The comparative genomics of T-box genes.

T-box genes are defined by the presence of a conserved sequence, the so-called T-box; this codes for the T-domain, which is involved in DNA-binding and protein dimerisation. Members of this gene family have been found in all metazoans, from diploblasts to humans, and mutations in T-box gene family members in humans have been linked to several congenital disorders. Sequencing of the complete genomes of a range of invertebrate and vertebrate species has allowed the classification of individual T-box genes into five subfamilies: Brachyury, T-brain1, Tbx1, Tbx2 and Tbx6. This review will largely focus on T-box genes identified in organisms whose genomes have been fully sequenced, emphasising how comparative studies of the T-box gene family will help to reveal the roles of these genes during development and in the adult.     

Identification of functional links between genes using phylogenetic profiles

MOTIVATION: Genes with identical patterns of occurrence across the phyla tend to function together in the same protein complexes or participate in the same biochemical pathway. However, the requirement that the profiles be identical (i) severely restricts the number of functional links that can be established by such phylogenetic profiling; (ii) limits detection to very strong functional links, failing to capture relations between genes that are not in the same pathway, but nevertheless subserve a common function and (iii) misses relations between analogous genes. Here we present and apply a method for relaxing the restriction, based on the probability that a given arbitrary degree of similarity between two profiles would occur by chance, with no biological pressure. Function is then inferred at any desired level of confidence. RESULTS: We derive an expression for the probability distribution of a given number of chance co-occurrences of a pair of non-homologous orthologs across a set of genomes. The method is applied to 2905 clusters of orthologous genes (COGs) from 44 fully sequenced microbial genomes representing all three domains of life. Among the results are the following. (1) Of the 51 000 annotated intrapathway gene pairs, 8935 are linked at a level of significance of 0.01. This is over 30-fold greater than the 271 intrapathway pairs obtained at the same confidence level when identical profiles are used. (2) Of the 540 000 interpathway genes pairs, some 65 000 are linked at the 0.01 level of significance, some 12 standard deviations beyond the number expected by chance at this confidence level. We speculate that many of these links involve nearest-neighbor path, and discuss some examples. (3) The difference in the percentage of linked interpathway and intrapathway genes is highly significant, consistent with the intuitive expectation that genes in the same pathway are generally under greater selective pressure than those that are not. (4) The method appears to recover well metabolic networks. This is illustrated by the TCA cycle which is recovered as a highly connected, weighted edge network of 30 of its 31 COGs. (5) The fraction of pairs having a common pathway is a symmetric function of the Hamming distance between their profiles. This finding, that the functional correlation between profiles with near maximum Hamming distance is as large as between profiles with near zero Hamming distance, and as statistically significant, is plausibly explained if the former group represents analogous genes.     

Identifying functional links between genes using conserved chromosomal proximity

Conservation of proximity of a pair of genes across multiple genomes generally indicates that their functions could be linked. Here, we present a systematic evaluation using 42 complete microbial genomes from 25 phylogenetic groups to test the reliability of this observation in predicting function for genes. We find a relationship between the number of phylogenetic groups in which a gene pair is proximate and the probability that the pair belongs to a common pathway. Our method produces 1586 links between ortholog families substantiated by observed proximity in genomes representing at least three phylogenetic groups. Of the pairs annotated in the KEGG database, 80% are in the same biological pathway in KEGG.     

Identification of functional differences in metabolic networks using comparative genomics and constraint-based models

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here.     

Missing genes in metabolic pathways: a comparative genomics approach

The new techniques of genome context analysis--chromosomal gene clustering, protein fusions, occurrence profiles and shared regulatory sites--infer functional coupling between genes. In combination with metabolic reconstructions, these techniques can dramatically accelerate the pace of gene discovery.     

The functional links between prion protein and copper

Prion diseases are fatal neurodegenerative disorders associated with the conversion of the cellular prion protein (PrPC) into a pathologic isoform. Although the physiological function of PrPC remains unknown, evidence relates PrPC to copper metabolism and oxidative stress as suggested by its copper-binding properties in the N-terminal octapeptide repeat region. This region also reduces copper ions in vitro, and this reduction ability is associated with the neuroprotection exerted by the octarepeat region against copper in vivo. In addition, the promoter region of the PrPC gene contains putative metal response elements suggesting it may be regulated by heavy metals. Here we address some of the evidence that support a physiological link between PrPC and copper. Also, in vivo experiments suggesting the physiological relevance of PrPC interaction with heparan sulfate proteoglycans are discussed.     

Bacterial endosymbionts of insects: insights from comparative genomics

The development of molecular techniques for the study of uncultured bacteria allowed the extensive study of the widespread association between insects and intracellular symbiotic bacteria. Most of the bacterial endosymbionts involved in such associations are gamma-proteobacteria, closely related to Escherichia coli. In recent years, five genomes from insect endosymbionts have been sequenced, allowing the performance of extensive genome comparative analysis that, as a complement of phylogenetic studies, and analysis on individual genes, can help to understand the different traits of this particular association, including how the symbiotic process is established, the explanation of the special features of these microbial genomes, the bases of this intimate association and the possible future that awaits the endosymbionts with extremely reduced genomes.     

Identification of metastasis genes by a functional genomics approach

Comment on: Gumireddy K, et al. Nat Cell Biol 2009; 11:1297-304.     

Predictome: a database of putative functional links between proteins

The current deluge of genomic sequences has spawned the creation of tools capable of making sense of the data. Computational and high-throughput experimental methods for generating links between proteins have recently been emerging. These methods effectively act as hypothesis machines, allowing researchers to screen large sets of data to detect interesting patterns that can then be studied in greater detail. Although the potential use of these putative links in predicting gene function has been demonstrated, a central repository for all such links for many genomes would maximize their usefulness. Here we present Predictome, a database of predicted links between the proteins of 44 genomes based on the implementation of three computational methods—chromosomal proximity, phylogenetic profiling and domain fusion—and large-scale experimental screenings of protein–protein interaction data. The combination of data from various predictive methods in one database allows for their comparison with each other, as well as visualization of their correlation with known pathway information. As a repository for such data, Predictome is an ongoing resource for the community, providing functional relationships among proteins as new genomic data emerges. Predictome is available at http://predictome.bu.edu.     

Identification of genes required for neural-specific glycosylation using functional genomics

Glycosylation plays crucial regulatory roles in various biological processes such as development, immunity, and neural functions. For example, α1,3-fucosylation, the addition of a fucose moiety abundant in Drosophila neural cells, is essential for neural development, function, and behavior. However, it remains largely unknown how neural-specific α1,3-fucosylation is regulated. In the present study, we searched for genes involved in the glycosylation of a neural-specific protein using a Drosophila RNAi library. We obtained 109 genes affecting glycosylation that clustered into nine functional groups. Among them, members of the RNA regulation group were enriched by a secondary screen that identified genes specifically regulating α1,3-fucosylation. Further analyses revealed that an RNA-binding protein, second mitotic wave missing (Swm), upregulates expression of the neural-specific glycosyltransferase FucTA and facilitates its mRNA export from the nucleus. This first large-scale genetic screen for glycosylation-related genes has revealed novel regulation of fucTA mRNA in neural cells.     

Evolution of prokaryotic two-component systems: insights from comparative genomics

Two-component systems (TCSs) are diverse and abundant signal transduction pathways found predominantly in prokaryotes. This review focuses on insights into TCS evolution made possible by the sequencing of whole prokaryotic genomes. Typical TCSs comprise an autophosphorylating protein (a histidine kinase), which transfers a phosphoryl group onto an effector protein (a response regulator), thus modulating its activity. Histidine kinases and response regulators are usually found encoded as pairs of adjacent genes within a genome, with multiple examples in most prokaryotes. Recent studies have shed light on major themes of TCS evolution, including gene duplication, gene gain/loss, gene fusion/fission, domain gain/loss, domain shuffling and the emergence of complexity. Coupled with an understanding of the structural and biophysical properties of many TCS proteins, it has become increasingly possible to draw inferences regarding the functional consequences of such evolutionary changes. In turn, this increase in understanding has the potential to enhance both our ability to rationally engineer TCSs, and also allow us to more powerfully correlate TCS evolution with behavioural phenotypes and ecological niche occupancy.     

从基因组学到功能蛋白质组学的研究

人类基因组草图绘制的完成,标志着生命科学已实质性地跨入了后基因组时代,研究重心已从揭示生命的所有遗传信息转移到在分子整体水平对功能的研究[1]。这种转向表明目前已进入功能基因组学(functional genom ics)以及随之产生的功能蛋白质组学(functional proteomics)等新学科领域的研究。     

Identifying directed links in large scale functional networks: application to brain fMRI

Background

Biological experiments increasingly yield data representing large ensembles of interacting variables, making the application of advanced analytical tools a forbidding task. We present a method to extract networks of correlated activity, specifically from functional MRI data, such that: (a) network nodes represent voxels, and (b) the network links can be directed or undirected, representing temporal relationships between the nodes. The method provides a snapshot of the ongoing dynamics of the brain without sacrificing resolution, as the analysis is tractable even for very large numbers of voxels.

Results

We find that, based on topological properties of the networks, the method provides enough information about the dynamics to discriminate between subtly different brain states. Moreover, the statistical regularities previously reported are qualitatively preserved, i.e. the resulting networks display scale-free and small-world topologies.

Conclusion

Our method expands previous approaches to render large scale functional networks, and creates the basis for an extensive and -due to the presence of mixtures of directed and undirected links- richer motif analysis of functional relationships.