Arabidopsis serine/threonine/tyrosine protein kinase phosphorylates oil body proteins that regulate oil content in the seeds

Genome-Wide Association Studies on Resistance to Pea Weevil: Identification of Novel Sources of Resistance and Associated Markers

Little resistance to the pea weevil insect pest (Bruchus pisorum) is available in pea (Pisum sativum) cultivars, highlighting the need to search for sources of resistance in Pisum germplasm and to decipher the genetic basis of resistance. To address this need, we screened the response to pea weevil in a Pisum germplasm collection (324 accession, previously genotyped) under field conditions over four environments. Significant variation for weevil seed infestation (SI) was identified, with resistance being frequent in P. fulvum, followed by P. sativum ssp. elatius, P. abyssinicum, and P. sativum ssp. humile. SI tended to be higher in accessions with lighter seed color. SI was also affected by environmental factors, being favored by high humidity during flowering and hampered by warm winter temperatures and high evapotranspiration during and after flowering. Merging the phenotypic and genotypic data allowed genome-wide association studies (GWAS) yielding 73 markers significantly associated with SI. Through the GWAS models, 23 candidate genes were found associated with weevil resistance, highlighting the interest of five genes located on chromosome 6. These included gene 127136761 encoding squalene epoxidase; gene 127091639 encoding a transcription factor MYB SRM1; gene 127097033 encoding a 60S ribosomal protein L14; gene 127092211, encoding a BolA-like family protein, which, interestingly, was located within QTL BpLD.I, earlier described as conferring resistance to weevil in pea; and gene 127096593 encoding a methyltransferase. These associated genes offer valuable potential for developing pea varieties resistant to Bruchus spp. and efficient utilization of genomic resources through marker-assisted selection (MAS).

Molecular Machinery of Lipid Droplet Degradation and Turnover in Plants

Lipid droplets (LDs) are important organelles conserved across eukaryotes with a fascinating biogenesis and consumption cycle. Recent intensive research has focused on uncovering the cellular biology of LDs, with emphasis on their degradation. Briefly, two major pathways for LD degradation have been recognized: (1) lipolysis, in which lipid degradation is catalyzed by lipases on the LD surface, and (2) lipophagy, in which LDs are degraded by autophagy. Both of these pathways require the collective actions of several lipolytic and proteolytic enzymes, some of which have been purified and analyzed for their in vitro activities. Furthermore, several genes encoding these proteins have been cloned and characterized. In seed plants, seed germination is initiated by the hydrolysis of stored lipids in LDs to provide energy and carbon equivalents for the germinating seedling. However, little is known about the mechanism regulating the LD mobilization. In this review, we focus on recent progress toward understanding how lipids are degraded and the specific pathways that coordinate LD mobilization in plants, aiming to provide an accurate and detailed outline of the process. This will set the stage for future studies of LD dynamics and help to utilize LDs to their full potential.

Construction of a genetic map and QTL mapping of seed size traits in soybean

Soybean seed size and seed shape traits are closely related to plant yield and appearance quality. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiang Chun 2 and JiYu 166 were selected as the mapping population to construct a molecular genetic linkage map, and the phenotypic data of hundred-grain weight, seed length, seed width, and seed length-to-width ratio of soybean under three generations of F2 single plants and F2:3 and F2:4 lines were combined to detect the QTL (quantitative trait loci) for the corresponding traits by ICIM mapping. A soybean genetic map containing 455 markers with an average distance of 6.15 cM and a total length of 2799.2 cM was obtained. Forty-nine QTLs related to the hundred-grain weight, seed length, seed width, and seed length-to-width ratio of soybean were obtained under three environmental conditions. A total of 10 QTLs were detected in more than two environments with a phenotypic variation of over 10%. Twelve QTL clusters were identified on chromosomes 1, 2, 5, 6, 8, 13, 18, and 19, with the majority of the overlapping intervals for hundred-grain weight and seed width. These results will lay the theoretical and technical foundation for molecularly assisted breeding in soybean seed weight and seed shape. Eighteen candidate genes that may be involved in the regulation of soybean seed size were screened by gene functional annotation and GO enrichment analysis.

4D genetic networks reveal the genetic basis of metabolites and seed oil-related traits in 398 soybean RILs

Background

The yield and quality of soybean oil are determined by seed oil-related traits, and metabolites/lipids act as bridges between genes and traits. Although there are many studies on the mode of inheritance of metabolites or traits, studies on multi-dimensional genetic network (MDGN) are limited.

Results

In this study, six seed oil-related traits, 59 metabolites, and 107 lipids in 398 recombinant inbred lines, along with their candidate genes and miRNAs, were used to construct an MDGN in soybean. Around 175 quantitative trait loci (QTLs), 36 QTL-by-environment interactions, and 302 metabolic QTL clusters, 70 and 181 candidate genes, including 46 and 70 known homologs, were previously reported to be associated with the traits and metabolites, respectively. Gene regulatory networks were constructed using co-expression, protein–protein interaction, and transcription factor binding site and miRNA target predictions between candidate genes and 26 key miRNAs. Using modern statistical methods, 463 metabolite–lipid, 62 trait–metabolite, and 89 trait–lipid associations were found to be significant. Integrating these associations into the above networks, an MDGN was constructed, and 128 sub-networks were extracted. Among these sub-networks, the gene–trait or gene–metabolite relationships in 38 sub-networks were in agreement with previous studies, e.g., oleic acid (trait)–GmSEI–GmDGAT1a–triacylglycerol (16:0/18:2/18:3), gene and metabolite in each of 64 sub-networks were predicted to be in the same pathway, e.g., oleic acid (trait)–GmPHS–d-glucose, and others were new, e.g., triacylglycerol (16:0/18:1/18:2)–GmbZIP123–GmHD-ZIPIII-10–miR166s–oil content.

Conclusions

This study showed the advantages of MGDN in dissecting the genetic relationships between complex traits and metabolites. Using sub-networks in MGDN, 3D genetic sub-networks including pyruvate/threonine/citric acid revealed genetic relationships between carbohydrates, oil, and protein content, and 4D genetic sub-networks including PLDs revealed the relationships between oil-related traits and phospholipid metabolism likely influenced by the environment. This study will be helpful in soybean quality improvement and molecular biological research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13068-022-02191-1.

RAF22, ABI1 and OST1 form a dynamic interactive network that optimizes plant growth and responses to drought stress in Arabidopsis

Plants adapt to their ever-changing environment via positive and negative signals induced by environmental stimuli. Drought stress, for instance, induces accumulation of the plant hormone abscisic acid (ABA), triggering ABA signal transduction. However, the molecular mechanisms for switching between plant growth promotion and stress response remain poorly understood. Here we report that RAF (rapidly accelerated fibrosarcoma)-LIKE MITOGEN-ACTIVATED PROTEIN KINASE KINASE KINASE 22 (RAF22) in Arabidopsis thaliana physically interacts with ABA INSENSITIVE 1 (ABI1) and phosphorylates ABI1 at Ser416 residue to enhance its phosphatase activity. Interestingly, ABI1 can also enhance the activity of RAF22 through dephosphorylation, reciprocally inhibiting ABA signaling and promoting the maintenance of plant growth under normal conditions. Under drought stress, however, the ABA-activated OPEN STOMATA1 (OST1) phosphorylates the Ser81 residue of RAF22 and inhibits its kinase activity, restraining its enhancement of ABI1 activity. Taken together, our study reveals that RAF22, ABI1, and OST1 form a dynamic regulatory network that plays crucial roles in optimizing plant growth and environmental adaptation under drought stress.

How plants solubilise seed fats: revisiting oleosin structure and function to inform commercial applications

Plants store triacylglycerides in organelles called oil bodies, which are important fuel sources for germination. Oil bodies consist of a lipid core surrounded by an interfacial single layer membrane of phospholipids and proteins. Oleosins are highly conserved plant proteins that are important for oil body formation, solubilising the triacylglycerides, stabilising oil bodies, and playing a role in mobilising the fuel during the germination process. The domain structure of oleosins is well established, with N- and C-terminal domains that are hydrophilic flanking a long hydrophobic domain that is proposed to protrude into the triacylglyceride core of the oil body. However, beyond this general understanding, little molecular level detail on the structure is available and what is known is disputed. This lack of knowledge limits our understanding of oleosin function and concomitantly our ability to engineer them. Here, we review the state of play in the literature regarding oleosin structure and function, and provide some examples of how oleosins can be used in commercial settings.

Genome-wide identification and analysis of Oleosin gene family in four cotton species and its involvement in oil accumulation and germination

BACKGROUND

Cotton is not only a major textile fiber crop but also a vital oilseed, industrial, and forage crop. Oleosins are the structural proteins of oil bodies, influencing their size and the oil content in seeds. In addition, the degradation of oleosins is involved in the mobilization of lipid and oil bodies during seed germination. However, comprehensive identification and the systematic analysis of the Oleosin gene (OLEOs) family have not been conducted in cotton.

RESULTS

An in-depth analysis has enabled us to identify 25 and 24 OLEOs in tetraploid cotton species G. hirsutum and G. barbadense, respectively, while 12 and 13 OLEOs were identified in diploid species G. arboreum and G. raimondii, respectively. The 74 OLEOs were further clustered into three lineages according to the phylogenetic tree. Synteny analysis revealed that most of the OLEOs were conserved and that WGD or segmental duplications might drive their expansion. The transmembrane helices in GhOLEO proteins were predicted, and three transmembrane models were summarized, in which two were newly proposed. A total of 24 candidate miRNAs targeting GhOLEOs were predicted. Three highly expressed oil-related OLEOs, GH_A07G0501 (SL), GH_D10G0941 (SH), and GH_D01G1686 (U), were cloned, and their subcellular localization and function were analyzed. Their overexpression in Arabidopsis increased seed oil content and decreased seed germination rates.

CONCLUSION

We identified OLEO gene family in four cotton species and performed comparative analyses of their relationships, conserved structure, synteny, and gene duplication. The subcellular localization and function of three highly expressed oil-related OLEOs were detected. These results lay the foundation for further functional characterization of OLEOs and improving seed oil content.

Plant Phosphoproteomics: Known Knowns, Known Unknowns, and Unknown Unknowns of an Emerging Systems Science Frontier

Plant systems science research depends on the dynamic functional maps of the biological substrates of plant phenotypes and host/environment interactions in diverse ecologies. In this context, high-resolution mass spectrometry platforms offer comprehensive insights into the molecular pathways regulated by protein phosphorylation. Reversible protein phosphorylation is a ubiquitous reaction in signal transduction mechanisms in biological systems. In contrast to human and animal biology research, a plethora of experimental options for functional mapping and regulation of plant biology are, however, not currently available. Plant phosphoproteomics is an emerging field of research that aims at addressing this gap in systems science and plant omics, and thus has a large scope to empower fundamental discoveries. To date, large-scale data-intensive identification of phosphorylation events in plants remained technically challenging. In this expert review, we present a critical analysis and overview of phosphoproteomic studies performed in the model plant Arabidopsis thaliana. We discuss the technical strategies used for the enrichment of phosphopeptides and methods used for their quantitative assessment. Various types of mass spectrometry data acquisition and fragmentation methods are also discussed. The insights gathered here can allow plant biology and systems science researchers to design high-throughput function-oriented experimental workflows that elucidate the regulatory signaling mechanisms impacting plant physiology and plant diseases.

Arabidopsis group C Raf-like protein kinases negatively regulate abscisic acid signaling and are direct substrates of SnRK2

The phytohormone abscisic acid (ABA) plays a major role in abiotic stress responses in plants, and subclass III SNF1-related protein kinase 2 (SnRK2) kinases mediate ABA signaling. In this study, we identified Raf36, a group C Raf-like protein kinase in Arabidopsis, as a protein that interacts with multiple SnRK2s. A series of reverse genetic and biochemical analyses revealed that 1) Raf36 negatively regulates ABA responses during postgermination growth, 2) the N terminus of Raf36 is directly phosphorylated by SnRK2s, and 3) Raf36 degradation is enhanced in response to ABA. In addition, Raf22, another C-type Raf-like kinase, functions partially redundantly with Raf36 to regulate ABA responses. A comparative phosphoproteomic analysis of ABA-induced responses of wild-type and raf22raf36-1 plants identified proteins that are phosphorylated downstream of Raf36 and Raf22 in planta. Together, these results support a model in which Raf36/Raf22 function mainly under optimal conditions to suppress ABA responses, whereas in response to ABA, the SnRK2 module promotes Raf36 degradation as a means of alleviating Raf36-dependent inhibition and allowing for heightened ABA signaling to occur.

Cellular alteration and differential protein profile explain effects of GA3 and ABA and their inhibitor on Trichocline catharinensis (Asteraceae) seed germination

Seed physiology of wild species has not been studied as deeply as that of domesticated crop species. Trichocline catharinensis (Asteraceae) is an endemic wildflower species from the high-altitude fields of southern Brazil. This species is of interest as a source of genes to improve cultivated Asteraceae because of its ornamental features, disease resistance and ability to tolerate drought and poor soil conditions. We studied the effects of abscisic acid (ABA) and gibberellic acid (GA₃ ) and their inhibitors, fluridone (FLU) and paclobutrazol (PAC), on seed germination. We individually assessed ultrastructural changes and differential protein accumulation. The principal component analysis explained 69.66% of differential accumulation for 32 proteins at phase II of seed germination in response to hormone and inhibitor treatment. GA₃ -imbibed seed germination (98.75%) resulted in increased protein accumulation to meet energy demand, redox regulation, and reserve metabolism activation. FLU-imbibed seeds showed a higher germination speed index as a consequence of metabolism activation. ABA-imbibed seeds (58.75%) showed osmotolerance and flattened cells in the hypocotyl-radicular axis, suggesting that ABA inhibits cell expansion. PAC-imbibed seeds remained at phase II for 300 h, and germination was suppressed (7.5%) because of the increased signaling proteins and halted reserve mobilization. Therefore, our findings provide insight into the behavior of Asteraceae non-dormant seed germination, which broadens our knowledge of seed germination in a wild and endemic plant species from a threatened ecosystem.

Detection of significant SNP associated with production and oil quality traits in interspecific oil palm hybrids using RARSeq

A RARSeq based Association mapping study was performed in a population of 104 Elaeis oleifera x E. guineensis hybrids of five origins with the aim of finding functional markers associated to six productive and 19 oil quality traits. For this purpose mRNA of each genotype was isolated and double stranded cDNA was synthesized. Following digestion with two restriction enzymes and adapter ligation, a size selected pool of barcoded amplicons was produced and sequenced using Illumina MiSeq. The obtained sequences were processed with a "snakemake" pipeline, filtered and missing values were imputed. For all traits except two significant effects of the origin was observed. Genetic diversity analyses revealed high variability within origins and an excess of heterozygosity in the population. Two GLM models with Q matrix or PCA matrix as covariates and two MLM models incorporating in addition a Kinship matrix were tested for genotype-phenotype associations using GAPIT software. Using unadjusted p values (< 0.01) 78 potential associations were detected involving 25 SNP and 20 traits. When applying FDR multiple testing with p <  0.05, 25 significant associations remained involving eight SNP and six quality traits. Four SNP were located in genes with a potential relevant biological meaning.

A Cytosolic Protein Kinase STY46 in Arabidopsis thaliana is Involved in Plant Growth and Abiotic Stress Response

Starch provides plants with carbon and energy during stressful periods; however, relatively few regulators of starch metabolism under stress-induced carbon starvation have been discovered. We studied a protein kinase Ser/Thr/Tyr (STY) 46, identified by gene co-expression network analysis as a potential regulator of the starch starvation response in Arabidopsis thaliana. We showed that STY46 was induced by (1) abscisic acid and prolonged darkness, (2) by abiotic stressors, including salinity and osmotic stress, and (3) by conditions associated with carbon starvation. Characterization of STY46 T-DNA knockout mutants indicated that there was functional redundancy among the STY gene family, as these genotypes did not show strong phenotypes. However, Arabidopsis with high levels of STY46 transcripts (OE-25) grew faster at the early seedling stage, had higher photosynthetic rates, and more carbon was stored as protein in the seeds under control conditions. Further, OE-25 source leaf accumulated more sugars under 100 mM NaCl stress, and salinity also accelerated root growth, which is consistent with an adaptive response. Salt-stressed OE-25 partitioned ¹⁴C towards sugars and amino acids, and away from starch and protein in source leaves. Together, these findings suggested that STY46 may be part of the salinity stress response pathway that utilizes starch during early plant growth.

Identification of transcriptome-wide, nut weight-associated SNPs in Castanea crenata

Nut weight is one of the most important traits that can affect a chestnut grower's returns. Due to the long juvenile phase of chestnut trees, the selection of desired characteristics at early developmental stages represents a major challenge for chestnut breeding. In this study, we identified single nucleotide polymorphisms (SNPs) in transcriptomic regions, which were significantly associated with nut weight in chestnuts (Castanea crenata), using a genome-wide association study (GWAS). RNA-sequencing (RNA-seq) data were generated from large and small nut-bearing trees, using an Illumina HiSeq. 2000 system, and 3,271,142 SNPs were identified. A total of 21 putative SNPs were significantly associated with chestnut weight (false discovery rate [FDR] < 10-5), based on further analyses. We also applied five machine learning (ML) algorithms, support vector machine (SVM), C5.0, k-nearest neighbour (k-NN), partial least squares (PLS), and random forest (RF), using the 21 SNPs to predict the nut weights of a second population. The average accuracy of the ML algorithms for the prediction of chestnut weights was greater than 68%. Taken together, we suggest that these SNPs have the potential to be used during marker-assisted selection to facilitate the breeding of large chestnut-bearing varieties.