Unravelling the Complex Interplay of Transcription Factors Orchestrating Seed Oil Content in Brassica napus L

Mechanisms of low nighttime temperature promote oil accumulation in Brassica napus L. based on in-depth transcriptome analysis

Rape (Brassica napus L.; AACC) is an important oil-bearing crop worldwide. Temperature significantly affects the production of oil crops; however, the mechanisms underlying temperature-promoted oil biosynthesis remain largely unknown. In this study, we found that a temperature-sensitive cultivar (O) could accumulate higher seed oil content under low nighttime temperatures (LNT,13°C) compared with a temperature-insensitive cultivar (S). We performed an in-depth transcriptome analysis of seeds from both cultivars grown under different nighttime temperatures. We found that low nighttime temperatures induced significant changes in the transcription patterns in the seeds of both cultivars. In contrast, the expression of genes associated with fatty acid and lipid pathways was higher in the O cultivar than in the S cultivar under low nighttime temperatures. Among these genes, we identified 14 genes associated with oil production, especially BnLPP and ACAA1, which were remarkably upregulated in the O cultivar in response to low nighttime temperatures compared to S. Further, a WGCNA analysis and qRT-PCR verification revealed that these genes were mainly regulated by five transcription factors, WRKY20, MYB86, bHLH144, bHLH95, and NAC12, whose expression was also increased in O compared to S under LNT. These results allowed the elucidation of the probable molecular mechanism of oil accumulation under LNT conditions in the O cultivar. Subsequent biochemical assays verified that BnMYB86 transcriptionally activated BnLPP expression, contributing to oil accumulation. Meanwhile, at LNT, the expression levels of these genes in the O plants were higher than at high nighttime temperatures, DEGs (SUT, PGK, PK, GPDH, ACCase, SAD, KAS II, LACS, FAD2, FAD3, KCS, KAR, ECR, GPAT, LPAAT, PAP, DGAT, STERO) related to lipid biosynthesis were also upregulated, most of which are used in oil accumulation.

Effects of Low Nighttime Temperature on Fatty Acid Content in Developing Seeds from Brassica napus L. Based on RNA-Seq and Metabolome

Brassica napus L. is a vital plant oil resource worldwide. The fatty acid biosynthesis and oil accumulation in its seeds are controlled by several genetic and environmental factors, including daytime and nighttime temperatures. We analyzed changes in oleic and erucic acid content in two double haploid (DH) lines, DH0729, a weakly temperature-sensitive line, and DH0815, a strongly temperature-sensitive line, derived from B. napus plants grown at different altitudes (1600, 1800, 2000, 2200, and 2400 m a.s.l., 28.85° N, 112.35° E) and nighttime temperatures (20/18, 20/16, 20/13 and 20/10 °C, daytime/nighttime temperature). Based on medium- and long-chain fatty acid metabolites, the total oleic acid content 35 and 43 days after flowering was significantly lower in low nighttime temperature (LNT, 20/13 °C) plants than in high nighttime temperature (HNT, 20/18 °C) plants (HNT: 58-62%; LNT: 49-54%; an average decrease of 9%), and the total erucic acid content was significantly lower in HNT than in LNT plants (HNT: 1-2%; LNT: 8-13%; an average increase of 10%). An RNA-seq analysis showed that the expression levels of SAD (LOC106366808), ECR (LOC106396280), KCS (LOC106419344), KAR (LOC106367337), HB1(LOC106430193), and DOF5 (LOC111211868) in STSL seeds increased under LNT conditions. In STSL seeds, a base mutation in the cis-acting element involved in low-temperature responsiveness (LTR), the HB1 and KCS promoter caused loss of sensitivity to low temperatures, whereas that of the KCS promoter caused increased sensitivity to low temperatures.

Gene regulatory network inference methodology for genomic and transcriptomic data acquired in genetically related heterozygote individuals

MOTIVATION

Inferring gene regulatory networks in non-independent genetically related panels is a methodological challenge. This hampers evolutionary and biological studies using heterozygote individuals such as in wild sunflower populations or cultivated hybrids.

RESULTS

First, we simulated 100 datasets of gene expressions and polymorphisms, displaying the same gene expression distributions, heterozygosities and heritabilities as in our dataset including 173 genes and 353 genotypes measured in sunflower hybrids. Secondly, we performed a meta-analysis based on six inference methods [least absolute shrinkage and selection operator (Lasso), Random Forests, Bayesian Networks, Markov Random Fields, Ordinary Least Square and fast inference of networks from directed regulation (Findr)] and selected the minimal density networks for better accuracy with 64 edges connecting 79 genes and 0.35 area under precision and recall (AUPR) score on average. We identified that triangles and mutual edges are prone to errors in the inferred networks. Applied on classical datasets without heterozygotes, our strategy produced a 0.65 AUPR score for one dataset of the DREAM5 Systems Genetics Challenge. Finally, we applied our method to an experimental dataset from sunflower hybrids. We successfully inferred a network composed of 105 genes connected by 106 putative regulations with a major connected component.

AVAILABILITY AND IMPLEMENTATION

Our inference methodology dedicated to genomic and transcriptomic data is available at https://forgemia.inra.fr/sunrise/inference_methods.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deciphering Pleiotropic Signatures of Regulatory SNPs in Zea mays L. Using Multi-Omics Data and Machine Learning Algorithms

Maize is one of the most widely grown cereals in the world. However, to address the challenges in maize breeding arising from climatic anomalies, there is a need for developing novel strategies to harness the power of multi-omics technologies. In this regard, pleiotropy is an important genetic phenomenon that can be utilized to simultaneously enhance multiple agronomic phenotypes in maize. In addition to pleiotropy, another aspect is the consideration of the regulatory SNPs (rSNPs) that are likely to have causal effects in phenotypic development. By incorporating both aspects in our study, we performed a systematic analysis based on multi-omics data to reveal the novel pleiotropic signatures of rSNPs in a global maize population. For this purpose, we first applied Random Forests and then Markov clustering algorithms to decipher the pleiotropic signatures of rSNPs, based on which hierarchical network models are constructed to elucidate the complex interplay among transcription factors, rSNPs, and phenotypes. The results obtained in our study could help to understand the genetic programs orchestrating multiple phenotypes and thus could provide novel breeding targets for the simultaneous improvement of several agronomic traits.

agReg-SNPdb-Plants: A Database of Regulatory SNPs for Agricultural Plant Species

Simple Summary

In breeding research, the investigation of regulatory SNPs (rSNPs) is becoming increasingly important due to their potential causal role for specific functional traits. Especially for crop species, there is still a lack of systematic analyses to detect rSNPs and their predicted effects on the binding of transcription factors. In this study, we present agReg-SNPdb-Plants, a database storing genome-wide collections of regulatory SNPs for agricultural plant species which can be queried via a web interface.

Abstract

Single nucleotide polymorphisms (SNPs) that are located in the promoter regions of genes and affect the binding of transcription factors (TFs) are called regulatory SNPs (rSNPs). Their identification can be highly valuable for the interpretation of genome-wide association studies (GWAS), since rSNPs can reveal the biologically causative variant and decipher the regulatory mechanisms behind a phenotype. In our previous work, we presented agReg-SNPdb, a database of regulatory SNPs for agriculturally important animal species. To complement this previous work, in this study we present the extension agReg-SNPdb-Plants storing rSNPs and their predicted effects on TF-binding for 13 agriculturally important plant species and subspecies (Brassica napus, Helianthus annuus, Hordeum vulgare, Oryza glaberrima, Oryza glumipatula, Oryza sativa Indica, Oryza sativa Japonica, Solanum lycopersicum, Sorghum bicolor, Triticum aestivum, Triticum turgidum, Vitis vinifera, and Zea mays). agReg-SNPdb-Plants can be queried via a web interface that allows users to search for SNP IDs, chromosomal regions, or genes. For a comprehensive interpretation of GWAS results or larger SNP-sets, it is possible to download the whole list of SNPs and their impact on transcription factor binding sites (TFBSs) from the website chromosome-wise.

Transcriptional Regulation of Quinoa Seed Quality: Identification of Novel Candidate Genetic Markers for Increased Protein Content

Quinoa (Chenopodium quinoa Willd.) is a crop that has great potential for increased cultivation in diverse climate regions. The seed protein quality obtained from this crop is high concerning the requirements to meet human nutritional needs, but the seed protein content is relatively low if compared to crops such as grain legumes. Increased seed protein content is desirable for increasing the economic viability of this crop in order for it to be used as a protein crop. In this study, we characterized three genotypes of quinoa with different levels of seed protein content. By performing RNA sequencing of developing seeds, we determined the genotype differences in gene expression and identified genetic polymorphisms that could be associated with increased protein content. Storage nutrient analyses of seeds of three quinoa genotypes (Titicaca, Pasankalla, and Regalona) from different ecoregions grown under controlled climate conditions showed that Pasankalla had the highest protein content (20%) and the lowest starch content (46%). Our seed transcriptome analyses revealed highly differentially expressed transcripts (DETs) in Pasankalla as compared to the other genotypes. These DETs encoded functions in sugar transport, starch and protein synthesis, genes regulating embryo size, and seed transcription factors. We selected 60 genes that encode functions in the central carbon metabolism and transcription factors as potential targets for the development of high-precision markers. Genetic polymorphisms, such as single nucleotide polymorphisms (SNPs) and base insertions and deletions (InDels), were found in 19 of the 60 selected genes, which can be further evaluated for the development of genetic markers for high seed protein content in quinoa. Increased cultivation of quinoa can contribute to a more diversified agriculture and support the plant protein diet shift. The identification of quinoa genotypes with contrasting seed quality can help establish a model system that can be used for the identification of precise breeding targets to improve the seed quality of quinoa. The data presented in this study based on nutrient and transcriptome analyses contribute to an enhanced understanding of the genetic regulation of seed quality traits in quinoa and suggest high-precision candidate markers for such traits.

Identification of the Complex Interplay Between Nematode-Related lncRNAs and Their Target Genes in Glycine max L

Soybean (Glycine max) is a major plant protein source and oilseed crop. However, plant-parasitic nematodes (PPNs) affect its annual yield. In the current study, in order to better understand the regulation of defense mechanism against PPNs in soybean, we investigated the role of long non-coding RNAs (lncRNAs) in response to two nematode species, Heterodera glycines (SCN: soybean cyst nematode) and Rotylenchulus reniformis (reniform). To this end, two publicly available RNA-seq data sets (SCN data set and RAD: reniform-associated data set) were employed to discover the lncRNAome profile of soybean under SCN and reniform infection, respectively. Upon identification of unannotated transcripts in these data sets, a seven-step pipeline was utilized to sieve these transcripts, which ended up in 384 and 283 potential lncRNAs in SCN data set and RAD, respectively. These transcripts were then used to predict cis and trans nematode-related targets in soybean genome. Computational prediction of target genes function, some of which were also among differentially expressed genes, revealed the involvement of putative nematode-responsive genes as well as enrichment of multiple stress responses in both data sets. Finally, 15 and six lncRNAs were proposed to be involved in microRNA-mediated regulation of gene expression in soybean in response to SNC and reniform infection, respectively. Collectively, this study provides a novel insight into the signaling and regulatory network of soybean-pathogen interactions and opens a new window for further research.