Variability of sodium channel toxins in Mesobuthus thersites Advised toolkit for comprehensive venomics.

IF 6.3 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Mikhail A Chernykh, Rustam H Ziganshin, Maria I Toropygina, Vladislav V Babenko, Ilya Y Toropygin, Alexander A Vassilevski
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引用次数: 0

Abstract

Natural venoms constitute vast combinatorial libraries of ion channel ligands with unprecedented properties including high affinity and selectivity. Scorpion venoms in particular are abundant in sodium and potassium channel ligands representing small proteins or peptides, which are traditionally studied by transcriptomics and proteomics approaches. However, a universal algorithm to pursue the examination of multicomponent peptide-rich mixtures such as venoms is lacking. Here we present a general scheme to the analysis of scorpion toxin arsenal with focus on sodium channel ligands. We explored the venom of the Central Asian scorpion Mesobuthus thersites using venom gland transcriptomics and three proteomic methods. We compared traditional automated annotation by Blast2GO with our custom approach based on regular expressions. We found that the traditional annotation is compromised yielding a considerable percentage of false annotations, whereas our approach is rigorous. As a result, we predicted 120 unique toxin-like peptides from transcriptomic data, 30 of which were omitted by Blast2GO. Using a combination of MALDI and LC-MS proteomic techniques, we detected the presence of 358 peptides in the venom. Our results demonstrate the limitations of the currently used methods in venomics and suggest a preferred framework for the investigation of toxin variability. Implementation of this framework will aid toxinologists to uncover previously elusive or undetected venom components.

钠通道毒素的变异性,在Mesobuthus thersites建议综合基因组学工具包。
天然毒液构成了巨大的离子通道配体组合库,具有前所未有的特性,包括高亲和力和选择性。特别是蝎子毒液富含钠和钾通道配体,这些配体代表小蛋白质或肽,传统上通过转录组学和蛋白质组学方法研究。然而,一种通用的算法来追求检查多组分多肽丰富的混合物,如毒液是缺乏的。本文提出了蝎子毒素库分析的一般方案,重点是钠通道配体。利用毒腺转录组学和三种蛋白质组学方法对中亚中毒蝎的毒液进行了研究。我们比较了Blast2GO的传统自动注释和我们基于正则表达式的自定义方法。我们发现传统的注释会产生相当大比例的错误注释,而我们的方法是严格的。因此,我们从转录组学数据中预测了120种独特的毒素样肽,其中30种被Blast2GO省略了。利用MALDI和LC-MS结合的蛋白质组学技术,我们检测到毒液中存在358个肽。我们的研究结果证明了目前在基因组学中使用的方法的局限性,并提出了毒素变异性研究的首选框架。这一框架的实施将有助于毒物学家发现以前难以捉摸或未检测到的毒液成分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Molecular & Cellular Proteomics
Molecular & Cellular Proteomics 生物-生化研究方法
CiteScore
11.50
自引率
4.30%
发文量
131
审稿时长
84 days
期刊介绍: The mission of MCP is to foster the development and applications of proteomics in both basic and translational research. MCP will publish manuscripts that report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life. Manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action. The journal also emphasizes articles that describe innovative new computational methods and technological advancements that will enable future discoveries. Manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data. Scope: -Fundamental studies in biology, including integrative "omics" studies, that provide mechanistic insights -Novel experimental and computational technologies -Proteogenomic data integration and analysis that enable greater understanding of physiology and disease processes -Pathway and network analyses of signaling that focus on the roles of post-translational modifications -Studies of proteome dynamics and quality controls, and their roles in disease -Studies of evolutionary processes effecting proteome dynamics, quality and regulation -Chemical proteomics, including mechanisms of drug action -Proteomics of the immune system and antigen presentation/recognition -Microbiome proteomics, host-microbe and host-pathogen interactions, and their roles in health and disease -Clinical and translational studies of human diseases -Metabolomics to understand functional connections between genes, proteins and phenotypes
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