淡水浮游植物的高通量测序和标记色素分析:与乌干达西部热带火山口湖显微计数数据的直接比较

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Heidi Tanttu , Dirk Verschuren , Wannes De Crop , Angela Nankabirwa , Christine Cocquyt , Bjorn Tytgat , Elie Verleyen
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引用次数: 0

摘要

高通量DNA测序(HTS)和光合标记色素分析可以为湖泊浮游植物基于形态学的显微鉴定和计数提供成本和时间效益的替代方法。然而,在热带湖泊的应用,特别是高温高温的应用仍然不多见。利用高效液相色谱(HPLC)和16s和18s小亚基rRNA基因的HTS分析了乌干达西部15个热带火山口湖蓝藻和真核浮游植物群落组成与营养状况和其他环境因子的关系。然后,我们将结果与基于显微鉴定和细胞计数的浮游植物组成数据进行比较,以评估前两种方法是否可以替代或补充分析这些湖泊的浮游植物群落。HTS记录到蓝藻105个操作分类单位(operational taxonomic units, otu),自养真核生物197个操作分类单位(operational taxonomic units, otu),主要来自鞭毛藻、隐生植物和绿藻。藻门(绿藻和硅藻)的丰度较低,其余的序列属于链藻门和附藻门。样品中标记色素的分布反映了蓝藻(主要是玉米黄素、粘黄素和紫紫酮)的广泛存在和经常占主导地位,其次是绿藻(叶绿素b、叶黄素、花青素、新黄素)和硅藻(岩藻黄素、硅藻-二硅藻黄质),在一些湖泊中隐藻(异黄质)也很重要。直接和间接的otu和标记色素排序表明,与细胞计数相似,浮游植物群落组成在深层贫营养湖泊和浅层高产湖泊之间存在差异,尽管蓝藻otu的分布与湖水电导率和季节的关系比与营养状况的关系更大。基于HTS数据对两种优势真核生物类群绿藻和绿藻的组成进行了Procrustes分析,结果显示其模式与基于细胞计数的组成数据非常相似,但真核生物或蓝藻的完整HTS数据与细胞计数数据之间没有显著相关性。虽然后一种差异可归因于HTS和显微镜方法相关的偏差,但与显微镜方法相比,HTS未能确定营养指标物种,也没有将所有湖泊分配到正确的营养类别。当色素丰度以绝对浓度表示时,基于标记色素的成分数据对15个湖泊的营养水平进行了聚类,而当色素丰度以分数丰度表示时则没有。我们将这种较低的辨别能力部分归因于蓝藻在这些热带火山口湖中的广泛分布,以及色素的低分类分辨率。然而,由于主要真核浮游植物类群的标记色素和OTUs的组成模式与细胞计数数据相似,因此这两种方法都具有在热带火山口湖生态系统水平监测的潜力。然而,高分辨率的HTS方法受到基因序列扩增不均匀的限制,而低分辨率的标记-色素方法则受到蓝藻在这些系统中广泛占主导地位的限制,无论它们的营养状态如何。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-throughput sequencing and marker pigment analysis of freshwater phytoplankton: A direct comparison with microscopic count data in the tropical crater lakes of Western Uganda

High-throughput DNA sequencing (HTS) and photosynthetic marker-pigment analysis can provide cost-and time-efficient alternatives for morphology-based microscopic identification and counting of lacustrine phytoplankton. However, application of particularly HTS in tropical lakes is still uncommon. We analyzed the community composition of cyanobacterial and eukaryotic phytoplankton in relation to trophic status and other environmental factors in 15 tropical crater lakes in western Uganda, using both high-performance liquid chromatography (HPLC) of photosynthetic marker pigments and HTS of 16 S and 18 S small subunit rRNA genes. We then compared the results with phytoplankton composition data based on microscopic identification and cell counting to evaluate whether the two former methods can be alternative or complementary approaches to analyze these lakes’ phytoplankton communities. HTS recorded 105 operational taxonomic units (OTUs) of cyanobacteria, and 197 OTUs of autotrophic eukaryotes mainly from dinoflagellates, cryptophytes and green algae. Ochrophyta (chrysophytes and diatoms) were present in low abundances only, and the few remaining sequences belonged to Streptophyta and Haptophyta. Marker pigment distribution among samples reflected the widespread and often dominant presence of cyanobacteria (primarily zeaxanthin, myxoxanthophyll and echinenone), followed by green algae (chlorophyll b, lutein, antheraxanthin, neoxanthin) and diatoms (fucoxanthin, diato-diadinoxanthin), and with cryptophyta (alloxanthin) important in some of the lakes. Direct and indirect ordinations of the OTUs and marker pigments revealed that, similar to patterns observed in cell counts, phytoplankton community composition differed between deep oligotrophic lakes and shallower more productive lakes, even though the distribution of cyanobacterial OTUs was more related to lake water conductivity and season than to trophic status. Procrustes analyses of the composition of green algae and Ochrophyta, the two dominant eukaryotic groups, based on HTS data showed patterns significantly similar to composition data based on cell counts, but no significant correlations were found between the complete eukaryotic or cyanobacterial HTS data and cell count data. While the latter discrepancies can be attributed to method-related biases in both HTS and microscopy, in contrast to the microscopic approach HTS failed to identify trophic indicator species and did not assign all lakes to the correct trophic category. Composition data based on marker pigments clustered the 15 lakes according to their trophic level only when pigment abundances were expressed as absolute concentrations, not when expressed as fractional abundances. We attribute this lower discriminating power partly to the wide distribution of cyanobacteria in these tropical crater lakes, besides the pigments’ low taxonomic resolution. Nevertheless, as the compositional patterns in relation to environmental variation observed in marker pigments and OTUs of the dominant eukaryotic phytoplankton groups are similar to those in cell count data, both methods hold potential for ecosystem-level monitoring of tropical crater lakes. However, the high-resolution HTS approach is handicapped by non-uniform amplification of gene sequences, while the low-resolution marker-pigment approach is handicapped by the widespread dominance of cyanobacteria in these systems irrespective of their trophic status.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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