Transcript level coordination of carbon pathways during silicon starvation‐induced lipid accumulation in the diatom Thalassiosira pseudonana

Abstract

Summary Diatoms are one of the most productive and successful photosynthetic taxa on Earth and possess attributes such as rapid growth rates and production of lipids, making them candidate sources of renewable fuels. Despite their significance, few details of the mechanisms used to regulate growth and carbon metabolism are currently known, hindering metabolic engineering approaches to enhance productivity.To characterize the transcript level component of metabolic regulation, genome‐wide changes in transcript abundance were documented in the model diatom Thalassiosira pseudonana on a time‐course of silicon starvation. Growth, cell cycle progression, chloroplast replication, fatty acid composition, pigmentation, and photosynthetic parameters were characterized alongside lipid accumulation.Extensive coordination of large suites of genes was observed, highlighting the existence of clusters of coregulated genes as a key feature of global gene regulation in T. pseudonana. The identity of key enzymes for carbon metabolic pathway inputs (photosynthesis) and outputs (growth and storage) reveals these clusters are organized to synchronize these processes.Coordinated transcript level responses to silicon starvation are probably driven by signals linked to cell cycle progression and shifts in photophysiology. A mechanistic understanding of how this is accomplished will aid efforts to engineer metabolism for development of algal‐derived biofuels.

Genome and methylome of the oleaginous diatom Cyclotella cryptica reveal genetic flexibility toward a high lipid phenotype

“…Multiple gene model prediction pipelines, including AUGUSTUS and MAKER, were evaluated to estimate the number of genes (Additional file 1: Table S1, Additional methods). The quality of predicted gene models was assessed by comparing C. cryptica RNAseq data and against manually curated T. pseudonana (version 3) gene models, which are supported by RNAseq and EST data [30–32]. MAKER predicted the fewest genes (Table 1; Additional file 1: Table S1), yet most of these models were supported by transcript data, and therefore, considered high confidence.…”

“…This data suggests that DNA methylation does not control the cell’s silicon status response in these conditions as is reported with nitrate metabolism in P. tricornutum. One explanation for these differences could be the fact that nitrogen stress on diatoms can cause severe cellular damage and detrimental effects, such as chlorosis and DNA and protein degradation [28, 50], whereas silicon starvation does not produce as severe phenotypes [32, 42, 51]. …”

“…5; Additional file 1: Additional methods; [54–58]). The first four in silico organellar prediction programs are developed for higher plants and other eukaryotes, but we have found their collective use to be suitable for some basic understanding of metabolic compartmentation in diatoms, particularly when overlaid with coordinate expression patterns of particular genes [27, 32]. The latter two programs, HECTAR and ASAFind are specific for heterokonts, and take advantage of the conserved amino acid sequence motif (ASA-FAP) which is part of the N-terminal bipartite signal peptide that is unique to these genera of algae.…”

“…7ii), and can be assumed to be non-functional. In contrast, the plastid PEPS gene is homologous to the single copy of PEPS found in T. pseudonana , which shows moderate levels of expression and is unmethylated (0.19% across gene body) under similar experimental conditions [32, 45].

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The ancestors of diatoms evolved a unique mitochondrial dehydrogenase to oxidize photorespiratory glycolate

“…An analysis of transcript-level changes after induction of photorespiration in response to silicon starvation in T. pseudonana also indicated coordinately increased levels of the PtGO1 ortholog and other photorespiratory genes (Claquin et al 2002;Smith et al 2016). In contrast, the PtGO2 homolog did not appear coordinately expressed with other photorespiratory genes (Hennon et al 2015;Smith et al 2016).…”