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Proteomic fingerprinting facilitates biodiversity assessments in understudied ecosystems: A case study on integrated taxonomy of deep sea copepods
Molecular Ecology Resources ( IF 7.7 ) Pub Date : 2021-04-26 , DOI: 10.1111/1755-0998.13405
Jasmin Renz 1 , Elena L Markhaseva 2 , Silke Laakmann 3, 4 , Sven Rossel 5, 6 , Pedro Martinez Arbizu 5, 6 , Janna Peters 1
Affiliation  

Accurate and reliable biodiversity estimates of marine zooplankton are a prerequisite to understand how changes in diversity can affect whole ecosystems. Species identification in the deep sea is significantly impeded by high numbers of new species and decreasing numbers of taxonomic experts, hampering any assessment of biodiversity. We used in parallel morphological, genetic, and proteomic characteristics of specimens of calanoid copepods from the abyssal South Atlantic to test if proteomic fingerprinting can accelerate estimating biodiversity. We cross-validated the respective molecular discrimination methods with morphological identifications to establish COI and proteomic reference libraries, as they are a pre-requisite to assign taxonomic information to the identified molecular species clusters. Due to the high number of new species only 37% of the individuals could be assigned to species or genus level morphologically. COI sequencing was successful for 70% of the specimens analysed, while proteomic fingerprinting was successful for all specimens examined. Predicted species richness based on morphological and molecular methods was 42 morphospecies, 56 molecular operational taxonomic units (MOTUs) and 79 proteomic operational taxonomic units (POTUs), respectively. Species diversity was predicted based on proteomic profiles using hierarchical cluster analysis followed by application of the variance ratio criterion for identification of species clusters. It was comparable to species diversity calculated based on COI sequence distances. Less than 7% of specimens were misidentified by proteomic profiles when compared with COI derived MOTUs, indicating that unsupervised machine learning using solely proteomic data could be used for quickly assessing species diversity.

中文翻译:

蛋白质组学指纹有助于未充分研究的生态系统中的生物多样性评估:深海桡足类综合分类学案例研究

准确可靠地估计海洋浮游动物的生物多样性是了解多样性变化如何影响整个生态系统的先决条件。大量新物种和分类专家数量的减少极大地阻碍了深海中的物种鉴定,从而阻碍了对生物多样性的任何评估。我们使用来自南大西洋深海的桡足类动物标本的平行形态学、遗传学和蛋白质组学特征来测试蛋白质组学指纹识别是否可以加速对生物多样性的估计。我们通过形态学识别交叉验证了各自的分子鉴别方法,以建立 COI 和蛋白质组学参考库,因为它们是将分类信息分配给已识别分子物种簇的先决条件。由于新物种的数量很多,只有 37% 的个体可以在形态上被分配到物种或属水平。COI 测序对 70% 的分析样本成功,而蛋白质组指纹对所有检查的样本都成功。基于形态学和分子方法预测的物种丰富度分别为 42 个形态物种、56 个分子操作分类单元 (MOTU) 和 79 个蛋白质组操作分类单元 (POTU)。物种多样性是基于蛋白质组学概况使用层次聚类分析预测的,然后应用方差比标准来识别物种聚类。它与基于 COI 序列距离计算的物种多样性相当。与 COI 衍生的 MOTU 相比,只有不到 7% 的样本被蛋白质组学特征错误识别,
更新日期:2021-04-26
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