The rapeseed team and the bioinformatics team revealed the gene regulatory network for oilseed content in rapeseed

Nanhu News Network News (Correspondent Tan Zengdong) On November 7, the rapeseed team of Huazhong Agricultural University and the bioinformatics team published a report entitled "Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus" in Genome Biology Research papers
.
This study comprehensively describes the regulation map of transcriptome variation during the development of rapeseed seeds, and combines machine learning and deep learning algorithms to mine new genes for the regulation of oil content in rapeseed seeds, and the results provide reference
for the asymmetric regulation of polyploid plants.

Gene expression regulation plays a crucial role
in plant phenotyping and adaptation.
Genetic variation can regulate plant phenotypes by influencing gene expression, and the study of expression quantitative trait loci (eQTL) links genomic variation and gene expression traits, and plays an important role
in the resolution of key genes and regulatory networks for plant traits.
Population transcriptome data bridges the gap between genetic variation and phenotype
.
The analysis of hot spot eQTLs can help mine key regulators and eventually build gene regulatory networks
.
For polyploid plants, eQTL can also help resolve intergenome regulatory signatures
.
Cabbage rape is an important oil crop in the world, and improving the oil content of seeds is one of the important breeding goals of
rapeseed.
Rapeseed is heterotetraploid, the genome is very complex, and the cloning and regulatory network elucidation of oil-content genes are still challenging
at this stage.

Figure 1 Genome-wide eQTL dynamic changes and characterization

In this study, the transcriptional variation of rapeseed seeds at two developmental stages 20 days (20 DAF) and 40 days (40 DAF) after flowering was comprehensively analyzed and a regulatory map
was constructed.
At 20 DAF and 40 DAF, 79,605 and 76,713 expressed genes and 53,759 and 53,550 independent eQTLs were detected, respectively (Figure 1).

For the first time, eQTL and chromatin accessibility were combined in rapeseed and it was found that when adjacent gene pairs were regulated by local eQTL and had the same chromatin open state, they showed stronger gene expression piggyback patterns
.

There is a general imbalance in regulation
between subgenomes of polyploid plants.
eQTL analysis also provides rich information
for dissecting the genetic regulation of polyploid plant subgenomes and understanding the expression characteristics of heteropolyploid intergenome homologous gene pairs (HGPs).
This study found that the rapeseed An subgenome has richer variation and higher eQTL distribution density
than the Cn subgenome.
Intergenome comparative analysis showed that there was feedback regulation of homologous gene pairs to maintain the expression dose balance
of some genes.

Figure 2 Machine learning predicts the key transcription factors regulating oil content in the regulatory region of distant hotspot eQTL

In addition, 141 hot spot eQTLs were identified in this study, and a key hot spot affecting oil content was identified on the A09 chromosome, and 69.
73% of the oil-content whole transcriptome association analysis of significant genes eQTL colocated with this hot spot (Figure 2).

。 In order to further predict the key regulators and regulatory networks affecting oil content in this hot spot, the XGBoost and Basenji models were constructed using 856 RNA-seq and 59 ATAC-seq datasets, and the transcription factors NAC13 and SCL31 were predicted and verified as positive regulators of oil content
.

This study comprehensively characterized the gene regulation characteristics of developing seeds of rapeseed and constructed an eQTL database
of rapeseed.
The candidate genes of hot eQTL regulating rapeseed oil content were predicted by multi-omics analysis methods, two transcription factors regulating oil content were successfully cloned, and a regulatory network
for oil content in rapeseed was constructed.
The results provide a basis and rich genetic resources for the subgenomic asymmetric regulation of polyploid plants, and also provide an important theoretical basis
for the genetic improvement of rapeseed oil content.

Tan Zengdong, a doctoral student at the National Key Laboratory of Crop Genetic Improvement of Huazhong Agricultural University, is the first author of the paper, and Dr.
Zhao Hu, Professor Guo Liang and Professor Xie Weibo are the corresponding authors
of the paper.
Professor Rod Snowdon and Dr.
Agnieszka Golicz of the University of Giessen, Professor Liu Kede, Associate Professor Yao Xuan and Associate Professor Lu Shaoping of Huazhong Agricultural University participated in the study
.
This research was supported
by the National Science Foundation for Outstanding Young Scholars, the National Natural Science Foundation of China Youth Fund, and the major project of Hubei Hongshan Laboratory.

Reviewer: Guo Liang

【English Abstract】

Background

Regulation of gene expression plays an essential role in controlling the phenotypes of plants.
Brassica napus (B.
napus) is an important source for the vegetable oil in the world, and the seed oil content is an important trait of B.
napus.

Results

We perform a comprehensive analysis of the transcriptional variability in the seeds of B.
napus at two developmental stages, 20 and 40 days after flowering (DAF).
We detect 53,759 and 53,550 independent expression quantitative trait loci (eQTLs) for 79,605 and 76,713 expressed genes at 20 and 40 DAF, respectively.
Among them, the local eQTLs are mapped to the adjacent genes more frequently.
The adjacent gene pairs are regulated by local eQTLs with the same open chromatin state and show a stronger mode of expression piggybacking.
Inter-subgenomic analysis indicates that there is a feedback regulation for the homoeologous gene pairs to maintain partial expression dosage.
We also identify 141 eQTL hotspots and find that hotspot87-88 co-localizes with a QTL for the seed oil content.
To further resolve the regulatory network of this eQTL hotspot, we construct the XGBoost model using 856 RNA-seq datasets and the Basenji model using 59 ATAC-seq datasets.
Using these two models, we predict the mechanisms affecting the seed oil content regulated by hotspot87-88 and experimentally validate that the transcription factors, NAC13 and SCL31, positively regulate the seed oil content.

Conclusions

We comprehensively characterize the gene regulatory features in the seeds of B.
napus and reveal the gene networks regulating the seed oil content of B.
napus.

Link to the paper: https://genomebiology.
biomedcentral.
com/articles/10.
1186/s13059-022-02801-z