Homozygous gene disruption in diploid yeast through a single transformation.

As industrial shochu yeast is a diploid strain, obtaining a strain with mutations in both allelic genes was considered difficult. We investigated a method for disrupting two copies of a homozygous gene with a single transformation. We designed a disruption cassette containing an intact LYS5 flanked by nonfunctional ura3 gene fragments divided into the 5'- and 3'-regions. These fragments had overlapping sequences that enabled LYS5 removal as well as URA3 regeneration through loop-out. Furthermore, both ends of the disruption cassette had an additional repeat sequence that allowed the cassette to be removed from the chromosome through loop-out. First, 45 bases of 5'- and 3'-regions of target gene sequences were added on both ends of this cassette using polymerase chain reaction; the resultant disruption cassette was introduced into a shochu yeast strain (ura3/ura3 lys5/lys5); then, single allele disrupted strains were selected on Lys drop-out plates; and after cultivation in YPD medium, double-disrupted strains, in which replacement of another allelic gene with disruption cassette by loss of heterozygosity and regeneration of URA3 in one of the cassettes by loop-out, were obtained by selection on Ura and Lys drop-out plates. The disruption cassettes were removed from the double-disrupted strain via loop-out between repeat sequences in the disruption cassette. The strains that lost either URA3 or LYS5 were counter-selected on 5-fluoroorotic acid or α-amino adipic acid plates, respectively. Using this method, we obtained leu2/leu2 and leu2/leu2 his3/his3 strains in shochu yeast, demonstrating the effectiveness and repeatability of this gene disruption technique in diploid yeast Saccharomyces cerevisiae.

Genome Sequence of Saccharomyces cerevisiae Strain Kagoshima No. 2, Used for Brewing the Japanese Distilled Spirit Shochu.

Here, we report a draft genome sequence of Saccharomyces cerevisiae strain Kagoshima no. 2, which is used for brewing shochu, a traditional distilled spirit in Japan. The genome data will facilitate an understanding of the evolutional traits and genetic background related to the characteristic features of strain Kagoshima no. 2.

Toward Network Biology in E. coli Cell.

E. coli has been a critically important model research organism for more than 50 years, particularly in molecular biology. In 1997, the E. coli draft genome sequence was published. Post-genomic techniques and resources were then developed that allowed E. coli to become a model organism for systems biology. Progress made since publication of the E. coli genome sequence will be summarized.

Development and mining of a volatile organic compound database.

Volatile organic compounds (VOCs) are small molecules that exhibit high vapor pressure under ambient conditions and have low boiling points. Although VOCs contribute only a small proportion of the total metabolites produced by living organisms, they play an important role in chemical ecology specifically in the biological interactions between organisms and ecosystems. VOCs are also important in the health care field as they are presently used as a biomarker to detect various human diseases. Information on VOCs is scattered in the literature until now; however, there is still no available database describing VOCs and their biological activities. To attain this purpose, we have developed KNApSAcK Metabolite Ecology Database, which contains the information on the relationships between VOCs and their emitting organisms. The KNApSAcK Metabolite Ecology is also linked with the KNApSAcK Core and KNApSAcK Metabolite Activity Database to provide further information on the metabolites and their biological activities. The VOC database can be accessed online.

GenoBase: comprehensive resource database of Escherichia coli K-12.

Comprehensive experimental resources, such as ORFeome clone libraries and deletion mutant collections, are fundamental tools for elucidation of gene function. Data sets by omics analysis using these resources provide key information for functional analysis, modeling and simulation both in individual and systematic approaches. With the long-term goal of complete understanding of a cell, we have over the past decade created a variety of clone and mutant sets for functional genomics studies of Escherichia coli K-12. We have made these experimental resources freely available to the academic community worldwide. Accordingly, these resources have now been used in numerous investigations of a multitude of cell processes. Quality control is extremely important for evaluating results generated by these resources. Because the annotation has been changed since 2005, which we originally used for the construction, we have updated these genomic resources accordingly. Here, we describe GenoBase (http://ecoli.naist.jp/GB/), which contains key information about comprehensive experimental resources of E. coli K-12, their quality control and several omics data sets generated using these resources.

PIERO ontology for analysis of biochemical transformations: effective implementation of reaction information in the IUBMB enzyme list.

Genomics is faced with the issue of many partially annotated putative enzyme-encoding genes for which activities have not yet been verified, while metabolomics is faced with the issue of many putative enzyme reactions for which full equations have not been verified. Knowledge of enzymes has been collected by IUBMB, and has been made public as the Enzyme List. To date, however, the terminology of the Enzyme List has not been assessed comprehensively by bioinformatics studies. Instead, most of the bioinformatics studies simply use the identifiers of the enzymes, i.e. the Enzyme Commission (EC) numbers. We investigated the actual usage of terminology throughout the Enzyme List, and demonstrated that the partial characteristics of reactions cannot be retrieved by simply using EC numbers. Thus, we developed a novel ontology, named PIERO, for annotating biochemical transformations as follows. First, the terminology describing enzymatic reactions was retrieved from the Enzyme List, and was grouped into those related to overall reactions and biochemical transformations. Consequently, these terms were mapped onto the actual transformations taken from enzymatic reaction equations. This ontology was linked to Gene Ontology (GO) and EC numbers, allowing the extraction of common partial reaction characteristics from given sets of orthologous genes and the elucidation of possible enzymes from the given transformations. Further future development of the PIERO ontology should enhance the Enzyme List to promote the integration of genomics and metabolomics.

Colony-live--a high-throughput method for measuring microbial colony growth kinetics--reveals diverse growth effects of gene knockouts in Escherichia coli.

BACKGROUND: Precise quantitative growth measurements and detection of small growth changes in high-throughput manner is essential for fundamental studies of bacterial cell. However, an inherent tradeoff for measurement quality in high-throughput methods sacrifices some measurement quality. A key challenge has been how to enhance measurement quality without sacrificing throughput. RESULTS: We developed a new high-throughput measurement system, termed Colony-live. Here we show that Colony-live provides accurate measurement of three growth values (lag time of growth (LTG), maximum growth rate (MGR), and saturation point growth (SPG)) by visualizing colony growth over time. By using a new normalization method for colony growth, Colony-live gives more precise and accurate growth values than the conventional method. We demonstrated the utility of Colony-live by measuring growth values for the entire Keio collection of Escherichia coli single-gene knockout mutants. By using Colony-live, we were able to identify subtle growth defects of single-gene knockout mutants that were undetectable by the conventional method quantified by fixed time-point camera imaging. Further, Colony-live can reveal genes that influence the length of the lag-phase and the saturation point of growth. CONCLUSIONS: Measurement quality is critical to achieving the resolution required to identify unique phenotypes among a diverse range of phenotypes. Sharing high-quality genome-wide datasets should benefit many researchers who are interested in specific gene functions or the architecture of cellular systems. Our Colony-live system provides a new powerful tool to accelerate accumulation of knowledge of microbial growth phenotypes.

Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach.

MOTIVATION: Metabolic pathway analysis is crucial not only in metabolic engineering but also in rational drug design. However, the biosynthetic/biodegradation pathways are known only for a small portion of metabolites, and a vast amount of pathways remain uncharacterized. Therefore, an important challenge in metabolomics is the de novo reconstruction of potential reaction networks on a metabolome-scale. RESULTS: In this article, we develop a novel method to predict the multistep reaction sequences for de novo reconstruction of metabolic pathways in the reaction-filling framework. We propose a supervised approach to learn what we refer to as 'multistep reaction sequence likeness', i.e. whether a compound-compound pair is possibly converted to each other by a sequence of enzymatic reactions. In the algorithm, we propose a recursive procedure of using step-specific classifiers to predict the intermediate compounds in the multistep reaction sequences, based on chemical substructure fingerprints/descriptors of compounds. We further demonstrate the usefulness of our proposed method on the prediction of enzymatic reaction networks from a metabolome-scale compound set and discuss characteristic features of the extracted chemical substructure transformation patterns in multistep reaction sequences. Our comprehensively predicted reaction networks help to fill the metabolic gap and to infer new reaction sequences in metabolic pathways. AVAILABILITY AND IMPLEMENTATION: Materials are available for free at http://web.kuicr.kyoto-u.ac.jp/supp/kot/ismb2014/

Modular architecture of metabolic pathways revealed by conserved sequences of reactions.

The metabolic network is both a network of chemical reactions and a network of enzymes that catalyze reactions. Toward better understanding of this duality in the evolution of the metabolic network, we developed a method to extract conserved sequences of reactions called reaction modules from the analysis of chemical compound structure transformation patterns in all known metabolic pathways stored in the KEGG PATHWAY database. The extracted reaction modules are repeatedly used as if they are building blocks of the metabolic network and contain chemical logic of organic reactions. Furthermore, the reaction modules often correspond to traditional pathway modules defined as sets of enzymes in the KEGG MODULE database and sometimes to operon-like gene clusters in prokaryotic genomes. We identified well-conserved, possibly ancient, reaction modules involving 2-oxocarboxylic acids. The chain extension module that appears as the tricarboxylic acid (TCA) reaction sequence in the TCA cycle is now shown to be used in other pathways together with different types of modification modules. We also identified reaction modules and their connection patterns for aromatic ring cleavages in microbial biodegradation pathways, which are most characteristic in terms of both distinct reaction sequences and distinct gene clusters. The modular architecture of biodegradation modules will have a potential for predicting degradation pathways of xenobiotic compounds. The collection of these and many other reaction modules is made available as part of the KEGG database.

Analysis of common substructures of metabolic compounds within the different organism groups.

With the increase in available post-genomic data and metabolic pathway information, we have been focusing on revealing the biological meaning of higher phenomena such as relationships of metabolic systems in different organisms. Metabolism plays an essential role in all cellular organisms, e.g. energy transportation, signal transduction and structural formation of cell components. The metabolic pathway of each organism has a different landscape from all others because of the different sets of enzymes encoded in the genome. The organisms that are incapable of producing their own essential chemical compounds should acquire them in some way from other organisms that can produce them. For example, several vitamins are required by animals to survive. In this manner we can assume that the different availabilities of metabolites may influence the relationship between organisms in nature. In this study, we focus on the differences in available metabolites among organisms. First, we divided 239 species with complete genomes into 9 organism groups in accordance with phylogeny and averaged out the annotation quality and the phylogenetic sparsity. Then, we calculated the commonly used chemical compounds between organism groups and the uniquely used chemical compounds in an organism group. The total number of metabolites we consider in this study is 1,074, which is about one-third of all metabolites that appear in the KEGG metabolic pathways. Finally we show the differences and the similarities between organism groups on every metabolic pathway map, illustrating the commonly observed substructures within the uniquely used metabolites. These results will help us to better comprehend the architecture of metabolic pathways and the relationships between organisms.