The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes

Separation of sequences from host-pathogen interface using triplet nucleotide frequencies

The identification of genes involved in host-pathogen interactions is important for the elucidation of mechanisms of disease resistance and host susceptibility. A traditional way to classify the origin of genes sampled from a pool of mixed cDNA is through sequence similarity to known genes from either the pathogen or host organism or other closely related species. This approach does not work when the identified sequence has no close homologues in the sequence databases. In our previous studies, we classified genes using their codon frequencies. This method, however, explicitly required the prediction of CDS regions and thus could not be applied to sequences composed from the non-coding regions of genes. In this study, we show that the use of sliding-window triplet frequencies extends the application of the algorithm to both coding and non-coding sequences and also increases the prediction accuracy of a Support Vector Machine classifier from 95.6+/-0.3 to 96.5+/-0.2. Thus the use of the triplet frequencies increased the prediction accuracy of the new method by more than 20% compared to our previous approach. A functional analysis of sequences detected gene families having significantly higher or lower probability to be correctly classified compared to the average accuracy of the method is described. The server to perform classification of EST sequences using triplet frequencies is available at (URL: http://mips.gsf.de/proj/est3).

A general framework for biclustering gene expression data

A large number of biclustering methods have been proposed to detect patterns in gene expression data. All these methods try to find some type of biclusters but no one can discover all the types of patterns in the data. Furthermore, researchers have to design new algorithms in order to find new types of biclusters/patterns that interest biologists. In this paper, we propose a novel approach for biclustering that, in general, can be used to discover all computable patterns in gene expression data. The method is based on the theory of Kolmogorov complexity. More precisely, we use Kolmogorov complexity to measure the randomness of submatrices as the merit of biclusters because randomness naturally consists in a lack of regularity, which is a common property of all types of patterns. On the basis of algorithmic probability measure, we develop a Markov Chain Monte Carlo algorithm to search for biclusters. Our method can also be easily extended to solve the problems of conventional clustering and checkerboard type biclustering. The preliminary experiments on simulated as well as real data show that our approach is very versatile and promising.

Microarray analysis of Fusarium graminearum-induced wheat genes: identification of organ-specific and differentially expressed genes

A wheat cDNA microarray consisting of 5739 expressed sequence tags (ESTs) was used to investigate the transcriptome patterns of the glume, lemma, palea, anther, ovary and rachis dissected from infected wheat spikes after inoculation with the fungus Fusarium graminearum, the causal agent of fusarium head blight (FHB) disease. Stringent conditions were employed to reduce the false discovery rate. The significance analysis of microarrays (SAM) was used to identify transcripts that showed a differential response between fungal-challenged vs. control plants. To verify the microarray data, Northern blot analysis was carried out on randomly selected up-regulated clones. We observed 185 (3.2%) up-regulated and 16 (0.28%) down-regulated ESTs in the six organs constituting the wheat spike. Many up-regulated ESTs (46.67%) showed no homology with sequences of known functions, whereas others showed homology with genes involved in defence and stress responses, the oxidative burst of H(2)O(2), regulatory functions, protein synthesis and the phenylpropanoid pathway. The monitoring of genes in specific organs avoided the averaging of expression values over multiple organs that occurs when using data from the whole spike. Our data allowed us to uncover new up-regulated genes expressed in specific organs. The study revealed that each organ had a defined and distinctive transcriptome pattern in response to F. graminearum infection.

Isolation of 151 mutants that have developmental defects from T-DNA tagging

In order to understand the mechanisms underlying plant development, a necessary first step involves the elucidation of the functions of the genes, via the analysis of mutants that exhibit developmental defects. In this study, an activation tagging mutant library harboring 80,650 independent Arabidopsis transformants was generated in order to screen for developmental mutants. A total of 129 mutants manifesting dominant developmental abnormalities were isolated, and their T-DNA insertion loci were mapped. The activation of one or more genes adjacent to a T-DNA insertion locus was confirmed in eight dominant mutants. A gene adjacent to the right border was usually activated by the 35S enhancers. Interestingly, the transcriptional activation of multiple genes within a broad range was observed in one of the mutants, which raises the possibility that activation by the 35S enhancers was not limited strictly to a single gene. In order to gain a better understanding of sexual reproduction in higher plants, we isolated 22 mutants exhibiting defects in female gametophyte development, and determined their T-DNA insertion loci. We propose that this mutant population may prove useful in the further determination of the functions of genes that play important roles in plant development.

Enabling a community to dissect an organism: overview of the Neurospora functional genomics project

A consortium of investigators is engaged in a functional genomics project centered on the filamentous fungus Neurospora, with an eye to opening up the functional genomic analysis of all the filamentous fungi. The overall goal of the four interdependent projects in this effort is to accomplish functional genomics, annotation, and expression analyses of Neurospora crassa, a filamentous fungus that is an established model for the assemblage of over 250,000 species of non yeast fungi. Building from the completely sequenced 43-Mb Neurospora genome, Project 1 is pursuing the systematic disruption of genes through targeted gene replacements, phenotypic analysis of mutant strains, and their distribution to the scientific community at large. Project 2, through a primary focus in Annotation and Bioinformatics, has developed a platform for electronically capturing community feedback and data about the existing annotation, while building and maintaining a database to capture and display information about phenotypes. Oligonucleotide-based microarrays created in Project 3 are being used to collect baseline expression data for the nearly 11,000 distinguishable transcripts in Neurospora under various conditions of growth and development, and eventually to begin to analyze the global effects of loss of novel genes in strains created by Project 1. cDNA libraries generated in Project 4 document the overall complexity of expressed sequences in Neurospora, including alternative splicing alternative promoters and antisense transcripts. In addition, these studies have driven the assembly of an SNP map presently populated by nearly 300 markers that will greatly accelerate the positional cloning of genes.

MitoP2, an integrated database for mitochondrial proteins

The impact of mitochondria on several fundamental cellular processes is reflected in their involvement in the pathophysiology of common diseases such as Parkinson's disease, diabetes, and obesity and a wide range of monogenic disorders primarily associated with energy impairment or metabolic diseases. The importance of mitochondria is also reflected by the steep increase of proteins, which has been localized to this organelle. In yeast, more than 500 of the expected 700-800 mitochondrial proteins are already annotated. In the mammalian species, the expected numbers are estimated to be in the range of 1500-2000 proteins, and the currently annotated entries reach almost 700. In addition to the studies dealing with single proteins, there are many high-throughput approaches that improve the description of the mitochondrial proteome. They include computational predictions of signaling sequences, proteome mapping, mutant screening, expression profiling, protein-protein interaction, and cellular sublocalization studies. The MitoP2 database (http://www.mitop2.de/) was established to structure, explore, and customize the available data on mitochondrial proteins, functions, and diseases. MitoP2 provides a comprehensive picture of the mitochondrial proteome by focusing on (1) the orthology between species, including Saccharomyces cerevisiae, mouse, humans, and Arabidopsis thaliana; (2) the definition of mitochondrial reference sets in these species; (3) the integration of data predictive for mitochondrial localization or function stemming from genomewide approaches; (4) the allocation of a gateway for functional data from model systems and genetics of mitochondriopathies; and (5) the calculation of a combined score for each protein summarizing the indirect evidence for a mitochondrial localization. All data are accessible via search tools and linked to the original data source. By providing an overview of functional annotations from different databases, the MitoP2 database lends itself to genetic investigations of human mitochondriopathies.

Phenotypic heterogeneity can enhance rare-cell survival in 'stress-sensitive' yeast populations

Individual cells within isogenic microbial cultures exhibit phenotypic heterogeneity, an issue that is attracting intense interest. Heterogeneity could confer benefits, in generating variant subpopulations that may be better equipped to persist during perturbation. We tested this hypothesis by comparing the survival of wild-type Saccharomyces cerevisiae with that of mutants which are considered stress-sensitive but which, we demonstrate, also have increased heterogeneity. The mutants (e.g. vma3, ctr1, sod1) exhibited the anticipated sensitivities to intermediate doses of nickel, copper, alkaline pH, menadione or paraquat. However, enhanced heterogeneity meant that the resistances of individual mutant cells spanned a broad range, and at high stress occasional-cell survival in most of these populations overtook that of the wild type. Green fluorescent protein (GFP) reporter studies showed that this heterogeneity-dependent advantage was not related to perturbation of buffered gene expression. Deletion strain screens combined with other approaches revealed that vacuolar alkalinization resulting from loss of Vma-dependent vacuolar H(+)-ATPase activity was not the cause of vma mutants' net stress sensitivities. An alternative Vma-dependent resistance mechanism was found to suppress an influence of variable vacuolar pH on the metal resistances of individual wild-type cells. In addition to revealing new mechanisms of heterogeneity generation, the results demonstrate experimentally a benefit under adverse conditions that arises specifically from heterogeneity, and in populations conventionally considered to be disadvantaged.

Evaluation of clustering algorithms for gene expression data

BACKGROUND

Cluster analysis is an integral part of high dimensional data analysis. In the context of large scale gene expression data, a filtered set of genes are grouped together according to their expression profiles using one of numerous clustering algorithms that exist in the statistics and machine learning literature. A closely related problem is that of selecting a clustering algorithm that is "optimal" in some sense from a rather impressive list of clustering algorithms that currently exist.

RESULTS

In this paper, we propose two validation measures each with two parts: one measuring the statistical consistency (stability) of the clusters produced and the other representing their biological functional congruence. Smaller values of these indices indicate better performance for a clustering algorithm. We illustrate this approach using two case studies with publicly available gene expression data sets: one involving a SAGE data of breast cancer patients and the other involving a time course cDNA microarray data on yeast. Six well known clustering algorithms UPGMA, K-Means, Diana, Fanny, Model-Based and SOM were evaluated.

CONCLUSION

No single clustering algorithm may be best suited for clustering genes into functional groups via expression profiles for all data sets. The validation measures introduced in this paper can aid in the selection of an optimal algorithm, for a given data set, from a collection of available clustering algorithms.

The pathogen-host interactions database (PHI-base) provides insights into generic and novel themes of pathogenicity

Fungal and oomycete pathogens of plants and animals are a major global problem. In the last 15 years, many genes required for pathogenesis have been determined for over 50 different species. Other studies have characterized effector genes (previously termed avirulence genes) required to activate host responses. By studying these types of pathogen genes, novel targets for control can be revealed. In this report, we describe the Pathogen-Host Interactions database (PHI-base), which systematically compiles such pathogenicity genes involved in pathogen-host interactions. Here, we focus on the biology that underlies this computational resource: the nature of pathogen-host interactions, the experimental methods that exist for the characterization of such pathogen-host interactions as well as the available computational resources. Based on the data, we review and analyze the specific functions of pathogenicity genes, the host-specific nature of pathogenicity and virulence genes, and the generic mechanisms of effectors that trigger plant responses. We further discuss the utilization of PHI-base for the computational identification of pathogenicity genes through comparative genomics. In this context, the importance of standardizing pathogenicity assays as well as integrating databases to aid comparative genomics is discussed.

Molecular characterization of the α-glucosidase activity in Enterobacter sakazakii reveals the presence of a putative gene cluster for palatinose metabolism

Enterobacter sakazakii is considered an opportunistic pathogen for premature infants and neonates. Although E. sakazakii has been isolated from various types of food, recontaminated dried infant formula has been epidemiologically identified as the major source of infection. Amongst others, alpha-glucosidase activity is one of the most important biochemical features, which differentiates E. sakazakii from other species in the family Enterobacteriaceae and has therefore been used as a selective marker in the development of differential media. However, it has been shown, that methods based on this biochemical feature are prone to producing false-positive results for presumptive E. sakazakii colonies due to the presence of this enzymatic activity in other species of the Enterobacteriaceae. Therefore, elucidation of the molecular basis responsible for the biochemical feature in E. sakazakii would provide novel targets suitable for the development of more specific and direct identification systems for this organism. By applying the bacterial artificial chromosome (BAC) approach, along with heterologous gene expression in Escherichia coli, the molecular basis of the alpha-glucosidase activity in E. sakazakii was characterized. Here we report the identification of two different alpha-glucosidase encoding genes. Homology searches of the deduced amino acid sequences revealed that the proteins belong to a cluster of gene products putatively responsible for the metabolism of isomaltulose (palatinose; 6-O-alpha-d-glucopyranosyl-d-fructose). The glycosyl-hydrolyzing activity of each protein was demonstrated by subcloning the respective open reading frames and screening of E. coli transformants for their ability to hydrolyze 4-methyl-umbelliferyl-alpha-d-glucoside. Analysis at the protein level revealed that both enzymes belong to the intracellular fraction of cell proteins. The presence of the postulated palatinose metabolism was proven by growth experiments using this sugar as a sole carbon source.

Identification of functional modules in a PPI network by clique percolation clustering

Large-scale experiments and data integration have provided the opportunity to systematically analyze and comprehensively understand the topology of biological networks and biochemical processes in cells. Modular architecture which encompasses groups of genes/proteins involved in elementary biological functional units is a basic form of the organization of interacting proteins. Here we apply a graph clustering algorithm based on clique percolation clustering to detect overlapping network modules of a protein-protein interaction (PPI) network. Our analysis of the yeast Sacchromyces cerevisiae suggests that most of the detected modules correspond to one or more experimentally functional modules and half of these annotated modules match well with experimentally determined protein complexes. Our method of analysis can of course be applied to protein-protein interaction data for any species and even other biological networks.

Signaling alkaline pH stress in the yeast Saccharomyces cerevisiae through the Wsc1 cell surface sensor and the Slt2 MAPK pathway

Alkalinization of the external environment represents a stress situation for Saccharomyces cerevisiae. Adaptation to this circumstance involves the activation of diverse response mechanisms, the components of which are still largely unknown. We show here that mutation of members of the cell integrity Pkc1/Slt2 MAPK module, as well as upstream and downstream elements of the system, confers sensitivity to alkali. Alkalinization resulted in fast and transient activation of the Slt2 MAPK, which depended on the integrity of the kinase module and was largely abolished by sorbitol. Lack of Wsc1, removal of specific extracellular and intracellular domains, or substitution of Tyr(303) in this putative membrane stress sensor rendered cells sensitive to alkali and considerably decreased alkali-induced Slt2 activation. In contrast, constitutive activation of Slt2 by the bck1-20 allele increased pH tolerance in the wsc1 mutant. DNA microarray analysis revealed that several genes encoding cell wall proteins, such as GSC2/FKS2, DFG5, SKT5, and CRH1, were induced, at least in part, by high pH in an Slt2-dependent manner. We observed that dfg5, skt5, and particularly dfg5 skt5 cells were alkali-sensitive. Therefore, our results show that an alkaline environment imposes a stress condition on the yeast cell wall. We propose that the Slt2-mediated MAPK pathway plays an important role in the adaptive response to this insult and that Wsc1 participates as an essential cell-surface pH sensor. Moreover, these results provide a new example of the complexity of the response of budding yeast to the alkalinization of the environment.

The transcription factors WRKY11 and WRKY17 act as negative regulators of basal resistance in Arabidopsis thaliana

Transcription factors are believed to play a pivotal role in the activation and fine-tuning of plant defense responses, but little is known about the exact function of individual transcription factors in this process. We analyzed the role of the IId subfamily of WRKY transcription factors in the regulation of basal resistance to Pseudomonas syringae pv tomato (Pst). The expression of four members of the subfamily was induced upon challenge with virulent and avirulent strains of Pst. Mutant analyses revealed that loss of WRKY11 function increased resistance toward avirulent and virulent Pst strains and that resistance was further enhanced in wrky11 wrky17 double mutant plants. Thus, WRKY11 and WRKY17 act as negative regulators of basal resistance to Pst. Genome-wide expression analysis and expression studies of selected genes in single and double mutants demonstrated that both transcription factors modulate transcriptional changes in response to pathogen challenge. Depending on the target gene, WRKY11 and WRKY17 act either specifically or in a partially redundant manner. We demonstrate complex cross-regulation within the IId WRKY subfamily and provide evidence that both WRKY transcription factors are involved in the regulation of Pst-induced jasmonic acid-dependent responses. These results provide genetic evidence for the importance of WRKY11 and WRKY17 in plant defense.

Transcriptional Profiling of the Protein Phosphatase 2C Family in Yeast Provides Insights into the Unique Functional Roles of Ptc1

Type 2C protein phosphatases are encoded in Saccharomyces cerevisiae by several related genes (PTC1-5 and PTC7). To gain insight into the functions attributable to specific members of this gene family, we have investigated the transcriptional profiles of ptc1-5 mutants. Two main patterns were obtained as follows: the one generated by the ptc1 mutation and the one resulting from the lack of Ptc2-5. ptc4 and ptc5 profiles were quite similar, whereas that of ptc2 was less related to this group. Mutation of PTC1 resulted in increased expression of numerous genes that are also induced by cell wall damage, such as YKL161c, SED1, or CRH1, as well as in higher amounts of active Slt2 mitogen-activated protein kinase, indicating that lack of the phosphatase activates the cell wall integrity pathway. ptc1 cells were even more sensitive than slt2 mutants to a number of cell wall-damaging agents, and both mutations had additive effects. The sensitivity of ptc1 cells was not dependent on Hog1. Besides these phenotypes, we observed that calcineurin was hyperactivated in ptc1 cells, which were also highly sensitive to calcium ions, heavy metals, and alkaline pH, and exhibited a random haploid budding pattern. Remarkably, many of these traits are found in certain mutants with impaired vacuolar function. As ptc1 cells also display fragmented vacuoles, we hypothesized that lack of Ptc1 would primarily cause vacuolar malfunction, from which other phenotypes would derive. In agreement with this scenario, overexpression of VPS73, a gene of unknown function involved in vacuolar protein sorting, largely rescues not only vacuolar fragmentation but also sensitivity to cell wall damage, high calcium, alkaline pH, as well as other ptc1-specific phenotypes.

Cloning and characterization of Enterobacter sakazakii pigment genes and in situ spectroscopic analysis of the pigment

Enterobacter sakazakii is considered an opportunistic foodborne pathogen that is characterized by formation of yellow-pigmented colonies. Because of the lack of basic knowledge about Enterobacter sakazakii genetics, the BAC approach and the heterologous expression of the pigment in Escherichia coli were used to elucidate the molecular structure of the genes responsible for pigment production in Enterobacter sakazakii strain ES5. Sequencing and annotation of a 33.025 bp fragment revealed seven ORFs that could be assigned to the carotenoid biosynthesis pathway. The gene cluster had the organization crtE-idi-XYIBZ, with the crtE-idi-XYIB genes putatively transcribed as an operon and the crtZ gene transcribed in the opposite orientation. The carotenogenic nature of the pigment of Enterobacter sakazakii wt was ascertained by in situ analysis using visible microspectroscopy and resonance Raman microspectroscopy.

A scalable method for integration and functional analysis of multiple microarray datasets

MOTIVATION

The diverse microarray datasets that have become available over the past several years represent a rich opportunity and challenge for biological data mining. Many supervised and unsupervised methods have been developed for the analysis of individual microarray datasets. However, integrated analysis of multiple datasets can provide a broader insight into genetic regulation of specific biological pathways under a variety of conditions.

RESULTS

To aid in the analysis of such large compendia of microarray experiments, we present Microarray Experiment Functional Integration Technology (MEFIT), a scalable Bayesian framework for predicting functional relationships from integrated microarray datasets. Furthermore, MEFIT predicts these functional relationships within the context of specific biological processes. All results are provided in the context of one or more specific biological functions, which can be provided by a biologist or drawn automatically from catalogs such as the Gene Ontology (GO). Using MEFIT, we integrated 40 Saccharomyces cerevisiae microarray datasets spanning 712 unique conditions. In tests based on 110 biological functions drawn from the GO biological process ontology, MEFIT provided a 5% or greater performance increase for 54 functions, with a 5% or more decrease in performance in only two functions.

Coding limits on the number of transcription factors

Background

Transcription factor proteins bind specific DNA sequences to control the expression of genes. They contain DNA binding domains which belong to several super-families, each with a specific mechanism of DNA binding. The total number of transcription factors encoded in a genome increases with the number of genes in the genome. Here, we examined the number of transcription factors from each super-family in diverse organisms.

Results

We find that the number of transcription factors from most super-families appears to be bounded. For example, the number of winged helix factors does not generally exceed 300, even in very large genomes. The magnitude of the maximal number of transcription factors from each super-family seems to correlate with the number of DNA bases effectively recognized by the binding mechanism of that super-family. Coding theory predicts that such upper bounds on the number of transcription factors should exist, in order to minimize cross-binding errors between transcription factors. This theory further predicts that factors with similar binding sequences should tend to have similar biological effect, so that errors based on mis-recognition are minimal. We present evidence that transcription factors with similar binding sequences tend to regulate genes with similar biological functions, supporting this prediction.

Conclusion

The present study suggests limits on the transcription factor repertoire of cells, and suggests coding constraints that might apply more generally to the mapping between binding sites and biological function.

When transcriptome meets metabolome: fast cellular responses of yeast to sudden relief of glucose limitation

Within the first 5 min after a sudden relief from glucose limitation, Saccharomyces cerevisiae exhibited fast changes of intracellular metabolite levels and a major transcriptional reprogramming. Integration of transcriptome and metabolome data revealed tight relationships between the changes at these two levels. Transcriptome as well as metabolite changes reflected a major investment in two processes: adaptation from fully respiratory to respiro-fermentative metabolism and preparation for growth acceleration. At the metabolite level, a severe drop of the AXP pools directly after glucose addition was not accompanied by any of the other three NXP. To counterbalance this loss, purine biosynthesis and salvage pathways were transcriptionally upregulated in a concerted manner, reflecting a sudden increase of the purine demand. The short-term dynamics of the transcriptome revealed a remarkably fast decrease in the average half-life of downregulated genes. This acceleration of mRNA decay can be interpreted both as an additional nucleotide salvage pathway and an additional level of glucose-induced regulation of gene expression.

Natural isolates of Saccharomyces cerevisiae display complex genetic variation in sporulation efficiency

Sporulation is a well-studied process executed with varying efficiency by diverse yeast strains. We developed a high-throughput method to quantify yeast sporulation efficiency and used this technique to analyze a line cross between a high-efficiency oak tree isolate and a low-efficiency wine strain. We find that natural variation in sporulation efficiency mirrors natural variation in higher eukaryotes: it shows divergence between isolated populations, arises from loci of major effect, and exhibits epistasis. We show that the lower sporulation efficiency of the wine strain results from a failure to initiate sporulation, rather than from slower kinetics of meiosis and spore formation. The two strains differentially regulate many genes involved in aerobic respiration, an essential pathway for sporulation, such that the oak tree strain appears better poised to generate energy from this pathway. We also report that a polymorphism in RME1 that affects sporulation efficiency in laboratory strains also cosegregates with significant phenotypic differences in our cross of natural isolates. These results lay the groundwork for the study of variation in sporulation efficiency among natural isolates of yeast.

Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes

Background

A cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of which used the standard hierarchical clustering, UPGMA, with one minus the Pearson's correlation coefficient as a measure of dissimilarity), often times such groupings could be misleading. More importantly, a systematic evaluation of the entire set of clusters produced by such unsupervised procedures is necessary since they also contain genes that are seemingly unrelated or may have more than one common function. Here we quantify the performance of a given unsupervised clustering algorithm applied to a given microarray study in terms of its ability to produce biologically meaningful clusters using a reference set of functional classes. Such a reference set may come from prior biological knowledge specific to a microarray study or may be formed using the growing databases of gene ontologies (GO) for the annotated genes of the relevant species.

Results

In this paper, we introduce two performance measures for evaluating the results of a clustering algorithm in its ability to produce biologically meaningful clusters. The first measure is a biological homogeneity index (BHI). As the name suggests, it is a measure of how biologically homogeneous the clusters are. This can be used to quantify the performance of a given clustering algorithm such as UPGMA in grouping genes for a particular data set and also for comparing the performance of a number of competing clustering algorithms applied to the same data set. The second performance measure is called a biological stability index (BSI). For a given clustering algorithm and an expression data set, it measures the consistency of the clustering algorithm's ability to produce biologically meaningful clusters when applied repeatedly to similar data sets. A good clustering algorithm should have high BHI and moderate to high BSI. We evaluated the performance of ten well known clustering algorithms on two gene expression data sets and identified the optimal algorithm in each case. The first data set deals with SAGE profiles of differentially expressed tags between normal and ductal carcinoma in situ samples of breast cancer patients. The second data set contains the expression profiles over time of positively expressed genes (ORF's) during sporulation of budding yeast. Two separate choices of the functional classes were used for this data set and the results were compared for consistency.

Conclusion

Functional information of annotated genes available from various GO databases mined using ontology tools can be used to systematically judge the results of an unsupervised clustering algorithm as applied to a gene expression data set in clustering genes. This information could be used to select the right algorithm from a class of clustering algorithms for the given data set.

Role-similarity based functional prediction in networked systems: application to the yeast proteome

We propose a general method to predict functions of vertices where (i) the wiring of the network is somehow related to the vertex functionality and (ii) a fraction of the vertices are functionally classified. The method is influenced by role-similarity measures of social network analysis. The two versions of our prediction scheme are tested on model networks where the functions of the vertices are designed to match their network surroundings. We also apply these methods to the proteome of the yeast Saccharomyces cerevisiae and find the results compatible with more specialized methods.

Complex functionality of gene groups identified from high-throughput data

Relating experimental data to biological knowledge is necessary to cope with the avalanches of new data emerging from recent developments in high-throughput technologies. Automatic functional profiling becomes the de facto standard approach for the secondary analysis of high-throughput data. A number of tools employing available gene functional annotations have been developed for this purpose. However, current annotations are derived mostly from traditional analysis of the individual gene function. The complex biological phenomena carried out by the concerted activity of many genes often requires the definition of new complex functionality (related to a group of genes), which is, in many cases, not available in current annotation vocabularies. Functional profiling with annotation terms related to the description of individual biological functions of a gene may fail to provide reasonable interpretation of biological relationships in a set of genes involved in complex biological phenomena. We introduce a novel procedure to profile a complex functionality of a gene set. Complex functionality is constructed as a combination of available annotation terms. By profiling ChIP-chip data from Saccharomyces cerevisiae we demonstrate that this technique produces deeper insights into the results of high-throughput experiments that are beyond the known facts described in the functional classifications.

AGMIAL: implementing an annotation strategy for prokaryote genomes as a distributed system

We have implemented a genome annotation system for prokaryotes called AGMIAL. Our approach embodies a number of key principles. First, expert manual annotators are seen as a critical component of the overall system; user interfaces were cyclically refined to satisfy their needs. Second, the overall process should be orchestrated in terms of a global annotation strategy; this facilitates coordination between a team of annotators and automatic data analysis. Third, the annotation strategy should allow progressive and incremental annotation from a time when only a few draft contigs are available, to when a final finished assembly is produced. The overall architecture employed is modular and extensible, being based on the W3 standard Web services framework. Specialized modules interact with two independent core modules that are used to annotate, respectively, genomic and protein sequences. AGMIAL is currently being used by several INRA laboratories to analyze genomes of bacteria relevant to the food-processing industry, and is distributed under an open source license.

Construction, database integration, and application of an Oenothera EST library

Coevolution of cellular genetic compartments is a fundamental aspect in eukaryotic genome evolution that becomes apparent in serious developmental disturbances after interspecific organelle exchanges. The genus Oenothera represents a unique, at present the only available, resource to study the role of the compartmentalized plant genome in diversification of populations and speciation processes. An integrated approach involving cDNA cloning, EST sequencing, and bioinformatic data mining was chosen using Oenothera elata with the genetic constitution nuclear genome AA with plastome type I. The Gene Ontology system grouped 1621 unique gene products into 17 different functional categories. Application of arrays generated from a selected fraction of ESTs revealed significantly differing expression profiles among closely related Oenothera species possessing the potential to generate fertile and incompatible plastid/nuclear hybrids (hybrid bleaching). Furthermore, the EST library provides a valuable source of PCR-based polymorphic molecular markers that are instrumental for genotyping and molecular mapping approaches.

Construction of quantitative proteome reference maps of mouse spleen and lymph node based on two-dimensional gel electrophoresis

Quantitative features of the proteome are extremely useful for studying cellular processes at a molecular level. In this study, we attempted to construct quantitative reference proteome maps of the mouse spleen and lymph node based on 2-DE followed by protein identification using MS. We analyzed more than 1000 spots on the 2-DE images and consequently were able to determine that 919 spots were derived from 328 different genes. To obtain statistically reliable information of the protein levels from these 2-DE images, we measured the volumes of the respective spots on 2-DE images obtained by four to six independent experimental runs. These measurements were used to calculate the variability of the volumes of the respective spots on 2-DE following subcellular fractionation, which enabled us to discriminate differentially produced proteins from those within the range of intrinsic variability. More importantly, while the 2-DE data have been traditionally collected in a gel image-based manner, the resultant quantitative 2-DE data could be analyzed using the same procedure as that for mRNA expression profiles. This greatly assists in bridging the gap between the analyses of transcriptomes and proteomes and enables the integration of this data on the same informational platform.

Proliferative phase endosperm promoters from Arabidopsis thaliana

Endosperm accounts for a large proportion of human nutrition and is also a major determinant of seed viability and size, not only in cereals, but also in species with ephemeral endosperms, such as soybean and oilseed rape. The extent of endosperm proliferation early in seed development is a crucial component in setting seed size; therefore, a biotechnological approach for the modification of this trait requires promoters active in early endosperm. To find such promoters, we constructed an array based on cDNAs extracted from developing Arabidopsis seeds enriched for proliferating endosperm. Hybridization with RNA extracted from vegetative and reproductive tissues, including endosperm, and subsequent data filtering yielded sets of endosperm-expressed and endosperm-preferred genes, including many hundreds not previously identified in array experiments designed to detect genes expressed in Arabidopsis seeds. Of eight promoters selected for validation, seven were active in early endosperm, three with no detected activity elsewhere in the plant. Therefore, this strategy has yielded proliferative phase endosperm promoters which should be useful in altering seed size.

Microarray analysis of transcript accumulation during perithecium development in the filamentous fungus Gibberella zeae (anamorph Fusarium graminearum)

Gibberella zeae (anamorph Fusarium graminearum) is the causal agent of Fusarium head blight (FHB) of wheat and barley in the United States. Ascospores forcibly discharged from mature fruiting bodies, the perithecia, serve as the primary inoculum for FHB epidemics. To identify genes important for perithecium development and function, a cDNA microarray that covered 11% of the G. zeae genome was constructed. The microarray was used to measure changes in transcription levels of genes expressed during three successive stages of perithecium development. When compared with vegetative mycelia, 651 (31%) cDNA clones showed changes in transcript levels in at least one of the three developmental stages. During perithecium development, 263 (13%) cDNA clones showed temporal changes in transcript profiles. Transcripts that showed the greatest changes in levels in maturing perithecia belonged to genes in the FunCat main functional categories of cell rescue, metabolism, cell type differentiation, energy, and cellular transport. For genes related to metabolism and cell type differentiation, transcripts showed the highest levels in immature perithecia, whereas for cellular transport-related genes, transcripts showed the highest levels in mature perithecia. This study represents the first large-scale investigation of both spatial and temporal changes in transcript levels during perithecium development. It provides clear evidence that the sexual development in fungi is a complex, multigenic process and identifies genes involved in sexual development of this agriculturally important fungus.

Transcription analysis of arabidopsis membrane transporters and hormone pathways during developmental and induced leaf senescence

A comparative transcriptome analysis for successive stages of Arabidopsis (Arabidopsis thaliana) developmental leaf senescence (NS), darkening-induced senescence of individual leaves attached to the plant (DIS), and senescence in dark-incubated detached leaves (DET) revealed many novel senescence-associated genes with distinct expression profiles. The three senescence processes share a high number of regulated genes, although the overall number of regulated genes during DIS and DET is about 2 times lower than during NS. Consequently, the number of NS-specific genes is much higher than the number of DIS- or DET-specific genes. The expression profiles of transporters (TPs), receptor-like kinases, autophagy genes, and hormone pathways were analyzed in detail. The Arabidopsis TPs and other integral membrane proteins were systematically reclassified based on the Transporter Classification system. Coordinate activation or inactivation of several genes is observed in some TP families in all three or only in individual senescence types, indicating differences in the genetic programs for remobilization of catabolites. Characteristic senescence type-specific differences were also apparent in the expression profiles of (putative) signaling kinases. For eight hormones, the expression of biosynthesis, metabolism, signaling, and (partially) response genes was investigated. In most pathways, novel senescence-associated genes were identified. The expression profiles of hormone homeostasis and signaling genes reveal additional players in the senescence regulatory network.

Transcription analysis of arabidopsis membrane transporters and hormone pathways during developmental and induced leaf senescence

A comparative transcriptome analysis for successive stages of Arabidopsis (Arabidopsis thaliana) developmental leaf senescence (NS), darkening-induced senescence of individual leaves attached to the plant (DIS), and senescence in dark-incubated detached leaves (DET) revealed many novel senescence-associated genes with distinct expression profiles. The three senescence processes share a high number of regulated genes, although the overall number of regulated genes during DIS and DET is about 2 times lower than during NS. Consequently, the number of NS-specific genes is much higher than the number of DIS- or DET-specific genes. The expression profiles of transporters (TPs), receptor-like kinases, autophagy genes, and hormone pathways were analyzed in detail. The Arabidopsis TPs and other integral membrane proteins were systematically reclassified based on the Transporter Classification system. Coordinate activation or inactivation of several genes is observed in some TP families in all three or only in individual senescence types, indicating differences in the genetic programs for remobilization of catabolites. Characteristic senescence type-specific differences were also apparent in the expression profiles of (putative) signaling kinases. For eight hormones, the expression of biosynthesis, metabolism, signaling, and (partially) response genes was investigated. In most pathways, novel senescence-associated genes were identified. The expression profiles of hormone homeostasis and signaling genes reveal additional players in the senescence regulatory network.

Analyses of expressed sequence tags from apple

The domestic apple (Malus domestica; also known as Malus pumila Mill.) has become a model fruit crop in which to study commercial traits such as disease and pest resistance, grafting, and flavor and health compound biosynthesis. To speed the discovery of genes involved in these traits, develop markers to map genes, and breed new cultivars, we have produced a substantial expressed sequence tag collection from various tissues of apple, focusing on fruit tissues of the cultivar Royal Gala. Over 150,000 expressed sequence tags have been collected from 43 different cDNA libraries representing 34 different tissues and treatments. Clustering of these sequences results in a set of 42,938 nonredundant sequences comprising 17,460 tentative contigs and 25,478 singletons, together representing what we predict are approximately one-half the expressed genes from apple. Many potential molecular markers are abundant in the apple transcripts. Dinucleotide repeats are found in 4,018 nonredundant sequences, mainly in the 5'-untranslated region of the gene, with a bias toward one repeat type (containing AG, 88%) and against another (repeats containing CG, 0.1%). Trinucleotide repeats are most common in the predicted coding regions and do not show a similar degree of sequence bias in their representation. Bi-allelic single-nucleotide polymorphisms are highly abundant with one found, on average, every 706 bp of transcribed DNA. Predictions of the numbers of representatives from protein families indicate the presence of many genes involved in disease resistance and the biosynthesis of flavor and health-associated compounds. Comparisons of some of these gene families with Arabidopsis (Arabidopsis thaliana) suggest instances where there have been duplications in the lineages leading to apple of biosynthetic and regulatory genes that are expressed in fruit. This resource paves the way for a concerted functional genomics effort in this important temperate fruit crop.

Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions

MOTIVATION

Most approaches in predicting protein function from protein-protein interaction data utilize the observation that a protein often share functions with proteins that interacts with it (its level-1 neighbours). However, proteins that interact with the same proteins (i.e. level-2 neighbours) may also have a greater likelihood of sharing similar physical or biochemical characteristics. We speculate that functional similarity between a protein and its neighbours from the two different levels arise from two distinct forms of functional association, and a protein is likely to share functions with its level-1 and/or level-2 neighbours. We are interested in finding out how significant is functional association between level-2 neighbours and how they can be exploited for protein function prediction.

RESULTS

We made a statistical study on recent interaction data and observed that functional association between level-2 neighbours is clearly observable. A substantial number of proteins are observed to share functions with level-2 neighbours but not with level-1 neighbours. We develop an algorithm that predicts the functions of a protein in two steps: (1) assign a weight to each of its level-1 and level-2 neighbours by estimating its functional similarity with the protein using the local topology of the interaction network as well as the reliability of experimental sources and (2) scoring each function based on its weighted frequency in these neighbours. Using leave-one-out cross validation, we compare the performance of our method against that of several other existing approaches and show that our method performs relatively well.

Combining gene expression profiles and protein-protein interaction data to infer gene functions

The ever-increasing flow of gene expression profiles and protein-protein interactions has catalyzed many computational approaches for inference of gene functions. Despite all the efforts, there is still room for improvement, for the information enriched in each biological data source has not been exploited to its fullness. A composite method is proposed for classifying unannotated genes based on expression data and protein-protein interaction (PPI) data, which extracts information from both data sources in novel ways. With the noise nature of expression data taken into consideration, importance is attached to the consensus expression patterns of gene classes instead of the actual expression profiles of individual genes, thus characterizing the composite method with enhanced robustness against microarray data variation. With regard to the PPI network, the traditional clear-cut binary attitude towards inter- and intra-functional interactions is abandoned, whereas a more objective perspective into the PPI network structure is formed through incorporating the varied function-function interaction probabilities into the algorithm. The composite method was implemented in two numerical experiments, where its improvement over single-data-source based methods was observed and the superiority of the novel data handling operations was discussed.

FGDB: a comprehensive fungal genome resource on the plant pathogen Fusarium graminearum

The MIPS Fusarium graminearum Genome Database (FGDB) is a comprehensive genome database on one of the most devastating fungal plant pathogens of wheat and barley. FGDB provides information on two gene sets independently derived by automated annotation of the F.graminearum genome sequence. A complete manually revised gene set will be completed within the near future. The initial results of systematic manual correction of gene calls are already part of the current gene set. The database can be accessed to retrieve information from bioinformatics analyses and functional classifications of the proteins. The data are also organized in the well established MIPS catalogs and novel query techniques are available to search the data. The comprehensive set of gene calls was also used for the design of an Affymetrix GeneChip. The resource is accessible on .

The Mouse Functional Genome Database (MfunGD): functional annotation of proteins in the light of their cellular context

MfunGD () provides a resource for annotated mouse proteins and their occurrence in protein networks. Manual annotation concentrates on proteins which are found to interact physically with other proteins. Accordingly, manually curated information from a protein–protein interaction database (MPPI) and a database of mammalian protein complexes is interconnected with MfunGD. Protein function annotation is performed using the Functional Catalogue (FunCat) annotation scheme which is widely used for the analysis of protein networks. The dataset is also supplemented with information about the literature that was used in the annotation process as well as links to the SIMAP Fasta database, the Pedant protein analysis system and cross-references to external resources. Proteins that so far were not manually inspected are annotated automatically by a graphical probabilistic model and/or superparamagnetic clustering. The database is continuously expanding to include the rapidly growing amount of functional information about gene products from mouse. MfunGD is implemented in GenRE, a J2EE-based component-oriented multi-tier architecture following the separation of concern principle.

MIPS: analysis and annotation of proteins from whole genomes in 2005

The Munich Information Center for Protein Sequences (MIPS at the GSF), Neuherberg, Germany, provides resources related to genome information. Manually curated databases for several reference organisms are maintained. Several of these databases are described elsewhere in this and other recent NAR database issues. In a complementary effort, a comprehensive set of >400 genomes automatically annotated with the PEDANT system are maintained. The main goal of our current work on creating and maintaining genome databases is to extend gene centered information to information on interactions within a generic comprehensive framework. We have concentrated our efforts along three lines (i) the development of suitable comprehensive data structures and database technology, communication and query tools to include a wide range of different types of information enabling the representation of complex information such as functional modules or networks Genome Research Environment System, (ii) the development of databases covering computable information such as the basic evolutionary relations among all genes, namely SIMAP, the sequence similarity matrix and the CABiNet network analysis framework and (iii) the compilation and manual annotation of information related to interactions such as protein–protein interactions or other types of relations (e.g. MPCDB, MPPI, CYGD). All databases described and the detailed descriptions of our projects can be accessed through the MIPS WWW server ().

MPact: the MIPS protein interaction resource on yeast

In recent years, the Munich Information Center for Protein Sequences (MIPS) yeast protein–protein interaction (PPI) dataset has been used in numerous analyses of protein networks and has been called a gold standard because of its quality and comprehensiveness [H. Yu, N. M. Luscombe, H. X. Lu, X. Zhu, Y. Xia, J. D. Han, N. Bertin, S. Chung, M. Vidal and M. Gerstein (2004) Genome Res., 14, 1107–1118]. MPact and the yeast protein localization catalog provide information related to the proximity of proteins in yeast. Beside the integration of high-throughput data, information about experimental evidence for PPIs in the literature was compiled by experts adding up to 4300 distinct PPIs connecting 1500 proteins in yeast. As the interaction data is a complementary part of CYGD, interactive mapping of data on other integrated data types such as the functional classification catalog [A. Ruepp, A. Zollner, D. Maier, K. Albermann, J. Hani, M. Mokrejs, I. Tetko, U. Güldener, G. Mannhaupt, M. Münsterkötter and H. W. Mewes (2004) Nucleic Acids Res., 32, 5539–5545] is possible. A survey of signaling proteins and comparison with pathway data from KEGG demonstrates that based on these manually annotated data only an extensive overview of the complexity of this functional network can be obtained in yeast. The implementation of a web-based PPI-analysis tool allows analysis and visualization of protein interaction networks and facilitates integration of our curated data with high-throughput datasets. The complete dataset as well as user-defined sub-networks can be retrieved easily in the standardized PSI-MI format. The resource can be accessed through .

MitoP2: the mitochondrial proteome database--now including mouse data

The MitoP2 database () integrates information on mitochondrial proteins, their molecular functions and associated diseases. The central database features are manually annotated reference proteins localized or functionally associated with mitochondria supplied for yeast, human and mouse. MitoP2 enables (i) the identification of putative orthologous proteins between these species to study evolutionarily conserved functions and pathways; (ii) the integration of data from systematic genome-wide studies such as proteomics and deletion phenotype screening; (iii) the prediction of novel mitochondrial proteins using data integration and the assignment of evidence scores; and (iv) systematic searches that aim to find the genes that underlie common and rare mitochondrial diseases. The data and analysis files are referenced to data sources in PubMed and other online databases and can be easily downloaded. MitoP2 users can explore the relationship between mitochondrial dysfunctions and disease and utilize this information to conduct systems biology approaches on mitochondria.

MaGe: a microbial genome annotation system supported by synteny results

Magnifying Genomes (MaGe) is a microbial genome annotation system based on a relational database containing information on bacterial genomes, as well as a web interface to achieve genome annotation projects. Our system allows one to initiate the annotation of a genome at the early stage of the finishing phase. MaGe's main features are (i) integration of annotation data from bacterial genomes enhanced by a gene coding re-annotation process using accurate gene models, (ii) integration of results obtained with a wide range of bioinformatics methods, among which exploration of gene context by searching for conserved synteny and reconstruction of metabolic pathways, (iii) an advanced web interface allowing multiple users to refine the automatic assignment of gene product functions. MaGe is also linked to numerous well-known biological databases and systems. Our system has been thoroughly tested during the annotation of complete bacterial genomes (Acinetobacter baylyi ADP1, Pseudoalteromonas haloplanktis, Frankia alni) and is currently used in the context of several new microbial genome annotation projects. In addition, MaGe allows for annotation curation and exploration of already published genomes from various genera (e.g. Yersinia, Bacillus and Neisseria). MaGe can be accessed at .

Chloroplast-mediated regulation of nuclear genes in Arabidopsis thaliana in the absence of light stress

Chloroplast signaling involves mechanisms to relay information from chloroplasts to the nucleus, to change nuclear gene expression in response to environmental cues. Aside from reactive oxygen species (ROS) produced under stress conditions, changes in the reduction/oxidation state of photosynthetic electron transfer components or coupled compounds in the stroma and the accumulation of photosynthesis-derived metabolites are likely origins of chloroplast signals. We attempted to investigate the origin of the signals from chloroplasts in mature Arabidopsis leaves by differentially modulating the redox states of the plastoquinone pool and components on the reducing side of photosystem I, as well as the rate of CO2 fixation, while avoiding the production of ROS by excess light. Differential expression of several nuclear photosynthesis genes, including a set of Calvin cycle enzymes, was recorded. These responded to the stromal redox conditions under prevailing light conditions but were independent of the redox state of the plastoquinone pool. The steady-state CO2 fixation rate was reflected in the orchestration of the expression of a number of genes encoding cytoplasmic proteins, including several glycolysis genes and the trehalose-6-phosphate synthase gene, and also the chloroplast-targeted chaperone DnaJ. Clearly, in mature leaves, the redox state of the compounds on the reducing side of photosystem I is of greater importance in light-dependent modulation of nuclear gene expression than the redox state of the plastoquinone pool, particularly at early signaling phases. It also became apparent that photosynthesis-mediated generation of metabolites or signaling molecules is involved in the relay of information from chloroplast to nucleus.

A systematic approach to infer biological relevance and biases of gene network structures

The development of high-throughput technologies has generated the need for bioinformatics approaches to assess the biological relevance of gene networks. Although several tools have been proposed for analysing the enrichment of functional categories in a set of genes, none of them is suitable for evaluating the biological relevance of the gene network. We propose a procedure and develop a web-based resource (BIOREL) to estimate the functional bias (biological relevance) of any given genetic network by integrating different sources of biological information. The weights of the edges in the network may be either binary or continuous. These essential features make our web tool unique among many similar services. BIOREL provides standardized estimations of the network biases extracted from independent data. By the analyses of real data we demonstrate that the potential application of BIOREL ranges from various benchmarking purposes to systematic analysis of the network biology.

Prediction and classification of protein functions

Data from large-scale genome projects, transcriptomics and proteomics experiments have provided scientists with a wealth of information establishing the basis for the investigation of cellular processes. To understand biological function beyond the single gene by the discovery and characterization of functional protein networks, bioinformatics analysis requires information about two additional attributes associated with the gene products: (i) high-level protein function prediction of experimentally uncharacterized proteins and (ii) systematic classification of protein function. This article describes the basic properties of protein classification systems and discusses examples of their implementation.:

Combining chemical genetics and proteomics to identify protein kinase substrates

Phosphorylation is a ubiquitous protein modification important for regulating nearly every aspect of cellular biology. Protein kinases are highly conserved and constitute one of the largest gene families. Identifying the substrates of a kinase is essential for understanding its cellular role, but doing so remains a difficult task. We have developed a high-throughput method to identify substrates of yeast protein kinases that employs a collection of yeast strains each expressing a single epitope-tagged protein and a chemical genetic strategy that permits kinase reactions to be performed in native, whole-cell extracts. Using this method, we screened 4,250 strains expressing epitope-tagged proteins and identified 24 candidate substrates of the Pho85-Pcl1 cyclin-dependent kinase, including the known substrate Rvs167. The power of this method to identify true kinase substrates is strongly supported by functional overlap and colocalization of candidate substrates and the kinase, as well as by the specificity of Pho85-Pcl1 for some of the substrates compared with another Pho85-cyclin kinase complex. This method is readily adaptable to other yeast kinases.

Long-oligomer microarray profiling in Neurospora crassa reveals the transcriptional program underlying biochemical and physiological events of conidial germination

To test the inferences of spotted microarray technology against a biochemically well-studied process, we performed transcriptional profiling of conidial germination in the filamentous fungus, Neurospora crassa. We first constructed a 70 base oligomer microarray that assays 3366 predicted genes. To estimate the relative gene expression levels and changes in gene expression during conidial germination, we analyzed a circuit design of competitive hybridizations throughout a time course using a Bayesian analysis of gene expression level. Remarkable consistency of mRNA profiles with previously published northern data was observed. Genes were hierarchically clustered into groups with respect to their expression profiles over the time course of conidial germination. A functional classification database was employed to characterize the global picture of gene expression. Consensus motif searches identified a putative regulatory component associated with genes involved in ribosomal biogenesis. Our transcriptional profiling data correlate well with biochemical and physiological processes associated with conidial germination and will facilitate functional predictions of novel genes in N.crassa and other filamentous ascomycete species. Furthermore, our dataset on conidial germination allowed comparisons to transcriptional mechanisms associated with germination processes of diverse propagules, such as teliospores of the phytopathogenic fungus Ustilago maydis and spores of the social amoeba Dictyostelium discoideum.

Microarray analysis of expressed sequence tags from haustoria of the rust fungus Uromyces fabae

Rust fungi are plant parasites which colonise host tissue with an intercellular mycelium that forms haustoria within living plant cells. To identify genes expressed during biotrophic growth, EST sequencing was performed with a haustorium-specific cDNA library from Uromyces fabae. One thousand seventeen ESTs were generated, which assembled into 530 contigs. Several of the most frequently represented sequences in the EST database were identical to the in planta induced genes (PIGs) identified previously (Hahn, M., Mendgen, K., 1997. Characterisation of in planta-induced rust genes isolated from a haustorium-specific cDNA library, Mol. Plant-Microbe Interact. 10, 427-437). Virus-encoded sequences were identified, providing evidence for two novel RNA mycoviruses in U. fabae. Microarray hybridisation revealed many cDNAs that were significantly activated in rust-infected leaves compared to germinated uredospores. Very strong in planta expression was found for two PIGs encoding putative metallothioneins. Furthermore, several genes involved in ribosome biogenesis and translation, glycolysis, amino acid metabolism, stress response, and detoxification showed an increased expression in the parasitic mycelium. These data indicate a strong shift in gene expression in rust fungi between germination and the biotrophic stage of development.

The Arabidopsis cold-responsive transcriptome and its regulation by ICE1

To understand the gene network controlling tolerance to cold stress, we performed an Arabidopsis thaliana genome transcript expression profile using Affymetrix GeneChips that contain approximately 24,000 genes. We statistically determined 939 cold-regulated genes with 655 upregulated and 284 downregulated. A large number of early cold-responsive genes encode transcription factors that likely control late-responsive genes, suggesting a multitude of transcriptional cascades. In addition, many genes involved in chromatin level and posttranscriptional regulation were also cold regulated, suggesting their involvement in cold-responsive gene regulation. A number of genes important for the biosynthesis or signaling of plant hormones, such as abscisic acid, gibberellic acid, and auxin, are regulated by cold stress, which is of potential importance in coordinating cold tolerance with growth and development. We compared the cold-responsive transcriptomes of the wild type and inducer of CBF expression 1 (ice1), a mutant defective in an upstream transcription factor required for chilling and freezing tolerance. The transcript levels of many cold-responsive genes were altered in the ice1 mutant not only during cold stress but also before cold treatments. Our study provides a global picture of the Arabidopsis cold-responsive transcriptome and its control by ICE1 and will be valuable for understanding gene regulation under cold stress and the molecular mechanisms of cold tolerance.

Genome-wide expression profiling of the host response to root-knot nematode infection in Arabidopsis

During a compatible interaction, root-knot nematodes (Meloidogyne spp.) induce the redifferentiation of root cells into multinucleate nematode feeding cells (giant cells). Hyperplasia and hypertrophy of the surrounding cells leads to the formation of a root gall. We investigated the plant response to root-knot nematodes by carrying out a global analysis of gene expression during gall formation in Arabidopsis, using giant cell-enriched root tissues. Among 22 089 genes monitored with the complete Arabidopsis transcriptome microarray gene-specific tag, we identified 3373 genes that display significant differential expression between uninfected root tissues and galls at different developmental stages. Quantitative PCR analysis and the use of promoter GUS fusions confirmed the changes in mRNA levels observed in our microarray analysis. We showed that a comparable number of genes were found to be up- and downregulated, indicating that gene downregulation might be essential to allow proper gall formation. Moreover, many genes belonging to the same family are differently regulated in feeding cells. This genome-wide overview of gene expression during plant-nematode interaction provides new insights into nematode feeding-cell formation, and highlights that the suppression of plant defence is associated with nematode feeding-site development.

The Arabidopsis cold-responsive transcriptome and its regulation by ICE1

To understand the gene network controlling tolerance to cold stress, we performed an Arabidopsis thaliana genome transcript expression profile using Affymetrix GeneChips that contain approximately 24,000 genes. We statistically determined 939 cold-regulated genes with 655 upregulated and 284 downregulated. A large number of early cold-responsive genes encode transcription factors that likely control late-responsive genes, suggesting a multitude of transcriptional cascades. In addition, many genes involved in chromatin level and posttranscriptional regulation were also cold regulated, suggesting their involvement in cold-responsive gene regulation. A number of genes important for the biosynthesis or signaling of plant hormones, such as abscisic acid, gibberellic acid, and auxin, are regulated by cold stress, which is of potential importance in coordinating cold tolerance with growth and development. We compared the cold-responsive transcriptomes of the wild type and inducer of CBF expression 1 (ice1), a mutant defective in an upstream transcription factor required for chilling and freezing tolerance. The transcript levels of many cold-responsive genes were altered in the ice1 mutant not only during cold stress but also before cold treatments. Our study provides a global picture of the Arabidopsis cold-responsive transcriptome and its control by ICE1 and will be valuable for understanding gene regulation under cold stress and the molecular mechanisms of cold tolerance.