Plant Genome Informatics Lab.

Five most important research papers

*Corresponding author 

1.        Atsushi Fukushima*, Mikiko Takahashi, Hideki Nagasaki, Yusuke Aono, Makoto Kobayashi, Miyako Kusano, Kazuki Saito, Norio Kobayashi, Masanori Arita,  Development of RIKEN Plant Metabolome MetaDatabase. Plant and Cell Physiology 63:433-440 (2022). [PDF]

2.        Takeshi Kuroha, Keisuke Nagai, Yusuke Kurokawa, Yoshiaki Nagamura, Miyako Kusano, Hideshi Yasui, Motoyuki Ashikari, Atsushi Fukushima*, eQTLs regulating transcript variations associated with rapid internode elongation in deepwater rice. Frontiers in Plant Science 8:1753 (2017). [PDF]

3.        Atsushi Fukushima*, Miyako Kusano, Ramon Francisco Mejia, Mami Iwasa, Makoto Kobayashi, Naomi Hayashi, Akiko Watanabe-Takahashi, Tomoko Narisawa, Takayuki Tohge, Manhoi Hur, Eve Syrkin Wurtele, Basil J. Nikolau, Kazuki Saito, Metabolomic characterization of knockout mutants in Arabidopsis: development of a metabolite profiling database for knockout mutants in Arabidopsis. Plant Physiology 165:948-961 (2014). [PDF]

4.        Atsushi Fukushima*, DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene 518:209-214 (2013). [PDF]

5.        Atsushi Fukushima*, Tomoko Nishizawa, Mariko Hayakumo, Shoko Hikosaka, Kazuki Saito, Eiji Goto, Miyako Kusano, Exploring tomato gene functions based on coexpression modules using graph clustering and differential coexpression approaches. Plant Physiology 158:1487-1502 (2012). [PDF]

Three most important reviews

1.        Miyako Kusano, Zhigang Yang, Yozo Okazaki, Ryo Nakabayashi, Atsushi Fukushima, Kazuki Saito, Using metabolomic approaches to explore chemical diversity in rice. Molecular Plant 8:58-67 (2015). [PDF]

2.        Atsushi Fukushima, Miyako Kusano, A network perspective on nitrogen metabolism from model to crop plants using integrated “omics” approaches. Journal of Experimental Botany 65:5619-5630 (2014). [PDF]

3.        Atsushi Fukushima, Miyako Kusano, Henning Redestig, Masanori Arita, Kazuki Saito, Integrated omics approaches in plant systems biology. Current Opinion in Chemical Biology 13:532-538 (2009). [Abstract]

Full list of publication

Introduction

Plants are a paramount source of food, energy, and valuable compounds. The developing field of plant systems biology has provided outstanding insights into how these products are synthesized; its ultimate goal is an understanding of the genotype-phenotype relationship in cellular systems. Recent technical advances in high-throughput sequencing and various analytical instruments have made it possible to comprehensively measure and analyze genes, transcripts, proteins, and metabolites.

Network analysis

Large-scale “omics” data, such as RNA sequencing and microarrays, can be used to infer underlying cellular regulatory networks in organisms, enabling us to better understand the molecular basis of disease and important traits. Correlation approaches, such as a hierarchical cluster analysis, have been widely used to analyze omics data. In addition to the changes in the mean levels of molecules in the omics data, it is important to know about the changes in the correlation relationship among molecules between 2 experimental conditions. We developed the DiffCorr package (Fukushima, Gene, 2013), a simple method for identifying pattern changes between 2 experimental conditions in correlation networks, which builds on a commonly used association measure, such as Pearson's correlation coefficient. DiffCorr calculates correlation matrices for each dataset, identifies the first principal component-based “eigen-molecules” in the correlation networks, and tests differential correlation between the 2 groups based on Fisher's z-test (Figure 1).

Meta-omics data analysis

Generally, large-scale datasets obtained from high-throughput experiments allow us to estimate relationships (or, edges in biological network) among cellular elements such as transcripts and metabolites. In silico analysis of genes and metabolites using publicly available data can further construct gene-to-metabolite networks. Such meta-analysis toward plant systems biology can accelerate the studies required to fill in the missing blank spots in our knowledge of how cellular processes cooperate (Figure 2) (Fukushima et al. Curr Opin Chem Biol, 2009). The hypothesis-experiment cycle with in silico simulation of a biological phenomenon is repeated within individual experiments. The data deposited in publicly available databases contributes greatly to the construction of a general prediction database (e.g., coexpression database). This also generates a further testable hypothesis and adjusts the hypothesis for the individual experiment.

Database

It is estimated that approximately 1 million metabolites are produced in the plant kingdom. Metabolomics, i.e., the measurement of the full suite of metabolites in a living tissue, has expanded greatly over the past 20 years and has become an invaluable diagnostic tool for biochemical phenotyping of biological systems (Fukushima and Kusano, Front Plant Sci, 2013). To date, a number of databases involving information related to mass spectra, compound names and structures, statistical/mathematical models and metabolic pathways, and metabolite profile data have been developed (e.g., Fukushima et al. Plant Physiol, 2014). Such databases complement each other and support efficient growth in this area. We have developed the RIKEN Plant Metabolome MetaDatabase (RIKEN PMM, http://metabobank.riken.jp/pmm/db/plantMetabolomics), which mainly provides MS-based metabolite profiling data of plants together with their detailed and structured metadata as Linked Open Data (LOD) based on the semantic web. To introduce our reanalysis approach with RIKEN PMM, we have shared our gas chromatography (GC)–MS data reanalysis workflow (Fukushima et al. Plant Cell Physiol, 2022).