Rikiya Takeuchi

High-throughput, quantitative analyses of genetic interactions in E. coli.

Large-scale genetic interaction studies provide the basis for defining gene function and pathway architecture. Recent advances in the ability to generate double mutants en masse in Saccharomyces cerevisiae have dramatically accelerated the acquisition of genetic interaction information and the biological inferences that follow. Here we describe a method based on F factor–driven conjugation, which allows for high-throughput generation of double mutants in Escherichia coli. This method, termed genetic interaction analysis technology for E. coli (GIANT-coli), permits us to systematically generate and array double-mutant cells on solid media in high-density arrays. We show that colony size provides a robust and quantitative output of cellular fitness and that GIANT-coli can recapitulate known synthetic interactions and identify previously unidentified negative (synthetic sickness or lethality) and positive (suppressive or epistatic) relationships. Finally, we describe a complementary strategy for genome-wide suppressor-mutant identification. Together, these methods permit rapid, large-scale genetic interaction studies in E. coli.

Genome-scale identification and characterization of moonlighting proteins

BackgroundMoonlighting proteins perform two or more cellular functions, which are selected based on various contexts including the cell type they are expressed, their oligomerization status, and the binding of different ligands at different sites. To understand overall landscape of their functional diversity, it is important to establish methods that can identify moonlighting proteins in a systematic fashion. Here, we have developed a computational framework to find moonlighting proteins on a genome scale and identified multiple proteomic characteristics of these proteins.ResultsFirst, we analyzed Gene Ontology (GO) annotations of known moonlighting proteins. We found that the GO annotations of moonlighting proteins can be clustered into multiple groups reflecting their diverse functions. Then, by considering the observed GO term separations, we identified 33 novel moonlighting proteins in Escherichia coli and confirmed them by literature review. Next, we analyzed moonlighting proteins in terms of protein-protein interaction, gene expression, phylogenetic profile, and genetic interaction networks. We found that moonlighting proteins physically interact with a higher number of distinct functional classes of proteins than non-moonlighting ones and also found that most of the physically interacting partners of moonlighting proteins share the latter’s primary functions. Interestingly, we also found that moonlighting proteins tend to interact with other moonlighting proteins. In terms of gene expression and phylogenetically related proteins, a weak trend was observed that moonlighting proteins interact with more functionally diverse proteins. Structural characteristics of moonlighting proteins, i.e. intrinsic disordered regions and ligand binding sites were also investigated.ConclusionAdditional functions of moonlighting proteins are difficult to identify by experiments and these proteins also pose a significant challenge for computational function annotation. Our method enables identification of novel moonlighting proteins from current functional annotations in public databases. Moreover, we showed that potential moonlighting proteins without sufficient functional annotations can be identified by analyzing available omics-scale data. Our findings open up new possibilities for investigating the multi-functional nature of proteins at the systems level and for exploring the complex functional interplay of proteins in a cell.ReviewersThis article was reviewed by Michael Galperin, Eugine Koonin, and Nick Grishin.

iML1515, a knowledgebase that computes Escherichia coli traits

iML1515, a knowledgebase that computes Escherichia coli traits To the Editor: Extracting knowledge from the many types of big data produced by high-throughput methods remains a challenge, even when data are from Escherichia coli, the best characterized bacterial species. Here, we present iML1515, the most complete genome-scale reconstruction of the metabolic network in E. coli K-12 MG1655 to date, and we demonstrate how it can be used to address this challenge. Enabling analysis of several data types, including transcriptomes, proteomes, and metabolomes, iML1515 accounts for 1,515 open reading frames and 2,719 metabolic reactions involving 1,192 unique metabolites. The iML1515 knowledgebase is linked to 1,515 protein structures to provide an integrated modeling framework bridging systems and structural biology. We apply iML1515 to build metabolic models of E. coli human gut microbiome strains from metagenomic sequencing data. We then use iML1515 to build metabolic models for E. coli clinical isolates and predict their metabolic capabilities. Finally, we use iML1515 to carry out a comparative structural proteome analysis of 1,122 E. coli strains and identify multi-strain sequence variations.

The tRNA Thiolation Pathway Modulates the Intracellular Redox State in Escherichia coli

We have performed a screening of hydroxyurea (HU)-sensitive mutants using a single-gene-deletion mutant collection in Escherichia coli K-12. HU inhibits ribonucleotide reductase (RNR), an enzyme that catalyzes the formation of deoxyribonucleotides. Unexpectedly, seven of the mutants lacked genes that are required for the incorporation of sulfur into a specific tRNA modification base, 5-methylaminomethyl-2-thiouridine (mnm(5)s(2)U), via persulfide relay. We found that the expression of RNR in the mutants was reduced to about one-third both in the absence and presence of HU, while sufficient deoxynucleoside triphosphate (dNTP) was maintained in the mutants in the absence of HU but a shortage occurred in the presence of HU. Trans-supply of an RNR R2 subunit rescued the HU sensitivity of these mutants. The mutants showed high intracellular ATP/ADP ratios, and overexpression of Hda, which catalyzes the conversion of DnaA-ATP to DnaA-ADP, rescued the HU sensitivity of the mutants, suggesting that DnaA-ATP represses RNR expression. The high intracellular ATP/ADP ratios were due to high respiration activity in the mutants. Our data suggested that intracellular redox was inclined toward the reduced state in these mutants, which may explain a change in RNR activity by reduction of the catalytically formed disulfide bond and high respiration activity by the NADH reducing potential. The relation between persulfide relay and intracellular redox is discussed.

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.

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

BackgroundPrecise 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.ResultsWe 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.ConclusionsMeasurement 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.

Identification of essential genes and synthetic lethal gene combinations in Escherichia coli K-12.

Here we describe the systematic identification of single genes and gene pairs, whose knockout causes lethality in Escherichia coli K-12. During construction of precise single-gene knockout library of E. coli K-12, we identified 328 essential gene candidates for growth in complex (LB) medium. Upon establishment of the Keio single-gene deletion library, we undertook the development of the ASKA single-gene deletion library carrying a different antibiotic resistance. In addition, we developed tools for identification of synthetic lethal gene combinations by systematic construction of double-gene knockout mutants. We introduce these methods herein.

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.

Comprehensive Libraries of Escherichia coli K-12 and Their Application

Escherichia coli K-12 was reported more than 100 years ago in Germany, and after the discovery of conjugative genetic material transfer in this bacterium, this organism quickly became widely used for biological research. Considering the extensive research in genetics, biochemistry, and molecular biology using this organism in the next half-century, it is not too much to say that the concept of genes and current genetic technology could not have been established without this organ- ism. After genome sequencing biology, the new approach, systems biology, was launched to elucidate the cell at the systems level. In this new approach in biology, E. coli is one of the most appropriate unicellular organisms for this approach because of the huge amount of biological knowledge. Thus, we started to construct comprehensively prepared experimental resources, such as ORF clones and deletion strain libraries. We introduce here these resources and their application in the omics approach in systems biology.

Towards Elucidation of the Escherichia coli K-12 Unknowneome

Advances in genome sequencing have revolutionized biology by providing the molecular blueprints for thousands of living organisms. Yet, the functions of a large fraction, as much as one-half, of the component parts remain unknown even for the best understood organisms, including Escherichia coli, Bacillus subtilis, and Saccharomyces cerevisiae. Here, we describe our development of comprehensive genomic resources (ORFeome clone sets and mutant libraries) for systematic functional analysis of E. coli, summaries on our use of these resources, the GenoBase information resource for handling high-throughput experimental data obtained with them, and our creation of user workspaces at our Protein Function Elucidation Team (http://www.PrFEcT.org) website.