ACaenorhabditis elegansMass Spectrometric Resource for Neuropeptidomics

秀丽隐杆线虫 神经肽 生物信息学 化学 计算生物学 功能(生物学) 神经激素 蛋白质组 生物化学 生物 细胞生物学 受体 基因 激素
作者
Sven Van Bael,Sven Zels,Kurt Boonen,Isabel Beets,Liliane Schoofs,Liesbet Temmerman
出处
期刊:Journal of the American Society for Mass Spectrometry [American Chemical Society]
卷期号:29 (5): 879-889 被引量:41
标识
DOI:10.1007/s13361-017-1856-z
摘要

Neuropeptides are important signaling molecules used by nervous systems to mediate and fine-tune neuronal communication. They can function as neurotransmitters or neuromodulators in neural circuits, or they can be released as neurohormones to target distant cells and tissues. Neuropeptides are typically cleaved from larger precursor proteins by the action of proteases and can be the subject of post-translational modifications. The short, mature neuropeptide sequences often entail the only evolutionarily reasonably conserved regions in these precursor proteins. Therefore, it is particularly challenging to predict all putative bioactive peptides through in silico mining of neuropeptide precursor sequences. Peptidomics is an approach that allows de novo characterization of peptides extracted from body fluids, cells, tissues, organs, or whole-body preparations. Mass spectrometry, often combined with on-line liquid chromatography, is a hallmark technique used in peptidomics research. Here, we used an acidified methanol extraction procedure and a quadrupole-Orbitrap LC-MS/MS pipeline to analyze the neuropeptidome of Caenorhabditis elegans. We identified an unprecedented number of 203 mature neuropeptides from C. elegans whole-body extracts, including 35 peptides from known, hypothetical, as well as from completely novel neuropeptide precursor proteins that have not been predicted in silico. This set of biochemically verified peptide sequences provides the most elaborate C. elegans reference neurpeptidome so far. To exploit this resource to the fullest, we make our in-house database of known and predicted neuropeptides available to the community as a valuable resource. We are providing these collective data to help the community progress, amongst others, by supporting future differential and/or functional studies. Graphical Abstract ᅟ.
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
刚刚
树妖三三完成签到,获得积分10
1秒前
1秒前
wsc完成签到 ,获得积分10
3秒前
xiaoyu完成签到,获得积分10
5秒前
在水一方应助小仙旺采纳,获得10
5秒前
科研通AI6.4应助kc135采纳,获得10
5秒前
慕青应助彩笔梯队采纳,获得10
5秒前
大个应助端庄的过客采纳,获得10
5秒前
6秒前
九星完成签到 ,获得积分10
6秒前
ding应助古月方源采纳,获得10
6秒前
zzx发布了新的文献求助10
7秒前
7秒前
少卿发布了新的文献求助10
9秒前
真实的采白完成签到 ,获得积分10
9秒前
pureheart完成签到,获得积分10
9秒前
梁子发布了新的文献求助10
10秒前
傲娇的唇彩完成签到,获得积分10
10秒前
CipherSage应助安详老鼠采纳,获得10
10秒前
cclyfan完成签到,获得积分10
10秒前
kpllll发布了新的文献求助10
11秒前
搜集达人应助张贵虎采纳,获得10
11秒前
飘逸桔子爱做饭关注了科研通微信公众号
12秒前
迷人以山完成签到 ,获得积分10
12秒前
Ava应助顺心秋天采纳,获得10
12秒前
Ava应助kc135采纳,获得10
13秒前
成功的院士完成签到,获得积分10
13秒前
zhu完成签到,获得积分10
14秒前
乐乐完成签到 ,获得积分10
14秒前
15秒前
15秒前
zz完成签到,获得积分10
15秒前
Mal完成签到,获得积分20
15秒前
17秒前
Gavin123完成签到,获得积分20
17秒前
非诚勿扰完成签到 ,获得积分10
18秒前
QAQ完成签到,获得积分10
18秒前
穷鬼爬行完成签到,获得积分20
20秒前
情怀应助lsy采纳,获得10
20秒前
高分求助中
(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
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 800
Navigating Normative Orders. Interdisciplinary Perspectives 800
A Psychological Understanding of Criticism and Mental Health 600
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
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 7753125
求助须知:如何正确求助?哪些是违规求助? 9299911
关于积分的说明 20255495
捐赠科研通 7335360
什么是DOI,文献DOI怎么找? 3310416
关于科研通互助平台的介绍 2461729
邀请新用户注册赠送积分活动 2323382