已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Integrated transcriptomic and volatilomic profiles to explore the potential mechanism of aroma formation in Toona sinensis

芳香 机制(生物学) 化学 转录组 食品科学 生物 生物化学 基因 基因表达 物理 量子力学
作者
Cheng Wang,Beibei Zhang,Yanfang Li,Jing Hou,Chendan Fu,Zihui Wang,Jingfang Zhang
出处
期刊:Food Research International [Elsevier BV]
卷期号:165: 112452-112452 被引量:19
标识
DOI:10.1016/j.foodres.2022.112452
摘要

As an important quality determinant of Toona sinensis, the unique aroma largely impacts the purchasing behavior of consumers. However, the underlying formation mechanism of the characteristic aroma of T. sinensis remains poorly understood. In this work, integrative volatile/nonvolatile compounds profiling and RNA sequencing were used to characterize six T. sinensis cultivars. Volatile sulfur compounds (VSCs) and terpenoids were the main volatile organic compounds (VOCs) in T. sinensis, accounting for 36.95-67.27% and 17.75-31.36% of the total VOC content, respectively. Notably, the VOCs originated from terpenoid biosynthesis, and the degradation of unsaturated fatty acids (UFAs) played important roles in reconciling the irritating odor of VSCs. The above differential metabolic profiles are the main sources of the specific aroma of different T. sinensis cultivars. Furthermore, 13 volatile organic compounds were identified as potential biomarkers to distinguish these T. sinensis cultivars by chemometric analysis. Based on the analysis of transcriptomic datasets, the potential biosynthetic pathways of the key VSCs were firstly confirmed in T. sinensis. It was found that 1-propenylsulfenic acid is a crucial precursor in the formation of characteristic VSCs in T. sinensis. Additionally, two potential mechanisms were proposed to explain the differences of the key VSCs among six T. sinensis cultivars. These results provide theoretical guidance for improving the aroma quality of T. sinensis.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
LC1028发布了新的文献求助10
1秒前
3秒前
5秒前
WML发布了新的文献求助10
7秒前
今天吃什么呢完成签到 ,获得积分10
7秒前
CodeCraft应助啦啦啦采纳,获得10
8秒前
安详鞋垫完成签到 ,获得积分10
8秒前
8秒前
9秒前
kmy完成签到 ,获得积分10
9秒前
钦钦发布了新的文献求助10
10秒前
大胆的白卉完成签到 ,获得积分10
15秒前
曹星星完成签到 ,获得积分10
17秒前
共享精神应助MutantKitten采纳,获得10
18秒前
19秒前
Jasper应助jungle采纳,获得10
22秒前
22秒前
桐桐应助英俊001采纳,获得10
23秒前
23秒前
洒脱发布了新的文献求助10
25秒前
26秒前
啦啦啦发布了新的文献求助10
26秒前
科研通AI6.2应助无限若云采纳,获得10
26秒前
lwp发布了新的文献求助30
27秒前
zara完成签到,获得积分10
28秒前
28秒前
28秒前
赘婿应助芋圆采纳,获得10
29秒前
初景发布了新的文献求助30
29秒前
科研通AI6.4应助洒脱采纳,获得10
31秒前
Olivia完成签到 ,获得积分10
32秒前
shijia完成签到 ,获得积分10
34秒前
Leungcc发布了新的文献求助10
35秒前
义气的代曼完成签到,获得积分10
36秒前
36秒前
ding应助蛋挞好好吃采纳,获得30
36秒前
上官若男应助梦幻时空采纳,获得30
39秒前
李娅发布了新的文献求助10
40秒前
CipherSage应助忧郁的期待采纳,获得10
41秒前
NexusExplorer应助x1采纳,获得10
42秒前
高分求助中
卤化钙钛矿人工突触的研究 2000
Malcolm Fraser : a biography 700
Signals, Systems, and Signal Processing 610
Software that combines deep learning,3D reconstruction and CFD to analyze the state of carotid arteries from ultrasound imaging 500
Bounds for Statistical Estimation in Semiparametric Models 500
Forced degradation and stability indicating LC method for Letrozole: A stress testing guide 500
Ideology and Meaning-Making under the Putin Regime 450
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6494723
求助须知:如何正确求助?哪些是违规求助? 8291762
关于积分的说明 17694039
捐赠科研通 5587959
什么是DOI,文献DOI怎么找? 2916277
邀请新用户注册赠送积分活动 1893208
关于科研通互助平台的介绍 1752086