Relating tissue specialization to the differentiation of expression of singleton and duplicate mouse proteins

BACKGROUND: Gene duplications have been hypothesized to be a major factor in enabling the evolution of tissue differentiation. Analyses of the expression profiles of duplicate genes in mammalian tissues have indicated that, with time, the expression patterns of duplicate genes diverge and become more tissue specific. We explored the relationship between duplication events, the time at which they took place, and both the expression breadth of the duplicated genes and the cumulative expression breadth of the gene family to which they belong. RESULTS: We show that only duplicates that arose through post-multicellularity duplication events show a tendency to become more specifically expressed, whereas such a tendency is not observed for duplicates that arose in a unicellular ancestor. Unlike the narrow expression profile of the duplicated genes, the overall expression of gene families tends to maintain a global expression pattern. CONCLUSION: The work presented here supports the view suggested by the subfunctionalization model, namely that expression divergence in different tissues, following gene duplication, promotes the retention of a gene in the genome of multicellular species. The global expression profile of the gene families suggests division of expression between family members, whose expression becomes specialized. Because specialization of expression is coupled with an increased rate of sequence divergence, it can facilitate the evolution of new, tissue-specific functions

Measuring genome conservation across taxa: divided strains and united kingdoms

Species evolutionary relationships have traditionally been defined by sequence similarities of phylogenetic marker molecules, recently followed by whole-genome phylogenies based on gene order, average ortholog similarity or gene content. Here, we introduce genome conservation—a novel metric of evolutionary distances between species that simultaneously takes into account, both gene content and sequence similarity at the whole-genome level. Genome conservation represents a robust distance measure, as demonstrated by accurate phylogenetic reconstructions. The genome conservation matrix for all presently sequenced organisms exhibits a remarkable ability to define evolutionary relationships across all taxonomic ranges. An assessment of taxonomic ranks with genome conservation shows that certain ranks are inadequately described and raises the possibility for a more precise and quantitative taxonomy in the future. All phylogenetic reconstructions are available at the genome phylogeny server: <>

Emergence, development and diversification of the TGF-β signalling pathway within the animal kingdom

<p>Abstract</p> <p>Background</p> <p>The question of how genomic processes, such as gene duplication, give rise to co-ordinated organismal properties, such as emergence of new body plans, organs and lifestyles, is of importance in developmental and evolutionary biology. Herein, we focus on the diversification of the transforming growth factor-<it>β </it>(TGF-<it>β</it>) pathway – one of the fundamental and versatile metazoan signal transduction engines.</p> <p>Results</p> <p>After an investigation of 33 genomes, we show that the emergence of the TGF-<it>β </it>pathway coincided with appearance of the first known animal species. The primordial pathway repertoire consisted of four Smads and four receptors, similar to those observed in the extant genome of the early diverging tablet animal (<it>Trichoplax adhaerens</it>). We subsequently retrace duplications in ancestral genomes on the lineage leading to humans, as well as lineage-specific duplications, such as those which gave rise to novel Smads and receptors in teleost fishes. We conclude that the diversification of the TGF-<it>β </it>pathway can be parsimoniously explained according to the 2R model, with additional rounds of duplications in teleost fishes. Finally, we investigate duplications followed by accelerated evolution which gave rise to an atypical TGF-<it>β </it>pathway in free-living bacterial feeding nematodes of the genus Rhabditis.</p> <p>Conclusion</p> <p>Our results challenge the view of well-conserved developmental pathways. The TGF-<it>β </it>signal transduction engine has expanded through gene duplication, continually adopting new functions, as animals grew in anatomical complexity, colonized new environments, and developed an active immune system.</p

Expansion of the BioCyc collection of pathway/genome databases to 160 genomes

The BioCyc database collection is a set of 160 pathway/genome databases (PGDBs) for most eukaryotic and prokaryotic species whose genomes have been completely sequenced to date. Each PGDB in the BioCyc collection describes the genome and predicted metabolic network of a single organism, inferred from the MetaCyc database, which is a reference source on metabolic pathways from multiple organisms. In addition, each bacterial PGDB includes predicted operons for the corresponding species. The BioCyc collection provides a unique resource for computational systems biology, namely global and comparative analyses of genomes and metabolic networks, and a supplement to the BioCyc resource of curated PGDBs. The Omics viewer available through the BioCyc website allows scientists to visualize combinations of gene expression, proteomics and metabolomics data on the metabolic maps of these organisms. This paper discusses the computational methodology by which the BioCyc collection has been expanded, and presents an aggregate analysis of the collection that includes the range of number of pathways present in these organisms, and the most frequently observed pathways. We seek scientists to adopt and curate individual PGDBs within the BioCyc collection. Only by harnessing the expertise of many scientists we can hope to produce biological databases, which accurately reflect the depth and breadth of knowledge that the biomedical research community is producing

Denoising inferred functional association networks obtained by gene fusion analysis.

BACKGROUND: Gene fusion detection - also known as the 'Rosetta Stone' method - involves the identification of fused composite genes in a set of reference genomes, which indicates potential interactions between its un-fused counterpart genes in query genomes. The precision of this method typically improves with an ever-increasing number of reference genomes. RESULTS: In order to explore the usefulness and scope of this approach for protein interaction prediction and generate a high-quality, non-redundant set of interacting pairs of proteins across a wide taxonomic range, we have exhaustively performed gene fusion analysis for 184 genomes using an efficient variant of a previously developed protocol. By analyzing interaction graphs and applying a threshold that limits the maximum number of possible interactions within the largest graph components, we show that we can reduce the number of implausible interactions due to the detection of promiscuous domains. With this generally applicable approach, we generate a robust set of over 2 million distinct and testable interactions encompassing 696,894 proteins in 184 species or strains, most of which have never been the subject of high-throughput experimental proteomics. We investigate the cumulative effect of increasing numbers of genomes on the fidelity and quantity of predictions, and show that, for large numbers of genomes, predictions do not become saturated but continue to grow linearly, for the majority of the species. We also examine the percentage of component (and composite) proteins with relation to the number of genes and further validate the functional categories that are highly represented in this robust set of detected genome-wide interactions. CONCLUSION: We illustrate the phylogenetic and functional diversity of gene fusion events across genomes, and their usefulness for accurate prediction of protein interaction and function

Construction, visualisation, and clustering of transcription networks from microarray expression data.

Network analysis transcends conventional pairwise approaches to data analysis as the context of components in a network graph can be taken into account. Such approaches are increasingly being applied to genomics data, where functional linkages are used to connect genes or proteins. However, while microarray gene expression datasets are now abundant and of high quality, few approaches have been developed for analysis of such data in a network context. We present a novel approach for 3-D visualisation and analysis of transcriptional networks generated from microarray data. These networks consist of nodes representing transcripts connected by virtue of their expression profile similarity across multiple conditions. Analysing genome-wide gene transcription across 61 mouse tissues, we describe the unusual topography of the large and highly structured networks produced, and demonstrate how they can be used to visualise, cluster, and mine large datasets. This approach is fast, intuitive, and versatile, and allows the identification of biological relationships that may be missed by conventional analysis techniques. This work has been implemented in a freely available open-source application named BioLayout Express(3D)

Stratification of co-evolving genomic groups using ranked phylogenetic profiles

<p>Abstract</p> <p>Background</p> <p>Previous methods of detecting the taxonomic origins of arbitrary sequence collections, with a significant impact to genome analysis and in particular metagenomics, have primarily focused on compositional features of genomes. The evolutionary patterns of phylogenetic distribution of genes or proteins, represented by phylogenetic profiles, provide an alternative approach for the detection of taxonomic origins, but typically suffer from low accuracy. Herein, we present <it>rank-BLAST</it>, a novel approach for the assignment of protein sequences into genomic groups of the same taxonomic origin, based on the ranking order of phylogenetic profiles of target genes or proteins across the reference database.</p> <p>Results</p> <p>The rank-BLAST approach is validated by computing the phylogenetic profiles of all sequences for five distinct microbial species of varying degrees of phylogenetic proximity, against a reference database of 243 fully sequenced genomes. The approach - a combination of sequence searches, statistical estimation and clustering - analyses the degree of sequence divergence between sets of protein sequences and allows the classification of protein sequences according to the species of origin with high accuracy, allowing taxonomic classification of 64% of the proteins studied. In most cases, a main cluster is detected, representing the corresponding species. Secondary, functionally distinct and species-specific clusters exhibit different patterns of phylogenetic distribution, thus flagging gene groups of interest. Detailed analyses of such cases are provided as examples.</p> <p>Conclusion</p> <p>Our results indicate that the rank-BLAST approach can capture the taxonomic origins of sequence collections in an accurate and efficient manner. The approach can be useful both for the analysis of genome evolution and the detection of species groups in metagenomics samples.</p

CORRIE: enzyme sequence annotation with confidence estimates

Using a previously developed automated method for enzyme annotation, we report the re-annotation of the ENZYME database and the analysis of local error rates per class. In control experiments, we demonstrate that the method is able to correctly re-annotate 91% of all Enzyme Classification (EC) classes with high coverage (755 out of 827). Only 44 enzyme classes are found to contain false positives, while the remaining 28 enzyme classes are not represented. We also show cases where the re-annotation procedure results in partial overlaps for those few enzyme classes where a certain inconsistency might appear between homologous proteins, mostly due to function specificity. Our results allow the interactive exploration of the EC hierarchy for known enzyme families as well as putative enzyme sequences that may need to be classified within the EC hierarchy. These aspects of our framework have been incorporated into a web-server, called CORRIE, which stands for Correspondence Indicator Estimation and allows the interactive prediction of a functional class for putative enzymes from sequence alone, supported by probabilistic measures in the context of the pre-calculated Correspondence Indicators of known enzymes with the functional classes of the EC hierarchy. The CORRIE server is available at:

The net of life: Reconstructing the microbial phylogenetic network

It has previously been suggested that the phylogeny of microbial species might be better described as a network containing vertical and horizontal gene transfer (HGT) events. Yet, all phylogenetic reconstructions so far have presented microbial trees rather than networks. Here, we present a first attempt to reconstruct such an evolutionary network, which we term the “net of life.” We use available tree reconstruction methods to infer vertical inheritance, and use an ancestral state inference algorithm to map HGT events on the tree. We also describe a weighting scheme used to estimate the number of genes exchanged between pairs of organisms. We demonstrate that vertical inheritance constitutes the bulk of gene transfer on the tree of life. We term the bulk of horizontal gene flow between tree nodes as “vines,” and demonstrate that multiple but mostly tiny vines interconnect the tree. Our results strongly suggest that the HGT network is a scale-free graph, a finding with important implications for genome evolution. We propose that genes might propagate extremely rapidly across microbial species through the HGT network, using certain organisms as hubs