Conservation and coevolution in the scale-free human gene coexpression network

Boolean implication networks derived from large scale, whole genome microarray datasets

A method for analysis of microarray data is presented that extracts statistically significant Boolean implication relationships between pairs of genes.

We describe a method for extracting Boolean implications (if-then relationships) in very large amounts of gene expression microarray data. A meta-analysis of data from thousands of microarrays for humans, mice, and fruit flies finds millions of implication relationships between genes that would be missed by other methods. These relationships capture gender differences, tissue differences, development, and differentiation. New relationships are discovered that are preserved across all three species.

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.

Functional organization of the transcriptome in human brain

The enormous complexity of the human brain ultimately derives from a finite set of molecular instructions encoded in the human genome. These instructions can be directly studied by exploring the organization of the brain's transcriptome through systematic analysis of gene coexpression relationships. We analyzed gene coexpression relationships in microarray data generated from specific human brain regions and identified modules of coexpressed genes that correspond to neurons, oligodendrocytes, astrocytes and microglia. These modules provide an initial description of the transcriptional programs that distinguish the major cell classes of the human brain and indicate that cell type-specific information can be obtained from whole brain tissue without isolating homogeneous populations of cells. Other modules corresponded to additional cell types, organelles, synaptic function, gender differences and the subventricular neurogenic niche. We found that subventricular zone astrocytes, which are thought to function as neural stem cells in adults, have a distinct gene expression pattern relative to protoplasmic astrocytes. Our findings provide a new foundation for neurogenetic inquiries by revealing a robust and previously unrecognized organization to the human brain transcriptome.

Comparable contributions of structural-functional constraints and expression level to the rate of protein sequence evolution

Background

Proteins show a broad range of evolutionary rates. Understanding the factors that are responsible for the characteristic rate of evolution of a given protein arguably is one of the major goals of evolutionary biology. A long-standing general assumption used to be that the evolution rate is, primarily, determined by the specific functional constraints that affect the given protein. These constrains were traditionally thought to depend both on the specific features of the protein's structure and its biological role. The advent of systems biology brought about new types of data, such as expression level and protein-protein interactions, and unexpectedly, a variety of correlations between protein evolution rate and these variables have been observed. The strongest connections by far were repeatedly seen between protein sequence evolution rate and the expression level of the respective gene. It has been hypothesized that this link is due to the selection for the robustness of the protein structure to mistranslation-induced misfolding that is particularly important for highly expressed proteins and is the dominant determinant of the sequence evolution rate.

Results

This work is an attempt to assess the relative contributions of protein domain structure and function, on the one hand, and expression level on the other hand, to the rate of sequence evolution. To this end, we performed a genome-wide analysis of the effect of the fusion of a pair of domains in multidomain proteins on the difference in the domain-specific evolutionary rates. The mistranslation-induced misfolding hypothesis would predict that, within multidomain proteins, fused domains, on average, should evolve at substantially closer rates than the same domains in different proteins because, within a mutlidomain protein, all domains are translated at the same rate. We performed a comprehensive comparison of the evolutionary rates of mammalian and plant protein domains that are either joined in multidomain proteins or contained in distinct proteins. Substantial homogenization of evolutionary rates in multidomain proteins was, indeed, observed in both animals and plants, although highly significant differences between domain-specific rates remained. The contributions of the translation rate, as determined by the effect of the fusion of a pair of domains within a multidomain protein, and intrinsic, domain-specific structural-functional constraints appear to be comparable in magnitude.

Conclusion

Fusion of domains in a multidomain protein results in substantial homogenization of the domain-specific evolutionary rates but significant differences between domain-specific evolution rates remain. Thus, the rate of translation and intrinsic structural-functional constraints both exert sizable and comparable effects on sequence evolution.

Reviewers

This article was reviewed by Sergei Maslov, Dennis Vitkup, Claus Wilke (nominated by Orly Alter), and Allan Drummond (nominated by Joel Bader). For the full reviews, please go to the Reviewers' Reports section.

An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer

Background

The most common application of microarray technology in disease research is to identify genes differentially expressed in disease versus normal tissues. However, it is known that, in complex diseases, phenotypes are determined not only by genes, but also by the underlying structure of genetic networks. Often, it is the interaction of many genes that causes phenotypic variations.

Results

In this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules. We propose an unsupervised method that take into account both co-expression dynamics and network topological information to simultaneously infer network modules and phenotype conditions in which they are activated or de-activated. Using our method, we have discovered network modules specific to cancer or subtypes of cancers. Many of these modules are consistent with or supported by their functional annotations or their previously known involvement in cancer. In particular, we identified a module that is predominately activated in breast cancer and is involved in tumor suppression. While individual components of this module have been suggested to be associated with tumor suppression, their coordinated function has never been elucidated. Here by adopting a network perspective, we have identified their interrelationships and, particularly, a hub gene PDGFRL that may play an important role in this tumor suppressor network.

Conclusion

Using a network-based approach, our method provides new insights into the complex cellular mechanisms that characterize cancer and cancer subtypes. By incorporating co-expression dynamics information, our approach can not only extract more functionally homogeneous modules than those based solely on network topology, but also reveal pathway coordination beyond co-expression.

Comprehensive detection of human terminal oligo-pyrimidine (TOP) genes and analysis of their characteristics

Although the knowledge accumulated on the transcriptional regulations of eukaryotes is significant, the knowledge on their translational regulations remains limited. Thus, we performed a comprehensive detection of terminal oligo-pyrimidine (TOP), which is one of the well-characterized cis-regulatory motifs for translational controls located immediately downstream of the transcriptional start sites of mRNAs. Utilizing our precise 5'-end information of the full-length cDNAs, we could screen 1645 candidate TOP genes by position specific matrix search. Among them, not only 75 out of 78 ribosomal protein genes but also eight previously identified non-ribosomal-protein TOP genes were included. We further experimentally validated the translational activities of 83 TOP candidate genes. Clear translational regulations exerted on the stimulation of 12-O-tetradecanoyl-1-phorbol-13-acetate for at least 41 of them was observed, indicating that there should be a few hundreds of human genes which are subjected to regulation at translation levels via TOPs. Our result suggests that TOP genes code not only formerly characterized ribosomal proteins and translation-related proteins but also a wider variety of proteins, such as lysosome-related proteins and metabolism-related proteins, playing pivotal roles in gene expression controls in the majority of cellular mRNAs.

Weak correlation between sequence conservation in promoter regions and in protein-coding regions of human-mouse orthologous gene pairs

Background

Interspecies sequence comparison is a powerful tool to extract functional or evolutionary information from the genomes of organisms. A number of studies have compared protein sequences or promoter sequences between mammals, which provided many insights into genomics. However, the correlation between protein conservation and promoter conservation remains controversial.

Results

We examined promoter conservation as well as protein conservation for 6,901 human and mouse orthologous genes, and observed a very weak correlation between them. We further investigated their relationship by decomposing it based on functional categories, and identified categories with significant tendencies. Remarkably, the 'ribosome' category showed significantly low promoter conservation, despite its high protein conservation, and the 'extracellular matrix' category showed significantly high promoter conservation, in spite of its low protein conservation.

Conclusion

Our results show the relation of gene function to protein conservation and promoter conservation, and revealed that there seem to be nonparallel components between protein and promoter sequence evolution.

Prediction of human disease genes by human-mouse conserved coexpression analysis

BACKGROUND

Even in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because the critical region may typically contain hundreds of positional candidates. Since genes implicated in similar phenotypes tend to share very similar expression profiles, high throughput gene expression data may represent a very important resource to identify the best candidates for sequencing. However, so far, gene coexpression has not been used very successfully to prioritize positional candidates.

METHODOLOGY/PRINCIPAL FINDINGS

We show that it is possible to reliably identify disease-relevant relationships among genes from massive microarray datasets by concentrating only on genes sharing similar expression profiles in both human and mouse. Moreover, we show systematically that the integration of human-mouse conserved coexpression with a phenotype similarity map allows the efficient identification of disease genes in large genomic regions. Finally, using this approach on 850 OMIM loci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases.

CONCLUSION

Our results demonstrate that conserved coexpression, even at the human-mouse phylogenetic distance, represents a very strong criterion to predict disease-relevant relationships among human genes.

Systems level analysis of osteoclastogenesis reveals intrinsic and extrinsic regulatory interactions

Osteoclasts are bone-resorbing cells derived from the myeloid lineage that play a central role in bone remodeling and inflammatory bone erosion diseases. The receptor activator of NF-kappaB ligand (RANKL) produced by osteoblasts and activated immune cells initiates the development of osteoclasts in the bone marrow. Using time series gene expression data, the intrinsic processes and the extrinsic factors that control osteoclastogenesis were identified. The gene expression profiles display distinct commitment and differentiation phases. Analysis of the time course revealed several mechanistic details, including the complex role of cholesterol in osteoclast development. Epistatic interactions and the coordination between cellular processes that regulate development were inferred from the coexpression network. The coexpression network indicated that osteoclasts induce angiogenesis and recruit T-cells to the site of osteoclastogenesis early in the commitment phase. The resulting model provides an essential framework for a better understanding of the epigenetic program of osteoclastogenesis.

A network of genes regulated by light in cyanobacteria

Oxygenic photosynthetic organisms require light for their growth and development. However, exposure to high light is detrimental to them. Using time series microarray data from a model cyanobacterium, Synechocystis 6803 transferred from low to high light, we generated a gene co-expression network. The network has twelve sub-networks connected hierarchically, each consisting of an interconnected hub-and-spoke architecture. Within each sub-network, edges formed between genes that recapitulate known pathways. Analysis of the expression profiles shows that the cells undergo a phase transition 6-hours post-shift to high light, characterized by core sub-network. The core sub-network is enriched in proteins that (putatively) bind Fe-S clusters and proteins that mediate iron and sulfate homeostasis. At the center of this core is a sulfate permease, suggesting sulfate is rate limiting for cells grown in high light. To validate this novel finding, we demonstrate the limited ability of cell growth in sulfate-depleted medium in high light. This study highlights how understanding the organization of the networks can provide insights into the coordination of physiologic responses.

Construction of a reference gene association network from multiple profiling data: application to data analysis

MOTIVATION

Gene expression profiling is an important tool for gaining insight into biology. Novel strategies are required to analyze the growing archives of microarray data and extract useful information from them. One area of interest is in the construction of gene association networks from collections of profiling data. Various approaches have been proposed to construct gene networks using profiling data, and these networks have been used in functional inference as well as in data visualization. Here, we investigated a non-parametric approach to translate profiling data into a gene network. We explored the characteristics and utility of the resulting network and investigated the use of network information in analysis of variance models and hypothesis testing.

RESULTS

Our work is composed of two parts: gene network construction and partitioning and hypothesis testing using sub-networks as groups. In the first part, multiple independently collected microarray datasets from the Gene Expression Omnibus data repository were analyzed to identify probe pairs that are positively co-regulated across the samples. A co-expression network was constructed based on a reciprocal ranking criteria and a false discovery rate analysis. We named this network Reference Gene Association (RGA) network. Then, the network was partitioned into densely connected sub-networks of probes using a multilevel graph partitioning algorithm. In the second part, we proposed a new, MANOVA-based approach that can take individual probe expression values as input and perform hypothesis testing at the sub-network level. We applied this MANOVA methodology to two published studies and our analysis indicated that the methodology is both effective and sensitive for identifying transcriptional sub-networks or pathways that are perturbed across treatments.

Detecting the coevolution of biosequences--an example of RNA interaction prediction

A probabilistic graphical model is proposed in order to detect the coevolution between different sites in biological sequences. The model extends the continuous-time Markov process of sequence substitution for single nucleic or amino acids and imposes general constraints regarding simultaneous changes on the substitution rate matrix. Given a multiple sequence alignment for each molecule of interest and a phylogenetic tree, the model can predict potential interactions within or between nucleic acids and proteins. Initial validation of the model is carried out using tRNA and 16S rRNA sequence data. The model accurately identifies the secondary interactions of tRNA as well as several known tertiary interactions. In addition, results on 16S rRNA data indicate this general and simple coevolutionary model outperforms several other parametric and nonparametric methods in predicting secondary interactions. Furthermore, the majority of the putative predictions exhibit either direct contact or proximity of the nucleotide pairs in the 3-dimensional structure of the Thermus thermophilus ribosomal small subunit. The results on RNA data suggest a general model of coevolution might be applied to other types of interactions between protein, DNA, and RNA molecules.

Evolutionarily conserved genes preferentially accumulate introns

Introns that interrupt eukaryotic protein-coding sequences are generally thought to be nonfunctional. However, for reasons still poorly understood, positions of many introns are highly conserved in evolution. Previous reconstructions of intron gain and loss events during eukaryotic evolution used a variety of simplified evolutionary models that yielded contradicting conclusions and are not suited to reveal some of the key underlying processes. We combine a comprehensive probabilistic model and an extended data set, including 391 conserved genes from 19 eukaryotes, to uncover previously unnoticed aspects of intron evolution--in particular, to assign intron gain and loss rates to individual genes. The rates of intron gain and loss in a gene show moderate positive correlation. A gene's intron gain rate shows a highly significant negative correlation with the coding-sequence evolution rate; intron loss rate also significantly, but positively, correlates with the sequence evolution rate. Correlations of the opposite signs, albeit less significant ones, are observed between intron gain and loss rates and gene expression level. It is proposed that intron evolution includes a neutral component, which is manifest in the positive correlation between the gain and loss rates and a selection-driven component as reflected in the links between intron gain and loss and sequence evolution. The increased intron gain and decreased intron loss in evolutionarily conserved genes indicate that intron insertion often might be adaptive, whereas some of the intron losses might be deleterious. This apparent functional importance of introns is likely to be due, at least in part, to their multiple effects on gene expression.

Evolution of the vertebrate twist family and synfunctionalization: a mechanism for differential gene loss through merging of expression domains

Twist genes are essential for embryonic development and are conserved from jellyfish to human. To study the vertebrate twist family and its evolution, the entire complement of twist genes was obtained for 9 representative species. Phylogenetic analysis showed that a single protochordate twist gene was duplicated at least twice before the teleost-tetrapod split to give rise to 3 ancestral genes, which were further duplicated or deleted, resulting in fluctuating number of twist paralogs in different vertebrate lineages. To find whether changes in gene copy number were associated with changes in gene function, embryonic expression patterns of twist orthologs were evaluated against the number of twist paralogs in different species. The results showed evidence for both neo- and subfunctionalization, and, in addition, for loss of an ancestral regulatory gene. For example, in Xenopus, twist2 was lost, but the twist1 paralog acquired, and therefore preserved, twist2 function. A general model is proposed to explain the data. In this process, termed synfunctionalization, one paralog acquires the expression domain(s) of another. The merging may lead to function shuffle. Alternatively, it may leave one paralog redundant and thus subject to deletion--while its function is retained by the surviving paralog(s). Synfunctionalization is a mechanism that, together with neo- and subfunctionalization, may work to establish equilibrium in the number of genes that regulate developmental processes; it may regulate the complexity of regulatory regions as well as gene copy number and therefore may play a role in evolution of gene function and the structure of genome.

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.

Conservation and evolution of gene coexpression networks in human and chimpanzee brains

Comparisons of gene expression between human and non-human primate brains have identified hundreds of differentially expressed genes, yet translating these lists into key functional distinctions between species has proved difficult. Here we provide a more integrated view of human brain evolution by examining the large-scale organization of gene coexpression networks in human and chimpanzee brains. We identify modules of coexpressed genes that correspond to discrete brain regions and quantify their conservation between the species. Module conservation in cerebral cortex is significantly weaker than module conservation in subcortical brain regions, revealing a striking gradient that parallels known evolutionary hierarchies. We introduce a method for identifying species-specific network connections and demonstrate how differential network connectivity can be used to identify key drivers of evolutionary change. By integrating our results with comparative genomic sequence data and estimates of protein sequence divergence rates, we confirm a number of network predictions and validate these findings. Our results provide insights into the molecular bases of primate brain organization and demonstrate the general utility of weighted gene coexpression network analysis.

Evolution of candidate transcriptional regulatory motifs since the human-chimpanzee divergence

BACKGROUND

Despite the recent completion of the chimpanzee genome project, few functionally significant sequence differences between humans and chimpanzees have thus far been identified. Alteration in transcriptional regulatory mechanisms represents an important platform for evolutionary change, suggesting that a significant proportion of functional human-chimpanzee sequence differences may affect regulatory elements.

RESULTS

To explore this hypothesis, we performed genome-wide identification of conserved candidate transcription-factor binding sites that have evolved since the divergence of humans and chimpanzees. Analysis of candidate transcription-factor binding sites conserved between mouse and chimpanzee yet absent in human indicated that loss of candidate transcription-factor binding sites in the human lineage was not random but instead correlated with the biologic functions of associated genes.

CONCLUSION

Our data support the notion that changes in transcriptional regulation have contributed to the recent evolution of humans. Moreover, genes associated with mutated candidate transcription-factor binding sites highlight potential pathways underlying human-chimpanzee divergence.

Global similarity and local divergence in human and mouse gene co-expression networks

BACKGROUND

A genome-wide comparative analysis of human and mouse gene expression patterns was performed in order to evaluate the evolutionary divergence of mammalian gene expression. Tissue-specific expression profiles were analyzed for 9,105 human-mouse orthologous gene pairs across 28 tissues. Expression profiles were resolved into species-specific coexpression networks, and the topological properties of the networks were compared between species.

RESULTS

At the global level, the topological properties of the human and mouse gene coexpression networks are, essentially, identical. For instance, both networks have topologies with small-world and scale-free properties as well as closely similar average node degrees, clustering coefficients, and path lengths. However, the human and mouse coexpression networks are highly divergent at the local level: only a small fraction (<10%) of coexpressed gene pair relationships are conserved between the two species. A series of controls for experimental and biological variance show that most of this divergence does not result from experimental noise. We further show that, while the expression divergence between species is genuinely rapid, expression does not evolve free from selective (functional) constraint. Indeed, the coexpression networks analyzed here are demonstrably functionally coherent as indicated by the functional similarity of coexpressed gene pairs, and this pattern is most pronounced in the conserved human-mouse intersection network. Numerous dense network clusters show evidence of dedicated functions, such as spermatogenesis and immune response, that are clearly consistent with the coherence of the expression patterns of their constituent gene members.

CONCLUSION

The dissonance between global versus local network divergence suggests that the interspecies similarity of the global network properties is of limited biological significance, at best, and that the biologically relevant aspects of the architectures of gene coexpression are specific and particular, rather than universal. Nevertheless, there is substantial evolutionary conservation of the local network structure which is compatible with the notion that gene coexpression networks are subject to purifying selection.

Evolutionary systems biology: links between gene evolution and function

The recent accumulation of genome-wide data on various facets of gene expression, function and evolution stimulated the emergence of a new field, evolutionary systems biology. Many significant correlations were detected between variables that characterize the functioning of a gene, such as expression level, knockout effect, connectivity of genetic and protein-protein interaction networks, and variables that describe gene evolution, such as sequence evolution rate and propensity for gene loss. The first attempts on multidimensional analysis of genomic data yielded composite variables that describe the 'status' of a gene in the genomic community. However, it remains uncertain whether different functional variables affect gene evolution synergistically or there is a single, dominant factor. The number of translation events, linked to selection for translational robustness, was proposed as a candidate for such a major determinant of protein evolution. These developments show that, although the methodological basis of evolutionary systems biology is not yet fully solidified, this area of research is already starting to yield fundamental biological insights.

Evolution of primate gene expression

It has been suggested that evolutionary changes in gene expression account for most phenotypic differences between species, in particular between humans and apes. What general rules can be described governing expression evolution? We find that a neutral model where negative selection and divergence time are the major factors is a useful null hypothesis for both transcriptome and genome evolution. Two tissues that stand out with regard to gene expression are the testes, where positive selection has exerted a substantial influence in both humans and chimpanzees, and the brain, where gene expression has changed less than in other organs but acceleration might have occurred in human ancestors.

Sharing of transcription factors after gene duplication in the yeast Saccharomyces cerevisiae

In a set of 190 duplicate gene pairs in yeast Saccharomyces cerevisiae, the sharing of transcription factors tended to decrease with increased divergence in coding sequence, at both synonymous and nonsynonymous sites. Our results showed a significantly higher sharing of transcription factors by duplicated gene pairs falling within duplicated genomic blocks than in other duplicated gene pairs; and genes in duplicated blocks also showed significantly greater conservation at the coding sequence level. In spite of the overall trends, there were certain gene pairs, both in duplicated blocks and in other genomic regions, which were highly divergent in coding sequence and yet had identical patterns of transcription factor binding. These results suggest that functional differentiation of genes after duplication is a multi-dimensional process, with different duplicate pairs differentiating in different ways.

Unifying measures of gene function and evolution

Recent genome analyses revealed intriguing correlations between variables characterizing the functioning of a gene, such as expression level (EL), connectivity of genetic and protein-protein interaction networks, and knockout effect, and variables describing gene evolution, such as sequence evolution rate (ER) and propensity for gene loss. Typically, variables within each of these classes are positively correlated, e.g. products of highly expressed genes also have a propensity to be involved in many protein-protein interactions, whereas variables between classes are negatively correlated, e.g. highly expressed genes, on average, evolve slower than weakly expressed genes. Here, we describe principal component (PC) analysis of seven genome-related variables and propose biological interpretations for the first three PCs. The first PC reflects a gene's 'importance', or the 'status' of a gene in the genomic community, with positive contributions from knockout lethality, EL, number of protein-protein interaction partners and the number of paralogues, and negative contributions from sequence ER and gene loss propensity. The next two PCs define a plane that seems to reflect the functional and evolutionary plasticity of a gene. Specifically, PC2 can be interpreted as a gene's 'adaptability' whereby genes with high adaptability readily duplicate, have many genetic interaction partners and tend to be non-essential. PC3 also might reflect the role of a gene in organismal adaptation albeit with a negative rather than a positive contribution of genetic interactions; we provisionally designate this PC 'reactivity'. The interpretation of PC2 and PC3 as measures of a gene's plasticity is compatible with the observation that genes with high values of these PCs tend to be expressed in a condition- or tissue-specific manner. Functional classes of genes substantially vary in status, adaptability and reactivity, with the highest status characteristic of the translation system and cytoskeletal proteins, highest adaptability seen in cellular processes and signalling genes, and top reactivity characteristic of metabolic enzymes.

An integrated view of protein evolution

Why do proteins evolve at different rates? Advances in systems biology and genomics have facilitated a move from studying individual proteins to characterizing global cellular factors. Systematic surveys indicate that protein evolution is not determined exclusively by selection on protein structure and function, but is also affected by the genomic position of the encoding genes, their expression patterns, their position in biological networks and possibly their robustness to mistranslation. Recent work has allowed insights into the relative importance of these factors. We discuss the status of a much-needed coherent view that integrates studies on protein evolution with biochemistry and functional and structural genomics.

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.

Multi-target strategies for the improved treatment of depressive states: Conceptual foundations and neuronal substrates, drug discovery and therapeutic application

Major depression is a debilitating and recurrent disorder with a substantial lifetime risk and a high social cost. Depressed patients generally display co-morbid symptoms, and depression frequently accompanies other serious disorders. Currently available drugs display limited efficacy and a pronounced delay to onset of action, and all provoke distressing side effects. Cloning of the human genome has fuelled expectations that symptomatic treatment may soon become more rapid and effective, and that depressive states may ultimately be "prevented" or "cured". In pursuing these objectives, in particular for genome-derived, non-monoaminergic targets, "specificity" of drug actions is often emphasized. That is, priority is afforded to agents that interact exclusively with a single site hypothesized as critically involved in the pathogenesis and/or control of depression. Certain highly selective drugs may prove effective, and they remain indispensable in the experimental (and clinical) evaluation of the significance of novel mechanisms. However, by analogy to other multifactorial disorders, "multi-target" agents may be better adapted to the improved treatment of depressive states. Support for this contention is garnered from a broad palette of observations, ranging from mechanisms of action of adjunctive drug combinations and electroconvulsive therapy to "network theory" analysis of the etiology and management of depressive states. The review also outlines opportunities to be exploited, and challenges to be addressed, in the discovery and characterization of drugs recognizing multiple targets. Finally, a diversity of multi-target strategies is proposed for the more efficacious and rapid control of core and co-morbid symptoms of depression, together with improved tolerance relative to currently available agents.

Protein evolutionary rates correlate with expression independently of synonymous substitutions in Helicobacter pylori

In free-living microorganisms, such as Escherichia coli and Saccharomyces cerevisiae, both synonymous and nonsynonymous substitution frequencies correlate with expression levels. Here, we have tested the hypothesis that the correlation between amino acid substitution rates and expression is a by-product of selection for codon bias and translational efficiency in highly expressed genes. To this end, we have examined the correlation between protein evolutionary rates and expression in the human gastric pathogen Helicobacter pylori, where the absence of selection on synonymous sites enables the two types of substitutions to be uncoupled. The results revealed a statistically significant negative correlation between expression levels and nonsynonymous substitutions in both H. pylori and E. coli. We also found that neighboring genes located on the same, but not on opposite strands, evolve at significantly more similar rates than random gene pairs, as expected by co-expression of genes located in the same operon. However, the two species differ in that synonymous substitutions show a strand-specific pattern in E. coli, whereas the weak similarity in synonymous substitutions for neighbors in H. pylori is independent of gene orientation. These results suggest a direct influence of expression levels on nonsynonymous substitution frequencies independent of codon bias and selective constraints on synonymous sites.

Uneven size distribution of mammalian genes in the number of tissues expressed and in the number of co-expressed genes

Tissue specificity, the traditional predictor of gene function, has recently been used to interpret the selective pressure associated with gene architecture. In this work, we examine gene structures and their relation to the number of tissues expressed and to the number of co-expressed genes, using a recent atlas of microarray-based mouse gene expression in 55 normal tissues. We define tissue specificity and expression-pattern specificity according to the number of tissues expressed and the number of co-expressed genes, respectively. We find that, consistent with previous findings, tissue non-specific (housekeeping) genes are short in all gene regions (coding regions, intron, 5' and 3' untranslated regions). However, in contrast to previous suggestion that tissue-specific genes are long, the genes that are the most tissue-specific (expressed only in one tissue) are also short. We further show that both expression-pattern-specific and non-specific genes are long in coding and non-coding regions. The origins for short tissue-specific genes and long expression-pattern-specific genes are not clear. Genes with highly non-specific expression patterns (i.e. genes with a large number of co-expressed genes) are composed of genes that spread all tissues but are overwhelmingly enriched in the central nervous system (e.g. brain). Thus, the large sizes of these genes are possibly related to the functional complexity and/or accelerated evolutions of the central nervous system.

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.

Asymmetrical Evolution of Cytochrome bd Subunits

Functionally linked genes generally evolve at similar rates and the knowledge of this particular feature of genomic evolution has been used as the basis for the phylogenetic profiling method. We illustrate here an exception to this rule in the evolution of the cytochrome bd complex. This is a two-component oxidase complex, with the subunits I and II known to be widely present in bacteria. The subunits within the cytochrome bd complex are under the same evolutionary pressure and most likely behave in the same evolutionary manner. However, the sequence similarity of genes encoding subunit II varies considerably across species. Genes encoding subunit II evolve 1.2 times faster on most of the branches of their phylogeny than subunit I genes. Furthermore, the genes encoding subunit II in Oceanobacillus iheyensis, Bacillus halodurans, and Staphylococcus species do not have detectable homologues within E. coli due to their large divergence. Together, the two subunits of cytochrome bd reveal an interesting example of an asymmetric pattern of evolutionary change.

Rapid and asymmetric divergence of duplicate genes in the human gene coexpression network

Background

While gene duplication is known to be one of the most common mechanisms of genome evolution, the fates of genes after duplication are still being debated. In particular, it is presently unknown whether most duplicate genes preserve (or subdivide) the functions of the parental gene or acquire new functions. One aspect of gene function, that is the expression profile in gene coexpression network, has been largely unexplored for duplicate genes.

Results

Here we build a human gene coexpression network using human tissue-specific microarray data and investigate the divergence of duplicate genes in it. The topology of this network is scale-free. Interestingly, our analysis indicates that duplicate genes rapidly lose shared coexpressed partners: after approximately 50 million years since duplication, the two duplicate genes in a pair have only slightly higher number of shared partners as compared with two random singletons. We also show that duplicate gene pairs quickly acquire new coexpressed partners: the average number of partners for a duplicate gene pair is significantly greater than that for a singleton (the latter number can be used as a proxy of the number of partners for a parental singleton gene before duplication). The divergence in gene expression between two duplicates in a pair occurs asymmetrically: one gene usually has more partners than the other one. The network is resilient to both random and degree-based in silico removal of either singletons or duplicate genes. In contrast, the network is especially vulnerable to the removal of highly connected genes when duplicate genes and singletons are considered together.

Conclusion

Duplicate genes rapidly diverge in their expression profiles in the network and play similar role in maintaining the network robustness as compared with singletons.

Contact: kdm16@psu.edu

Supplementary information: Please see additional files.

Gene expression omnibus: microarray data storage, submission, retrieval, and analysis

The Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information archives and freely distributes high-throughput molecular abundance data, predominantly gene expression data generated by DNA microarray technology. The database has a flexible design that can handle diverse styles of both unprocessed and processed data in a Minimum Information About a Microarray Experiment-supportive infrastructure that promotes fully annotated submissions. GEO currently stores about a billion individual gene expression measurements, derived from over 100 organisms, submitted by over 1500 laboratories, addressing a wide range of biological phenomena. To maximize the utility of these data, several user-friendly web-based interfaces and applications have been implemented that enable effective exploration, query, and visualization of these data at the level of individual genes or entire studies. This chapter describes how data are stored, submission procedures, and mechanisms for data retrieval and query. GEO is publicly accessible at http://www.ncbi.nlm.nih.gov/projects/geo/.

Evolutionary Conservation of Expression Profiles Between Human and Mouse Orthologous Genes

Mouse models are often used to study human genes because it is believed that the expression and function are similar for the majority of orthologous genes between the two species. However, recent comparisons of microarray data from thousands of orthologous human and mouse genes suggested rapid evolution of gene expression profiles under minimal or no selective constraint. These findings appear to contradict non-array-based observations from many individual genes and imply the uselessness of mouse models for studying human genes. Because absolute levels of gene expression are not comparable between species when the data are generated by species-specific microarrays, use of relative mRNA abundance among tissues (RA) is preferred to that of absolute expression signals. We thus reanalyze human and mouse genome-wide gene expression data generated by oligonucleotide microarrays. We show that the mean correlation coefficient among expression profiles detected by different probe sets of the same gene is only 0.38 for humans and 0.28 for mice, indicating that current measures of expression divergence are flawed because the large estimation error (discrepancy in expression signal detected by different probe sets of the same gene) is mistakenly included in the between-species divergence. When this error is subtracted, 84% of human-mouse orthologous gene pairs show significantly lower expression divergence than that of random gene pairs. In contrast to a previous finding, but consistent with the common sense, expression profiles of orthologous tissues between species are more similar to each other than to those of nonorthologous tissues. Furthermore, the evolutionary rate of expression divergence and that of coding sequence divergence are found to be weakly, but significantly positively correlated, when RA and the Euclidean distance are used to measure expression-profile divergence. These results highlight the importance of proper consideration of various estimation errors in comparing the microarray data between species.

Scale-freeness and biological networks

The notion of scale-freeness and its prevalence in both natural and artificial networks have recently attracted much attention. The concept of scale-freeness is enthusiastically applied to almost any conceivable network, usually with affirmative conclusions. Well-known scale-free examples include the internet, electric lines among power plants, the co-starring of movie actors, the co-authorship of researchers, food webs, and neural, protein-protein interactional, genetic, and metabolic networks. The purpose of this review is to clarify the relationship between scale-freeness and power-law distribution, and to assess critically the previous related works, especially on biological networks. In addition, I will focus on the close relationship between power-law distribution and lognormal distribution to show that power-law distribution is not a special characteristic of natural selection.

Parallel patterns of evolution in the genomes and transcriptomes of humans and chimpanzees

The determination of the chimpanzee genome sequence provides a means to study both structural and functional aspects of the evolution of the human genome. Here we compare humans and chimpanzees with respect to differences in expression levels and protein-coding sequences for genes active in brain, heart, liver, kidney, and testis. We find that the patterns of differences in gene expression and gene sequences are markedly similar. In particular, there is a gradation of selective constraints among the tissues so that the brain shows the least differences between the species whereas liver shows the most. Furthermore, expression levels as well as amino acid sequences of genes active in more tissues have diverged less between the species than have genes active in fewer tissues. In general, these patterns are consistent with a model of neutral evolution with negative selection. However, for X-chromosomal genes expressed in testis, patterns suggestive of positive selection on sequence changes as well as expression changes are seen. Furthermore, although genes expressed in the brain have changed less than have genes expressed in other tissues, in agreement with previous work we find that genes active in brain have accumulated more changes on the human than on the chimpanzee lineage.

Reverse engineering of regulatory networks in human B cells

Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genome-wide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new method called ARACNe (algorithm for the reconstruction of accurate cellular networks), we report the reconstruction of regulatory networks from expression profiles of human B cells. The results are suggestive a hierarchical, scale-free network, where a few highly interconnected genes (hubs) account for most of the interactions. Validation of the network against available data led to the identification of MYC as a major hub, which controls a network comprising known target genes as well as new ones, which were biochemically validated. The newly identified MYC targets include some major hubs. This approach can be generally useful for the analysis of normal and pathologic networks in mammalian cells.

Modularity and evolutionary constraint on proteins

Modularity, which has been found in the functional and physical protein interaction networks of many organisms, has been postulated to affect both the mode and tempo of evolution. Here I show that in the yeast Saccharomyces cerevisiae, protein interaction hubs situated in single modules are highly constrained, whereas those connecting different modules are more plastic. This pattern of change could reflect a tendency for evolutionary innovations to occur by altering the proteins and interactions between rather than within modules, in a manner somewhat similar to the evolution of new proteins through the shuffling of conserved protein domains.

Global properties of biological networks

This article discusses the most recent achievements in understanding the biological implications of the small-world and scale-free global topological properties of genetic, proteomic and metabolic networks. Most importantly, these networks are highly clustered and have small node-to-node distances. With their few very connected nodes, which are statistically unlikely to fail under random conditions, the proper functioning of these systems is maintained under external perturbations.

Evolutionary significance of gene expression divergence

Recent large-scale studies of evolutionary changes in gene expression among mammalian species have led to the proposal that gene expression divergence may be neutral with respect to organismic fitness. Here, we employ a comparative analysis of mammalian gene sequence divergence and gene expression divergence to test the hypothesis that the evolution of gene expression is predominantly neutral. Two models of neutral gene expression evolution are considered: 1-purely neutral evolution (i.e., no selective constraint) of gene expression levels and patterns and 2-neutral evolution accompanied by selective constraint. With respect to purely neutral evolution, levels of change in gene expression between human-mouse orthologs are correlated with levels of gene sequence divergence that are determined largely by purifying selection. In contrast, evolutionary changes of tissue-specific gene expression profiles do not show such a correlation with sequence divergence. However, divergence of both gene expression levels and profiles are significantly lower for orthologous human-mouse gene pairs than for pairs of randomly chosen human and mouse genes. These data clearly point to the action of selective constraint on gene expression divergence and are inconsistent with the purely neutral model; however, there is likely to be a neutral component in evolution of gene expression, particularly, in tissues where the expression of a given gene is low and functionally irrelevant. The model of neutral evolution with selective constraint predicts a regular, clock-like accumulation of gene expression divergence. However, relative rate tests of the divergence among human-mouse-rat orthologous gene sets reveal clock-like evolution for gene sequence divergence, and to a lesser extent for gene expression level divergence, but not for the divergence of tissue-specific gene expression profiles. Taken together, these results indicate that gene expression divergence is subject to the effects of purifying selective constraint and suggest that it might also be substantially influenced by positive Darwinian selection.