Shotgun proteomics of quinoa seeds reveals chitinases enrichment under rainfed conditions

Integrated transcriptomic and proteomic analyses revealed molecular mechanisms underlying nutritional changes during seed development of Chenopodium quinoa

Quinoa (Chenopodium quinoa) is a pseudocereal crop of the Amaranthacea family containing highly nutritious seeds which undergo complex physiological and biochemical changes during their development, resulting in final yield and seed nutritional quality (SN-quality). To obtain new insights into the underlying molecular mechanisms, integrated transcriptomic and proteomic analyses of developing seeds from 7 days after flowering (DAF) to maturation (57 DAF) were conducted. A total of 44,776 genes and 8235 proteins were detected; among them, 4130 genes and 3978 proteins were significantly different in pairwise comparisons of samples at various seed developmental stages. Results showed that genes and proteins associated with pathways of sucrose, fructose, mannose, pentose, glucuronate, starch, amino sugar and nucleotide sugar in carbohydrate metabolism; cyano amino acid, taurine & hypotaurine and storage proteins in amino acid and protein metabolisms; cutin, suberin and wax biosynthesis in lipid metabolism and phenylpropanoid and terpenoid biosynthesis in secondary metabolisms of flavonoids and triterpenoidal saponins play a key role in seed developmental process and SN-quality control. Gene regulatory networks correlated with SN-quality traits identified ABA independent (CqDREB2A, Cyclic dof factor 2 (CqCDF2) and AINTEGUMENTA-like5 (CqAIL5),) as well as dependent (CqABI4 and CqWRKY24) associated transcription factors play dynamic role in quinoa SN-quality control by regulating potential target genes and their encoding proteins related to above-mentioned metabolic pathways. The provided multi-omic data sets presented a dynamic picture regarding the developmental process of quinoa seeds, revealing the temporal specific expression of key candidate genes and proteins and providing the basis for crop improvement.

Comprehensive Characterization of Raw and Processed Quinoa from Conventional and Organic Farming by Label-Free Shotgun Proteomics

Quinoa is widely recognized for its exceptional nutritional properties, particularly its complete protein content. This study, for the first time, investigates the effects of processing methods (boiling and extrusion) and farming conditions (conventional and organic) on the proteomic profile. Following a label-free shotgun proteomics approach, a total of 1796 proteins were identified and quantified across all quinoa samples. Regarding processing, both boiling and extrusion produced protein extracts with lower total protein content, with the number of identified proteins decreasing from 1695 in raw quinoa to 957 in processed quinoa. Boiling led to a reduction in protein diversity and expression, while extrusion, which involves high temperatures and pressures, specifically decreased the abundance of high molecular mass proteins. Concerning cultivation practices, organic farming was associated with a broader protein diversity, especially proteins related to translation (28 vs 5%), while conventional farming showed a higher abundance of catalytic and enzymatic proteins (67 vs 46%). These findings highlight the distinct proteomic changes induced by different processing methods and farming conditions, offering valuable insights to manage quinoa's nutritional, bioactive, and functional properties across various production practices.

Shotgun proteomics profiling of chia seeds (Salvia hispanica L.) reveals genotypic differential responses to viability loss

Introduction

Exposure to elevated temperatures and relative humidity expedites the seed aging process, finally leading to seed viability loss. In this context, certain proteins play a pivotal role in safeguarding the longevity of seeds. However, the seedproteomic response to loss viability in Salvia hispanica L., commonly known as chia, remains incompletely understood.

Methods

This work explores the application of proteomics as a potent tool for uncovering molecular responses to viability loss caused by artificial aging in two chia genotypes, WN and MN.

Results

By using a quantitative label-free proteomics analysis (LC-MS/MS), 1787 proteins wereidentified in chia seeds at a 95% confidence level, including storage proteins, heat shock proteins (HSPs), late embryogenesis abundant proteins (LEA),oleosins, reactive oxygen species (ROS)-related enzymes, and ribosomal proteins. A relatively low percentage of exclusive proteins were identified in viable and non-viable seeds. However, proteins exhibiting differential abundancebetween samples indicated variations in the genotype and physiological status. Specifically, the WN genotype showed 130 proteins with differential abundancecomparing viable and non-viable seeds, while MN displayed changes in the abundance of 174 proteins. While both showed a significant decrease in keyproteins responsible for maintaining seed functionality, longevity, and vigor withhigh-temperature and humidity conditions, such as LEA proteins or HSPs, ROS, and oleosins, distinct responses between genotypes were noted, particularly in ribosomal proteins that were accumulated in MN and diminished in WN seeds.

Discussion

Overall, the results emphasize the importance of evaluating changes in proteins of viable and non-viable seeds as they offer valuable insights into the underlying biological mechanisms responsible for the maintenance of chia seed integrity throughout high-temperature and humidity exposure.

Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry Combined with Chemometrics for Protein Profiling and Classification of Boiled and Extruded Quinoa from Conventional and Organic Crops

Quinoa is an Andean crop that stands out as a high-quality protein-rich and gluten-free food. However, its increasing popularity exposes quinoa products to the potential risk of adulteration with cheaper cereals. Consequently, there is a need for novel methodologies to accurately characterize the composition of quinoa, which is influenced not only by the variety type but also by the farming and processing conditions. In this study, we present a rapid and straightforward method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to generate global fingerprints of quinoa proteins from white quinoa varieties, which were cultivated under conventional and organic farming and processed through boiling and extrusion. The mass spectra of the different protein extracts were processed using the MALDIquant software (version 1.19.3), detecting 49 proteins (with 31 tentatively identified). Intensity values from these proteins were then considered protein fingerprints for multivariate data analysis. Our results revealed reliable partial least squares-discriminant analysis (PLS-DA) classification models for distinguishing between farming and processing conditions, and the detected proteins that were critical for differentiation. They confirm the effectiveness of tracing the agricultural origins and technological treatments of quinoa grains through protein fingerprinting by MALDI-TOF-MS and chemometrics. This untargeted approach offers promising applications in food control and the food-processing industry.

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.