Shotgun proteomics of quinoa seeds reveals chitinases enrichment under rainfed conditions

Analysis of chitinase gene family in barley and function study of HvChi22 involved in drought tolerance

BACKGROUND

Chitinase (Chi) is a pathogenesis-related protein, also reported to play an important role in plant responses to abiotic stress. However, its role in response to abiotic stress in barley is still unclear.

RESULTS

In this study, a total of 61 Chi gene family members were identified from the whole genome of wild barley EC_S1. Phylogenetic analysis suggested that these family genes were divided into five groups. Among these genes, four pairs of collinearity genes were discovered. Besides, abundant cis-regulatory elements, including drought response element and abscisic acid response element were identified in the promoter regions of HvChi gene family members. The expression profiles revealed that most HvChi family members were significantly up-regulated under drought stress, which was also validated by RT-qPCR measurements. To further explore the role of Chi under drought stress, HvChi22 was overexpressed in Arabidopsis. Compared to wild-type plants, overexpression of HvChi22 enhanced drought tolerance by increasing the activity of oxidative protective enzymes, which caused less MDA accumulation.

CONCLUSION

Our study improved the understanding of the Chi gene family under drought stress in barley, and provided a theoretical basis for crop improvement strategies to address the challenges posed by changing environmental conditions.

Genomic basis of seed colour in quinoa inferred from variant patterns using extreme gradient boosting

Quinoa is an agriculturally important crop species originally domesticated in the Andes of central South America. One of its most important phenotypic traits is seed colour. Seed colour variation is determined by contrasting abundance of betalains, a class of strong antioxidant and free radicals scavenging colour pigments only found in plants of the order Caryophyllales. However, the genetic basis for these pigments in seeds remains to be identified. Here we demonstrate the application of machine learning (extreme gradient boosting) to identify genetic variants predictive of seed colour. We show that extreme gradient boosting outperforms the classical genome-wide association approach. We provide re-sequencing and phenotypic data for 156 South American quinoa accessions and identify candidate genes potentially controlling betalain content in quinoa seeds. Genes identified include novel cytochrome P450 genes and known members of the betalain synthesis pathway, as well as genes annotated as being involved in seed development. Our work showcases the power of modern machine learning methods to extract biologically meaningful information from large sequencing data sets.

Proteomic profile of tepary bean seed storage proteins in germination with low water potential

BACKGROUND

Tepary bean (Phaseolus acutifolius A. Gray) is one of the five species domesticated from the genus Phaseolus with genetic resistance to biotic and abiotic stress. To understand the mechanisms underlying drought responses in seed storage proteins germinated on water and polyethylene glycol (PEG-6000) at -0.49 MPa, we used a proteomics approach to identify potential molecular target proteins associated with the low water potential stress response.

METHODS

Storage proteins from cotyledons of Tepary bean seeds germinated at 24, 48 and 72 h on water and PEG-6000 at -0.49 MPa were analyzed by one-dimensional electrophoresis (DE) with 2-DE analysis and shotgun mass spectrometry. Using computational database searching and bioinformatics analyses, we performed Gene Ontology (GO) and protein interactome (functional protein association network) String analyses.

RESULTS

Comparative analysis showed that the effect of PEG-6000 on root growth was parallel to that on germination. Based on the SDS‒PAGE protein banding patterns and 2-DE analysis, ten differentially abundant seed storage proteins showed changes in storage proteins, principally in the phaseolin and lectin fractions. We found many proteins that are recognized as drought stress-responsive proteins, and several of them are predicted to be intrinsically related to abiotic stress. The shotgun analysis searched against UniProt's legume database, and Gene Ontology (GO) analysis indicated that most of the seed proteins were cytosolic, with catalytic activity and associated with carbohydrate metabolism. The protein‒protein interaction networks from functional enrichment analysis showed that phytohemagglutinin interacts with proteins associated with the degradation of storage proteins in the cotyledons of common bean during germination.

CONCLUSION

These findings suggest that Tepary bean seed proteins provide valuable information with the potential to be used in genetic improvement and are part of the drought stress response, making our approach a potentially useful strategy for discovering novel drought-responsive proteins in other plant models.

Transcriptional survey of abiotic stress response in maize (Zea mays) in the level of gene co-expression network and differential gene correlation analysis

Abstract. Maize may be exposed to several abiotic stresses in the field. Therefore, identifying the tolerance mechanisms of natural field stress is mandatory. Gene expression data of maize upon abiotic stress were collected, and 560 differentially expressed genes (DEGs) were identified through meta-analysis. The most significant gene ontology terms in up-regulated genes were 'response to abiotic stress' and 'chitinase activity'. 'Phosphorelay signal transduction system' was the most significant enriched biological process in down-regulated DEGs. The co-expression analysis unveiled seven modules of DEGs, with a notable positive correlation between the modules and abiotic stress. Furthermore, the statistical significance was strikingly high for the turquoise, green and yellow modules. The turquoise group played a central role in orchestrating crucial adaptations in metabolic and stress response pathways in maize when exposed to abiotic stress. Within three up-regulated modules, Zm.7361.1.A1_at, Zm.10386.1.A1_a_at and Zm.10151.1.A1_at emerged as hub genes. These genes might introduce novel candidates implicated in stress tolerance mechanisms, warranting further comprehensive investigation and research. In parallel, the R package glmnet was applied to fit a logistic LASSO regression model on the DEGs profile to select candidate genes associated with abiotic responses in maize. The identified hub genes and LASSO regression genes were validated on an independent microarray dataset. Additionally, Differential Gene Correlation Analysis (DGCA) was performed on LASSO and hub genes to investigate the gene-gene regulatory relationship. The P value of DGCA of 16 pairwise gene comparisons was lower than 0.01, indicating a gene-gene significant change in correlation between control and abiotic stress. Integrated weighted gene correlation network analysis and logistic LASSO analysis revealed Zm.11185.1.S1_at, Zm.2331.1.S1_x_at and Zm.17003.1.S1_at. Notably, these 3 genes were identified in the 16 gene-pair comparisons. This finding highlights the notable significance of these genes in the abiotic stress response. Additional research into maize stress tolerance may focus on these three genes.

Identification of Reference Genes for Precise Expression Analysis during Germination in Chenopodium quinoa Seeds under Salt Stress

Chenopodium quinoa Willd. (quinoa), a member of the Amaranthaceae family, is an allotetraploid annual plant, endemic to South America. The plant of C. quinoa presents significant ecological plasticity with exceptional adaptability to several environmental stresses, including salinity. The resilience of quinoa to several abiotic stresses, as well as its nutritional attributes, have led to significant shifts in quinoa cultivation worldwide over the past century. This work first defines germination sensu stricto in quinoa where the breakage of the pericarp and the testa is followed by endosperm rupture (ER). Transcriptomic changes in early seed germination stages lead to unstable expression levels in commonly used reference genes that are typically stable in vegetative tissues. Noteworthy, no suitable reference genes have been previously identified specifically for quinoa seed germination under salt stress conditions. This work aims to identify these genes as a prerequisite step for normalizing qPCR data. To this end, germinating seeds from UDEC2 and UDEC4 accessions, with different tolerance to salt, have been analyzed under conditions of absence (0 mM NaCl) and in the presence (250 mM NaCl) of sodium chloride. Based on the relevant literature, six candidate reference genes, Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), Monensin sensitivity1 (MON1), Polypyrimidine tract-binding protein (PTB), Actin-7 (ACT7), Ubiquitin-conjugating enzyme (UBC), and 18S ribosomal RNA (18S), were selected and assessed for stability using the RefFinder Tool encompassing the statistical algorithms geNorm, NormFinder, BestKeeper, and ΔCt in the evaluation. The data presented support the suitability of CqACT7 and CqUBC as reference genes for normalizing gene expression during seed germination under salinity stress. These recommended reference genes can be valuable tools for consistent qPCR studies on quinoa seeds.