Deciphering the Molecular Diversity of an Ant Venom Peptidome through a Venomics Approach

毒液 生物 转录组 计算生物学 功能多样性 基因 遗传学 生物化学 基因表达 生态学
作者
Axel Touchard,Nathan Téné,Philippe Chan Tchi Song,Benjamin Lefranc,Jérôme Leprince,Michel Treilhou,Elsa Bonnafé
出处
期刊:Journal of Proteome Research [American Chemical Society]
卷期号:17 (10): 3503-3516 被引量:23
标识
DOI:10.1021/acs.jproteome.8b00452
摘要

The peptide toxins in the venoms of small invertebrates such as stinging ants have rarely been studied due to the limited amount of venom available per individual. We used a venomics strategy to identify the molecular diversity of the venom peptidome for the myrmicine ant Tetramorium bicarinatum. The methodology included (i) peptidomics, in which the venom peptides are sequenced through a de novo mass spectrometry approach or Edman degradation; (ii) transcriptomics, based on RT-PCR-cloning and DNA sequencing; and (iii) the data mining of the RNA-seq in the available transcriptome. Mass spectrometry analysis revealed about 2800 peptides in the venom. However, the de novo sequencing suggested that most of these peptides arose from processing or the artifactual fragmentations of full-length mature peptides. These peptides, called "myrmicitoxins", are produced by a limited number of genes. Thirty-seven peptide precursors were identified and classified into three superfamilies. These precursors are related to pilosulin, secapin or are new ant venom prepro-peptides. The mature myrmicitoxins display sequence homologies with antimicrobial, cytolytic and neurotoxic peptides. The venomics strategy enabled several post-translational modifications in some peptides such as O-glycosylation to be identified. This study provides novel insights into the molecular diversity and evolution of ant venoms.

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
小二郎应助知行合一采纳,获得50
1秒前
澄澄橙橙紫完成签到,获得积分10
1秒前
郑哈哈发布了新的文献求助10
1秒前
从容的柜子完成签到 ,获得积分10
2秒前
chen发布了新的文献求助10
2秒前
3秒前
3秒前
3秒前
乐乐应助米崽采纳,获得10
4秒前
4秒前
在水一方应助ZYB采纳,获得10
4秒前
CipherSage应助莲蓉采纳,获得10
4秒前
木昆发布了新的文献求助10
5秒前
暴扣三米线完成签到,获得积分10
5秒前
5秒前
爆米花应助Msong采纳,获得10
6秒前
不摇碧莲完成签到 ,获得积分10
6秒前
个性又菱完成签到,获得积分10
6秒前
7秒前
bkagyin应助Chen采纳,获得10
7秒前
8秒前
MOhy发布了新的文献求助10
8秒前
9秒前
galaxy应助蓝天采纳,获得10
9秒前
123456发布了新的文献求助10
9秒前
Orange应助黑白菜采纳,获得10
10秒前
windli发布了新的文献求助10
10秒前
10秒前
10秒前
爱撒娇的又菡完成签到 ,获得积分20
10秒前
11秒前
11秒前
ma完成签到 ,获得积分10
11秒前
12秒前
12秒前
李秋秋发布了新的文献求助30
12秒前
CT完成签到,获得积分10
12秒前
12秒前
12秒前
上官若男应助chen采纳,获得10
12秒前
高分求助中
Overcoming Stigma and Bias in Obesity Management 1200
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
Adhesion Science: Principles & Practice 400
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 6492768
求助须知:如何正确求助?哪些是违规求助? 8290294
关于积分的说明 17690743
捐赠科研通 5584744
什么是DOI,文献DOI怎么找? 2915445
邀请新用户注册赠送积分活动 1892541
关于科研通互助平台的介绍 1750782