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Background Sesame (Sesamum indicum L.) leaves, flowers, especially seeds are used in traditional medicine to prevent or cure various diseases. Its seed’s market is expanding. However, the other tissues are still underexploited due to the lack of information related to metabolites distribution and variability in the plant. Herein, the metabolite profiles of five sesame tissues (leaves, fresh seeds, white and purple flowers, and fresh carpels) have been investigated using ultra-high-performance liquid chromatography-mass spectrometry (UPLC-MS/MS)-based widely targeted metabolomics analysis platform. Results In total, 776 metabolites belonging to diverse classes were qualitatively and quantitatively identified. The different tissues exhibited obvious differences in metabolites composition. The majority of flavonoids predominantly accumulated in flowers. Amino acids and derivatives, and lipids were identified predominantly in fresh seeds followed by flowers. Many metabolites, including quinones, coumarins, tannins, vitamins, terpenoids and some bioactive phenolic acids (acteoside, isoacteoside, verbascoside, plantamajoside, etc.) accumulated mostly in leaves. Lignans were principally detected in seeds. 238 key significantly differential metabolites were filtered out. KEGG annotation and enrichment analyses of the differential metabolites revealed that flavonoid biosynthesis, amino acids biosynthesis, and phenylpropanoid biosynthesis were the main differently regulated pathways. In addition to the tissue-specific accumulation of metabolites, we noticed a cooperative relationship between leaves, fresh carpels, and developing seeds in terms of metabolites transfer. Delphinidin-3-O-(6ʺ-O-p-coumaroyl)glucoside and most of the flavonols were up-regulated in the purple flowers indicating they might be responsible for the purple coloration. Conclusion This study revealed that the metabolic processes in the sesame tissues are differently regulated. It offers valuable resources for investigating gene-metabolites interactions in sesame tissues and examining metabolic transports during seed development in sesame. Furthermore, our findings provide crucial knowledge that will facilitate sesame biomass valorization.

Sesame (Sesamum indicum L.) is an important and ancient oilseed crop. Sesame seed coat color is related to biochemical functions involved in protein and oil metabolism, and antioxidant content. Because of its complication, the genetic basis of sesame seed coat color remains poorly understood. To elucidate the factors affecting the genetic architecture of seed coat color, 366 sesame germplasm lines were evaluated for seed coat color in 12 environments. The genome-wide association studies (GWAS) for three seed coat color space values, best linear unbiased prediction (BLUP) values from a multi-environment trial analysis and principal component scores (PCs) of three seed coat color space values were conducted. GWAS for three seed coat color space values identified a total of 224 significant single nucleotide polymorphisms (SNPs, P < 2.34×10−7), with phenotypic variation explained (PVE) ranging from 1.01% to 22.10%, and 35 significant SNPs were detected in more than 6 environments. Based on BLUP values, 119 significant SNPs were identified, with PVE ranging from 8.83 to 31.98%. Comparing the results of the GWAS using phenotypic data from different environments and the BLUP values, all significant SNPs detected in more than 6 environments were also detected using the BLUP values. GWAS for PCs identified 197 significant SNPs, and 30 were detected in more than 6 environments. GWAS results for PCs were consistent with those for three color space values. Out of 224 significant SNPs, 22 were located in the confidence intervals of previous reported quantitative trait loci (QTLs). Finally, 92 candidate genes were identified in the vicinity of the 4 SNPs that were most significantly associated with sesame seed coat color. The results in this paper will provide new insights into the genetic basis of sesame seed coat color, and should be useful for molecular breeding in sesame.

Leaf size is a crucial component of sesame (Sesamum indicum L.) plant architecture and further influences yield potential. Despite that it is well known that leaf size traits are quantitative traits controlled by large numbers of genes, quantitative trait loci (QTL) and candidate genes for sesame leaf size remain poorly understood. In the present study, we combined the QTL-seq approach and SSR marker mapping to identify the candidate genomic regions harboring QTL controlling leaf size traits in an RIL population derived from a cross between sesame varieties Zhongzhi No. 13 (with big leaves) and ZZM2289 (with small leaves). The QTL mapping revealed 56 QTL with phenotypic variation explained (PVE) from 1.87 to 27.50% for the length and width of leaves at the 1/3 and 1/2 positions of plant height. qLS15-1, a major and environmentally stable pleiotropic locus for both leaf length and width explaining 5.81 to 27.50% phenotypic variation, was located on LG15 within a 408-Kb physical genomic region flanked by the markers ZMM6185 and ZMM6206. In this region, a combination of transcriptome analysis with gene annotations revealed three candidate genes SIN_1004875, SIN_1004882, and SIN_1004883 associated with leaf growth and development in sesame. These findings provided insight into the genetic characteristics and variability for sesame leaf and set up the foundation for future genomic studies on sesame leaves and will serve as gene resources for improvement of sesame plant architecture.

Ego depletion has been found to moderate the effect of implicit preferences on food consumption, such that implicit preferences predict consumption only under a depleted state. The present study tested how trait impulsivity impacts the effect of implicit preferences on food consumption in a depleted condition. Trait impulsivity was measured by means of self-report and a stop signal task. Results showed that both self-reported impulsivity and behavioral impulsivity moderated the ‘depletion and then eating according to implicit preferences’ effect, albeit in different ways. Participants high in self-reported impulsivity and low in behavioral impulsivity were more vulnerable to the effect of depletion on eating. The implications of these results for extant theories are discussed. Future research is needed to verify whether or not trait impulsivity is associated with vulnerability to depletion across different self-control domains.