Animal Physiology and Neurobiology, Department of Biology, KU Leuven (University of Leuven), Leuven, Belgium.
J Am Soc Mass Spectrom. 2018 May;29(5):879-889. doi: 10.1007/s13361-017-1856-z. Epub 2018 Jan 3.
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.
神经肽是神经系统用来介导和微调神经元通讯的重要信号分子。它们可以作为神经递质或神经调质在神经回路中发挥作用,也可以作为神经激素释放,以靶向远处的细胞和组织。神经肽通常通过蛋白酶的作用从较大的前体蛋白中切割下来,并可以进行翻译后修饰。短的、成熟的神经肽序列通常包含这些前体蛋白中唯一进化上合理保守的区域。因此,通过对神经肽前体序列的计算机挖掘来预测所有潜在的生物活性肽是特别具有挑战性的。肽组学是一种从体液、细胞、组织、器官或全身制剂中提取肽进行从头鉴定的方法。质谱分析,通常与在线液相色谱法结合使用,是肽组学研究中使用的标志性技术。在这里,我们使用酸化甲醇提取程序和四极杆-Orbitrap LC-MS/MS 分析了秀丽隐杆线虫的神经肽组。我们从秀丽隐杆线虫全身体提取物中鉴定出了数量空前的 203 种成熟神经肽,包括 35 种来自已知、假设和全新的神经肽前体蛋白的肽,这些蛋白以前在计算机上没有预测到。这组经过生物化学验证的肽序列提供了迄今为止最详细的秀丽隐杆线虫参考神经肽组。为了充分利用这一资源,我们将我们内部的已知和预测神经肽数据库作为一个有价值的资源提供给社区。我们提供这些集体数据,以帮助社区取得进展,支持未来的差异和/或功能研究。