Introduction
Diatoms (Bacillariophyceae) rank among the most important components of
aquatic food webs and play an important role in carbon fixation (Mann
1999). Because of their fast response and narrow optima for multiple
environmental variables, diatoms are excellent indicators of ecosystem
health (Dixit, Smol, Kingston & Charles 1992; Pan, Stevenson, Hill,
Herlihy & Collins 1996), and may provide early warning signals for
aquatic ecosystem changes in face of anthropogenic pressures such as
eutrophication (Wang et al. 2012) or heavy metal contamination
(Chen et al. 2015). The standard methods for assessing diatom
communities rely on counting and identifying their silicified cell walls
(valves) using mostly light microscopy (e.g.
European-Committee-for-Standardization 2014). But with the rapid
development and continuously decreasing costs of high-throughput
sequencing (HTS) technologies, the metabarcoding approach, allowing
simultaneous identification of multiple species from environmental
samples, has become an alternative tool for fast biodiversity assessment
(Ruppert, Kline & Rahman 2019). Because morphological analyses of
diatoms (and other microorganisms) are labor-intensive, require
expertise and are prone to inter-investigator variation, metabarcoding,
referred to as ‘Biomonitoring 2.0’ (Baird & Hajibabaei 2012), may have
the potential to outperform the traditional, low throughput, monitoring
methods.
Metabarcoding-based biodiversity studies, however, may face various
difficulties, starting from DNA extraction to data processing in complex
bioinformatics pipelines (Sinha et al. 2017; Anslan et al.2018; Hardge et al. 2018). Therefore, the suitability of
metabarcoding approach for assessing diatom communities have been the
research focus for several studies. Although the DNA barcoding library
for accurate species level detection is still incomplete for diatoms,
metabarcoding is a promising tool for biomonitoring of community
assemblages of these organisms as it has been shown to produce similar
results compared with morphological analyses (Zimmermann, Glöckner,
Jahn, Enke & Gemeinholzer 2015; Apotheloz-Perret-Gentil et al.2017; Vasselon, Rimet, Tapolczai & Bouchez 2017; Keck, Vasselon, Rimet,
Bouchez & Kahlert 2018; Rimet et al. 2018; Rimet, Vasselon,
Barbara & Bouchez 2018; Rivera et al. 2018). The majority of
diatom community studies are applied to biofilms of epilithic diatom
species from rivers and lakes, with the goal of assessing current-state
water quality. Because diatom silicified valves are usually well
preserved in sediments, they also constitute important indicators for
inferring paleo-environmental conditions such as water pH, nutrient
dynamics, and temperature (Douglas & Smol 2010). However, only few
studies have estimated the suitability of metabarcoding for identifying
diatom communities directly from sediment samples and have assessed its
consistency with microscopy (Dulias, Stoof-Leichsenring, Pestryakova &
Herzschuh 2017; Piredda et al. 2017). Although morphological and
metabarcoding data sets from these studies have demonstrated highly
correlated results, it is not clear how this pattern is related to the
quantity of sediment used for DNA extraction or affected by the use of
different bioinformatics pipelines. The quantity of sediment used
strongly depends on the approach taken for DNA extraction; it is common
to use DNA isolation kits which allow input of ‘large’ quantities
(usually up to 10 g) of environmental sample, to potentially capture the
complete community represented in the sample. However, DNA extraction
methods, for example the ‘universal’ Power Soil Kit (Hermans, Buckley &
Lear 2018), which process much less material and thus use less
chemicals, cost only a fractional amount and may represent attractive
alternatives for DNA metabarcoding of large numbers of samples. Multiple
publicly available tools exist for bioinformatics processing of large
sets of sequencing data, amongst which QIIME (Caporaso et al.2010) and mothur (Schloss et al. 2009) are the most commonly
used, but some studies have highlighted that an inappropriate choice of
software and settings may heavily affect the final results (Majaneva,
Hyytiäinen, Varvio, Nagai & Blomster 2015; Anslan et al. 2018).
Also for diatom communities, recent studies have suggested that the
choice of bioinformatics pipelines may affect the outcome of
metabarcoding studies (Tapolczai, Keck, Bouchez, Rimet & Vasselon 2019;
Rivera, Vasselon, Bouchez & Rimet 2020). Here, we investigate diatom
communities from Nam Co, a saline lake on the Tibetan Plateau, and from
nearby ponds and tributaries. Our aim is to explore whether the
characterization of diatom community structure via metabarcoding is
dependent on the quantity of sediment used for DNA extraction by
comparing the two most commonly used DNA isolation kits, PowerMax Soil
and Power Soil (Qiagen, Germany), and by applying those to 10 g and 0.5
g (wet weight) of surface sediment samples, respectively. We further
tested the consistency of the metabarcoding results obtained via three
different bioinformatics pipelines by applying exact sequence variants
(ESV) and two OTU clustering approaches. In addition, we assess how the
metabarcoding data sets (from 10 g vs . 0.5 g of sediments)
compare with the morphological analyses of diatoms from the same
samples, and how these datasets relate with environmental variables.