A strategy for predicting gene functions from genome and metagenome sequences on the basis of oligopeptide frequency distance.

As a result of the extensive decoding of a massive amount of genomic and metagenomic sequence data, a large number of genes whose functions cannot be predicted by sequence similarity searches are accumulating, and such genes are of little use to science or industry. Current genome and metagenome sequencing largely depend on high-throughput and low-cost methods. In the case of genome sequencing for a single species, high-density sequencing can reduce sequencing errors. For metagenome sequences, however, high-density sequencing does not necessarily increase the sequence quality because multiple and unknown genomes, including those of closely related species, are likely to exist in the sample. Therefore, a function prediction method that is robust against sequence errors becomes an increased need. Here, we present a method for predicting protein gene function that does not depend on sequence similarity searches. Using an unsupervised machine learning method called BLSOM (batch-learning self-organizing map) for short oligopeptide frequencies, we previously developed a sequence alignment-free method for clustering bacterial protein genes according to clusters of orthologous groups of proteins (COGs), without using information from COGs during machine learning. This allows function-unknown proteins to cluster with function-known proteins, based solely on similarity with respect to oligopeptide frequency, although the method required high-performance supercomputers (HPCs). Based on a wide range of knowledge obtained with HPCs, we have now developed a strategy to correlate function-unknown proteins with COG categories, using only oligopeptide frequency distances (OPDs), which can be conducted with PC-level computers. The OPD strategy is suitable for predicting the functions of proteins with low sequence similarity and is applied here to predict the functions of a large number of gene candidates discovered using metagenome sequencing.

Changes in catecholamine levels in short day-induced cotyledons of Pharbitis nil.

We investigated the effects of catecholamine on flower-induction in P. nil (cv. Violet). GC-SIM analysis identified dopamine for the first time in P. nil seedlings. Dopamine levels in the cotyledons did not show a significant change during the inducing dark treatment. The dopamine content of cotyledons exposed to various durations of darkness were 0.1-0.2 nmol/g fresh weight. The same content was found when cotyledons were exposed to continuous light.

Endogenous alpha-ketol linolenic acid levels in short day-induced cotyledons are closely related to flower induction in Pharbitis nil.

Alpha-ketol linolenic acid [KODA, 9,10-ketol-octadecadienoic acid, that is 9-hydroxy-10-oxo-12(Z),15(Z)-octadecadienoic acid] is a signal compound found in Lemna paucicostata after exposure to stress, such as drought, heat or osmotic stress. KODA reacts with catecholamines to generate products that strongly induce flowering, although KODA itself is inactive [Yokoyama et al. (2000) Plant Cell Physiol. 41: 110; Yamaguchi et al. (2001) Plant Cell Physiol. 42: 1201]. We examined the role of KODA in the flower-induction process of Pharbitis nil (violet). KODA was identified for the first time in seedlings of P. nil grown under a flower-inductive condition (16-h dark exposure), by means of LC-SIM and LC-MS/MS. In addition, the changes in endogenous KODA levels (evaluated after esterification of KODA with 9-anthryldiazomethane) during the flower-inductive phase in short day-induced cotyledons were closely related to flower induction. The KODA concentration sharply increased in seedlings during the last 2 h of a 16-h dark period, while the KODA level showed no significant elevation under continuous light. The increase of KODA level occurred in cotyledonal blades, but not in other parts (petiole, hypocotyls and shoot tip). When the 16-h dark period was interrupted with a 10-min light exposure at the 8th h, flower induction was blocked and KODA level also failed to increase. The degree of elevation of KODA concentration in response to 16-h dark exposure was the highest when the cotyledons had just unfolded, and gradually decreased in seedlings grown under continuous light for longer periods, reaching the basal level at the 3rd day after unfolding. Flower-inducing ability also decreased in a similar manner. These results suggest that KODA may be involved in flower induction in P. nil.

Identification of (3S, 9R)- and (3S, 9S)-megastigma-6,7-dien-3,5,9-triol 9-O-beta-D-glucopyranosides as damascenone progenitors in the flowers of Rosa damascena Mill.

The progenitors of damascenone (1), the most intensive C13-norisoprenoid volatile aroma constituent of rose essential oil, were surveyed in the flowers of Rosa damascena Mill. Besides 9-O-beta-D-glucopyranosyl-3-hydroxy-7,8-didehydro-beta-ionol (4b), a stable progenitor already isolated from the residual water after steam distillation of flowers of R. damascena Mill., two labile progenitors were identified to be (3S, 9R)- and (3S, 9S)-megastigma-6,7-dien-3,5,9-triol 9-O-beta-D-glucopyranosides (2b) based on their synthesis and HPLC-MS analytical data. Compound 2b gave damascenone (1), 3-hydroxy-beta-damascone (3) and 4b upon heating under acidic conditions.