Meta-analysis of transcriptome reveals key genes relating to oil quality in olive.

BACKGROUND: Olive oil contains monounsaturated oleic acid up to 83% and phenolic compounds, making it an excellent source of fat. Due to its economic importance, the quantity and quality of olive oil should be improved in parallel with international standards. In this study, we analyzed the raw RNA-seq data with a meta-analysis approach to identify important genes and their metabolic pathways involved in olive oil quality. RESULTS: A deep search of RNA-seq published data shed light on thirty-nine experiments associated with the olive transcriptome, four of these proved to be ideal for meta-analysis. Meta-analysis confirmed the genes identified in previous studies and released new genes, which were not identified before. According to the IDR index, the meta-analysis had good power to identify new differentially expressed genes. The key genes were investigated in the metabolic pathways and were grouped into four classes based on the biosynthetic cycle of fatty acids and factors that affect oil quality. Galactose metabolism, glycolysis pathway, pyruvate metabolism, fatty acid biosynthesis, glycerolipid metabolism, and terpenoid backbone biosynthesis were the main pathways in olive oil quality. In galactose metabolism, raffinose is a suitable source of carbon along with other available sources for carbon in fruit development. The results showed that the biosynthesis of acetyl-CoA in glycolysis and pyruvate metabolism is a stable pathway to begin the biosynthesis of fatty acids. Key genes in oleic acid production as an indicator of oil quality and critical genes that played an important role in production of triacylglycerols were identified in different developmental stages. In the minor compound, the terpenoid backbone biosynthesis was investigated and important enzymes were identified as an interconnected network that produces important precursors for the synthesis of a monoterpene, diterpene, triterpene, tetraterpene, and sesquiterpene biosynthesis. CONCLUSIONS: The results of the current investigation can produce functional data related to the quality of olive oil and would be a useful step in reducing the time of cultivar screening by developing gene specific markers in olive breeding programs, releasing also new genes that could be applied in the genome editing approach.

Comprehensive functional analysis and mapping of SSR markers in the chickpea genome (Cicer arietinum L.).

Plant molecular breeding largely depends on the relationship between molecular markers and major traits. Herein, a total of 32,962 genomic simple sequence repeats (SSRs) were detected in the whole genome of chickpea with an average density of 94.93 SSRs/Mb. Chickpea chromosomes uniformity test indicated that the genomic SSRs (gSSRs) were steadily distributed across the genome. Moreover, 48,667 transcriptome sequences were analyzed and 1949 SSR-containing transcript assembly contigs (TACs) were identified. The analysis showed that di- and trinucleotide SSRs were the most frequent SSR motifs within the transcriptome sequences. Among them, AT and TTA and AG and TTC motifs within the transcriptome showed the highest frequencies among di- and trinucleotide repeat motifs, respectively. The SSRs-containing TACs were compared to the GenBank non-redundant database using BLASTX, and subsequently, gene ontology (GO) analysis was performed using QuickGO browser to reduce complexity and highlight biological processes associated with the SSRs-containing TACs. The identified SSRs-containing TACs were categorized into 35 enriched functional-related gene group. The mapping of characterized SSRs-containing TACs onto chickpea chromosomes was performed using BLASTN. The mapping result showed that, a total of 1798 SSRs-containing TACs were mapped onto the chickpea genome. Based on the functional analysis result, 249 and 242 of the mapped SSRs-containing TACs were found in the genes encoding for putative stress-related proteins and transcription factors, respectively. The results presented here can be applied to improve and speed up the chickpea breeding programs.