Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification

Evolutionary processes acting on candidate cis-regulatory regions in humans inferred from patterns of polymorphism and divergence

Analysis of polymorphism and divergence in the non-coding portion of the human genome yields crucial information about factors driving the evolution of gene regulation. Candidate cis-regulatory regions spanning more than 15,000 genes in 15 African Americans and 20 European Americans were re-sequenced and aligned to the chimpanzee genome in order to identify potentially functional polymorphism and to characterize and quantify departures from neutral evolution. Distortions of the site frequency spectra suggest a general pattern of selective constraint on conserved non-coding sites in the flanking regions of genes (CNCs). Moreover, there is an excess of fixed differences that cannot be explained by a Gamma model of deleterious fitness effects, suggesting the presence of positive selection on CNCs. Extensions of the McDonald-Kreitman test identified candidate cis-regulatory regions with high probabilities of positive and negative selection near many known human genes, the biological characteristics of which exhibit genome-wide trends that differ from patterns observed in protein-coding regions. Notably, there is a higher probability of positive selection in candidate cis-regulatory regions near genes expressed in the fetal brain, suggesting that a larger portion of adaptive regulatory changes has occurred in genes expressed during brain development. Overall we find that natural selection has played an important role in the evolution of candidate cis-regulatory regions throughout hominid evolution.

A complex suite of forces drives gene traffic from Drosophila X chromosomes

Theoretical studies predict X chromosomes and autosomes should be under different selection pressures, and there should therefore be differences in sex-specific and sexually antagonistic gene content between the X and the autosomes. Previous analyses have identified an excess of genes duplicated by retrotransposition from the X chromosome in Drosophila melanogaster. A number of hypotheses may explain this pattern, including mutational bias, escape from X-inactivation during spermatogenesis, and the movement of male-favored (sexually antagonistic) genes from a chromosome that is predominantly carried by females. To distinguish among these processes and to examine the generality of these patterns, we identified duplicated genes in nine sequenced Drosophila genomes. We find that, as in D. melanogaster, there is an excess of genes duplicated from the X chromosome across the genus Drosophila. This excess duplication is due almost completely to genes duplicated by retrotransposition, with little to no excess from the X among genes duplicated via DNA intermediates. The only exception to this pattern appears within the burst of duplication that followed the creation of the Drosophila pseudoobscura neo-X chromosome. Additionally, we examined genes relocated among chromosomal arms (i.e., genes duplicated to new locations coupled with the loss of the copy in the ancestral locus) and found an excess of genes relocated off the ancestral X and neo-X chromosomes. Interestingly, many of the same genes were duplicated or relocated from the independently derived neo-X chromosomes of D. pseudoobscura and Drosophila willistoni, suggesting that natural selection favors the traffic of genes from X chromosomes. Overall, we find that the forces driving gene duplication from X chromosomes are dependent on the lineage in question, the molecular mechanism of duplication considered, the preservation of the ancestral copy, and the age of the X chromosome.

Clustering of codons with rare cognate tRNAs in human genes suggests an extra level of expression regulation

In species with large effective population sizes, highly expressed genes tend to be encoded by codons with highly abundant cognate tRNAs to maximize translation rate. However, there has been little evidence for a similar bias of synonymous codons in highly expressed human genes. Here, we ask instead whether there is evidence for the selection for codons associated with low abundance tRNAs. Rather than averaging the codon usage of complete genes, we scan the genes for windows with deviating codon usage. We show that there is a significant over representation of human genes that contain clusters of codons with low abundance cognate tRNAs. We name these regions, which on average have a 50% reduction in the amount of cognate tRNA available compared to the remainder of the gene, RTS (rare tRNA score) clusters. We observed a significant reduction in the substitution rate between the human RTS clusters and their orthologous chimp sequence, when compared to non-RTS cluster sequences. Overall, the genes with an RTS cluster have higher tissue specificity than the non-RTS cluster genes. Furthermore, these genes are functionally enriched for transcription regulation. As genes that regulate transcription in lower eukaryotes are known to be involved in translation on demand, this suggests that the mechanism of translation level expression regulation also exists within the human genome.

Lack of laminin gamma1 in embryonic stem cell-derived cardiomyocytes causes inhomogeneous electrical spreading despite intact differentiation and function

Laminins form a large family of extracellular matrix (ECM) proteins, and their expression is a prerequisite for normal embryonic development. Herein we investigated the role of the laminin gamma1 chain for cardiac muscle differentiation and function using cardiomyocytes derived from embryonic stem cells deficient in the LAMC1 gene. Laminin gamma1 (-/-) cardiomyocytes lacked basement membranes (BM), whereas their sarcomeric organization was unaffected. Accordingly, electrical activity and hormonal regulation were found to be intact. However, the inadequate BM formation led to an increase of ECM deposits between adjacent cardiomyocytes, and this resulted in defects of the electrical signal propagation. Furthermore, we also found an increase in the number of pacemaker areas. Thus, although laminin and intact BM are not essential for cardiomyocyte development and differentiation per se, they are required for the normal deposition of matrix molecules and critical for intact electrical signal propagation.

Similarly strong purifying selection acts on human disease genes of all evolutionary ages

A number of studies have showed that recently created genes differ from the genes created in deep evolutionary past in many aspects. Here, we determined the age of emergence and propensity for gene loss (PGL) of all human protein–coding genes and compared disease genes with non-disease genes in terms of their evolutionary rate, strength of purifying selection, mRNA expression, and genetic redundancy. The older and the less prone to loss, non-disease genes have been evolving 1.5- to 3-fold slower between humans and chimps than young non-disease genes, whereas Mendelian disease genes have been evolving very slowly regardless of their ages and PGL. Complex disease genes showed an intermediate pattern. Disease genes also have higher mRNA expression heterogeneity across multiple tissues than non-disease genes regardless of age and PGL. Young and middle-aged disease genes have fewer similar paralogs as non-disease genes of the same age. We reasoned that genes were more likely to be involved in human disease if they were under a strong functional constraint, expressed heterogeneously across tissues, and lacked genetic redundancy. Young human genes that have been evolving under strong constraint between humans and chimps might also be enriched for genes that encode important primate or even human-specific functions.

Gene expression during Drosophila melanogaster egg development before and after reproductive diapause

Background

Despite the importance of egg development to the female life cycle in Drosophila, global patterns of gene expression have not been examined in detail, primarily due to the difficulty in isolating synchronised developmental stages in sufficient quantities for gene expression profiling. Entry into vitellogenesis is a key stage of oogenesis and by forcing females into reproductive diapause we are able to arrest oogenesis at the pre-vitellogenic stages. Releasing females from diapause allows collection of relatively synchronous developing egg populations and an investigation of some of the transcriptional dynamics apparent before and after reproductive diapause.

Results

Focusing on gender-biased transcription, we identified mechanisms of egg development suppressed during reproductive dormancy as well as other molecular changes unique to the diapausing female. A microarray based analysis generated a set of 3565 transcripts with at least 2-fold greater expression in females as compared to control males, 1392 such changes were biased during reproductive dormancy. In addition, we also detect 1922 up-regulated transcriptional changes after entry into vitellogenesis, which were classified into discrete blocks of co-expression. We discuss some of the regulatory aspects apparent after re-initiation of egg development, exploring the underlying functions, maternal contribution and evolutionary conservation of co-expression patterns involved in egg production.

Conclusion

Although much of the work we present is descriptive, fundamental aspects of egg development and gender-biased transcription can be derived from our time-series experiment. We believe that our dataset will facilitate further exploration of the developmental and evolutionary characteristics of oogenesis as well as the nature of reproductive arrest in Drosophila.

Tissue-specific genetic control of splicing: implications for the study of complex traits

Numerous genome-wide screens for polymorphisms that influence gene expression have provided key insights into the genetic control of transcription. Despite this work, the relevance of specific polymorphisms to in vivo expression and splicing remains unclear. We carried out the first genome-wide screen, to our knowledge, for SNPs that associate with alternative splicing and gene expression in human primary cells, evaluating 93 autopsy-collected cortical brain tissue samples with no defined neuropsychiatric condition and 80 peripheral blood mononucleated cell samples collected from living healthy donors. We identified 23 high confidence associations with total expression and 80 with alternative splicing as reflected by expression levels of specific exons. Fewer than 50% of the implicated SNPs however show effects in both tissue types, reflecting strong evidence for distinct genetic control of splicing and expression in the two tissue types. The data generated here also suggest the possibility that splicing effects may be responsible for up to 13 out of 84 reported genome-wide significant associations with human traits. These results emphasize the importance of establishing a database of polymorphisms affecting splicing and expression in primary tissue types and suggest that splicing effects may be of more phenotypic significance than overall gene expression changes.

Although humans have a relatively small complement of genes, the proteins encoded by those genes and their biologic function are far more complex. The increased complexity is achieved in part through processes that create different messages from the same gene sequence (alternative splicing) and that regulate the expression of those messages in a tissue-specific fashion. These processes expand the functional capacity of the human genome, but also can create predisposition to disease when these processes go awry. In this study, we investigated how single nucleotide polymorphisms influence both overall gene expression and alternative splicing in two important cell types (brain and blood) highly relevant to human disease. Extensive and tissue-specific regulation of gene expression and alternative splicing were observed in the two tissue types, and some of these polymorphisms were shown to be connected to other polymorphsims that have been recently implicated in human diseases through genome-wide association studies. Most of these connections appeared to relate to alternative splicing as opposed to overall expression changes, suggesting that changes in splicing patterns may be more consequential for disease than those affecting only expression. These data emphasize the importance of comprehensive studies into genetic regulation of gene expression in all human tissue types in order to help understand how genetic variation influences risk of common diseases.

We investigated tissue-specific genetic control of gene expression and alternative splicing in primary human cells, and we describe here the implications for understanding how genetic variation influences human disease.

Human self-protein CD8+ T-cell epitopes are both positively and negatively selected

The cellular immune system recognizes self-epitopes in the context of MHC-I molecules. The immunological general view presumes that these self-epitopes are just a background, both positively and negatively selecting T cells. We here estimate the number of epitopes in each human protein for many frequent HLA alleles, and a score representing over or under presentation of epitopes on these proteins. We further show that there is a clear selection for the presentation of specific self-protein types. Proteins presenting many epitopes include, for example, autoimmune regulator (AIRE) upregulated tissue-specific antigens, immune system receptors and proteins with a high expression level. On the other hand, proteins that may be considered less "useful" for the immune system, such as low expression level proteins, are under-presented. We combine our epitope estimate with single nucleotide polymorphism (SNP) measures to show that this selection can be directly observed through the fraction of non-synonymous SNP (replacement fraction), which is significantly higher inside epitopes than outside.

Multifunctionality dominantly determines the rate of human housekeeping and tissue specific interacting protein evolution

Elucidation of the determinants of the rate of protein sequence evolution is one of the great challenges in evolutionary biology. It has been proposed that housekeeping genes are evolutionarily slower than tissue specific genes. In the present communication, we have examined different determinants that influence the evolutionary rate variation in human housekeeping and tissue specific proteins present in protein-protein interaction network. Studies on yeast proteome, revealed a predominant role of protein connectivity in determining the rate of protein evolution. However, in human, we did not observe any significant influence of protein connectivity on its evolutionary rate. Rather, a significant impact of the proportion of protein's interacting length (amount of protein interface involved in interaction with its partners), expression level and multifunctionality has been observed in determining the rate of protein evolution. We also observed that multi interface proteins are evolutionarily conserved between housekeeping and tissue specific genes and it has been found that the average number of biological processes they associated in these two sets of genes is similar. Moreover, single interface proteins in housekeeping genes evolve more slowly as compared to tissue specific genes owing to their involvement in different number of biological processes. Partial correlation analysis suggests that the relative importance of three individual factors in determining the evolutionary rate variation between housekeeping and tissue specific proteins is in the order of protein multifunctionality>protein expression level>interacting protein length.

A computational approach for genome-wide mapping of splicing factor binding sites

A computational method is presented for genome-wide mapping of splicing factor binding sites that considers both the genomic environment and evolutionary conservation.

Alternative splicing is regulated by splicing factors that serve as positive or negative effectors, interacting with regulatory elements along exons and introns. Here we present a novel computational method for genome-wide mapping of splicing factor binding sites that considers both the genomic environment and the evolutionary conservation of the regulatory elements. The method was applied to study the regulation of different alternative splicing events, uncovering an interesting network of interactions among splicing factors.

Recombination rates of human microRNA

The fact that microRNAs play a role in almost all biological processes is well established, as is the importance of recombination in generating genome variability. However, the association between microRNAs and recombination remains largely unknown. In order to investigate the recombination patterns of microRNAs, we performed a comprehensive analysis of the recombination rate of human microRNAs. We observed that microRNAs that are expressed in several tissues tend to have lower recombination rates than tissue-specific microRNAs. Additionally, microRNAs that are associated with a number of diseases are also likely to have lower recombination rates. Furthermore, microRNAs with higher expression levels are found to have fewer recombination events. These findings reveal patterns in recombination rates of microRNAs that could help in understanding the function, evolution, and disease-related roles of microRNAs.

Are sex-biased genes more dispensable?

Many genes show different expression levels in males and females, and these form the basis of sexually dimorphic phenotypes. Sex-biased genes experience accelerated rates of protein evolution, which has been attributed to sexual selection. However, it is possible that the increased rates of molecular evolution, and more importantly the sex-biased gene expression pattern itself, are due to decreased selective constraint. This notion may explain many of the patterns associated with sex-biased gene expression, and changes how we should view the role of natural and sexual selection in relation to these genes.

Adaptive history of single copy genes highly expressed in the term human placenta

The chorioallantoic placenta is a shared derived feature of "placental" mammals essential for the success of eutherian reproduction. Identifying the genes involved in the emergence of the placenta may provide clues for understanding the biology of this organ. Here we identify among 4960 single copy genes in mammals, 222 that show high expression levels in human placentas at term. Further, we present evidence that 94 of these 222 genes evolved adaptively during human evolutionary history since the time of the last common ancestor of eutherian mammals. Remarkably, the majority of positive selection occurred on the eutherian stem lineage suggesting that ancient adaptations have been retained in the human placenta. Of these positively selected genes, 28 have been shown to play a role in human pregnancy and placental biology, and at least 26 have important pregnancy-related phenotypes in mice. Adaptations in genes highly expressed in human placenta are attractive candidates for functional and clinical studies.

Network-based prediction of human tissue-specific metabolism

Direct in vivo investigation of mammalian metabolism is complicated by the distinct metabolic functions of different tissues. We present a computational method that successfully describes the tissue specificity of human metabolism on a large scale. By integrating tissue-specific gene- and protein-expression data with an existing comprehensive reconstruction of the global human metabolic network, we predict tissue-specific metabolic activity in ten human tissues. This reveals a central role for post-transcriptional regulation in shaping tissue-specific metabolic activity profiles. The predicted tissue specificity of genes responsible for metabolic diseases and tissue-specific differences in metabolite exchange with biofluids extend markedly beyond tissue-specific differences manifest in enzyme-expression data, and are validated by large-scale mining of tissue-specificity data. Our results establish a computational basis for the genome-wide study of normal and abnormal human metabolism in a tissue-specific manner.

Transient gene delivery for functional enrichment of differentiating embryonic stem cells

There is a critical need for new sources of hepatocytes, both clinically to provide support for patients with liver failure and in drug discovery for toxicity, metabolic and pharmacokinetic screening of new drug entities. We have reported previously a variety of methods for differentiating murine embryonic stem (ES) cells into hepatocyte-like cells. One major challenge of our work and others in the field has been the ability to selectively purify and enrich these cells from a heterogeneous population. Traditional approaches for inserting new genes (e.g., stable transfection, knock-in, retroviral transduction) involve permanent alterations in the genome. These approaches can lead to mutations and involve the extra costs and time of developing, validating and maintaining new cell lines. We have developed a transient gene delivery system that uses fluorescent gene reporters for purification of the cells. Following a transient transfection, the cells are purified through a fluorescence-activated cell sorter (FACS), re-plated in secondary culture and subsequent phenotypic analysis is performed. In an effort to test the ability of the reporters to work in a transient environment for our differentiation system, we engineered two non-viral plasmid reporters, the first driven by the mouse albumin enhancer/promoter and the second by the mouse cytochrome P450 7A1 (Cyp7A1) promoter. We optimized the transfection efficiency of delivering these genes into spontaneously differentiated ES cells and sorted independent fractions positive for each reporter 17 days after inducing differentiation. We found that cells sorted based on the Cyp7A1 promoter showed significant enrichment in terms of albumin secretion, urea secretion and cytochrome P450 1A2 detoxification activity as compared to enrichment garnered by the albumin promoter-based cell sort. Development of gene reporter systems that allow us to identify, purify and assess homogeneous populations of cells is important in better understanding stem cell differentiation pathways. And engineering cellular systems without making permanent gene changes will be critical for the generation of clinically acceptable cellular material in the future.

Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification

Background

Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal genes in the human and mouse genomes. These switch-like genes consist of 15% of known human genes, and are enriched with genes coding for extracellular and membrane proteins. It is of interest to determine the prediction potential of bimodal genes for class discovery in large-scale datasets.

Results

Use of a model-based clustering algorithm accurately classified more than 400 microarray samples into 19 different tissue types on the basis of bimodal gene expression. Bimodal expression patterns were also highly effective in differentiating between infectious diseases in model-based clustering of microarray data. Supervised classification with feature selection restricted to switch-like genes also recognized tissue specific and infectious disease specific signatures in independent test datasets reserved for validation. Determination of "on" and "off" states of switch-like genes in various tissues and diseases allowed for the identification of activated/deactivated pathways. Activated switch-like genes in neural, skeletal muscle and cardiac muscle tissue tend to have tissue-specific roles. A majority of activated genes in infectious disease are involved in processes related to the immune response.

Conclusion

Switch-like bimodal gene sets capture genome-wide signatures from microarray data in health and infectious disease. A subset of bimodal genes coding for extracellular and membrane proteins are associated with tissue specificity, indicating a potential role for them as biomarkers provided that expression is altered in the onset of disease. Furthermore, we provide evidence that bimodal genes are involved in temporally and spatially active mechanisms including tissue-specific functions and response of the immune system to invading pathogens.

An analysis of human microRNA and disease associations

It has been reported that increasingly microRNAs are associated with diseases. However, the patterns among the microRNA-disease associations remain largely unclear. In this study, in order to dissect the patterns of microRNA-disease associations, we performed a comprehensive analysis to the human microRNA-disease association data, which is manually collected from publications. We built a human microRNA associated disease network. Interestingly, microRNAs tend to show similar or different dysfunctional evidences for the similar or different disease clusters, respectively. A negative correlation between the tissue-specificity of a microRNA and the number of diseases it associated was uncovered. Furthermore, we observed an association between microRNA conservation and disease. Finally, we uncovered that microRNAs associated with the same disease tend to emerge as predefined microRNA groups. These findings can not only provide help in understanding the associations between microRNAs and human diseases but also suggest a new way to identify novel disease-associated microRNAs.

Differential selective constraints shaping codon usage pattern of housekeeping and tissue-specific homologous genes of rice and arabidopsis

Intra-genomic variation between housekeeping and tissue-specific genes has always been a study of interest in higher eukaryotes. To-date, however, no such investigation has been done in plants. Availability of whole genome expression data for both rice and Arabidopsis has made it possible to examine the evolutionary forces in shaping codon usage pattern in both housekeeping and tissue-specific genes in plants. In the present work, we have taken 4065 rice-Arabidopsis homologous gene pairs to study evolutionary forces responsible for codon usage divergence between housekeeping and tissue-specific genes. In both rice and Arabidopsis, it is mutational bias that regulates error minimization in highly expressed genes of both housekeeping and tissue-specific genes. Our results show that, in comparison to tissue-specific genes, housekeeping genes are under strong selective constraint in plants. However, in tissue-specific genes, lowly expressed genes are under stronger selective constraint compared with highly expressed genes. We demonstrated that constraint acting on mRNA secondary structure is responsible for modulating codon usage variations in rice tissue-specific genes. Thus, different evolutionary forces must underline the evolution of synonymous codon usage of highly expressed genes of housekeeping and tissue-specific genes in rice and Arabidopsis.

Tissue- and cell-specific alternative splicing of NFIC

Alternative splicing is an important mechanism for increasing genetic complexity leading to multiple transcripts from single genes and gene regulation through alternative promoters. Splicing often leads to unique tissue-specific patterns of mRNAs with specific biological functions. Nuclear factor I-C (NFI-C), a member of the NFI gene family, is expressed in numerous tissues including brain, liver, spleen and heart. However, the unique dental phenotype of Nfic(-/-) mice lacking molar roots demonstrates a critical role for this transcription factor in root formation. In humans, the NFI-C gene is alternatively spliced producing 4 isoforms. However, different spliced variants have not been studied in association with tissue specificity. The main objective of this study is to identify the NFI-C isoforms expressed in dental cells/tissues, comparing them to the spliced variants in nondental cells/tissues and to analyze their relative expression levels in various cell types. Using bioinformatics, we analyzed the NFI-C gene structure, identifying 2 potential alternative promoters driving expression of selective mRNA transcripts. Our studies show the expression of 3 NFI-C transcripts with the overall splicing pattern conserved between dental and nondental cells tested. Furthermore, by quantitative real-time PCR analysis, we found that although the relative levels of these transcripts were similar in dental and nondental cells, significant differences were observed within the dental cells tested. These are the first studies to analyze the expression of NFI-C isoforms in dental versus nondental cells/tissues, finding subtle cell-/tissue-specific expression patterns that could explain the dental phenotype of Nfic(-/-) mice.

SigWin-detector: a Grid-enabled workflow for discovering enriched windows of genomic features related to DNA sequences

Background

Chromosome location is often used as a scaffold to organize genomic information in both the living cell and molecular biological research. Thus, ever-increasing amounts of data about genomic features are stored in public databases and can be readily visualized by genome browsers. To perform in silico experimentation conveniently with this genomics data, biologists need tools to process and compare datasets routinely and explore the obtained results interactively. The complexity of such experimentation requires these tools to be based on an e-Science approach, hence generic, modular, and reusable. A virtual laboratory environment with workflows, workflow management systems, and Grid computation are therefore essential.

Findings

Here we apply an e-Science approach to develop SigWin-detector, a workflow-based tool that can detect significantly enriched windows of (genomic) features in a (DNA) sequence in a fast and reproducible way. For proof-of-principle, we utilize a biological use case to detect regions of increased and decreased gene expression (RIDGEs and anti-RIDGEs) in human transcriptome maps. We improved the original method for RIDGE detection by replacing the costly step of estimation by random sampling with a faster analytical formula for computing the distribution of the null hypothesis being tested and by developing a new algorithm for computing moving medians. SigWin-detector was developed using the WS-VLAM workflow management system and consists of several reusable modules that are linked together in a basic workflow. The configuration of this basic workflow can be adapted to satisfy the requirements of the specific in silico experiment.

Conclusion

As we show with the results from analyses in the biological use case on RIDGEs, SigWin-detector is an efficient and reusable Grid-based tool for discovering windows enriched for features of a particular type in any sequence of values. Thus, SigWin-detector provides the proof-of-principle for the modular e-Science based concept of integrative bioinformatics experimentation.

Patterns of positive selection in six Mammalian genomes

Genome-wide scans for positively selected genes (PSGs) in mammals have provided insight into the dynamics of genome evolution, the genetic basis of differences between species, and the functions of individual genes. However, previous scans have been limited in power and accuracy owing to small numbers of available genomes. Here we present the most comprehensive examination of mammalian PSGs to date, using the six high-coverage genome assemblies now available for eutherian mammals. The increased phylogenetic depth of this dataset results in substantially improved statistical power, and permits several new lineage- and clade-specific tests to be applied. Of approximately 16,500 human genes with high-confidence orthologs in at least two other species, 400 genes showed significant evidence of positive selection (FDR<0.05), according to a standard likelihood ratio test. An additional 144 genes showed evidence of positive selection on particular lineages or clades. As in previous studies, the identified PSGs were enriched for roles in defense/immunity, chemosensory perception, and reproduction, but enrichments were also evident for more specific functions, such as complement-mediated immunity and taste perception. Several pathways were strongly enriched for PSGs, suggesting possible co-evolution of interacting genes. A novel Bayesian analysis of the possible "selection histories" of each gene indicated that most PSGs have switched multiple times between positive selection and nonselection, suggesting that positive selection is often episodic. A detailed analysis of Affymetrix exon array data indicated that PSGs are expressed at significantly lower levels, and in a more tissue-specific manner, than non-PSGs. Genes that are specifically expressed in the spleen, testes, liver, and breast are significantly enriched for PSGs, but no evidence was found for an enrichment for PSGs among brain-specific genes. This study provides additional evidence for widespread positive selection in mammalian evolution and new genome-wide insights into the functional implications of positive selection.

Global organization of replication time zones of the mouse genome

The division of genomes into distinct replication time zones has long been established. However, an in-depth understanding of their organization and their relationship to transcription is incomplete. Taking advantage of a novel synchronization method ("baby machine") and of genomic DNA microarrays, we have, for the first time, mapped replication times of the entire mouse genome at a high temporal resolution. Our data revealed that although most of the genome has a distinct time of replication either early, middle, or late S phase, a significant portion of the genome is replicated asynchronously. Analysis of the replication map revealed the genomic scale organization of the replication time zones. We found that the genomic regions between early and late replication time zones often consist of extremely large replicons. Analysis of the relationship between replication and transcription revealed that early replication is frequently correlated with the transcription potential of a gene and not necessarily with its actual transcriptional activity. These findings, along with the strong conservation found between replication timing in human and mouse genomes, emphasize the importance of replication timing in transcription regulation.

Natural selection of protein structural and functional properties: a single nucleotide polymorphism perspective

BACKGROUND

The rates of molecular evolution for protein-coding genes depend on the stringency of functional or structural constraints. The Ka/Ks ratio has been commonly used as an indicator of selective constraints and is typically calculated from interspecies alignments. Recent accumulation of single nucleotide polymorphism (SNP) data has enabled the derivation of Ka/Ks ratios for polymorphism (SNP A/S ratios).

RESULTS

Using data from the dbSNP database, we conducted the first large-scale survey of SNP A/S ratios for different structural and functional properties. We confirmed that the SNP A/S ratio is largely correlated with Ka/Ks for divergence. We observed stronger selective constraints for proteins that have high mRNA expression levels or broad expression patterns, have no paralogs, arose earlier in evolution, have natively disordered regions, are located in cytoplasm and nucleus, or are related to human diseases. On the residue level, we found higher degrees of variation for residues that are exposed to solvent, are in a loop conformation, natively disordered regions or low complexity regions, or are in the signal peptides of secreted proteins. Our analysis also revealed that histones and protein kinases are among the protein families that are under the strongest selective constraints, whereas olfactory and taste receptors are among the most variable groups.

CONCLUSION

Our study suggests that the SNP A/S ratio is a robust measure for selective constraints. The correlations between SNP A/S ratios and other variables provide valuable insights into the natural selection of various structural or functional properties, particularly for human-specific genes and constraints within the human lineage.

Natural selection of protein structural and functional properties: a single nucleotide polymorphism perspective

A large-scale survey using single nucleotide polymorphism data from dbSNP provides insights into the evolutionary selection constraints on human proteins of different structural and functional categories.

Background

The rates of molecular evolution for protein-coding genes depend on the stringency of functional or structural constraints. The Ka/Ks ratio has been commonly used as an indicator of selective constraints and is typically calculated from interspecies alignments. Recent accumulation of single nucleotide polymorphism (SNP) data has enabled the derivation of Ka/Ks ratios for polymorphism (SNP A/S ratios).

Results

Using data from the dbSNP database, we conducted the first large-scale survey of SNP A/S ratios for different structural and functional properties. We confirmed that the SNP A/S ratio is largely correlated with Ka/Ks for divergence. We observed stronger selective constraints for proteins that have high mRNA expression levels or broad expression patterns, have no paralogs, arose earlier in evolution, have natively disordered regions, are located in cytoplasm and nucleus, or are related to human diseases. On the residue level, we found higher degrees of variation for residues that are exposed to solvent, are in a loop conformation, natively disordered regions or low complexity regions, or are in the signal peptides of secreted proteins. Our analysis also revealed that histones and protein kinases are among the protein families that are under the strongest selective constraints, whereas olfactory and taste receptors are among the most variable groups.

Conclusion

Our study suggests that the SNP A/S ratio is a robust measure for selective constraints. The correlations between SNP A/S ratios and other variables provide valuable insights into the natural selection of various structural or functional properties, particularly for human-specific genes and constraints within the human lineage.

Evolution of protein-coding genes in Drosophila

Several contributing factors have been implicated in evolutionary rate heterogeneity among proteins, but their evolutionary mechanisms remain poorly characterized. The recently sequenced 12 Drosophila genomes provide a unique opportunity to shed light on these unresolved issues. Here, we focus on the role of natural selection in shaping evolutionary rates. We use the Drosophila genomic data to distinguish between factors that increase the strength of purifying selection on proteins and factors that affect the amount of positive selection experienced by proteins. We confirm the importance of translational selection in shaping protein evolution in Drosophila and show that factors such as tissue bias in expression, gene essentiality, intron number, and recombination rate also contribute to evolutionary rate variation among proteins.

Excess of microRNAs in large and very 5' biased introns

Many of microRNAs (miRNAs) and small nucleolar RNAs (snoRNAs) are located within the introns of genes in eukaryotes. Contrary to intronic snoRNAs, intronic miRNAs are processed from unspliced intronic regions before the catalysis of splicing in vertebrates. By analyzing the distribution patterns of the length and position of the introns hosting these two groups of small RNA genes, we observed that both human and mouse intronic miRNAs tended to be present in large introns, and miRNA host introns have a more 5'-biased position distribution compared with all other introns among the two genomes. These observations indicate that the negative selection of functional constraints might affect the intron size in both genomes. Interestingly, the very 5'-biased positions of miRNA host introns may be necessary for the transcription and regulation of intronic miRNAs to utilize the regulatory signals within the 5'-UTRs of their host genes.

Pleiotropic constraint hampers the resolution of sexual antagonism in vertebrate gene expression

The numerous physiological and phenotypic differences between the sexes, as well as the disparity between male and female reproductive interests, result in sexual conflicts, which are often manifested at the genomic level. Sexually antagonistic genes benefit one sex at the expense of the other and experience strong pressure to evolve male- and female-specific expression patterns to resolve sexual conflicts and maximize fitness for both sexes. Sex-biased gene expression has recently been demonstrated for much of the metazoan transcriptome, suggesting that many loci are sexually antagonistic. However, many coding regions function in multiple processes throughout the organism. This pleiotropy increases the complexity of selection for any given gene, which in turn may obscure sex-specific selective pressures and hamper the evolution of sex-biased gene expression. Here we use microarray gene expression data, in conjunction with data on transcript abundance from expressed sequence tag libraries, to demonstrate that loci with sex-biased gene expression are significantly less pleiotropic than unbiased genes. This relationship was independent of sex chromosome gene dosage effects, and the results were concordant across two study organisms, chicken and mouse. These results suggest that the resolution of sexually antagonistic gene expression is determined by the evolutionary constraints acting on any given antagonistic locus.

Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells

Characterization of the transcriptional regulatory network of the normal cell cycle is essential for understanding the perturbations that lead to cancer. However, the complete set of cycling genes in primary cells has not yet been identified. Here, we report the results of genome-wide expression profiling experiments on synchronized primary human foreskin fibroblasts across the cell cycle. Using a combined experimental and computational approach to deconvolve measured expression values into "single-cell" expression profiles, we were able to overcome the limitations inherent in synchronizing nontransformed mammalian cells. This allowed us to identify 480 periodically expressed genes in primary human foreskin fibroblasts. Analysis of the reconstructed primary cell profiles and comparison with published expression datasets from synchronized transformed cells reveals a large number of genes that cycle exclusively in primary cells. This conclusion was supported by both bioinformatic analysis and experiments performed on other cell types. We suggest that this approach will help pinpoint genetic elements contributing to normal cell growth and cellular transformation.

Switch-like genes populate cell communication pathways and are enriched for extracellular proteins

BACKGROUND

Recent studies have placed gene expression in the context of distribution profiles including housekeeping, graded, and bimodal (switch-like). Single-gene studies have shown bimodal expression results from healthy cell signaling and complex diseases such as cancer, however developing a comprehensive list of human bimodal genes has remained a major challenge due to inherent noise in human microarray data. This study presents a two-component mixture analysis of mouse gene expression data for genes on the Affymetrix MG-U74Av2 array for the detection and annotation of switch-like genes. Two-component normal mixtures were fit to the data to identify bimodal genes and their potential roles in cell signaling and disease progression.

RESULTS

Seventeen percent of the genes on the MG-U74Av2 array (1519 out of 9091) were identified as bimodal or switch-like. KEGG pathways significantly enriched for bimodal genes included ECM-receptor interaction, cell communication, and focal adhesion. Similarly, the GO biological process "cell adhesion" and cellular component "extracellular matrix" were significantly enriched. Switch-like genes were found to be associated with such diseases as congestive heart failure, Alzheimer's disease, arteriosclerosis, breast neoplasms, hypertension, myocardial infarction, obesity, rheumatoid arthritis, and type I and type II diabetes. In diabetes alone, over two hundred bimodal genes were in a different mode of expression compared to normal tissue.

CONCLUSION

This research identified and annotated bimodal or switch-like genes in the mouse genome using a large collection of microarray data. Genes with bimodal expression were enriched within the cell membrane and extracellular environment. Hundreds of bimodal genes demonstrated alternate modes of expression in diabetic muscle, pancreas, liver, heart, and adipose tissue. Bimodal genes comprise a candidate set of biomarkers for a large number of disease states because their expressions are tightly regulated at the transcription level.

Evolution of genes and genomes on the Drosophila phylogeny

Comparative analysis of multiple genomes in a phylogenetic framework dramatically improves the precision and sensitivity of evolutionary inference, producing more robust results than single-genome analyses can provide. The genomes of 12 Drosophila species, ten of which are presented here for the first time (sechellia, simulans, yakuba, erecta, ananassae, persimilis, willistoni, mojavensis, virilis and grimshawi), illustrate how rates and patterns of sequence divergence across taxa can illuminate evolutionary processes on a genomic scale. These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution. Despite remarkable similarities among these Drosophila species, we identified many putatively non-neutral changes in protein-coding genes, non-coding RNA genes, and cis-regulatory regions. These may prove to underlie differences in the ecology and behaviour of these diverse species.

Organismal complexity, cell differentiation and gene expression: human over mouse

We present a molecular and cellular phenomenon underlying the intriguing increase in phenotypic organizational complexity. For the same set of human-mouse orthologous genes (11 534 gene pairs) and homologous tissues (32 tissue pairs), human shows a greater fraction of tissue-specific genes and a greater ratio of the total expression of tissue-specific genes to housekeeping genes in each studied tissue, which suggests a generally higher level of evolutionary cell differentiation (specialization). This phenomenon is spectacularly more pronounced in those human tissues that are more directly involved in the increase of complexity, longevity and body size (i.e. it is reflected on the organismal level as well). Genes with a change in expression breadth show a greater human-mouse divergence of promoter regions and encoded proteins (i.e. the functional genomics data are supported by the structural analysis). Human also shows the higher expression of translation machinery. The upstream untranslated regions (5'UTRs) of human mRNAs are longer than mouse 5'UTRs (even after correction for the difference in genome sizes) and contain more uAUG codons, which suggest a more complex regulation at the translational level in human cells (and agrees well with the augmented cell specialization).

Genes overexpressed in different human solid cancers exhibit different tissue-specific expression profiles

We have analyzed gene expression in different normal human tissues and different types of solid cancers derived from these tissues. The cancers analyzed include brain (astrocytoma and glioblastoma), breast, colon, endometrium, kidney, liver, lung, ovary, prostate, skin, and thyroid cancers. Comparing gene expression in each normal tissue to 12 other normal tissues, we identified 4,917 tissue-selective genes that were selectively expressed in different normal tissues. We also identified 2,929 genes that are overexpressed at least 4-fold in the cancers compared with the normal tissue from which these cancers were derived. The overlap between these two gene groups identified 1,340 tissue-selective genes that are overexpressed in cancers. Different types of cancers, including different brain cancers arising from the same lineage, showed differences in the tissue-selective genes they overexpressed. Melanomas overexpressed the highest number of brain-selective genes and this may contribute to melanoma metastasis to the brain. Of all of the genes with tissue-selective expression, those selectively expressed in testis showed the highest frequency of genes that are overexpressed in at least two types of cancer. However, colon and prostate cancers did not overexpress any testis-selective gene. Nearly all of the genes with tissue-selective expression that are overexpressed in cancers showed selective expression in tissues different from the cancers' tissue of origin. Cancers aberrantly expressing such genes may acquire phenotypic alterations that contribute to cancer cell viability, growth, and metastasis.

Selection for the miniaturization of highly expressed genes

Most widely expressed genes are also highly expressed. Based on high or wide expression, different models were proposed to explain the small sizes of highly/widely expressed genes. We found that housekeeping genes are not more compact than narrowly expressed genes with similar expression levels, but compactness and expression level are correlated in housekeeping genes (except that highly expressed Arabidopsis HK genes have longer intron length). Meanwhile, we found evidence that genes with high functional/regulatory complexity do not have longer introns and longer proteins. The genome design hypothesis is thus not supported. Furthermore, we found that housekeeping genes are not more compact than the narrowly expressed somatic genes with similar average expression levels. Because housekeeping genes are expected to have much higher germline expression levels than narrowly expressed somatic genes, transcription-associated deletion bias is not supported. Selection of the compactness of highly expressed genes for economy is supported.

Large-scale integration of cancer microarray data identifies a robust common cancer signature

BACKGROUND

There is a continuing need to develop molecular diagnostic tools which complement histopathologic examination to increase the accuracy of cancer diagnosis. DNA microarrays provide a means for measuring gene expression signatures which can then be used as components of genomic-based diagnostic tests to determine the presence of cancer.

RESULTS

In this study, we collect and integrate ~1500 microarray gene expression profiles from 26 published cancer data sets across 21 major human cancer types. We then apply a statistical method, referred to as the Top-Scoring Pair of Groups (TSPG) classifier, and a repeated random sampling strategy to the integrated training data sets and identify a common cancer signature consisting of 46 genes. These 46 genes are naturally divided into two distinct groups; those in one group are typically expressed less than those in the other group for cancer tissues. Given a new expression profile, the classifier discriminates cancer from normal tissues by ranking the expression values of the 46 genes in the cancer signature and comparing the average ranks of the two groups. This signature is then validated by applying this decision rule to independent test data.

CONCLUSION

By combining the TSPG method and repeated random sampling, a robust common cancer signature has been identified from large-scale microarray data integration. Upon further validation, this signature may be useful as a robust and objective diagnostic test for cancer.

Classification of heterogeneous microarray data by maximum entropy kernel

Background

There is a large amount of microarray data accumulating in public databases, providing various data waiting to be analyzed jointly. Powerful kernel-based methods are commonly used in microarray analyses with support vector machines (SVMs) to approach a wide range of classification problems. However, the standard vectorial data kernel family (linear, RBF, etc.) that takes vectorial data as input, often fails in prediction if the data come from different platforms or laboratories, due to the low gene overlaps or consistencies between the different datasets.

Results

We introduce a new type of kernel called maximum entropy (ME) kernel, which has no pre-defined function but is generated by kernel entropy maximization with sample distance matrices as constraints, into the field of SVM classification of microarray data. We assessed the performance of the ME kernel with three different data: heterogeneous kidney carcinoma, noise-introduced leukemia, and heterogeneous oral cavity carcinoma metastasis data. The results clearly show that the ME kernel is very robust for heterogeneous data containing missing values and high-noise, and gives higher prediction accuracies than the standard kernels, namely, linear, polynomial and RBF.

Conclusion

The results demonstrate its utility in effectively analyzing promiscuous microarray data of rare specimens, e.g., minor diseases or species, that present difficulty in compiling homogeneous data in a single laboratory.

Statistical methods in cardiac gene expression profiling: from image to function

By providing genome-scale information on gene expression, microarray technology has gained popularity in diverse areas including clinical medicine. However, the analysis and interpretation of microarray data are often complicated. This chapter describes various strategies for microarray data analysis. The analysis starts with the scanned image of a microarray. The image information is processed and summarized to numerical values that represent the abundance of transcripts. Technical variability and systematic biases can be minimized with the proper procedures of background correction and normalization. Considerable numbers of genes are not expressed or not detected by microarray technology. Those genes can be filtered out before further statistical comparison to reduce the dimensionality of the problem. The next step in analysis involves statistical comparison, cluster analysis, and visualization. Genes from the same cluster are considered to be coexpressed and/or coregulated. Also, we can group coexpressed genes into categories by their biological function and cellular location. By combining prior knowledge and statistical results, we can make an inference based on the gene expression profiles.

Stage specific gene expression of serpins and their cognate proteases during myeloid differentiation

Proteases and their serpin inhibitors are abundantly expressed in haemopoietic and peripheral blood cells. There is, however, relatively little information about the role played by serpins in the control of protease activity within these cells and in the pericellular region. The observation that mutations in the neutrophil elastase gene, which cause cyclic and severe congenital neutropenia, are associated with protease maldistribution gives some clue as to the potential importance of inhibitor proteins. To begin to address the role of protease/inhibitor balance in blood cells we used reverse transcription polymerase chain reaction to examine protease and serpin gene expression in mature peripheral blood cells, differentiating haemopoietic progenitors, leukaemic blasts and haemopoietic cell lines. The results demonstrate stage-specific expression of proteases together with widespread expression of intra- and extra-cellular serpins. The elastase inhibitors monocyte neutrophil elastase inhibitor (MNEI) and antitrypsin (AT) showed overlapping expression. MNEI is predominantly expressed in early haemopoietic progenitors while antitrypsin is mainly expressed in more mature myeloid precursors, peripheral blood granulocytes and mononuclear cells. Our results give an overall picture of serpin and protease gene expression and draws attention to the potential importance of elastase regulators at all stages of myelopoiesis.

Evaluation of Two Outlier-Detection-Based Methods for Detecting Tissue-Selective Genes from Microarray Data

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent's non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent's method is not suitable for ROKU.

Defining the gene expression signature of rhabdomyosarcoma by meta-analysis

BACKGROUND

Rhabdomyosarcoma is a highly malignant soft tissue sarcoma in childhood and arises as a consequence of regulatory disruption of the growth and differentiation pathways of myogenic precursor cells. The pathogenic pathways involved in this tumor are mostly unknown and therefore a better characterization of RMS gene expression profile would represent a considerable advance. The availability of publicly available gene expression datasets have opened up new challenges especially for the integration of data generated by different research groups and different array platforms with the purpose of obtaining new insights on the biological process investigated.

RESULTS

In this work we performed a meta-analysis on four microarray and two SAGE datasets of gene expression data on RMS in order to evaluate the degree of agreement of the biological results obtained by these different studies and to identify common regulatory pathways that could be responsible of tumor growth. Regulatory pathways and biological processes significantly enriched has been investigated and a list of differentially meta-profiles have been identified as possible candidate of aggressiveness of RMS.

CONCLUSION

Our results point to a general down regulation of the energy production pathways, suggesting a hypoxic physiology for RMS cells. This result agrees with the high malignancy of RMS and with its resistance to most of the therapeutic treatments. In this context, different isoforms of the ANT gene have been consistently identified for the first time as differentially expressed in RMS. This gene is involved in anti-apoptotic processes when cells grow in low oxygen conditions. These new insights in the biological processes responsible of RMS growth and development demonstrate the effective advantage of the use of integrated analysis of gene expression studies.

Nuclear myosin is ubiquitously expressed and evolutionary conserved in vertebrates

Nuclear myosin I (NMI) is a single-headed member of myosin superfamily localized in the cell nucleus which participates along with nuclear actin in transcription and chromatin remodeling. We demonstrate that NMI is present in cell nuclei of all mouse tissues examined except for cells in terminal stages of spermiogenesis. Quantitative PCR and western blots demonstrate that the expression of NMI in tissues varies with the highest levels in the lungs. The expression of NMI is lower in serum-starved cells and it increases after serum stimulation. The lifespan of NMI is longer than 16 h as determined by cycloheximide translation block. A homologous protein is expressed in human, chicken, Xenopus, and zebrafish as shown by RACE analysis. The analysis of genomic sequences indicates that almost identical homologous NMI genes are expressed in mammals, and similar NMI genes in vertebrates.

Selection of relevant genes in cancer diagnosis based on their prediction accuracy

MOTIVATIONS

One of the main problems in cancer diagnosis by using DNA microarray data is selecting genes relevant for the pathology by analyzing their expression profiles in tissues in two different phenotypical conditions. The question we pose is the following: how do we measure the relevance of a single gene in a given pathology?

METHODS

A gene is relevant for a particular disease if we are able to correctly predict the occurrence of the pathology in new patients on the basis of its expression level only. In other words, a gene is informative for the disease if its expression levels are useful for training a classifier able to generalize, that is, able to correctly predict the status of new patients. In this paper we present a selection bias free, statistically well founded method for finding relevant genes on the basis of their classification ability.

RESULTS

We applied the method on a colon cancer data set and produced a list of relevant genes, ranked on the basis of their prediction accuracy. We found, out of more than 6500 available genes, 54 overexpressed in normal tissues and 77 overexpressed in tumor tissues having prediction accuracy greater than 70% with p-value <or=0.05.

CONCLUSIONS

The relevance of the selected genes was assessed (a) statistically, evaluating the p-value of the estimate prediction accuracy of each gene; (b) biologically, confirming the involvement of many genes in generic carcinogenic processes and in particular for the colon; (c) comparatively, verifying the presence of these genes in other studies on the same data-set.

Impacts of Gene Essentiality, Expression Pattern, and Gene Compactness on the Evolutionary Rate of Mammalian Proteins

Understanding the determinants of the rate of protein sequence evolution is of fundamental importance in evolutionary biology. Many recent studies have focused on the yeast because of the availability of many genome-wide expressional and functional data. Yeast studies revealed a predominant role of gene expression level and a minor role of gene essentiality in determining the rate of protein sequence evolution. Whether these rules apply to complex organisms such as mammals is unclear. Here we assemble a list of 1,138 essential and 2,341 nonessential mouse genes based on targeted gene deletion experiments and report a significant impact of gene essentiality on the rate of mammalian protein evolution. Gene expression level has virtually no effect, although tissue specificity in expression pattern has a strong influence. Unexpectedly, gene compactness, measured by average intron size and untranslated region length, has the greatest influence. Hence, the relative importance of the various factors in determining the rate of mammalian protein evolution is gene compactness > gene essentiality approximately tissue specificity > expression level. Our results suggest a considerable variation in rate determinants between unicellular organisms such as the yeast and multicellular organisms such as mammals.

Prediction of a key role of motifs binding E2F and NR2F in down-regulation of numerous genes during the development of the mouse hippocampus

Background

We previously demonstrated that gene expression profiles during neuronal differentiation in vitro and hippocampal development in vivo were very similar, due to a conservation of the important second singular value decomposition (SVD) mode (Mode 2) of expression. The conservation of Mode 2 suggests that it reflects a regulatory mechanism conserved between the two systems. In either dataset, the expression vectors of all the genes form two large clusters that differ in the sign of the contribution of Mode 2, which for the majority of them reflects the difference between down- or up-regulation.

Results

In the current work, we used a novel approach of analyzing cis-regulation of gene expression in a subspace of a single SVD mode of temporal expression profiles. In the putative upstream regulatory sequences identified by mouse-human homology for all the genes represented in either dataset, we searched for simple features (motifs and pairs of motifs) associated with either sign of the loading of Mode 2. Using a cross-system training-test set approach, we identified E2F binding sites as predictors of down-regulation of gene expression during hippocampal development. NR2F binding sites, for the transcription factors Nr2f/COUP and Hnf4, and also NR2F_SP1 pairs of binding sites, were predictors of down-regulation of expression both during hippocampal development and neuronal differentiation. Analysis of another dataset, from gene profiling of myoblast differentiation in vitro, shows that the conservation of Mode 2 extends to the differentiation of mesenchymal cells. This permitted the identification of two more pairs of motifs, one of which included the CDE/CHR tandem element, as features associated with down-regulation both in the differentiating myoblasts and in the developing hippocampus. Of the features we identified, the E2F and CDE/CHR motifs may be associated with the cycling progenitor cell status, while NR2F may be related to the entry into differentiation along the neuronal pathway.

Conclusion

Our results constitute the first prediction of an expression pattern from the genomic sequence for the developing mammalian brain, and demonstrate a potential for the analysis of gene regulation in a subspace of a single SVD mode of expression.

trnp: A conserved mammalian gene encoding a nuclear protein that accelerates cell-cycle progression

We herein describe a novel protein encoded by a single exon in a single-copy conserved mammalian gene. This protein, termed TMF regulated nuclear protein (TRNP), was identified in a yeast "two-hybrid" screen in which the "BC box" containing protein-TMF/ARA160 served as a bait. TRNP is a basic protein which accumulates in an insoluble nuclear fraction in mammalian cells. It is 227 aa long in humans and chimps and 223 aa long in mice. Enforced expression of TRNP in cells that do not express this protein significantly increased their proliferation rate by enhancing their cell-cycle progression from the G0/G1 to the S phase. Like another proliferation promoting factor, Stat3, TRNP was directed to proteasomal degradation by TMF/ ARA160. Thus, the trnp gene encodes a novel mammalian conserved nuclear protein that can accelerate cellcycle progression and is regulated by TMF/ARA160.

Widespread ectopic expression of olfactory receptor genes

BACKGROUND

Olfactory receptors (ORs) are the largest gene family in the human genome. Although they are expected to be expressed specifically in olfactory tissues, some ectopic expression has been reported, with special emphasis on sperm and testis. The present study systematically explores the expression patterns of OR genes in a large number of tissues and assesses the potential functional implication of such ectopic expression.

RESULTS

We analyzed the expression of hundreds of human and mouse OR transcripts, via EST and microarray data, in several dozens of human and mouse tissues. Different tissues had specific, relatively small OR gene subsets which had particularly high expression levels. In testis, average expression was not particularly high, and very few highly expressed genes were found, none corresponding to ORs previously implicated in sperm chemotaxis. Higher expression levels were more common for genes with a non-OR genomic neighbor. Importantly, no correlation in expression levels was detected for human-mouse orthologous pairs. Also, no significant difference in expression levels was seen between intact and pseudogenized ORs, except for the pseudogenes of subfamily 7E which has undergone a human-specific expansion.

CONCLUSION

The OR superfamily as a whole, show widespread, locus-dependent and heterogeneous expression, in agreement with a neutral or near neutral evolutionary model for transcription control. These results cannot reject the possibility that small OR subsets might play functional roles in different tissues, however considerable care should be exerted when offering a functional interpretation for ectopic OR expression based only on transcription information.

Transcriptome-scale similarities between mouse and human skeletal muscles with normal and myopathic phenotypes

BACKGROUND

Mouse and human skeletal muscle transcriptome profiles vary by muscle type, raising the question of which mouse muscle groups have the greatest molecular similarities to human skeletal muscle.

METHODS

Orthologous (whole, sub-) transcriptome profiles were compared among four mouse-human transcriptome datasets: (M) six muscle groups obtained from three mouse strains (wildtype, mdx, mdx5cv); (H1) biopsied human quadriceps from controls and Duchenne muscular dystrophy patients; (H2) four different control human muscle types obtained at autopsy; and (H3) 12 different control human tissues (ten non-muscle).

RESULTS

Of the six mouse muscles examined, mouse soleus bore the greatest molecular similarities to human skeletal muscles, independent of the latters' anatomic location/muscle type, disease state, age and sampling method (autopsy versus biopsy). Significant similarity to any one mouse muscle group was not observed for non-muscle human tissues (dataset H3), indicating this finding to be muscle specific.

CONCLUSION

This observation may be partly explained by the higher type I fiber content of soleus relative to the other mouse muscles sampled.

Low rates of expression profile divergence in highly expressed genes and tissue-specific genes during mammalian evolution

Evolutionary rates provide important information about the pattern and mechanism of evolution. Although the rate of gene sequence evolution has been well studied, the rate of gene expression evolution is poorly understood. In particular, it is unclear whether the gene expression level and tissue specificity influence the divergence of expression profiles between orthologous genes. Here we address this question using a microarray data set comprising the expression signals of 10,607 pairs of orthologous human and mouse genes from over 60 tissues per species. We show that the level of gene expression and the degree of tissue specificity are generally conserved between the human and mouse orthologs. The rate of gene expression profile change during evolution is negatively correlated with the level of gene expression, measured by either the average or the highest level among all tissues examined. This is analogous to the observation that the rate of gene (or protein) sequence evolution is negatively correlated with the gene expression level. The impacts of the degree of tissue specificity on the evolutionary rate of gene sequence and that of expression profile, however, are opposite. Highly tissue-specific genes tend to evolve rapidly at the gene sequence level but slowly at the expression profile level. Thus, different forces and selective constraints must underlie the evolution of gene sequence and that of gene expression.

An integrated approach of immunogenomics and bioinformatics to identify new Tumor Associated Antigens (TAA) for mammary cancer immunological prevention

Background

Neoplastic transformation is a multistep process in which distinct gene products of specific cell regulatory pathways are involved at each stage. Identification of overexpressed genes provides an unprecedented opportunity to address the immune system against antigens typical of defined stages of neoplastic transformation. HER-2/neu/ERBB2 (Her2) oncogene is a prototype of deregulated oncogenic protein kinase membrane receptors. Mice transgenic for rat Her2 (BALB-neuT mice) were studied to evaluate the stage in which vaccines can prevent the onset of Her2 driven mammary carcinomas. As Her2 is not overexpressed in all mammary carcinomas, definition of an additional set of tumor associated antigens (TAAs) expressed at defined stages by most breast carcinomas would allow a broader coverage of vaccination. To address this question, a meta-analysis was performed on two transcription profile studies [1,2] to identify a set of new TAA targets to be used instead of or in conjunction with Her2.

Results

The five TAAs identified (Tes, Rcn2, Rnf4, Cradd, Galnt3) are those whose expression is linearly related to the tumor mass increase in BALB-neuT mammary glands. Moreover, they have a low expression in normal tissues and are generally expressed in human breast tumors, though at a lower level than Her2.

Conclusion

Although the number of putative TAAs identified is limited, this pilot study suggests that meta-analysis of expression profiles produces results that could assist in the designing of pre-clinical immunopreventive vaccines.