Combined Analysis of BSA-Seq Based Mapping, RNA-Seq, and Metabolomic Unraveled Candidate Genes Associated with Panicle Grain Number in Rice (Oryza sativa L.)

Abstract

Rice grain yield is a complex and highly variable quantitative trait consisting of several key components, including the grain weight, the effective panicles per unit area, and the grain number per panicle (GNPP). The GNPP is a significant contributor to grain yield controlled by multiple genes (QTL) and is crucial for improvement. Attempts have been made to find genes for this trait, which has always been a challenging and arduous task through conventional methods. We combined a BSA analysis, RNA profiling, and a metabolome analysis in the present study to identify new candidate genes involved in the GNPP. The F2 population from crossing R4233 (high GNPP) and Ce679 (low GNPP) revealed a frequency distribution fitting two segregated genes. Three pools, including low, middle, and high GNPP, were constructed and a BSA analysis revealed six candidate regions spanning 5.38 Mb, containing 739 annotated genes. Further, a conjunctive analysis of BSA-Seq and RNA-Seq showed 31 differentially expressed genes (DEGs) in the candidate intervals. Subsequently, a metabolome analysis showed 1024 metabolites, with 71 significantly enriched, including 44 up and 27 downregulated in Ce679 vs. R4233. A KEGG enrichment analysis of these 31 DEGs and 71 differentially enriched metabolites (DEMs) showed two genes, Os12g0102100 and Os01g0580500, significantly enriched in the metabolic pathways’ biosynthesis of secondary metabolites, cysteine and methionine metabolism, and fatty acid biosynthesis. Os12g0102100, which encodes for the alcohol dehydrogenase superfamily and a zinc-containing protein, is a novel gene whose contribution to the GNPP is not yet elucidated. This gene coding for mitochondrial trans-2-enoyl-CoA reductase is involved in the biosynthesis of myristic acid, also known as tetradecanoic acid. The Os01g0580500 coding for the enzyme 1-aminoclopropane-1-carboxylate oxidase (OsACO7) is responsible for the final step of the ethylene biosynthesis pathway through the conversion of 1-aminocyclopropane-1-carboxylic acid (ACC) into ethylene. Unlike Os12g0102100, this gene was significantly upregulated in R4233, downregulated in Ce679, and significantly enriched in two of the three metabolite pathways. This result pointed out that these two genes are responsible for the difference in the GNPP in the two cultivars, which has never been identified. Further validation studies may disclose the physiological mechanisms through which they regulate the GNPP in rice.

New Genes Identified as Modulating Salt Tolerance in Maize Seedlings Using the Combination of Transcriptome Analysis and BSA

“…The cetyltrimethylammonium bromide (CTAB) method was used to obtain high-quality genomic DNA [ 45 ]. DNA from 30 plants representing tolerance and sensitivity was equally mixed to form tolerance and sensitivity pools [ 25 ].…”

“…This research indicates that natural genetic diversities provide valuable sources for identifying new candidate genes for breeding salt-tolerant crops. Recently, new strategies that combine several emerging technologies (Bulked Segregate Analysis, RNA-sequencing, metabolome analysis, or phosphoproteomic analysis) have been carried out to find new genes associated with several traits, such as the anthocyanin in cucumber ( Cucumis sativus L.) fruit skin [ 24 ], the panicle grain number in rice [ 25 ], and the plant architecture in Brassica napus [ 26 ].…”

Detection of QTLs Regulating Six Agronomic Traits of Rice Based on Chromosome Segment Substitution Lines of Common Wild Rice (Oryza rufipogon Griff.) and Mapping of qPH1.1 and qLMC6.1

“…The phenotypic characteristics of the DP15-CSSLs mature plants, including grain number (GN), TGW, GL, GW, GLWR, PH, LMC, AC, and LSC, were recorded under natural conditions in the experimental field during 2020 (from 20 November 2019 to 10 May 2020) in Sanya, China, and 2021 (from 2 March 2021 to 10 July 2021) in Nanning, Guangxi Province, China, respectively ( Figure 3 , Figures S2 and S3, and Table S5 ). When the seeds were matured under natural conditions, the dry seeds of each line were selected to measure the values of grain traits as previously described [ 47 ]. The tissue slices were processed by the frozen section machine Hestion CM2850.…”

The GW5‐WRKY53‐SGW5 module regulates grain size variation in rice

“…Rice grain yield is a complex trait, which influenced by many other agronomic traits, such as the number of panicles per plant, grain numbers per panicle, grain weight, and grain filling ratio (Ma et al ., 2022). Grain size is one of the most important factors determining grain yield, appearance, and milling quality in rice.…”