SummaryTrehalose-6-phosphate is a signal of sucrose status in plants and forms part of a homeostatic mechanism that maintains sucrose levels within a range that is appropriate for the cell type and stage of development.

ORCID ID: 0000-0001-7502-6940 (R.B.).All cells produce reactive oxygen species (ROS) as by-products of their metabolism. In addition to being cytotoxic, ROS act as regulators of a wide range of developmental and physiological processes. Little is known about the molecular mechanisms underlying the perception of ROS and initiation of cellular responses in eukaryotes. Using the unicellular green alga Chlamydomonas reinhardtii, we developed a genetic screen for early components of singlet oxygen signaling. Here, we report the identification of a small zinc finger protein, METHYLENE BLUE SENSITIVITY (MBS), that is required for induction of singlet oxygendependent gene expression and, upon oxidative stress, accumulates in distinct granules in the cytosol. Loss-of-function mbs mutants produce singlet oxygen but are unable to fully respond to it at the level of gene expression. Knockout or knockdown of the homologous genes in the higher plant model Arabidopsis thaliana results in mutants that are hypersensitive to photooxidative stress, whereas overexpression produces plants with elevated stress tolerance. Together, our data indicate an important and evolutionarily conserved role of the MBS protein in ROS signaling and provide a strategy for engineering stress-tolerant plants.

Computational methods can be used to predict the effects of single amino acid substitutions (single-point mutations). In contrast to previous methods that need many protein sequence and structural features, we applied support vector machines (SVMs) to predict protein function changes associated with amino acid substitutions using only sequence information, and crossvalidated them on a large dataset extracted from the Protein Mutant Database (PMD). By three SVM classifiers, we investigated three local sequence features of proteins (residue composition, hydrophobic interaction, and evolutionary property), and examined their effects on the prediction accuracy. As a main result, a novel SVM named substitution-matrix-based kernel SVM was constructed to make speedy and accurate prediction, and its value was shown in an application case. Furthermore, our findings confirmed results from other studies.