefore, cultivating high-yield and low-N-tolerant maize varieties is crucial for minimizing the nitrate pollution of land and water bodies. Here, based on the analysis of the maize leaf transcriptome and proteome at the grain filling stage, we identified 3957 differentially expressed genes (DEGs) and 329 differentially abundant proteins (DAPs) from the two maize hybrids contrasting in N stress tolerance (low-N-tolerant XY335 and low-N-sensitive HN138) and screened four sets of low-N-responsive genes and proteins through Venn diagram analysis. We identified 761 DEGs (253 up- and 508 down-regulated) specific to XY335, whereas 259 DEGs (198 up- and 61 down-regulated) were specific to HN138, and 59 DEGs (41 up- and 18 down-regulated) were shared between the two cultivars under low-N-stress conditions. Meanwhile, among the low-N-responsive DAPs, thirty were unique to XY335, thirty were specific to HN138, and three DAPs were shared between the two cultivars under low-N treatment. Key among those genes/proteins were leucine-rich repeat protein, DEAD-box ATP-dependent RNA helicase family proteins, copper transport protein, and photosynthesis-related proteins. These genes/proteins were involved in the MAPK signaling pathway, regulating membrane lipid peroxidation, and photosynthesis. Our results may suggest that XY335 better tolerates low-N stress than HN138, possibly through robust low-N-stress sensing and signaling, amplified protein phosphorylation and stress response, and increased photosynthesis efficiency, as well as the down-regulation of 'lavish' or redundant proteins to minimize N demand. Additionally, we screened glutathione transferase 42 (ZmGST42) and performed physiological and biochemical characterizations of the wild-type (B73) and gst42 mutant at the seedling stage. Resultantly, the wild-type exhibited stronger tolerance to low N than the mutant line. Our findings provide a better understanding of the molecular mechanisms underlying low-N tolerance during the maize grain filling stage and reveal key candidate genes for low-N-tolerance breeding in maize.

Emerging Roles of Receptor-like Protein Kinases in Plant Response to Abiotic Stresses

The productivity of plants is hindered by unfavorable conditions. To perceive stress signals and to transduce these signals to intracellular responses, plants rely on membrane-bound receptor-like kinases (RLKs). These play a pivotal role in signaling events governing growth, reproduction, hormone perception, and defense responses against biotic stresses; however, their involvement in abiotic stress responses is poorly documented. Plant RLKs harbor an N-terminal extracellular domain, a transmembrane domain, and a C-terminal intracellular kinase domain. The ectodomains of these RLKs are quite diverse, aiding their responses to various stimuli. We summarize here the sub-classes of RLKs based on their domain structure and discuss the available information on their specific role in abiotic stress adaptation. Furthermore, the current state of knowledge on RLKs and their significance in abiotic stress responses is highlighted in this review, shedding light on their role in influencing plant-environment interactions and opening up possibilities for novel approaches to engineer stress-tolerant crop varieties.

Cloning and Functional Characterization of Cold-Inducible MYB-like 17 Transcription Factor in Rapeseed (Brassica napus L.)

Rapeseed (Brassica napus L.) is an important crop for edible oil, vegetables, and biofuel. Rapeseed growth and development require a minimum temperature of ~1-3 °C. Notably, frost damage occurs during overwintering, posing a serious threat to the productivity and yield of rapeseed. MYB proteins are important transcription factors (TFs) in plants, and have been proven to be involved in the regulation of stress responses. However, the roles of the MYB TFs in rapeseed under cold stress conditions are yet to be fully elucidated. To better understand the molecular mechanisms of one MYB-like 17 gene, BnaMYBL17, in response to low temperature, the present study found that the transcript level of BnaMYBL17 is induced by cold stress. To characterize the gene's function, the 591 bp coding sequence (CDS) from rapeseed was isolated and stably transformed into rapeseed. The further functional analysis revealed significant sensitivity in BnaMYBL17 overexpression lines (BnaMYBL17-OE) after freezing stress, suggesting its involvement in freezing response. A total of 14,298 differentially expressed genes relative to freezing response were found based on transcriptomic analysis of BnaMYBL17-OE. Overall, 1321 candidate target genes were identified based on differential expression, including Phospholipases C1 (PLC1), FCS-like zinc finger 8 (FLZ8), and Kinase on the inside (KOIN). The qPCR results confirmed that the expression levels of certain genes showed fold changes ranging from two to six when compared between BnaMYBL17-OE and WT lines after exposure to freezing stress. Furthermore, verification indicated that BnaMYBL17 affects the promoter of BnaPLC1, BnaFLZ8, and BnaKOIN genes. In summary, the results suggest that BnaMYBL17 acts as a transcriptional repressor in regulating certain genes related to growth and development during freezing stress. These findings provide valuable genetic and theoretical targets for molecular breeding to enhance freezing tolerance in rapeseed.

Transcriptomic profiling of wheat stem during meiosis in response to freezing stress

Low temperature injury in spring has seriously destabilized the production and grain quality of common wheat. However, the molecular mechanisms underlying spring frost tolerance remain elusive. In this study, we investigated the response of a frost-tolerant wheat variety Zhongmai8444 to freezing stress at the meiotic stage. Transcriptome profiles over a time course were subsequently generated by high-throughput sequencing. Our results revealed that the prolonged freezing temperature led to the significant reductions in plant height and seed setting rate. Cell wall thickening in the vascular tissue was also observed in the stems. RNA-seq analyses demonstrated the identification of 1010 up-regulated and 230 down-regulated genes shared by all time points of freezing treatment. Enrichment analysis revealed that gene activity related to hormone signal transduction and cell wall biosynthesis was significantly modulated under freezing. In addition, among the identified differentially expressed genes, 111 transcription factors belonging to multiple gene families exhibited dynamic expression pattern. This study provided valuable gene resources beneficial for the breeding of wheat varieties with improved spring frost tolerance.

Protein-Protein Interactions in Abiotic Stress Signaling: An Overview of Biochemical and Biophysical Methods of Characterization

The identification and characterization of bona fide abiotic stress signaling proteins can occur at different levels of the complete in vivo signaling cascade or network. Knowledge of a particular abiotic stress signaling protein could theoretically lead to the characterization of complete networks through the analysis of unknown proteins that interact with the previously known protein. Such signaling proteins of interest can indeed be experimentally used as bait proteins to catch interacting prey proteins, provided that the association of bait proteins and prey proteins should yield a biochemical or biophysical signal that can be detected. To this end, several biochemical and biophysical techniques are available to provide experimental evidence for specific protein-protein interactions, such as co-immunoprecipitation, bimolecular fluorescence complementation, tandem affinity purification coupled to mass spectrometry, yeast two hybrid, protein microarrays, Förster resonance energy transfer, or fluorescence correlation spectroscopy. This array of methods can be implemented to establish the biochemical reality of putative protein-protein interactions between two proteins of interest or to identify previously unknown partners related to an initially known protein of interest. The ultimate validity of these methods however depends on the in vitro/in vivo nature of the approach and on the heterologous/homologous context of the analysis. This chapter will review the application and success of some classical methods of protein-protein interaction analysis in the field of plant abiotic stress signaling.

Silveira S, Brommonschenkel SH, Fontes EPB. RLPredictiOme, a Machine Learning-Derived Method for High-Throughput Prediction of Plant Receptor-like Proteins, Reveals Novel Classes of Transmembrane Receptors

Cell surface receptors play essential roles in perceiving and processing external and internal signals at the cell surface of plants and animals. The receptor-like protein kinases (RLK) and receptor-like proteins (RLPs), two major classes of proteins with membrane receptor configuration, play a crucial role in plant development and disease defense. Although RLPs and RLKs share a similar single-pass transmembrane configuration, RLPs harbor short divergent C-terminal regions instead of the conserved kinase domain of RLKs. This RLP receptor structural design precludes sequence comparison algorithms from being used for high-throughput predictions of the RLP family in plant genomes, as has been extensively performed for RLK superfamily predictions. Here, we developed the RLPredictiOme, implemented with machine learning models in combination with Bayesian inference, capable of predicting RLP subfamilies in plant genomes. The ML models were simultaneously trained using six types of features, along with three stages to distinguish RLPs from non-RLPs (NRLPs), RLPs from RLKs, and classify new subfamilies of RLPs in plants. The ML models achieved high accuracy, precision, sensitivity, and specificity for predicting RLPs with relatively high probability ranging from 0.79 to 0.99. The prediction of the method was assessed with three datasets, two of which contained leucine-rich repeats (LRR)-RLPs from Arabidopsis and rice, and the last one consisted of the complete set of previously described Arabidopsis RLPs. In these validation tests, more than 90% of known RLPs were correctly predicted via RLPredictiOme. In addition to predicting previously characterized RLPs, RLPredictiOme uncovered new RLP subfamilies in the Arabidopsis genome. These include probable lipid transfer (PLT)-RLP, plastocyanin-like-RLP, ring finger-RLP, glycosyl-hydrolase-RLP, and glycerophosphoryldiester phosphodiesterase (GDPD, GDPDL)-RLP subfamilies, yet to be characterized. Compared to the only Arabidopsis GDPDL-RLK, molecular evolution studies confirmed that the ectodomain of GDPDL-RLPs might have undergone a purifying selection with a predominance of synonymous substitutions. Expression analyses revealed that predicted GDPGL-RLPs display a basal expression level and respond to developmental and biotic signals. The results of these biological assays indicate that these subfamily members have maintained functional domains during evolution and may play relevant roles in development and plant defense. Therefore, RLPredictiOme provides a framework for genome-wide surveys of the RLP superfamily as a foundation to rationalize functional studies of surface receptors and their relationships with different biological processes.