Glucosinolates (GSLs) in the plant order of the Brassicales are sulfur-rich secondary metabolites that harbor antipathogenic and antiherbivory plant-protective functions and have medicinal properties, such as carcinopreventive and antibiotic activities. Plants repress GSL biosynthesis upon sulfur deficiency (−S); hence, field performance and medicinal quality are impaired by inadequate sulfate supply. The molecular mechanism that links –S to GSL biosynthesis has remained understudied. We report here the identification of the –S marker genes sulfur deficiency induced 1 (SDI1) and SDI2 acting as major repressors controlling GSL biosynthesis in Arabidopsis under –S condition. SDI1 and SDI2 expression negatively correlated with GSL biosynthesis in both transcript and metabolite levels. Principal components analysis of transcriptome data indicated that SDI1 regulates aliphatic GSL biosynthesis as part of –S response. SDI1 was localized to the nucleus and interacted with MYB28, a major transcription factor that promotes aliphatic GSL biosynthesis, in both yeast and plant cells. SDI1 inhibited the transcription of aliphatic GSL biosynthetic genes by maintaining the DNA binding composition in the form of an SDI1-MYB28 complex, leading to down-regulation of GSL biosynthesis and prioritization of sulfate usage for primary metabolites under sulfur-deprived conditions.

Allantoin is a metabolic intermediate of purine catabolism that often accumulates in stressed plants. Recently, we used Arabidopsis knockout mutants (aln) of ALLANTOINASE to show that this purine metabolite activates abscisic acid (ABA) production, thereby stimulating stress-related gene expression and enhancing seedling tolerance to abiotic stress. A detailed re-examination of the microarray data of an aln mutant (aln-1) confirmed the increased expression of ABA-related genes and also revealed altered expression of genes involved in jasmonic acid (JA) responses, probably under the control of MYC2, a master switch in the JA signaling pathway. Consistent with the transcriptome profiles, the aln-1 mutant displayed increased JA levels and enhanced responses to mechanical wounding and exogenous JA. Moreover, aln mutants demonstrated modestly increased susceptibility to Pseudomonas syringae and Pectobacterium carotovorum, probably reflecting the antagonistic action of MYC2 on the defense against these bacterial phytopathogens. Exogenously administered allantoin elicited the expression of JA-responsive genes, including MYC2, in wild-type plants, supporting the idea that allantoin might be responsible for the observed JA-related phenotypes of aln mutants. However, mutants deficient in bioactive JA (jar1-1), insensitive to JA (myc2-3), or deficient in ABA (aba2-1 and bglu18) suppressed the effect of exogenous allantoin. The suppression was further confirmed in aln-1 jar1-1 and aln-1 bglu18 double mutants. These results indicate that allantoin can activate the MYC2-regulated JA signaling pathway through ABA production. Overall, this study suggests a possible connection of purine catabolism with stress hormone homeostasis and signaling, and highlights the potential importance of allantoin in these interactions.

Principal component analysis is used to summarize matrix data, such as found in transcriptome, proteome or metabolome and medical examinations, into fewer dimensions by fitting the matrix to orthogonal axes. Although this methodology is frequently used in multivariate analyses, it has disadvantages when applied to experimental data. First, the identified principal components have poor generality; since the size and directions of the components are dependent on the particular data set, the components are valid only within the data set. Second, the method is sensitive to experimental noise and bias between sample groups. It cannot reflect the experimental design that is planned to manage the noise and bias; rather, it estimates the same weight and independence to all the samples in the matrix. Third, the resulting components are often difficult to interpret. To address these issues, several options were introduced to the methodology. First, the principal axes were identified using training data sets and shared across experiments. These training data reflect the design of experiments, and their preparation allows noise to be reduced and group bias to be removed. Second, the center of the rotation was determined in accordance with the experimental design. Third, the resulting components were scaled to unify their size unit.

Caloric restriction (CR) can delay onset of several age-related pathophysiologies and extend lifespan in various species, including rodents. CR also induces metabolic remodeling involved in activation of lipid metabolism, enhancement of mitochondrial biogenesis, and reduction of oxidative stress in white adipose tissue (WAT). In studies using genetically modified mice with extended lifespans, WAT characteristics influenced mammalian lifespans. However, molecular mechanisms underlying CR-associated metabolic remodeling of WAT remain unclear. Sterol regulatory element-binding protein-1c (Srebp-1c), a master transcription factor of fatty acid (FA) biosynthesis, is responsible for the pathogenesis of fatty liver (steatosis). Our study showed that, under CR conditions, Srebp-1c enhanced mitochondrial biogenesis via increased expression of peroxisome proliferator-activated receptor gamma coactivator-1α (Pgc-1α) and upregulated expression of proteins involved in FA biosynthesis within WAT. However, via Srebp-1c, most of these CR-associated metabolic alterations were not observed in other tissues, including the liver. Moreover, our data indicated that Srebp-1c may be an important factor both for CR-associated suppression of oxidative stress, through increased synthesis of glutathione in WAT, and for the prolongevity action of CR. Our results strongly suggested that Srebp-1c, the primary FA biosynthesis-promoting transcriptional factor implicated in fatty liver disease, is also the food shortage-responsive factor in WAT. This indicated that Srebp-1c is a key regulator of metabolic remodeling leading to the beneficial effects of CR.

Two new ribonucleoproteins (RNPs) have been identified from a tobacco chloroplast lysate. These two proteins (cp29A and cp29B) are nuclear-encoded and have a less affinity to single-stranded DNA as compared with three other chloroplast RNPs (cp28, cp31 and cp33) previously isolated. DNA sequencing revealed that both contain two consensus sequence-type homologous RNA-binding domains (CS-RBDs) and a very acidic amino-terminal domain but shorter than that of cp28, cp31 and cp33. Comparison of cp29A and cp29B showed a 19 amino acid insertion in the region separating the two CS-RBDs in cp29B. This insertion results in three tandem repeats of a glycine-rich sequence of 10 amino acids, which is a novel feature in RNPs. The two proteins are encoded by different single nuclear genes and no alternatively spliced transcripts could be identified. We constructed a phylogenetic tree for the ten chloroplast CS-RBDs. These results suggest that there is a sizable RNP family in chloroplasts and the diversity was mainly generated through a series of gene duplications rather than through alternative pre-mRNA splicing. The gene for cp29B contains three introns. The first and second introns interrupt the first CS-RBD and the third intron does the second CS-RBD. The position of the first intron site is the same as that in the human hnRNP A1 protein gene.

Sequence data is now widely used to observe relationships among organisms. However, understanding structure of the qualitative data is challenging. Conventionally, the relationships are analysed using a dendrogram that estimates a tree shape. This approach has difficulty in verifying the appropriateness of the tree shape; rather, horizontal gene transfers and mating can make the shape of the relationship as networks. As a connection-free approach, principal component analysis (PCA) is used to summarize the distance matrix, which records distances between each combination of samples. However, this approach is limited regarding the treatment of information of sequence motifs; distances caused by different motifs are mixed up. This hides clues to figure out how the samples are different. As any bases may change independently, a sequence is multivariate data essentially. Hence, differences among samples and bases that contribute to the difference should be observed coincidentally. To archive this, the sequence matrix is transferred to boolean vector and directly analysed by using PCA. The effects are confirmed in diversity of Asiatic lion and human as well as environmental DNA. Resolution of samples and robustness of calculation is improved. Relationship of a direction of difference and causative nucleotides has become obvious at a glance.