Next Generation Sequencing Reveals the Hidden Diversity of Zooplankton Assemblages

Zooplankton play an important role in our oceans, in biogeochemical cycling and providing a food source for commercially important fish larvae. However, difficulties in correctly identifying zooplankton hinder our understanding of their roles in marine ecosystem functioning, and can prevent detection of long term changes in their community structure. The advent of massively parallel next generation sequencing technology allows DNA sequence data to be recovered directly from whole community samples. Here we assess the ability of such sequencing to quantify richness and diversity of a mixed zooplankton assemblage from a productive time series site in the Western English Channel. Methodology/Principle Findings Plankton net hauls (200 µm) were taken at the Western Channel Observatory station L4 in September 2010 and January 2011. These samples were analysed by microscopy and metagenetic analysis of the 18S nuclear small subunit ribosomal RNA gene using the 454 pyrosequencing platform. Following quality control a total of 419,041 sequences were obtained for all samples. The sequences clustered into 205 operational taxonomic units using a 97% similarity cut-off. Allocation of taxonomy by comparison with the National Centre for Biotechnology Information database identified 135 OTUs to species level, 11 to genus level and 1 to order, <2.5% of sequences were classified as unknowns. By comparison a skilled microscopic analyst was able to routinely enumerate only 58 taxonomic groups. Conclusions Metagenetics reveals a previously hidden taxonomic richness, especially for Copepoda and hard-to-identify meroplankton such as Bivalvia, Gastropoda and Polychaeta. It also reveals rare species and parasites. We conclude that Next Generation Sequencing of 18S amplicons is a powerful tool for elucidating the true diversity and species richness of zooplankton communities. While this approach allows for broad diversity assessments of plankton it may become increasingly attractive in future if sequence reference libraries of accurately identified individuals are better populated.

High-quality RNA extraction from copepods for Next Generation Sequencing: A comparative study

Despite the ecological importance of copepods, few Next Generation Sequencing studies (NGS) have been performed on small crustaceans, and a standard method for RNA extraction is lacking. In this study, we compared three commonly-used methods: TRIzol®, Aurum Total RNA Mini Kit and Qiagen RNeasy Micro Kit, in combination with preservation reagents TRIzol® or RNAlater®, to obtain high-quality and quantity of RNA from copepods for NGS. Total RNA was extracted from the copepods Calanus helgolandicus, Centropages typicus and Temora stylifera and its quantity and quality were evaluated using NanoDrop, agarose gel electrophoresis and Agilent Bioanalyzer. Our results demonstrate that preservation of copepods in RNAlater® and extraction with Qiagen RNeasy Micro Kit were the optimal isolation method for high-quality and quantity of RNA for NGS studies of C. helgolandicus. Intriguingly, C. helgolandicus 28S rRNA is formed by two subunits that separate after heat-denaturation and migrate along with 18S rRNA. This unique property of protostome RNA has never been reported in copepods. Overall, our comparative study on RNA extraction protocols will help increase gene expression studies on copepods using high-throughput applications, such as RNA-Seq and microarrays.

Modelling and Prediction of NOx Emission in a Coal-Fired Power Generation Plant

In this paper NOx emissions modelling for real-time operation and control of a 200 MWe coal-fired power generation plant is studied. Three model types are compared. For the first model the fundamentals governing the NOx formation mechanisms and a system identification technique are used to develop a grey-box model. Then a linear AutoRegressive model with eXogenous inputs (ARX) model and a non-linear ARX model (NARX) are built. Operation plant data is used for modelling and validation. Model cross-validation tests show that the developed grey-box model is able to consistently produce better overall long-term prediction performance than the other two models.