Knowledge-based gene expression classification via matrix factorization

MOTIVATION: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks. RESULTS: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.     

The Arabidopsis co-expression tool (ACT): a WWW-based tool and database for microarray-based gene expression analysis

We present a new WWW-based tool for plant gene analysis, the Arabidopsis Co-Expression Tool (ACT), based on a large Arabidopsis thaliana microarray data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression analysis tool allows users to identify genes whose expression patterns are correlated across selected experiments or the complete data set. Results are accompanied by estimates of the statistical significance of the correlation relationships, expressed as probability (P) and expectation (E) values. Additionally, highly ranked genes on a correlation list can be examined using the novel clique finder tool to determine the sets of genes most likely to be regulated in a similar manner. In combination, these tools offer three levels of analysis: creation of correlation lists of co-expressed genes, refinement of these lists using two-dimensional scatter plots, and dissection into cliques of co-regulated genes. We illustrate the applications of the software by analysing genes encoding functionally related proteins, as well as pathways involved in plant responses to environmental stimuli. These analyses demonstrate novel biological relationships underlying the observed gene co-expression patterns. To demonstrate the ability of the software to develop testable hypotheses on gene function within a defined biological process we have used the example of cell wall biosynthesis genes. The resource is freely available at http://www.arabidopsis.leeds.ac.uk/ACT/     

Systematic approaches to using the FOX hunting system to identify useful rice genes

Ectopic gene expression, or the gain-of-function approach, has the advantage that once the function of a gene is known the gene can be transferred to many different plants by transformation. We previously reported a method, called FOX hunting, that involves ectopic expression of Arabidopsis full-length cDNAs in Arabidopsis to systematically generate gain-of-function mutants. This technology is most beneficial for generating a heterologous gene resource for analysis of useful plant gene functions. As an initial model we generated more than 23 000 independent Arabidopsis transgenic lines that expressed rice fl-cDNAs (Rice FOX Arabidopsis lines). The short generation time and rapid and efficient transformation frequency of Arabidopsis enabled the functions of the rice genes to be analyzed rapidly. We screened rice FOX Arabidopsis lines for alterations in morphology, photosynthesis, element accumulation, pigment accumulation, hormone profiles, secondary metabolites, pathogen resistance, salt tolerance, UV signaling, high light tolerance, and heat stress tolerance. Some of the mutant phenotypes displayed by rice FOX Arabidopsis lines resulted from the expression of rice genes that had no homologs in Arabidopsis . This result demonstrated that rice fl-cDNAs could be used to introduce new gene functions in Arabidopsis. Furthermore, these findings showed that rice gene function could be analyzed by employing Arabidopsis as a heterologous host. This technology provides a framework for the analysis of plant gene function in a heterologous host and of plant improvement by using heterologous gene resources.     

Comparative analysis of divergent and convergent gene pairs and their expression patterns in rice, Arabidopsis, and populus

Comparative analysis of the organization and expression patterns of divergent and convergent gene pairs in multiple plant genomes can identify patterns that are shared by more than one species or are unique to a particular species. Here, we study the coexpression and interspecies conservation of divergent and convergent gene pairs in three plant species: rice (Oryza sativa), Arabidopsis (Arabidopsis thaliana), and black cottonwood (Populus trichocarpa). Strongly correlated expression levels between divergent and convergent genes were found to be quite common in all three species, and the frequency of strong correlation appears to be independent of intergenic distance. Conservation of divergent or convergent arrangement among these species appears to be quite rare. However, conserved arrangement is significantly more frequent when the genes display strongly correlated expression levels or have one or more Gene Ontology (GO) classes in common. A correlation between intergenic distance in divergent and convergent gene pairs and shared GO classes was observed, in varying degrees, in rice and Populus but not in Arabidopsis. Furthermore, multiple GO classes were either overrepresented or underrepresented in Arabidopsis and Populus gene pairs, while only two GO classes were underrepresented in rice divergent gene pairs. Three cis-regulatory elements common to both Arabidopsis and rice were overrepresented in the intergenic regions of strongly correlated divergent gene pairs compared to those of noncorrelated pairs. Our results suggest that shared as well as unique mechanisms operate in shaping the organization and function of divergent and convergent gene pairs in different plant species.     

Systematic prediction of gene function in Arabidopsis thaliana using a probabilistic functional gene network

AraNet is a functional gene network for the reference plant Arabidopsis and has been constructed in order to identify new genes associated with plant traits. It is highly predictive for diverse biological pathways and can be used to prioritize genes for functional screens. Moreover, AraNet provides a web-based tool with which plant biologists can efficiently discover novel functions of Arabidopsis genes (http://www.functionalnet.org/aranet/). This protocol explains how to conduct network-based prediction of gene functions using AraNet and how to interpret the prediction results. Functional discovery in plant biology is facilitated by combining candidate prioritization by AraNet with focused experimental tests.     

ATTED-II updates: condition-specific gene coexpression to extend coexpression analyses and applications to a broad range of flowering plants

ATTED-II (http://atted.jp) is a gene coexpression database for a wide variety of experimental designs, such as prioritizations of genes for functional identification and analyses of the regulatory relationships among genes. Here, we report updates of ATTED-II focusing on two new features: condition-specific coexpression and homologous coexpression with rice. To analyze a broad range of biological phenomena, it is important to collect data under many diverse experimental conditions, but the meaning of coexpression can become ambiguous under these conditions. One approach to overcome this difficulty is to calculate the coexpression for each set of conditions with a clear biological meaning. With this viewpoint, we prepared five sets of experimental conditions (tissue, abiotic stress, biotic stress, hormones and light conditions), and users can evaluate the coexpression by employing comparative gene lists and switchable gene networks. We also developed an interactive visualization system, using the Cytoscape web system, to improve the network representation. As the second update, rice coexpression is now available. The previous version of ATTED-II was specifically developed for Arabidopsis, and thus coexpression analyses for other useful plants have been difficult. To solve this problem, we extended ATTED-II by including comparison tables between Arabidopsis and rice. This representation will make it possible to analyze the conservation of coexpression among flowering plants. With the ability to investigate condition-specific coexpression and species conservation, ATTED-II can help researchers to clarify the functional and regulatory networks of genes in a broad array of plant species.     

Approaches for extracting practical information from gene co-expression networks in plant biology

Gene co-expression, in many cases, implies the presence of a functional linkage between genes. Co-expression analysis has uncovered gene regulatory mechanisms in model organisms such as Escherichia coli and yeast. Recently, accumulation of Arabidopsis microarray data has facilitated a genome-wide inspection of gene co-expression profiles in this model plant. An approach using network analysis has provided an intuitive way to represent complex co-expression patterns between many genes. Co-expression network analysis has enabled us to extract modules, or groups of tightly co-expressed genes, associated with biological processes. Furthermore, integrated analysis of gene expression and metabolite accumulation has allowed us to hypothesize the functions of genes associated with specific metabolic processes. Co-expression network analysis is a powerful approach for data-driven hypothesis construction and gene prioritization, and provides novel insights into the system-level understanding of plant cellular processes.     

Intron-exon organization and phylogeny in a large superfamily, the paralogous cytochrome P450 genes of Arabidopsis thaliana

The cytochrome P450 gene superfamily is represented by 80 genes in animal genomes and perhaps more than 300 genes in plant genomes. We analyzed about half of all Arabidopsis P450 genes, a very large dataset of truly paralogous genes. Sequence alignments were used to draw phylogenetic trees, and this information was compared with the intron-exon organization of each P450 gene. We found 60 unique intron positions, of which 37 were phase 0 introns. Our results confirm the polyphyletic origin of plant P450 genes. One group of these genes, the A-type P450s, are plant specific and characterized by a simple organization, with one highly conserved intron. Closely related A-type P450 genes are often clustered in the genome with as many as a dozen genes (e.g., of the CYP71 subfamily) on a short stretch of chromosome. The other P450 genes (non-A-type) form several distinct clades and are characterized by numerous introns. One such clade contains the two CYP51 genes, which are thought to encode obtusifoliol 14a demethylase. The two CYP51 genes have a single intron that is not shared with CYP51 genes from vertebrates or fungi, or with any other Arabidopsis P450 gene. Only a few of the Arabidopsis P450 genes are intronless (e.g., the CYP710A and CYP96A subfamilies). There was a relatively good correlation between intron conservation and phylogenetic relationships between members of the P450 subfamilies. Gene organization appears to be a useful tool in establishing the evolutionary relatedness of P450 genes, which may help in predictions of P450 function.     

Using transgenic modulation of protein synthesis and accumulation to probe protein signaling networks in Arabidopsis thaliana

Deployment of new model species in the plant biology community requires the development and/or improvement of numerous genetic tools. Sequencing of the Arabidopsis thaliana genome opened up a new challenge of assigning biological function to each gene. As many genes exhibit spatiotemporal or other conditional regulation of biological processes, probing for gene function necessitates applications that can be geared toward temporal, spatial and quantitative functional analysis in vivo. The continuing quest to establish new platforms to examine plant gene function has resulted in the availability of numerous genomic and proteomic tools. Classical and more recent genome-wide experimental approaches include conventional mutagenesis, tagged DNA insertional mutagenesis, ectopic expression of transgenes, activation tagging, RNA interference and two-component transactivation systems. The utilization of these molecular tools has resulted in conclusive evidence for the existence of many genes, and expanded knowledge on gene structure and function. This review covers several molecular tools that have become increasingly useful in basic plant research. We discuss their advantages and limitations for probing cellular protein function while emphasizing the contributions made to lay the fundamental groundwork for genetic manipulation of crops using plant biotechnology.Key words: enhancer trap, RNAi, plant transformation, transactivation, transgenic     

Distinctive features of plant organs characterized by global analysis of gene expression in Arabidopsis.

The distinctive features of plant organs are primarily determined by organ-specific gene expression. We analyzed the expression specificity of 8809 genes in 7 organs of Arabidopsis using a cDNA macroarray system. Using relative expression (RE) values between organs, many known and unknown genes specifically expressed in each organ were identified. We also analyzed the organ specificity of various gene groups using the GRE (group relative expression) value, the average of the REs of all genes in a group. Consequently, we found that many gene groups even ribosomal protein genes, have strong organ-specific expression. Clustering of the expression profiles revealed that the 8809 genes were classified into 9 major categories. Although 3451 genes were clustered into the largest category, which showed constitutive gene expression, 266 and 1005 genes were found to be root- and silique-specific genes, respectively. By this clustering, particular gene groups which showed multi-organ-specific expression profiles, such as bud-flower-specific, stem-silique-specific or bud-flower-root-specific profiles, could be effectively identified. From these results, major features of plant organs could be characterized by their distinct profiles of global gene expression. These data of organ-specific gene expression are available at our web site: Arabidopsis thaliana Tissue-Specific Expression Database, ATTED (http://www.atted.bio.titech.ac.jp/).