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Defining the Neuropeptidome of the Spiny Lobster Panulirus interruptus Brain Using a Multidimensional Mass Spectrometry-Based Platform

机译:使用基于多维质谱的平台定义多刺龙虾中断脑的神经肽组

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摘要

Decapod crustaceans are important animal models for neurobiologists due to their relatively simple nervous systems with well-defined neural circuits and extensive neuromodulation by a diverse set of signaling peptides. However, biochemical characterization of these endogenous neuropeptides is often challenging due to limited sequence information about these neuropeptide genes and the encoded preprohormones. By taking advantage of sequence homology in neuropeptides observed in related species using a home-built crustacean neuropeptide database, we developed a semi-automated sequencing strategy to characterize the neuropeptidome of Panulirus interruptus, an important aquaculture species, with few known neuropeptide preprohormone sequences. Our streamlined process searched the high mass accuracy and high-resolution data acquired on a LTQ-Orbitrap with a flexible algorithm in ProSight that allows for sequence discrepancy from reported sequences in our database, resulting in the detection of 32 neuropeptides, including 19 novel ones. We further improved the overall coverage to 51 neuropeptides with our multidimensional platform that employed multiple analytical techniques including dimethylation-assisted fragmentation, de novo sequencing using nanoliquid chromatog raphy-electrospray ionization-quadrupole-time-of-flight (nanoLC–ESI–Q-TOF), direct tissue analysis, and mass spectrometry imaging on matrix-assisted laser desorption/ionization (MALDI)-TOF/TOF. The high discovery rate from this unsequenced model organism demonstrated the utility of our neuropeptide discovery pipeline and highlighted the advantage of utilizing multiple sequencing strategies. Collectively, our study expands the catalog of crustacean neuropeptides and more importantly presents an approach that can be adapted to exploring neuropeptidome from species that possess limited sequence information.
机译:十足纲甲壳动物是神经生物学家的重要动物模型,因为它们的神经系统相对简单,具有清晰的神经回路,并通过多种信号肽进行广泛的神经调节。然而,由于关于这些神经肽基因和编码的前激素的序列信息有限,这些内源性神经肽的生化表征通常具有挑战性。利用自制的甲壳类神经肽数据库,利用相关物种中观察到的神经肽中的序列同源性,我们开发了一种半自动化测序策略来表征Panulirus interruptus的神经肽组,这是一种重要的水产养殖物种,几乎没有已知的神经肽前激素序列。我们简化的过程使用ProSight中的灵活算法搜索了在LTQ-Orbitrap上获得的高质量准确性和高分辨率数据,该算法可以使数据库中报告的序列与序列差异,从而检测出32种神经肽,包括19种新的神经肽。我们使用多维平台,使用多种分析技术,包括二甲基化辅助片段化,使用纳米液相色谱-电喷雾电离-四极杆飞行时间(nanoLC–ESI–Q-TOF)的从头测序,进一步提高了对51种神经肽的总体覆盖范围),直接组织分析和基于基质辅助激光解吸/电离(MALDI)-TOF / TOF的质谱成像。这种未测序的模型生物的高发现率证明了我们的神经肽发现管道的实用性,并突出了利用多种测序策略的优势。总体而言,我们的研究扩展了甲壳类神经肽的目录,更重要的是,提出了一种可用于探索序列信息有限的物种的神经肽组的方法。

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