Functional genomic study to identify key genes involved in terpenoid and rosmarinic acid biosynthesis in lemon balm (Melissa officinalis)

作者
Mehdi Mansouri,Fatemeh Mohammadi
出处
期刊:CERN European Organization for Nuclear Research - Zenodo [European Organization for Nuclear Research]
标识
DOI:10.5281/zenodo.3685522
摘要

Melissa officinalis (lemon balm) is a medicinal plant producing high-value secondary metabolites that are used in traditional medicine all over the world since the earliest times. Despite pharmacological importance, there is a lack of knowledge about the genes and enzymes involved in secondary metabolite biosynthetic pathways in lemon balm. We identified the key genes and pathways associated with biosynthesis of terpenoid and rosmarinic acid through functional analysis of transcriptomic data. In this study, a comprehensive transcriptome assembly containing 37,055 unigenes was generated by analyzing 42,837,601 Illumina paired-end reads by employing an efficient pipeline. Functional annotation of the unigenes showed that 35,822 (96.67%) and 27,363 (73.84%) had BLAST hits to known proteins in the NR and SwissProt databases, respectively. The KEGG pathway analysis revealed that 10,062 (36.83%) unigenes were assigned to 399 KEGG pathways. The focus of this study was on pathways associated with the production of important metabolites such as terpenes and rosmarinic acid. A total of 149 unigenes were identified that are associated with biosynthesis of terpenoids, including 75 mevalonate and methyl-erythritol phosphate (MEP) pathway genes, terpenoid backbone biosynthesis genes, and 74 terpene synthase genes. Furthermore, 144 and 30 unigenes were detected that are related to the phenylpropanoid biosynthesis and the rosmarinic acid pathway. Therefore, this study lays an accurate and comprehensive transcriptome foundation for future research in the metabolic engineering and identification of novel genes and pathways in lemon balm.

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
Chen发布了新的文献求助10
1秒前
chewy完成签到 ,获得积分10
1秒前
rainy发布了新的文献求助10
2秒前
酸海椒发布了新的文献求助10
3秒前
Xin发布了新的文献求助10
3秒前
4秒前
黎先生完成签到 ,获得积分10
6秒前
愤怒的茉莉完成签到,获得积分10
6秒前
英姑应助壮观人达采纳,获得10
7秒前
枕星河完成签到,获得积分10
8秒前
9秒前
麦奇完成签到,获得积分10
10秒前
4114完成签到,获得积分10
11秒前
小二郎应助下着星星雨采纳,获得10
13秒前
华仔应助大力的图图采纳,获得10
15秒前
王淳发布了新的文献求助10
15秒前
16秒前
orixero应助唐兴田采纳,获得10
16秒前
wanci应助小猪采纳,获得10
18秒前
怡然的复天完成签到,获得积分10
18秒前
19秒前
我是老大应助xuan采纳,获得10
19秒前
20秒前
20秒前
票子完成签到 ,获得积分10
21秒前
22秒前
ThreegoldHu发布了新的文献求助10
24秒前
番茄完成签到,获得积分10
24秒前
24秒前
张帅发布了新的文献求助10
28秒前
28秒前
一只小郭发布了新的文献求助10
30秒前
akion完成签到,获得积分10
31秒前
邢一完成签到 ,获得积分10
31秒前
上官若男应助清秀成败采纳,获得10
32秒前
32秒前
32秒前
张艺兴的咩咩完成签到,获得积分10
33秒前
JIA完成签到 ,获得积分10
34秒前
完美亦竹发布了新的文献求助10
34秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
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 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7583462
求助须知:如何正确求助?哪些是违规求助? 9162196
关于积分的说明 19606301
捐赠科研通 7165505
什么是DOI,文献DOI怎么找? 3266283
关于科研通互助平台的介绍 2431182
邀请新用户注册赠送积分活动 2257737