Institute of Environmental Sciences (CML), Leiden University, P.O. Box 9518, 2300 RA, Leiden, The Netherlands.
Naturalis Biodiversity Center, Darwinweg 2, 2333 CR, Leiden, The Netherlands.
BMC Ecol Evol. 2023 Feb 6;23(1):4. doi: 10.1186/s12862-023-02104-2.
Diatoms are present in all waters and are highly sensitive to pollution gradients. Therefore, they are ideal bioindicators for water quality assessment. Current indices used in these applications are based on identifying diatom species and counting their abundances using traditional light microscopy. Several molecular techniques have been developed to help automate different steps of this process, but obtaining reliable estimates of diatom community composition and species abundance remains challenging.
Here, we evaluated a recently developed quantification method based on Genotyping by Sequencing (GBS) for the first time in diatoms to estimate the relative abundances within a species complex. For this purpose, a reference database comprised of thousands of genomic DNA clusters was generated from cultures of Nitzschia palea. The sequencing reads from calibration and mock samples were mapped against this database for parallel quantification. We sequenced 25 mock diatom communities containing up to five taxa per sample in different abundances. Taxon abundances in these communities were also quantified by a diatom expert using manual counting of cells on light microscopic slides. The relative abundances of strains across mock samples were over- or under-estimated by the manual counting method, and a majority of mock samples had stronger correlations using GBS. Moreover, one previously recognized putative hybrid had the largest number of false positive detections demonstrating the limitation of the manual counting method when morphologically similar and/or phylogenetically close taxa are analyzed.
Our results suggest that GBS is a reliable method to estimate the relative abundances of the N. palea taxa analyzed in this study and outperformed traditional light microscopy in terms of accuracy. GBS provides increased taxonomic resolution compared to currently available quantitative molecular approaches, and it is more scalable in the number of species that can be analyzed in a single run. Hence, this is a significant step forward in developing automated, high-throughput molecular methods specifically designed for the quantification of [diatom] communities for freshwater quality assessments.
硅藻存在于所有水域中,对污染梯度高度敏感。因此,它们是水质评估的理想生物指标。目前应用于这些目的的指数是基于识别硅藻物种并使用传统的光学显微镜计数其丰度。已经开发了几种分子技术来帮助自动化这个过程的不同步骤,但获得硅藻群落组成和物种丰度的可靠估计仍然具有挑战性。
在这里,我们首次在硅藻中评估了一种基于测序的基因分型(GBS)的新定量方法,以估计物种复杂体内的相对丰度。为此,从 palea 中培养的数千个基因组 DNA 簇生成了一个参考数据库。校准和模拟样本的测序读取与该数据库进行映射,以进行平行定量。我们对 25 个含有高达五个分类群的模拟硅藻群落进行了测序,每个样本的丰度不同。使用手动在光学显微镜载玻片上计数细胞的方法对这些群落中的分类群丰度进行了定量。与手动计数方法相比,模拟样本中的菌株相对丰度被高估或低估,并且大多数模拟样本使用 GBS 具有更强的相关性。此外,一个以前公认的假定杂种具有最多的假阳性检测,这表明在分析形态相似和/或系统发育上接近的分类群时,手动计数方法存在局限性。
我们的结果表明,GBS 是一种可靠的方法,可以估计本研究中分析的 palea 硅藻类群的相对丰度,并且在准确性方面优于传统的光学显微镜。GBS 提供了比目前可用的定量分子方法更高的分类分辨率,并且在一次运行中可以分析的物种数量上更具可扩展性。因此,这是朝着为淡水质量评估专门开发自动化、高通量分子方法以定量[硅藻]群落迈出的重要一步。