Oil accumulation by the oleaginous diatom Fistulifera solaris as revealed by the genome and transcriptome.

Oleaginous photosynthetic organisms such as microalgae are promising sources for biofuel production through the generation of carbon-neutral sustainable energy. However, the metabolic mechanisms driving high-rate lipid production in these oleaginous organisms remain unclear, thus impeding efforts to improve productivity through genetic modifications. We analyzed the genome and transcriptome of the oleaginous diatom Fistulifera solaris JPCC DA0580. Next-generation sequencing technology provided evidence of an allodiploid genome structure, suggesting unorthodox molecular evolutionary and genetic regulatory systems for reinforcing metabolic efficiencies. Although major metabolic pathways were shared with nonoleaginous diatoms, transcriptome analysis revealed unique expression patterns, such as concomitant upregulation of fatty acid/triacylglycerol biosynthesis and fatty acid degradation (ß-oxidation) in concert with ATP production. This peculiar pattern of gene expression may account for the simultaneous growth and oil accumulation phenotype and may inspire novel biofuel production technology based on this oleaginous microalga.

Linear regression models predicting strength of transcriptional activity of promoters.

We developed linear regression models which predict strength of transcriptional activity of promoters from their sequences. Intrinsic transcriptional strength data of 451 human promoter sequences in three cell lines (HEK293, MCF7 and 3T3), which were measured by systematic luciferase reporter gene assays, were used to build the models. The models sum up contributions of CG dinucleotide content and transcription factor binding sites (TFBSs) to transcriptional strength. We evaluated prediction accuracies of the models by cross validation tests and found that they have adequate ability for predicting transcriptional strength of promoters in spite of their simple formalization. We also evaluated statistical significance of the contributions and proposed a picture of regulatory code hidden in promoter sequences. That is, CG dinucleotide content and TFBSs mainly determine strength of transcriptional activity under ubiquitous and specific environments, respectively.

Profiles of Chemical Effects on Cells (pCEC): a toxicogenomics database with a toxicoinformatics system for risk evaluation and toxicity prediction of environmental chemicals.

Profiles of Chemical Effects on Cells (pCEC) is a toxicogenomics database with a system of classifying chemicals that have effects on human health. This database stores and handles gene expression profiling information and categories of toxicity data. Chemicals are classified according to the specific tissues and cells they affect, the gene expression changes they induce, their toxicity and biological functions in this database system. The pCEC system also analyzes relationships between chemicals and the genes they affect in specific tissues and cells. The reason why we developed pCEC is to support decision-making within the context of environmental regulation. Especially, exposure to environmental chemicals during fetal and newborn development may result in a predisposition to various disorders such as cancer, learning disabilities and allergies later in life. The identification and prediction of hazardous chemicals using limited information are important issues in human health risk management. Therefore, various toxicity information including lethal dose 50 (LD50), toxicity pathways and pathological data were loaded into pCEC. pCEC is also a facility for query, analysis and prediction of unknown toxicochemical reaction pathways and biomarkers which are based on toxicoinformatical data mining approaches. This database is available online at http://project.nies.go.jp/eCA/cgi-bin/index.cgi. The current version of the database has information on the hepatotoxicity, reproductive toxicity and embryotoxicity of chemicals.