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MicroRNAs (miRNAs) are key regulators of gene expression in eukaryotes where they can function to downregulate expression levels or functioning of messenger RNAs (mRNAs) that are targeted by mature miRNAs displaying sequence homology. The 'active' mature miRNA forms are short RNAs which are processed from longer precursor miRNA molecules that have a stem-loop structure. While artificial miRNAs have been developed for gene knockdown experiments in a range of eukaryotes, it is not known whether artificial or endogenous miRNAs can functionally knockdown mRNA levels in the model marine diatom Phaeodactylum tricornutum. Here, we investigate the potential use of artificial microRNAs (amiRNAs) for targeted gene knockdowns in P. tricornutum, by generation of transformants harbouring a transgene cassette for the generation of amiRNAs designed to target the endogenous phytoene synthase (PSY) gene. In P. tricornutum, the amiRNA stem-loop precursor was processed to produce a mature amiRNA that successfully targeted the PSY mRNA and reduced PSY mRNA levels. As the PSY gene is a key component of the carotenoid biosynthetic pathway, the levels of carotenoids in the P. tricornutum amiRNA knockdown lines were reduced relative to untransformed control lines. This study demonstrates that artificial miRNAs can be successfully deployed for gene knockdown experiments in the model diatom P. tricornutum, providing a powerful tool for future metabolic engineering and synthetic biology experimentation in this model marine diatom.

Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

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