Hiromi Kajiya-Kanegae

CyanoBase and RhizoBase: databases of manually curated annotations for cyanobacterial and rhizobial genomes

To understand newly sequenced genomes of closely related species, comprehensively curated reference genome databases are becoming increasingly important. We have extended CyanoBase (http://genome.microbedb.jp/cyanobase), a genome database for cyanobacteria, and newly developed RhizoBase (http://genome.microbedb.jp/rhizobase), a genome database for rhizobia, nitrogen-fixing bacteria associated with leguminous plants. Both databases focus on the representation and reusability of reference genome annotations, which are continuously updated by manual curation. Domain experts have extracted names, products and functions of each gene reported in the literature. To ensure effectiveness of this procedure, we developed the TogoAnnotation system offering a web-based user interface and a uniform storage of annotations for the curators of the CyanoBase and RhizoBase databases. The number of references investigated for CyanoBase increased from 2260 in our previous report to 5285, and for RhizoBase, we perused 1216 references. The results of these intensive annotations are displayed on the GeneView pages of each database. Advanced users can also retrieve this information through the representational state transfer-based web application programming interface in an automated manner.

Complete genome sequence of Bradyrhizobium sp. S23321: insights into symbiosis evolution in soil oligotrophs.

Bradyrhizobium sp. S23321 is an oligotrophic bacterium isolated from paddy field soil. Although S23321 is phylogenetically close to Bradyrhizobium japonicum USDA110, a legume symbiont, it is unable to induce root nodules in siratro, a legume often used for testing Nod factor-dependent nodulation. The genome of S23321 is a single circular chromosome, 7,231,841 bp in length, with an average GC content of 64.3%. The genome contains 6,898 potential protein-encoding genes, one set of rRNA genes, and 45 tRNA genes. Comparison of the genome structure between S23321 and USDA110 showed strong colinearity; however, the symbiosis islands present in USDA110 were absent in S23321, whose genome lacked a chaperonin gene cluster (groELS3) for symbiosis regulation found in USDA110. A comparison of sequences around the tRNA-Val gene strongly suggested that S23321 contains an ancestral-type genome that precedes the acquisition of a symbiosis island by horizontal gene transfer. Although S23321 contains a nif (nitrogen fixation) gene cluster, the organization, homology, and phylogeny of the genes in this cluster were more similar to those of photosynthetic bradyrhizobia ORS278 and BTAi1 than to those on the symbiosis island of USDA110. In addition, we found genes encoding a complete photosynthetic system, many ABC transporters for amino acids and oligopeptides, two types (polar and lateral) of flagella, multiple respiratory chains, and a system for lignin monomer catabolism in the S23321 genome. These features suggest that S23321 is able to adapt to a wide range of environments, probably including low-nutrient conditions, with multiple survival strategies in soil and rhizosphere.

Rice has two distinct classes of protein kinase genes related to SNF1 of Saccharomyces cerevisiae, which are differently regulated in early seed development

Abstract We have isolated five cDNA clones (osk1–5) for protein kinases from rice which are related to SNF1 protein kinase of Saccharomyces cerevisiae. Based on the sequence homology, these cDNAs can be classified into two groups, group 1 (osk1) and group 2 (osk2–5). The products of these genes were demonstrated to be functional SNF1-related protein kinases by in vitro and in vivo experiments. Recombinant proteins expressed from both groups of genes were fully active as protein kinases and could phosphorylate SAMS peptide, a substrate specific for the SNF1/AMPK family, as well as themselves (autophosphorylation). Moreover, expression of osk3 cDNA in yeast snf1 mutants restored SNF1 function. Northern blot analyses showed differential expression of these two gene groups; group 1 is expressed uniformly in growing tissues (young roots, young shoots, flowers, and immature seeds), whereas group 2 is strongly expressed in immature seeds. SNF1-related protein kinases have been reported from different plant species, such as rye, barley, Arabidopsis, tobacco, and potato, while the type of gene strongly expressed in immature seeds is known only in cereals such as rye, barley, and, from our findings, in rice. Expression levels of the group 2 genes were further analyzed in seeds during seed maturation. Expression is transiently increased in the early stages of seed maturation and then decreases. The expression peak precedes those of the sbe1 and waxy genes, which are involved in starch synthesis in rice. Taken together, these findings suggest that group 2 OSK genes play important roles in the early stages of endosperm development in rice seeds.

High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling

Genomics-assisted breeding methods have been rapidly developed with novel technologies such as next-generation sequencing, genomic selection and genome-wide association study. However, phenotyping is still time consuming and is a serious bottleneck in genomics-assisted breeding. In this study, we established a high-throughput phenotyping system for sorghum plant height and its response to nitrogen availability; this system relies on the use of unmanned aerial vehicle (UAV) remote sensing with either an RGB or near-infrared, green and blue (NIR-GB) camera. We evaluated the potential of remote sensing to provide phenotype training data in a genomic prediction model. UAV remote sensing with the NIR-GB camera and the 50th percentile of digital surface model, which is an indicator of height, performed well. The correlation coefficient between plant height measured by UAV remote sensing (PHUAV) and plant height measured with a ruler (PHR) was 0.523. Because PHUAV was overestimated (probably because of the presence of taller plants on adjacent plots), the correlation coefficient between PHUAV and PHR was increased to 0.678 by using one of the two replications (that with the lower PHUAV value). Genomic prediction modeling performed well under the low-fertilization condition, probably because PHUAV overestimation was smaller under this condition due to a lower plant height. The predicted values of PHUAV and PHR were highly correlated with each other (r = 0.842). This result suggests that the genomic prediction models generated with PHUAV were almost identical and that the performance of UAV remote sensing was similar to that of traditional measurements in genomic prediction modeling. UAV remote sensing has a high potential to increase the throughput of phenotyping and decrease its cost. UAV remote sensing will be an important and indispensable tool for high-throughput genomics-assisted plant breeding.

Genomics-assisted breeding in fruit trees

Recent advancements in genomic analysis technologies have opened up new avenues to promote the efficiency of plant breeding. Novel genomics-based approaches for plant breeding and genetics research, such as genome-wide association studies (GWAS) and genomic selection (GS), are useful, especially in fruit tree breeding. The breeding of fruit trees is hindered by their long generation time, large plant size, long juvenile phase, and the necessity to wait for the physiological maturity of the plant to assess the marketable product (fruit). In this article, we describe the potential of genomics-assisted breeding, which uses these novel genomics-based approaches, to break through these barriers in conventional fruit tree breeding. We first introduce the molecular marker systems and whole-genome sequence data that are available for fruit tree breeding. Next we introduce the statistical methods for biparental linkage and quantitative trait locus (QTL) mapping as well as GWAS and GS. We then review QTL mapping, GWAS, and GS studies conducted on fruit trees. We also review novel technologies for rapid generation advancement. Finally, we note the future prospects of genomics-assisted fruit tree breeding and problems that need to be overcome in the breeding.

A simulation-based breeding design that uses whole-genome prediction in tomato

Efficient plant breeding methods must be developed in order to increase yields and feed a growing world population, as well as to meet the demands of consumers with diverse preferences who require high-quality foods. We propose a strategy that integrates breeding simulations and phenotype prediction models using genomic information. The validity of this strategy was evaluated by the simultaneous genetic improvement of the yield and flavour of the tomato (Solanum lycopersicum), as an example. Reliable phenotype prediction models for the simulation were constructed from actual genotype and phenotype data. Our simulation predicted that selection for both yield and flavour would eventually result in morphological changes that would increase the total plant biomass and decrease the light extinction coefficient, an essential requirement for these improvements. This simulation-based genome-assisted approach to breeding will help to optimise plant breeding, not only in the tomato but also in other important agricultural crops.

Genome-wide association study and genomic prediction in citrus: Potential of genomics-assisted breeding for fruit quality traits

Novel genomics-based approaches such as genome-wide association studies (GWAS) and genomic selection (GS) are expected to be useful in fruit tree breeding, which requires much time from the cross to the release of a cultivar because of the long generation time. In this study, a citrus parental population (111 varieties) and a breeding population (676 individuals from 35 full-sib families) were genotyped for 1,841 single nucleotide polymorphisms (SNPs) and phenotyped for 17 fruit quality traits. GWAS power and prediction accuracy were increased by combining the parental and breeding populations. A multi-kernel model considering both additive and dominance effects improved prediction accuracy for acidity and juiciness, implying that the effects of both types are important for these traits. Genomic best linear unbiased prediction (GBLUP) with linear ridge kernel regression (RR) was more robust and accurate than GBLUP with non-linear Gaussian kernel regression (GAUSS) in the tails of the phenotypic distribution. The results of this study suggest that both GWAS and GS are effective for genetic improvement of citrus fruit traits. Furthermore, the data collected from breeding populations are beneficial for increasing the detection power of GWAS and the prediction accuracy of GS.

Genome-wide association study and genomic prediction using parental and breeding populations of Japanese pear ( Pyrus pyrifolia Nakai)

Breeding of fruit trees is hindered by their large size and long juvenile period. Genome-wide association study (GWAS) and genomic selection (GS) are promising methods for circumventing this hindrance, but preparing new large datasets for these methods may not always be practical. Here, we evaluated the potential of breeding populations evaluated routinely in breeding programs for GWAS and GS. We used a pear parental population of 86 varieties and breeding populations of 765 trees from 16 full-sib families, which were phenotyped for 18 traits and genotyped for 1,506 single nucleotide polymorphisms (SNPs). The power of GWAS and accuracy of genomic prediction were improved when we combined data from the breeding populations and the parental population. The accuracy of genomic prediction was improved further when full-sib data of the target family were available. The results suggest that phenotype data collected in breeding programs can be beneficial for GWAS and GS when they are combined with genome-wide marker data. The potential of GWAS and GS will be further extended if we can build a system for routine collection of the phenotype and marker genotype data for breeding populations.

Transcriptional switch for programmed cell death in pith parenchyma of sorghum stems

Significance Sugar and ethanol productivity from the sugar juice of grass stems depends on their water content. Pith parenchyma cells function as a water storage tissue in plant stems, and the death of these cells reduces stem water content. In this study, we identified a gene, long referred to as D, in a promising energy grass, Sorghum bicolor, that is responsible for reducing stem water content. D and its Arabidopsis ortholog encode master transcriptional switches that induce programmed death of stem pith parenchyma cells by activating autolytic enzymes. Identifying D as the gene involved in programmed death of plant pith parenchyma cells will provide an approach to breeding crops for sugar and ethanol production. Pith parenchyma cells store water in various plant organs. These cells are especially important for producing sugar and ethanol from the sugar juice of grass stems. In many plants, the death of pith parenchyma cells reduces their stem water content. Previous studies proposed that a hypothetical D gene might be responsible for the death of stem pith parenchyma cells in Sorghum bicolor, a promising energy grass, although its identity and molecular function are unknown. Here, we identify the D gene and note that it is located on chromosome 6 in agreement with previous predictions. Sorghum varieties with a functional D allele had stems enriched with dry, dead pith parenchyma cells, whereas those with each of six independent nonfunctional D alleles had stems enriched with juicy, living pith parenchyma cells. D expression was spatiotemporally coupled with the appearance of dead, air-filled pith parenchyma cells in sorghum stems. Among D homologs that are present in flowering plants, Arabidopsis ANAC074 also is required for the death of stem pith parenchyma cells. D and ANAC074 encode previously uncharacterized NAC transcription factors and are sufficient to ectopically induce programmed death of Arabidopsis culture cells via the activation of autolytic enzymes. Taken together, these results indicate that D and its Arabidopsis ortholog, ANAC074, are master transcriptional switches that induce programmed death of stem pith parenchyma cells. Thus, targeting the D gene will provide an approach to breeding crops for sugar and ethanol production.

Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data

Abstract Recent availability of large-scale genomic resources enables us to conduct so called genome-wide association studies (GWAS) and genomic prediction (GP) studies, particularly with next-generation sequencing (NGS) data. The effectiveness of GWAS and GP depends on not only their mathematical models, but the quality and quantity of variants employed in the analysis. In NGS single nucleotide polymorphism (SNP) calling, conventional tools ideally require more reads for higher SNP sensitivity and accuracy. In this study, we aimed to develop a tool, Heap, that enables robustly sensitive and accurate calling of SNPs, particularly with a low coverage NGS data, which must be aligned to the reference genome sequences in advance. To reduce false positive SNPs, Heap determines genotypes and calls SNPs at each site except for sites at the both ends of reads or containing a minor allele supported by only one read. Performance comparison with existing tools showed that Heap achieved the highest F-scores with low coverage (7X) restriction-site associated DNA sequencing reads of sorghum and rice individuals. This will facilitate cost-effective GWAS and GP studies in this NGS era. Code and documentation of Heap are freely available from https://github.com/meiji-bioinf/heap (29 March 2017, date last accessed) and our web site (http://bioinf.mind.meiji.ac.jp/lab/en/tools.html (29 March 2017, date last accessed)).