Flo Snorre, Vader Anna, Præbel Kim
Department of Arctic Biology The University Centre in Svalbard Longyearbyen, Svalbard Norway.
Faculty of Biosciences, Fisheries and Economics UiT The Arctic University of Norway Tromsø Norway.
Ecol Evol. 2024 May 6;14(5):e11369. doi: 10.1002/ece3.11369. eCollection 2024 May.
Prey metabarcoding has become a popular tool in molecular ecology for resolving trophic interactions at high resolution, from various sample types and animals. To date, most predator-prey studies of small-sized animals (<1 mm) have met the problem of overabundant predator DNA in dietary samples by adding blocking primers/peptide nucleic acids. These primers aim to limit the PCR amplification and detection of the predator DNA but may introduce bias to the prey composition identified by interacting with sequences that are similar to those of the predator. Here we demonstrate the use of an alternative method to explore the prey of small marine copepods using whole-body DNA extracts and deep, brute force metabarcoding of an 18S rDNA fragment. After processing and curating raw data from two sequencing runs of varying depths (0.4 and 5.4 billion raw reads), we isolated 1.3 and 52.2 million prey reads, with average depths of ~15,900 and ~120,000 prey reads per copepod individual, respectively. While data from both sequencing runs were sufficient to distinguish dietary compositions from disparate seasons, locations, and copepod species, greater sequencing depth led to better separation of clusters. As computation and sequencing are becoming ever more powerful and affordable, we expect the brute force approach to become a general standard for prey metabarcoding, as it offers a simple and affordable solution to consumers that is impractical to dissect or unknown to science.
猎物代谢条形码技术已成为分子生态学中一种流行的工具,可用于从各种样本类型和动物中高分辨率解析营养级相互作用。迄今为止,大多数针对小型动物(<1毫米)的捕食者-猎物研究都遇到了饮食样本中捕食者DNA过多的问题,为此采用了添加阻断引物/肽核酸的方法。这些引物旨在限制捕食者DNA的PCR扩增和检测,但可能会与与捕食者相似的序列相互作用,从而给所识别的猎物组成带来偏差。在此,我们展示了一种替代方法的应用,即使用小型海洋桡足类动物的全身DNA提取物以及对18S rDNA片段进行深度、强力代谢条形码分析来探索其猎物。在处理和整理了来自两个不同深度(0.4亿和5.4亿条原始读数)测序运行的原始数据后,我们分别分离出了130万和5220万条猎物读数,每个桡足类个体的平均深度分别约为15900条和120000条猎物读数。虽然两次测序运行的数据都足以区分不同季节、地点和桡足类物种的饮食组成,但测序深度越高,聚类分离效果越好。随着计算和测序变得越来越强大且成本越来越低,我们预计这种强力方法将成为猎物代谢条形码分析的通用标准,因为它为消费者提供了一种简单且经济实惠的解决方案,而对于那些难以解剖或科学界未知的物种来说,这是一种切实可行的方法。