Modification of AtGRDP1 gene expression affects silique and seed development in Arabidopsis thaliana

Rodríguez-Hernández, Aida Araceli; Muro-Medina, Carlos Vladimir; Ramírez-Alonso, Jocelin Itzel; Jiménez-Bremont, Juan Francisco

doi:10.1016/j.bbrc.2017.03.015

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…The Arabidopsis GRDP1 encodes the glycine‐rich domain protein involved in the abscisic acid‐activated signalling pathway. The Arabidopsis grdp1 mutant lines exhibited many developmental defects, including shortened siliques and aborted ovules as well as reduced seed number and weight (Rodríguez‐Hernández et al , ). The Arabidopsis CSP2 encodes a glycine‐rich protein that responds to cold stress through the ABA pathway, which is highly expressed in shoot apical meristems and siliques (Nakaminami et al , ).…”

Section: Fruit Size Regulatorsmentioning

confidence: 99%

Genetic and signalling pathways of dry fruit size: targets for genome editing‐based crop improvement

Hussain

Shi

Scheben

et al. 2020

Plant Biotechnology Journal

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Summary Fruit is seed‐bearing structures specific to angiosperm that form from the gynoecium after flowering. Fruit size is an important fitness character for plant evolution and an agronomical trait for crop domestication/improvement. Despite the functional and economic importance of fruit size, the underlying genes and mechanisms are poorly understood, especially for dry fruit types. Improving our understanding of the genomic basis for fruit size opens the potential to apply gene‐editing technology such as CRISPR/Cas to modulate fruit size in a range of species. This review examines the genes involved in the regulation of fruit size and identifies their genetic/signalling pathways, including the phytohormones, transcription and elongation factors, ubiquitin‐proteasome and microRNA pathways, G‐protein and receptor kinases signalling, arabinogalactan and RNA‐binding proteins. Interestingly, different plant taxa have conserved functions for various fruit size regulators, suggesting that common genome edits across species may have similar outcomes. Many fruit size regulators identified to date are pleiotropic and affect other organs such as seeds, flowers and leaves, indicating a coordinated regulation. The relationships between fruit size and fruit number/seed number per fruit/seed size, as well as future research questions, are also discussed.

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Section: Fruit Size Regulatorsmentioning

confidence: 99%

Genetic and signalling pathways of dry fruit size: targets for genome editing‐based crop improvement

Hussain

Shi

Scheben

et al. 2020

Plant Biotechnology Journal

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“…For the central group, we found genes implicated in the regulation of flowering time ( HAC1 ) [ 41 , 42 ], blue light responses ( CRY1 and CRY2 ) [ 43 ], toxic heavy metal ion responses ( CNGC1 and CNGC10 ) [ 44 ], response to changes in humidity ( SAGL1 ) [ 45 ], and so on. For the southern group, we found genes related to plant development ( At5g45160 ) [ 46 ], fruit development ( GRDP1 ) [ 47 ], and chemical-induced genotoxic and oxidative stress ( NPC1 ) [ 48 ]. For the eastern group, we discovered several genes involved in regulating plant growth ( CGR2 , ERG28 , TOR ) [ 49 – 51 ].…”

Section: Resultsmentioning

confidence: 99%

Genomic population structure and local adaptation of the wild strawberry Fragaria nilgerrensis

Feng

Yang

et al. 2022

Horticulture Research

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The crop wild relative, Fragaria nilgerrensis, is adapted to a variety of diverse habitats across its native range in China. Thus, discoveries made in this species could serve useful to guide the development of new superior strawberry cultivars that are resilient to new or variable environments. However, the genetic diversity and genetic architecture of traits in this species underlying important adaptive traits remain poorly understood. Here, we used whole-genome resequencing data from 193 F. nilgerrensis individuals spanning the distribution range in China to investigate the genetic diversity, population structure and the genomic basis of local adaptation. We identified four genetic groups, with the western group located in Hengduan Mountains exhibited the highest genetic diversity. Redundancy analysis suggests that both environment and geographic variables shaped a significant proportion of genomic variation. Our analyses revealed that the environmental difference explains more of the observed genetic variation than geographic distance. This suggests that adaptation to distinct habitats, unique combination of abiotic factors, likely drove genetic differentiation. Lastly, by implementing selective sweeps scans and genome-environment association analysis throughout the genome, we identified the genetic variation associated with local adaptation and investigated the functions of putative candidate genes in F. nilgerrensis.

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“…GRDP1 is a trans-eGene in GRN_302, but a cis-eQTL in GRN_96(Figures 4C and 7A). The GRDP1 homolog in Arabidopsis, AtGRDP1 (AT2G22660), is predominantly expressed in the embryo during seed development and is involved with seed germination and ABA response 58,59 . Cultivars of the AA haploid type, which predominated in CUCP1, exhibited relatively low GRDP1 expression (P < 10 -16 , Mann-Whitney test) (Figure 7C), and variation in GRDP1 expression was found to be positively correlated with LP and negatively with SI (Figure 7C).…”

Section: Nf-yb3 Fla2mentioning

confidence: 99%

Integration of eQTL and Machine Learning Methods to Dissect Causal Genes with Pleiotropic effects in Genetic Regulation Networks of Seed Cotton Yield

Zhao

Wang

et al. 2023

Preprint

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Expression quantitative trait loci (eQTL) provide a powerful means of investigating the biological basis of genome-wide association study (GWAS) results and exploring complex traits or phenotypes. In addition to identifying the causal gene in cis, eQTL analysis also reveals a large number of trans-regulated genes located on different chromosomes, which form a gene regulatory network (GRN) that complements the GWAS locus. However, the dissection of a GRN and the crosstalk underlying multiple agronomical traits, along with prioritizing important genes in eQTL-derived GRNs, remains a major challenge. In this study, we generated 558 transcriptional profiles of lint-bearing ovules at one day post-anthesis (DPA) from a selective core cotton germplasm, from which we identified 12,207 eQTLs. By integrating with a GWAS catalog, we found that 66 out of 187 (35.29%) known phenotypic GWAS loci are colocalized with 1,090 eQTLs, forming 38 major functional GRNs predominantly (30 out of 38) associated with seed size-related phenotypes. Of the eGenes, 34 were shared between at least two functional GRNs, exhibiting pleiotropic effects, such as NF-YB3, GRDP1, and IDD7. Narrow-sense heritability analysis showed that the heritability increased with combining the eQTLs with GRNs compared to those with previous yield trait GWAS loci. The extreme gradient boosting (XGBoost) machine learning approach was then applied to predict seed cotton yield phenotypes based on gene expression. Top-ranking eGenes (NF-YB3, FLA2, and GRDP1) derived by XGBoost with pleiotropic effects on yield traits were validated, along with their potential roles by correlation analysis, domestication selection analysis, and transgenic plants. This study provides insights into the mining of GRNs in relation to the pleiotropy of phenotype. The combination of eQTL and machine learning approaches is efficient in improving the genetic dissection of agricultural traits.

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Modification of AtGRDP1 gene expression affects silique and seed development in Arabidopsis thaliana

Cited by 11 publications

References 15 publications

Genetic and signalling pathways of dry fruit size: targets for genome editing‐based crop improvement

Genetic and signalling pathways of dry fruit size: targets for genome editing‐based crop improvement

Genomic population structure and local adaptation of the wild strawberry Fragaria nilgerrensis

Integration of eQTL and Machine Learning Methods to Dissect Causal Genes with Pleiotropic effects in Genetic Regulation Networks of Seed Cotton Yield

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