A new method for gene discovery in large-scale microarray data

Assessment of Greenhouse Tomato Anthesis Rate Through Metabolomics Using LASSO Regularized Linear Regression Model

While the high year-round production of tomatoes has been facilitated by solar greenhouse cultivation, these yields readily fluctuate in response to changing environmental conditions. Mathematic modeling has been applied to forecast phenotypes of tomatoes using environmental measurements (e.g., temperature) as indirect parameters. In this study, metabolome data, as direct parameters reflecting plant internal status, were used to construct a predictive model of the anthesis rate of greenhouse tomatoes. Metabolome data were obtained from tomato leaves and used as variables for linear regression with the least absolute shrinkage and selection operator (LASSO) for prediction. The constructed model accurately predicted the anthesis rate, with an R² value of 0.85. Twenty-nine of the 161 metabolites were selected as candidate markers. The selected metabolites were further validated for their association with anthesis rates using the different metabolome datasets. To assess the importance of the selected metabolites in cultivation, the relationships between the metabolites and cultivation conditions were analyzed via correspondence analysis. Trigonelline, whose content did not exhibit a diurnal rhythm, displayed major contributions to the cultivation, and is thus a potential metabolic marker for predicting the anthesis rate. This study demonstrates that machine learning can be applied to metabolome data to identify metabolites indicative of agricultural traits.

Rapidly evolving phosphoenolpyruvate carboxylase Gmppc1 and Gmppc7 are highly expressed in the external seed coat of immature soybean seeds

Phosphoenolpyruvate carboxylase (PEPC) is a carbon fixation enzyme which probably plays crucial roles in seed development. A greater number of PEPC isoforms are encoded in the soybean genome, while most of the PEPC isoforms are functionally unknown. In this study, we investigated on soybean PEPC expressed in the external layer of seed coat (ELSC) during seed formation. PEPC activity in ELSC ranged from 0.24 to 1.0 U/g F.W., which could be comparable to those in whole seeds at U per dry matter. Public RNA-Seq data in separated soybean seed tissues revealed that six plant-type PEPC isogenes were substantially expressed in ELSC, and Gmppc1 and Gmppc7 were highly expressed in hourglass cells of ELSC. Gene Ontology enrichment of co-expressed genes with Gmppc1 and Gmppc7 implicated a role of these isogenes in assisting energy production and cellulose biosynthesis. Comparison of PEPC sequences from 16 leguminous species hypothesized adaptive evolution of the Gmppc1 and Gmppc7 lineage after divergence from the other plant-type PEPC lineages. Molecular diversification of these plant-type PEPC was possibly accomplished by adaptation to the functions of the soybean seed tissues. This study indicates that energy demand in immature seeds may be a driving force for the molecular evolution of PEPC.

Time course analysis of large-scale gene expression in incised muscle using correspondence analysis

Studying the time course of gene expression in injured skeletal muscle would help to estimate the timing of injuries. In this study, we investigated large-scale gene expression in incision-injured mouse skeletal muscle by DNA microarray using correspondence analysis (CA). Biceps femoris muscle samples were collected 6, 12, and 24 hours after injury, and RNA was extracted and prepared for microarray analysis. On a 2-dimensional plot by CA, the genes (row score coordinate) located farther from each time series (column score coordinate) had more upregulation at particular times. Each gene was situated in 6 subdivided triangular areas according to the magnitude of the relationship of the fold change (FC) value at each time point compared to the control. In each area, genes for which the ratios of two particular FC values were close to 1 were distributed along the two border lines. There was a tendency for genes whose FC values were almost equal to be distributed near the intersection of these 6 areas. Therefore, the gene marker candidates for estimation of the timing of injuries were detectable according to the location on the CA plot. Moreover, gene sets created by a specific gene and its surrounding genes were composed of genes that showed similar or identical fluctuation patterns to the specific gene. In various analyses on these sets, significant gene ontology term and pathway activity may reflect changes in specific genes. In conclusion, analyses of gene sets based on CA plots is effective for investigation of the time-dependent fluctuation in gene expression after injury.

The plant-type phosphoenolpyruvate carboxylase Gmppc2 is developmentally induced in immature soy seeds at the late maturation stage: a potential protein biomarker for seed chemical composition

Phosphoenolpyruvate carboxylase (PEPC) is a carbon-fixing enzyme with critical roles in seed development. Previously we observed a positive correlation between PEPC activity and protein content in mature seeds among soybean cultivars and varietal differences of PEPC activity in immature seeds, which is concordant with seed protein accumulation. Here, we report a PEPC isoform (Gmppc2) which is preferentially expressed in immature soybean seeds at the late maturation stage. Gmppc2 was co-expressed with enzyme genes involved in starch degradation: α-amylase, hexokinase, and α-glucan phosphorylase. Gmppc2 was developmentally induced in the external seed coats, internal seed coats, hypocotyls, and cotyledons at the late maturation stage. The expression of Gmppc2 protein was negatively regulated by the application of a nitrogen fertilizer, which suppressed nodule formation. These results imply that Gmppc2 is involved in the metabolism of nitrogen originated from nodules into seeds, and Gmppc2 might be applicable as a biomarker of seed protein content.Abbreviations: PEP: phosphoenolpyruvate; PEPC: phosphoenolpyruvate carboxylase; RNA-Seq: RNA sequencing; PCA: principal component analysis; SE: standard error.

PlantExpress: A Database Integrating OryzaExpress and ArthaExpress for Single-species and Cross-species Gene Expression Network Analyses with Microarray-Based Transcriptome Data

Publicly available microarray-based transcriptome data on plants are remarkably valuable in terms of abundance and variation of samples, particularly for Oryza sativa (rice) and Arabidopsis thaliana (Arabidopsis). Here, we introduce the web database PlantExpress (http://plantomics.mind.meiji.ac.jp/PlantExpress/) as a platform for gene expression network (GEN) analysis with the public microarray data of rice and Arabidopsis. PlantExpress has two functional modes. The single-species mode is specialized for GEN analysis within one of the species, while the cross-species mode is optimized for comparative GEN analysis between the species. The single-species mode for rice is the new version of OryzaExpress, which we have maintained since 2006. The single-species mode for Arabidopsis, named ArthaExpress, was newly developed. PlantExpress stores data obtained from three microarrays, the Affymetrix Rice Genome Array, the Agilent Rice Gene Expression 4x44K Microarray, and the Affymetrix Arabidopsis ATH1 Genome Array, with respective totals of 2,678, 1,206, and 10,940 samples. This database employs a ‘MyList’ function with which users may save lists of arbitrary genes and samples (experimental conditions) to use in analyses. In cross-species mode, the MyList function allows performing comparative GEN analysis between rice and Arabidopsis. In addition, the gene lists saved in MyList can be directly exported to the PODC database, which provides information and a platform for comparative GEN analysis based on RNA-seq data and knowledge-based functional annotation of plant genes. PlantExpress will facilitate understanding the biological functions of plant genes.

Identification of reference genes for quantitative expression analysis using large-scale RNA-seq data of Arabidopsis thaliana and model crop plants

In quantitative gene expression analysis, normalization using a reference gene as an internal control is frequently performed for appropriate interpretation of the results. Efforts have been devoted to exploring superior novel reference genes using microarray transcriptomic data and to evaluating commonly used reference genes by targeting analysis. However, because the number of specifically detectable genes is totally dependent on probe design in the microarray analysis, exploration using microarray data may miss some of the best choices for the reference genes. Recently emerging RNA sequencing (RNA-seq) provides an ideal resource for comprehensive exploration of reference genes since this method is capable of detecting all expressed genes, in principle including even unknown genes. We report the results of a comprehensive exploration of reference genes using public RNA-seq data from plants such as Arabidopsis thaliana (Arabidopsis), Glycine max (soybean), Solanum lycopersicum (tomato) and Oryza sativa (rice). To select reference genes suitable for the broadest experimental conditions possible, candidates were surveyed by the following four steps: (1) evaluation of the basal expression level of each gene in each experiment; (2) evaluation of the expression stability of each gene in each experiment; (3) evaluation of the expression stability of each gene across the experiments; and (4) selection of top-ranked genes, after ranking according to the number of experiments in which the gene was expressed stably. Employing this procedure, 13, 10, 12 and 21 top candidates for reference genes were proposed in Arabidopsis, soybean, tomato and rice, respectively. Microarray expression data confirmed that the expression of the proposed reference genes under broad experimental conditions was more stable than that of commonly used reference genes. These novel reference genes will be useful for analyzing gene expression profiles across experiments carried out under various experimental conditions.

DREMECELS: A Curated Database for Base Excision and Mismatch Repair Mechanisms Associated Human Malignancies

DNA repair mechanisms act as a warrior combating various damaging processes that ensue critical malignancies. DREMECELS was designed considering the malignancies with frequent alterations in DNA repair pathways, that is, colorectal and endometrial cancers, associated with Lynch syndrome (also known as HNPCC). Since lynch syndrome carries high risk (~40–60%) for both cancers, therefore we decided to cover all three diseases in this portal. Although a large population is presently affected by these malignancies, many resources are available for various cancer types but no database archives information on the genes specifically for only these cancers and disorders. The database contains 156 genes and two repair mechanisms, base excision repair (BER) and mismatch repair (MMR). Other parameters include some of the regulatory processes that have roles in these disease progressions due to incompetent repair mechanisms, specifically BER and MMR. However, our unique database mainly provides qualitative and quantitative information on these cancer types along with methylation, drug sensitivity, miRNAs, copy number variation (CNV) and somatic mutations data. This database would serve the scientific community by providing integrated information on these disease types, thus sustaining diagnostic and therapeutic processes. This repository would serve as an excellent accompaniment for researchers and biomedical professionals and facilitate in understanding such critical diseases. DREMECELS is publicly available at http://www.bioinfoindia.org/dremecels.

Comprehensive analysis of transcriptome response to salinity stress in the halophytic turf grass Sporobolus virginicus

The turf grass Sporobolus virginicus is halophyte and has high salinity tolerance. To investigate the molecular basis of its remarkable tolerance, we performed Illumina high-throughput RNA sequencing on roots and shoots of a S. virginicus genotype under normal and saline conditions. The 130 million short reads were assembled into 444,242 unigenes. A comparative analysis of the transcriptome with rice and Arabidopsis transcriptome revealed six turf grass-specific unigenes encoding transcription factors. Interestingly, all of them showed root specific expression and five of them encode bZIP type transcription factors. Another remarkable transcriptional feature of S. virginicus was activation of specific pathways under salinity stress. Pathway enrichment analysis suggested transcriptional activation of amino acid, pyruvate, and phospholipid metabolism. Up-regulation of several unigenes, previously shown to respond to salt stress in other halophytes was also observed. Gene Ontology enrichment analysis revealed that unigenes assigned as proteins in response to water stress, such as dehydrin and aquaporin, and transporters such as cation, amino acid, and citrate transporters, and H(+)-ATPase, were up-regulated in both shoots and roots under salinity. A correspondence analysis of the enriched pathways in turf grass cells, but not in rice cells, revealed two groups of unigenes similarly up-regulated in the turf grass in response to salt stress; one of the groups, showing excessive up-regulation under salinity, included unigenes homologos to salinity responsive genes in other halophytes. Thus, the present study identified candidate genes involved in salt tolerance of S. virginicus. This genetic resource should be valuable for understanding the mechanisms underlying high salt tolerance in S. virginicus. This information can also provide insight into salt tolerance in other halophytes.

Plant Omics Data Center: an integrated web repository for interspecies gene expression networks with NLP-based curation

Comprehensive integration of large-scale omics resources such as genomes, transcriptomes and metabolomes will provide deeper insights into broader aspects of molecular biology. For better understanding of plant biology, we aim to construct a next-generation sequencing (NGS)-derived gene expression network (GEN) repository for a broad range of plant species. So far we have incorporated information about 745 high-quality mRNA sequencing (mRNA-Seq) samples from eight plant species (Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, Solanum tuberosum, Medicago truncatula and Glycine max) from the public short read archive, digitally profiled the entire set of gene expression profiles, and drawn GENs by using correspondence analysis (CA) to take advantage of gene expression similarities. In order to understand the evolutionary significance of the GENs from multiple species, they were linked according to the orthology of each node (gene) among species. In addition to other gene expression information, functional annotation of the genes will facilitate biological comprehension. Currently we are improving the given gene annotations with natural language processing (NLP) techniques and manual curation. Here we introduce the current status of our analyses and the web database, PODC (Plant Omics Data Center; http://bioinf.mind.meiji.ac.jp/podc/), now open to the public, providing GENs, functional annotations and additional comprehensive omics resources.

Effect of Target Length on Specificity and Sensitivity of Oligonucleotide Microarrays: A Comparison between Dendrimer and Modified PCR based Labelling Methods

DNA microarrays are widely used as end point detectors for gene expression analysis. Several methods have been developed for target labelling to enable quantification but without taking target length into consideration. Here we highlight the importance of choosing the optimum target length that would ensure specificity without compromising sensitivity of the assay. For this, eight plasmids that are identical to each other except for a closely related 23 bp unique reporter (UR) sequence were used to examine the hybridization efficiency for these URs. Targets of various lengths were generated and labelled as follows: full length and 330 bases transcripts using a dendrimer labelling method, 120 bp amplicons by the modified PCR end labelling method and synthetic labelled targets of 33 bases. This report also shows the advantages of using the modified PCR method over other labelling methods in generating labelled amplicons of the desired lengths to maximize hybridization efficiency.

A new omics data resource of Pleurocybella porrigens for gene discovery

Background

Pleurocybellaporrigens is a mushroom-forming fungus, which has been consumed as a traditional food in Japan. In 2004, 55 people were poisoned by eating the mushroom and 17 people among them died of acute encephalopathy. Since then, the Japanese government has been alerting Japanese people to take precautions against eating the P. porrigens mushroom. Unfortunately, despite efforts, the molecular mechanism of the encephalopathy remains elusive. The genome and transcriptome sequence data of P. porrigens and the related species, however, are not stored in the public database. To gain the omics data in P. porrigens, we sequenced genome and transcriptome of its fruiting bodies and mycelia by next generation sequencing.

Methodology/Principal Findings

Short read sequences of genomic DNAs and mRNAs in P. porrigens were generated by Illumina Genome Analyzer. Genome short reads were de novo assembled into scaffolds using Velvet. Comparisons of genome signatures among Agaricales showed that P. porrigens has a unique genome signature. Transcriptome sequences were assembled into contigs (unigenes). Biological functions of unigenes were predicted by Gene Ontology and KEGG pathway analyses. The majority of unigenes would be novel genes without significant counterparts in the public omics databases.

Conclusions

Functional analyses of unigenes present the existence of numerous novel genes in the basidiomycetes division. The results mean that the omics information such as genome, transcriptome and metabolome in basidiomycetes is short in the current databases. The large-scale omics information on P. porrigens, provided from this research, will give a new data resource for gene discovery in basidiomycetes.

Exploring the transcription factor activity in high-throughput gene expression data using RLQ analysis

Background

Interpretation of gene expression microarray data in the light of external information on both columns and rows (experimental variables and gene annotations) facilitates the extraction of pertinent information hidden in these complex data. Biologists classically interpret genes of interest after retrieving functional information from a subset of genes of interest. Transcription factors play an important role in orchestrating the regulation of gene expression. Their activity can be deduced by examining the presence of putative transcription factors binding sites in the gene promoter regions.

Results

In this paper we present the multivariate statistical method RLQ which aims to analyze microarray data where additional information is available on both genes and samples. As an illustrative example, we applied RLQ methodology to analyze transcription factor activity associated with the time-course effect of steroids on the growth of primary human lung fibroblasts. RLQ could successfully predict transcription factor activity, and could integrate various other sources of external information in the main frame of the analysis. The approach was validated by means of alternative statistical methods and biological validation.

Conclusions

RLQ provides an efficient way of extracting and visualizing structures present in a gene expression dataset by directly modeling the link between experimental variables and gene annotations.

Computational Sequence Design Techniques for DNA Microarray Technologies

In systems biology and biomedical research, microarray technology is a method of choice that enables the complete quantitative and qualitative ascertainment of gene expression patterns for whole genomes. The selection of high quality oligonucleotide sequences that behave consistently across multiple experiments is a key step in the design, fabrication and experimental performance of DNA microarrays. The aim of this chapter is to outline recent algorithmic developments in microarray probe design, evaluate existing probe sequences used in commercial arrays, and suggest methodologies that have the potential to improve on existing design techniques.

The Rice Oligonucleotide Array Database: an atlas of rice gene expression

BACKGROUND

Microarray technologies facilitate high-throughput gene expression analysis. However, the diversity of platforms for rice gene expression analysis hinders efficient analysis. Tools to broadly integrate microarray data from different platforms are needed.

RESULTS

In this study, we developed the Rice Oligonucleotide Array Database (ROAD, http://www.ricearray.org) to explore gene expression across 1,867 publicly available rice microarray hybridizations. The ROAD's user-friendly web interface and variety of visualization tools facilitate the extraction of gene expression profiles using gene and microarray element identifications. The ROAD supports meta-analysis of genes expressed in different tissues and at developmental stages. Co-expression analysis tool provides information on co-regulation between genes under general, abiotic and biotic stress conditions. Additionally, functional analysis tools, such as Gene Ontology and KEGG (Kyoto Encyclopedia of Genes and Genomes) Orthology, are embedded in the ROAD. These tools facilitate the identification of meaningful biological patterns in a list of query genes.

CONCLUSIONS

The Rice Oligonucleotide Array Database provides comprehensive gene expression profiles for all rice genes, and will be a useful resource for researchers of rice and other grass species.

Options available for labelling nucleic acid samples in DNA microarray-based detection methods

DNA microarrays are considered by many researchers to be the platform of choice for the high-throughput analysis of nucleic acids. Since the past two decades, they have been used constantly as powerful tools in differential gene expression, SNP genotyping, DNA sequencing, gene discovery, disease diagnostic and pathways reconstruction. Several methods have been developed to enable samples of limited amounts of RNA to be quantified. Here we evaluate classical and up-to-date assays made available for labelling those samples. This review also sheds light on the recently developed strategies that ensure high sensitivity such as sample and signal amplification, quantum dot, surface plasmom resonance, nanoparticles and cationinc polythiophenes.

Genome Signature Difference between Deinococcus radiodurans and Thermus thermophilus

The extremely radioresistant bacteria of the genus Deinococcus and the extremely thermophilic bacteria of the genus Thermus belong to a common taxonomic group. Considering the distinct living environments of Deinococcus and Thermus, different genes would have been acquired through horizontal gene transfer after their divergence from a common ancestor. Their guanine-cytosine (GC) contents are similar; however, we hypothesized that their genomic signatures would be different. Our findings indicated that the genomes of Deinococcus radiodurans and Thermus thermophilus have different tetranucleotide frequencies. This analysis showed that the genome signature of D. radiodurans is most similar to that of Pseudomonas aeruginosa, whereas the genome signature of T. thermophilus is most similar to that of Thermanaerovibrio acidaminovorans. This difference in genome signatures may be related to the different evolutionary backgrounds of the 2 genera after their divergence from a common ancestor.

Comprehensive functional analyses of expressed sequence tags in common wheat (Triticum aestivum)

About 1 million expressed sequence tag (EST) sequences comprising 125.3 Mb nucleotides were accreted from 51 cDNA libraries constructed from a variety of tissues and organs under a range of conditions, including abiotic stresses and pathogen challenges in common wheat (Triticum aestivum). Expressed sequence tags were assembled with stringent parameters after processing with inbuild scripts, resulting in 37,138 contigs and 215,199 singlets. In the assembled sequences, 10.6% presented no matches with existing sequences in public databases. Functional characterization of wheat unigenes by gene ontology annotation, mining transcription factors, full-length cDNA, and miRNA targeting sites were carried out. A bioinformatics strategy was developed to discover single-nucleotide polymorphisms (SNPs) within our large EST resource and reported the SNPs between and within (homoeologous) cultivars. Digital gene expression was performed to find the tissue-specific gene expression, and correspondence analysis was executed to identify common and specific gene expression by selecting four biotic stress-related libraries. The assembly and associated information cater a framework for future investigation in functional genomics.

QTL/microarray approach using pathway information

BACKGROUND

A combined quantitative trait loci (QTL) and microarray-based approach is commonly used to find differentially expressed genes which are then identified based on the known function of a gene in the biological process governing the trait of interest. However, a low cutoff value in individual gene analyses may result in many genes with moderate but meaningful changes in expression being missed.

RESULTS

We modified a gene set analysis to identify intersection sets with significantly affected expression for which the changes in the individual gene sets are less significant. The gene expression profiles in liver tissues of four strains of mice from publicly available microarray sources were analyzed to detect trait-associated pathways using information on the QTL regions of blood concentrations of high density lipoproteins (HDL) cholesterol and insulin-like growth factor 1 (IGF-1). Several metabolic pathways related to HDL levels, including lipid metabolism, ABC transporters and cytochrome P450 pathways were detected for HDL QTL regions. Most of the pathways identified for the IGF-1 phenotype were signal transduction pathways associated with biological processes for IGF-1's regulation.

CONCLUSION

We have developed a method of identifying pathways associated with a quantitative trait using information on QTL. Our approach provides insights into genotype-phenotype relations at the level of biological pathways which may help to elucidate the genetic architecture underlying variation in phenotypic traits.

Computational Sequence Design Techniques for DNA Microarray Technologies

In systems biology and biomedical research, microarray technology is a method of choice that enables the complete quantitative and qualitative ascertainment of gene expression patterns for whole genomes. The selection of high quality oligonucleotide sequences that behave consistently across multiple experiments is a key step in the design, fabrication and experimental performance of DNA microarrays. The aim of this chapter is to outline recent algorithmic developments in microarray probe design, evaluate existing probe sequences used in commercial arrays, and suggest methodologies that have the potential to improve on existing design techniques.

OryzaExpress: an integrated database of gene expression networks and omics annotations in rice

Similarity of gene expression profiles provides important clues for understanding the biological functions of genes, biological processes and metabolic pathways related to genes. A gene expression network (GEN) is an ideal choice to grasp such expression profile similarities among genes simultaneously. For GEN construction, the Pearson correlation coefficient (PCC) has been widely used as an index to evaluate the similarities of expression profiles for gene pairs. However, calculation of PCCs for all gene pairs requires large amounts of both time and computer resources. Based on correspondence analysis, we developed a new method for GEN construction, which takes minimal time even for large-scale expression data with general computational circumstances. Moreover, our method requires no prior parameters to remove sample redundancies in the data set. Using the new method, we constructed rice GENs from large-scale microarray data stored in a public database. We then collected and integrated various principal rice omics annotations in public and distinct databases. The integrated information contains annotations of genome, transcriptome and metabolic pathways. We thus developed the integrated database OryzaExpress for browsing GENs with an interactive and graphical viewer and principal omics annotations (http://riceball.lab.nig.ac.jp/oryzaexpress/). With integration of Arabidopsis GEN data from ATTED-II, OryzaExpress also allows us to compare GENs between rice and Arabidopsis. Thus, OryzaExpress is a comprehensive rice database that exploits powerful omics approaches from all perspectives in plant science and leads to systems biology.