Proteomics and Co-expression Network Analysis Reveal the Importance of Hub Proteins and Metabolic Pathways in Nicotine Synthesis and Accumulation in Tobacco (Nicotiana tabacum L.).

Nicotine is a unique alkaloid present in tobacco that is widely used in cigarettes and in the agricultural, chemical, and pharmaceutical industries. However, the research on nicotine is mostly limited to its synthesis pathways, and only a few studies have explored the effects of other metabolic pathways on nicotine precursors. Regulating the nicotine content in tobacco can greatly promoting the application of nicotine in other fields. In this study, we performed global data-independent acquisition proteomics analysis of four tobacco varieties. Of the four varieties, one had high nicotine content and three had a low nicotine content. A total of 31,259 distinct peptides and 6,018 proteins across two samples were identified. A total of 45 differentially expressed proteins (DEPs) co-existed in the three comparison groups and were mainly involved in the transport and metallic processes of the substances. Most DEPs were enriched in the biosynthesis of secondary metals, glutathione metabolism, carbon metabolism, and glycolysis/gluconeogenesis. In addition, the weighted gene co-expression network analysis identified an expression module closely related to the nicotine content (Brown, r = 0.74, P = 0.006). Gene Ontology annotation and Kyoto Encyclopaedia of Genes and Genomes enrichment analysis showed that the module proteins were mainly involved in the synthesis and metabolism of nicotine precursors such as arginine, ornithine aspartate, proline, and glutathione. The increased levels of these precursors lead to the synthesis and accumulation of nicotine in plants. More importantly, these proteins regulate nicotine synthesis by affecting the formation of putrescine, which is the core intermediate product in nicotine anabolism. Our results provide a reference for tobacco variety selection with a suitable nicotine content and regulation of the nicotine content. Additionally, the results highlight the importance of other precursor metabolism in nicotine synthesis.

Effect of the over-dominant expression of proteins on nicotine heterosis via proteomic analysis.

Heterosis is a common biological phenomenon that can be used to optimize yield and quality of crops. Using heterosis breeding, hybrids with suitable nicotine content have been applied to tobacco leaf production. However, the molecular mechanism of the formation of nicotine heterosis has never been explained from the perspective of protein. The DIA proteomics technique was used to compare the differential proteomics of the hybrid Va116 × Basma, showing strong heterosis in nicotine content from its parent lines Va116 and Basma. Proteomics analysis indicated that 65.2% of DEPs showed over-dominant expression patterns, and these DEPs included QS, BBL, GS, ARAF and RFC1 which related to nicotine synthesis. In addition, some DEPs (including GST, ABCE2 and ABCF1 and SLY1) that may be associated with nicotinic transport exhibited significant heterosis over the parental lines. These findings demonstrated that the efficiency of the synthesis and transport of nicotine in hybrids was significantly higher than that in the parent lines, and the accumulation of over-dominant expression proteins may be the cause of heterosis of nicotinic content in hybrids.

Gene Regulatory Relationship Mining Using Improved Three-Phase Dependency Analysis Approach.

How to mine the gene regulatory relationship and construct gene regulatory network (GRN) is of utmost interest within the whole biological community, however, which has been consistently a challenging problem since the tremendous complexity in cellular systems. In present work, we construct gene regulatory network using an improved three-phase dependency analysis algorithm (TPDA) Bayesian network learning method, which includes the steps of Drafting, Thickening, and Thinning. In order to solve the problem of learning result is not reliable due to the high order conditional independence test, we use the entropy estimation approach of Gaussian kernel probability density estimator to calculate the (conditional) mutual information between genes. The experiment on the public benchmark data sets show the improved method outperforms the other nine kinds of Bayesian network learning methods when to process the data with large sample size, with small number of discrete values, and the frequency of different discrete values is about same. In addition, the improved TPDA method was further applied on a real large gene expression data set on RNA-seq from a global collection with 368 elite maize inbred lines. Experiment results show it performs better than the original TPDA method and the other nine kinds of Bayesian network learning algorithms significantly.

Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining.

BACKGROUND AND OBJECTIVE: Mining the genes related to maize carotenoid components is important to improve the carotenoid content and the quality of maize. METHODS: On the basis of using the entropy estimation method with Gaussian kernel probability density estimator, we use the three-phase dependency analysis (TPDA) Bayesian network structure learning method to construct the network of maize gene and carotenoid components traits. RESULTS: In the case of using two discretization methods and setting different discretization values, we compare the learning effect and efficiency of 10 kinds of Bayesian network structure learning methods. The method is verified and analyzed on the maize dataset of global germplasm collection with 527 elite inbred lines. CONCLUSIONS: The result confirmed the effectiveness of the TPDA method, which outperforms significantly another 9 kinds of Bayesian network learning methods. It is an efficient method of mining genes for maize carotenoid components traits. The parameters obtained by experiments will help carry out practical gene mining effectively in the future.

MODEM: multi-omics data envelopment and mining in maize.

MODEM is a comprehensive database of maize multidimensional omics data, including genomic, transcriptomic, metabolic and phenotypic information from the cellular to individual plant level. This initial release contains approximately 1.06 M high quality SNPs for 508 diverse inbred lines obtained by combining variations from RNA sequencing on whole kernels (15 days after pollination) of 368 lines and a 50 K array for all 508 individuals. As all of these data were derived from the same diverse panel of lines, the database also allows various types of genetic mapping (including characterization of phenotypic QTLs, pQTLs; expression QTLs, eQTLs and metabolic QTLs, mQTLs). MODEM is thus designed to promote a better understanding of maize genetic architecture and deep functional annotation of the complex maize genome (and potentially those of other crop plants) and to explore the genotype-phenotype relationships and regulation of maize kernel development at multiple scales, which is also comprehensive for developing novel methods. MODEM is additionally designed to link with other databases to make full use of current resources, and it provides visualization tools for easy browsing. All of the original data and the related mapping results are freely available for easy query and download. This platform also provides helpful tools for general analyses and will be continually updated with additional materials, features and public data related to maize genetics or regulation as they become available.Database URL: (http://modem.hzau.edu.cn).