Microarray analysis of orthologous genes: conservation of the translational machinery across species at the sequence and expression level

High resolution RNA-seq profiling of genes encoding ribosomal proteins across different organs and developmental stages in Arabidopsis thaliana

In Arabidopsis thaliana, each ribosomal protein (RP) is encoded by a small gene family consisting of two or more highly homologous paralogues, which results in ribosome heterogeneity. It is largely unknown that how genes from multiple member containing RP families are regulated at transcriptional level to accommodate the needs of different plant organs and developmental stages. In this study, we investigated the transcript accumulation profiles of RP genes and found that the expression levels of RP genes are varied dramatically in different organs and developmental stages. Although most RP genes are found to be ubiquitously transcribed, some are obviously transcribed with spatiotemporal specificity. The hierarchical clustering trees of transcript accumulation intensity of RP genes revealed that different organs and developmental stages have different population of RP gene transcripts. By interrogating of the expression fluctuation trend of RP genes, we found that in spite of the fact that most groups of paralogous RP genes are transcribed in concerted manners, some RPs gene have contrasting expression patterns. When transcripts of paralogous RP genes from the same family are considered together, the expression level of most RP genes are well-matched but some are obviously higher or lower, therefore we speculate that some superfluous RPs may act outside the ribosome and a portion of ribosomes may lack one or even more RP(s). Altogether, our analysis results suggested that functional divergence may exist among heterogeneous ribosomes that resulted from different combination of RP paralogues, and substoichiometry of several RP gene families may lead to another layer of heterogeneous ribosomes which also have divergent functions in plants.

Ceres: software for the integrated analysis of transcription factor binding sites and nucleosome positions in Saccharomyces cerevisiae

MOTIVATION

There is accumulating evidence that the chromatin environment of transcription factor (TF) binding sites in promoter regions has a critical influence on their regulatory potential. Recent studies have mapped TF binding sites and nucleosome positions throughout the yeast genome; however, there is a lack of computation tools to integrate these data types.

RESULTS

We have developed the Ceres software to facilitate the integrated analysis of TF binding sites and nucleosome positions in the model eukaryote S. cerevisiae. Ceres enables users to dynamically display the spatial organization of TF binding sites and nucleosome positions of individual genes, or the average profiles for large gene sets. Ceres provides novel statistical tools to test for the enrichment of TF binding sites and chromatin environments for user-selected gene sets. Ceres also enables users to search the genome for combinations of TF binding sites that are associated with specific chromatin environments. Preliminary analysis using the Ceres software indicates that functional and conserved TF binding sites are often associated with specific chromatin environments.

AVAILABILITY

http://bioinformatics1.smb.wsu.edu/Ceres.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genome adaptation to chemical stress: clues from comparative transcriptomics in Saccharomyces cerevisiae and Candida glabrata

Comparative transcriptomics of Saccharomyces cerevisiae and Candida glabrata revealed a remarkable conservation of response to drug-induced stress, despite underlying differences in the regulatory networks.

Background

Recent technical and methodological advances have placed microbial models at the forefront of evolutionary and environmental genomics. To better understand the logic of genetic network evolution, we combined comparative transcriptomics, a differential clustering algorithm and promoter analyses in a study of the evolution of transcriptional networks responding to an antifungal agent in two yeast species: the free-living model organism Saccharomyces cerevisiae and the human pathogen Candida glabrata.

Results

We found that although the gene expression patterns characterizing the response to drugs were remarkably conserved between the two species, part of the underlying regulatory networks differed. In particular, the roles of the oxidative stress response transcription factors ScYap1p (in S. cerevisiae) and Cgap1p (in C. glabrata) had diverged. The sets of genes whose benomyl response depends on these factors are significantly different. Also, the DNA motifs targeted by ScYap1p and Cgap1p are differently represented in the promoters of these genes, suggesting that the DNA binding properties of the two proteins are slightly different. Experimental assays of ScYap1p and Cgap1p activities in vivo were in accordance with this last observation.

Conclusions

Based on these results and recently published data, we suggest that the robustness of environmental stress responses among related species contrasts with the rapid evolution of regulatory sequences, and depends on both the coevolution of transcription factor binding properties and the versatility of regulatory associations within transcriptional networks.

DNA microarray data integration by ortholog gene analysis reveals potential molecular mechanisms of estrogen-dependent growth of human uterine fibroids

Background

Uterine fibroids or leiomyoma are a common benign smooth muscle tumor. The tumor growth is well known to be estrogen-dependent. However, the molecular mechanisms of its estrogen-dependency is not well understood.

Methods

Differentially expressed genes in human uterine fibroids were either retrieved from published papers or from our own statistical analysis of downloaded array data. Probes for the same genes on different Affymetrix chips were mapped based on probe comparison information provided by Affymetrix. Genes identified by two or three array studies were submitted for ortholog analysis. Human and rat ortholog genes were identified by using ortholog gene databases, HomoloGene and TOGA and were confirmed by synteny analysis with MultiContigView tool in the Ensembl genome browser.

Results

By integrated analysis of three recently published DNA microarray studies with human tissue, thirty-eight genes were found to be differentially expressed in the same direction in fibroid compared to adjacent uterine myometrium by at least two research groups. Among these genes, twelve with rat orthologs were identified as estrogen-regulated from our array study investigating uterine expression in ovariectomized rats treated with estrogen. Functional and pathway analyses of the twelve genes suggested multiple molecular mechanisms for estrogen-dependent cell survival and tumor growth. Firstly, estrogen increased expression of the anti-apoptotic PCP4 gene and suppressed the expression of growth inhibitory receptors PTGER3 and TGFBR2. Secondly, estrogen may antagonize PPARγ signaling, thought to inhibit fibroid growth and survival, at two points in the PPAR pathway: 1) through increased ANXA1 gene expression which can inhibit phospholipase A2 activity and in turn decrease arachidonic acid synthesis, and 2) by decreasing L-PGDS expression which would reduce synthesis of PGJ2, an endogenous ligand for PPARγ. Lastly, estrogen affects retinoic acid (RA) synthesis and mobilization by regulating expression of CRABP2 and ALDH1A1. RA has been shown to play a significant role in the development of uterine fibroids in an animal model.

Conclusion

Integrated analysis of multiple array datasets revealed twelve human and rat ortholog genes that were differentially expressed in human uterine fibroids and transcriptionally responsive to estrogen in the rat uterus. Functional and pathway analysis of these genes suggest multiple potential molecular mechanisms for the poorly understood estrogen-dependent growth of uterine fibroids. Fully understanding the exact molecular interactions among these gene products requires further study to validate their roles in uterine fibroids. This work provides new avenues of study which could influence the future direction of therapeutic intervention for the disease.

Cross-species microarray hybridizations: a developing tool for studying species diversity

The use of cross-species hybridization (CSH) to DNA microarrays, in which the target RNA and microarray probe are from different species, has increased in the past few years. CSH is used in comparative, evolutionary and ecological studies of closely related species, and for gene-expression profiling of many species that lack a representative microarray platform. However, unlike species-specific hybridization, CSH is still considered a non-standard use of microarrays. Here, we present the recent developments in the field of CSH for cDNA and oligomer microarray platforms. We discuss issues that influence the quality of CSH results, including platform choice, experiment design and data analysis, and suggest strategies that can lead to improvement of CSH studies to investigate species diversity.

LymphTF-DB: a database of transcription factors involved in lymphocyte development

B and T cells develop following a similar early stepwise progression to later stages where multiple developmental options are available. These developmental regimes necessitate differential gene expression regulated by a large number of transcription factors (TFs). The resultant burgeoning amount of information has opened a knowledge gap between TF activities during lymphocyte development and a researcher's experiments. We have created the LymphTF database (DB) to fill this gap. This DB holds interactions between individual TFs and their specific targets at a given developmental time. By storing such interactions as a function of developmental progression, we hope to advance the elucidation of regulatory networks that guide lymphocyte development. Besides queries for TF-target gene interactions in developmental stages, the DB provides a graphical representation of downloadable target gene regulatory sequences with locations of the transcriptional start sites and TF-binding sites. The LymphTF-DB can be accessed freely on the web at http://www.iupui.edu/~tfinterx/.

GO-2D: identifying 2-dimensional cellular-localized functional modules in Gene Ontology

BACKGROUND

Rapid progress in high-throughput biotechnologies (e.g. microarrays) and exponential accumulation of gene functional knowledge make it promising for systematic understanding of complex human diseases at functional modules level. Based on Gene Ontology, a large number of automatic tools have been developed for the functional analysis and biological interpretation of the high-throughput microarray data.

RESULTS

Different from the existing tools such as Onto-Express and FatiGO, we develop a tool named GO-2D for identifying 2-dimensional functional modules based on combined GO categories. For example, it refines biological process categories by sorting their genes into different cellular component categories, and then extracts those combined categories enriched with the interesting genes (e.g., the differentially expressed genes) for identifying the cellular-localized functional modules. Applications of GO-2D to the analyses of two human cancer datasets show that very specific disease-relevant processes can be identified by using cellular location information.

CONCLUSION

For studying complex human diseases, GO-2D can extract functionally compact and detailed modules such as the cellular-localized ones, characterizing disease-relevant modules in terms of both biological processes and cellular locations. The application results clearly demonstrate that 2-dimensional approach complementary to current 1-dimensional approach is powerful for finding modules highly relevant to diseases.

Evolutionary-conserved gene expression response profiles across mammalian tissues

Gene expression responses are complex and frequently involve the actions of many genes to effect coordinated patterns. We hypothesized these coordinated responses are evolutionarily conserved and used a comparison of human and mouse gene expression profiles to identify the most prominent conserved features across a set of normal mammalian tissues. Based on data from multiple studies across multiple tissues in human and mouse, 13 gene expression modes across multiple tissues were identified in each of these species using principal component analysis. Strikingly, 1-to-1 pairing of human and mouse modes was observed in 12 out of 13 modes obtained from the two species independently. These paired modes define evolutionarily conserved gene expression response modes (CGEMs). Notably, in this study we were able to extract biological responses that are not overwhelmed by laboratory-to-laboratory or species-to-species variation. Of the variation in our gene expression dataset, 84% can be explained using these CGEMs. Functional annotation was performed using Gene Ontology, pathway, and transcription factor binding site over representation. Our conclusion is that we found an unbiased way of obtaining conserved gene response modes that accounts for a considerable portion of gene expression variation in a given dataset, as well as validates the conservation of major gene expression response modes across the mammals.

Peeling off the hidden genetic heterogeneities of cancers based on disease-relevant functional modules

Discovering molecular heterogeneities in phenotypically defined disease is of critical importance both for understanding pathogenic mechanisms of complex diseases and for finding efficient treatments. Recently, it has been recognized that cellular phenotypes are determined by the concerted actions of many functionally related genes in modular fashions. The underlying modular mechanisms should help the understanding of hidden genetic heterogeneities of complex diseases. We defined a putative disease module to be the functional gene groups in terms of both biological process and cellular localization, which are significantly enriched with genes highly variably expressed across the disease samples. As a validation, we used two large cancer datasets to evaluate the ability of the modules for correctly partitioning samples. Then, we sought the subtypes of complex diffuse large B-cell lymphoma (DLBCL) using a public dataset. Finally, the clinical significance of the identified subtypes was verified by survival analysis. In two validation datasets, we achieved highly accurate partitions that best fit the clinical cancer phenotypes. Then, for the notoriously heterogeneous DLBCL, we demonstrated that two partitioned subtypes using an identified module ("cellular response to stress") had very different 5-year overall rates (65% vs. 14%) and were highly significantly (P < 0.007) correlated with the clinical survival rate. Finally, we built a multivariate Cox proportional-hazard prediction model that included 4 genes as risk predictors for survival over DLBCL. The proposed modular approach is a promising computational strategy for peeling off genetic heterogeneities and understanding the modular mechanisms of human diseases such as cancers.

Derivation of species-specific hybridization-like knowledge out of cross-species hybridization results

BACKGROUND

One of the approaches for conducting genomics research in organisms without extant microarray platforms is to profile their expression patterns by using Cross-Species Hybridization (CSH). Several different studies using spotted microarray and CSH produced contradicting conclusions in the ability of CSH to reflect biological processes described by species-specific hybridization (SSH).

RESULTS

We used a tomato-spotted cDNA microarray to examine the ability of CSH to reflect SSH data. Potato RNA was hybridized to spotted cDNA tomato and potato microarrays to generate CSH and SSH data, respectively. Difficulties arose in obtaining transcriptomic data from CSH that reflected those obtained from SSH. Nevertheless, once the data was filtered for those corresponding to matching probe sets, by restricting proper cutoffs of probe homology, the CSH transcriptome data showed improved reflection of those of the SSH.

CONCLUSIONS

This study evaluated the relative performance of CSH compared to SSH, and proposes methods to ensure that CSH closely reflects the biological process analyzed by SSH.

Associative clustering for exploring dependencies between functional genomics data sets

High-throughput genomic measurements, interpreted as cooccurring data samples from multiple sources, open up a fresh problem for machine learning: What is in common in the different data sets, that is, what kind of statistical dependencies are there between the paired samples from the different sets? We introduce a clustering algorithm for exploring the dependencies. Samples within each data set are grouped such that the dependencies between groups of different sets capture as much of pairwise dependencies between the samples as possible. We formalize this problem in a novel probabilistic way, as optimization of a Bayes factor. The method is applied to reveal commonalities and exceptions in gene expression between organisms and to suggest regulatory interactions in the form of dependencies between gene expression profiles and regulator binding patterns.

Computational prediction of conserved operons and phylogenetic footprinting of transcription regulatory elements in the metal-reducing bacterial family Geobacteraceae

Members of the family Geobacteraceae are an important group of microorganisms from the delta subdivision of Proteobacteria that couple the oxidation of organic compounds to metal reduction. In order to uncover transcription regulatory interactions in these organisms, we used computational methods to identify conserved operons and putative cis-regulatory transcription elements. We identified 26 putative operons with gene order and function conserved among two species of Geobacteraceae, Geobacter sulfurreducens and Geobacter metallireducens. Most of these operons were also conserved in Desulfovibrio vulgaris, an additional metal reducing organism from family Desulfovibrionaceae of the delta subdivision of Proteobacteria. The predicted conserved operons were investigated for the presence of transcription factor binding sites by two different methods, (i) comparison of non-coding regions in conserved operons, and (ii) neural network promoter prediction. Predicted motifs were screened to identify most likely transcription factor binding sites and ribosome-binding sites. We provide information on motifs in Geobacteraceae similar to known transcription factor binding sites in Escherichia coli, conserved motifs in other bacterial species, putative palindromic sites, and predicted ribosome-binding sites. These predictions will aid in further elucidation of regulatory networks of gene interactions in Geobacteraceae.

The 3′ Untranslated Region of the Membrane-Bound IL-1R Accessory Protein mRNA Confers Tissue-Specific Destabilization

IL-1alpha and IL-1beta are proinflammatory cytokines that promote activation of intracellular signaling cascades, leading to stabilization of certain mRNAs and activation of transcription factors. IL-1R type I (IL-1RI) binds IL-1alpha and IL-1beta, and subsequent recruitment of the membrane-bound IL-1R accessory protein (mIL-1RAcP) facilitates signal transduction. Two alternatively spliced isoforms, soluble IL-1RAcP (sIL-1RAcP) and sIL-1RAcP-beta, which lack transmembrane and intracellular domains, have been described. The sIL-1RAcP and possibly sIL-1RAcP-beta can inhibit IL-1 signaling. Proportional expression of the different IL-1RAcP splice variants may be an important determinant of responsiveness to IL-1. We show that although both mIL-1RAcP and sIL-1RAcP mRNAs are widely expressed in human tissue, their relative proportions differ significantly in a tissue-specific manner. Turnover studies revealed that the sIL-1RAcP mRNA has a half-life of approximately 48 h in both the kidney cell line 293 and the hepatoma cell line HepG2. The mIL-1RAcP mRNA has a similar half-life in 293 cells, but a considerably shorter half-life of approximately 5 h in HepG2 cells. Using luciferase reporter constructs, we demonstrated that this specific destabilization of the mIL-1RAcP mRNA in the latter cell type is mediated by its 2.8-kb 3'-untranslated region. Deletion analysis further established that the cell line-specific instability does not involve AU-rich elements, but is mediated by several novel elements that appear to act independently; such elements may be recognized by proteins expressed specifically in some, but not all, tissues. These data demonstrate that the cellular capacity to respond to IL-1 is tightly regulated in a tissue-specific manner.

Cross-species hybridisation of human and bovine orthologous genes on high density cDNA microarrays

BACKGROUND

Cross-species gene-expression comparison is a powerful tool for the discovery of evolutionarily conserved mechanisms and pathways of expression control. The usefulness of cDNA microarrays in this context is that broad areas of homology are compared and hybridization probes are sufficiently large that small inter-species differences in nucleotide sequence would not affect the analytical results. This comparative genomics approach would allow a common set of genes within a specific developmental, metabolic, or disease-related gene pathway to be evaluated in experimental models of human diseases. The objective of this study was to investigate the feasibility and reproducibility of cross-species analysis employing a human cDNA microarray as probe.

RESULTS

As a proof of principle, total RNA derived from human and bovine fetal brains was used as a source of labelled targets for hybridisation onto a human cDNA microarray composed of 349 characterised genes. Each gene was spotted 20 times representing 6,980 data points thus enabling highly reproducible spot quantification. Employing high stringency hybridisation and washing conditions, followed by data analysis, revealed slight differences in the expression levels and reproducibility of the signals between the two species. We also assigned each of the genes into three expression level categories- i.e. high, medium and low. The correlation co-efficient of cross hybridisation between the orthologous genes was 0.94. Verification of the array data by semi-quantitative RT-PCR using common primer sequences enabled co-amplification of both human and bovine transcripts. Finally, we were able to assign gene names to previously uncharacterised bovine ESTs.

CONCLUSIONS

Results of our study demonstrate the harnessing and utilisation power of comparative genomics and prove the feasibility of using human microarrays to facilitate the identification of co-expressed orthologous genes in common tissues derived from different species.