Search

Marine algae are central to global carbon fixation, and their productivity is dictated largely by resource availability. Reduced nutrient availability is predicted for vast oceanic regions as an outcome of climate change; however, there is much to learn regarding response mechanisms of the tiny picoplankton that thrive in these environments, especially eukaryotic phytoplankton. Here, we investigate responses of the picoeukaryote Micromonas commoda , a green alga found throughout subtropical and tropical oceans. Under shifting phosphate availability scenarios, transcriptomic analyses revealed altered expression of transfer RNA modification enzymes and biased codon usage of transcripts more abundant during phosphate-limiting versus phosphate-replete conditions, consistent with the role of transfer RNA modifications in regulating codon recognition. To associate the observed shift in the expression of the transfer RNA modification enzyme complement with the transfer RNAs encoded by M. commoda , we also determined the transfer RNA repertoire of this alga revealing potential targets of the modification enzymes. Codon usage bias was particularly pronounced in transcripts encoding proteins with direct roles in managing phosphate limitation and photosystem-associated proteins that have ill-characterized putative functions in "light stress." The observed codon usage bias corresponds to a proposed stress response mechanism in which the interplay between stress-induced changes in transfer RNA modifications and skewed codon usage in certain essential response genes drives preferential translation of the encoded proteins. Collectively, we expose a potential underlying mechanism for achieving growth under enhanced nutrient limitation that extends beyond the catalog of up- or downregulated protein-encoding genes to the cell biological controls that underpin acclimation to changing environmental conditions. [ABSTRACT FROM AUTHOR]

In many high-dimensional microarray classification problems, an important task is to identify subsets of genes that best discriminate the classes. Nevertheless, existing gene selection methods for microarray classification cannot identify which classes are discriminable by these selected genes. In this paper, we propose an improved linear discriminant analysis (LDA) method that simultaneously selects important genes and identifies the discriminable classes. Specifically, a pairwise fusion penalty for LDA was used to shrink the differences of the class centroids in pairs for each variable and fuse the centroids of indiscriminable classes altogether. The numerical results in analyzing 2 gene expression profiles demonstrate the proposed approach help improve the interpretation of important genes in microarray classification problems. [ABSTRACT FROM PUBLISHER]