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A computational method is proposed for inferring protein interactions from genome sequences on the basis of the observation that some pairs of interacting proteins have homologs in another organism fused into a single protein chain. Searching sequences from many genomes revealed 6809 such putative protein-protein interactions in Escherichia coli and 45,502 in yeast. Many members of these pairs were confirmed as functionally related; computational filtering further enriches for interactions. Some proteins have links to several other proteins; these coupled links appear to represent functional interactions such as complexes or pathways. Experimentally confirmed interacting pairs are documented in a Database of Interacting Proteins.

Background Phylogenetic methods are well-established bioinformatic tools for sequence analysis, allowing to describe the non-independencies of sequences because of their common ancestor. However, the evolutionary profiles of bacterial genes are often complicated by hidden paralogy and extensive and/or (multiple) horizontal gene transfer (HGT) events which make bifurcating trees often inappropriate. In this context, plasmid sequences are paradigms of network-like relationships characterizing the evolution of prokaryotes. Actually, they can be transferred among different organisms allowing the dissemination of novel functions, thus playing a pivotal role in prokaryotic evolution. However, the study of their evolutionary dynamics is complicated by the absence of universally shared genes, a prerequisite for phylogenetic analyses. Results To overcome such limitations we developed a bioinformatic package, named Blast2Network (B2N), allowing the automatic phylogenetic profiling and the visualization of homology relationships in a large number of plasmid sequences. The software was applied to the study of 47 completely sequenced plasmids coming from Escherichia, Salmonella and Shigella spps. Conclusion The tools implemented by B2N allow to describe and visualize in a new way some of the evolutionary features of plasmid molecules of Enterobacteriaceae; in particular it helped to shed some light on the complex history of Escherichia, Salmonella and Shigella plasmids and to focus on possible roles of unannotated proteins. The proposed methodology is general enough to be used for comparative genomic analyses of bacteria.

In the past few years, great efforts have been made on the improvement of phylogenetic profile method. Most of the researches focused on the selection of reference genome, and so far as we know, no published literature proposed a weight-based phylogenetic profile. In this study, we improved phylogenetic profile method in two aspects: one was to construct weight-based profiles for target proteins; the other was to combine hierarchical cluster method and K-means algorithm to analyze similarity among profiles. Our method make the construction of phylogenetic profile independent on the selection of reference genome, and the results indicated that our refined method performed a better performance.

Conserved domains carry many of the functional features found in the proteins of an organism. This includes not only catalytic activity, substrate binding, and structural features but also molecular adapters, which mediate the physical interactions between proteins or proteins with other molecules. In addition, two conserved domains can be linked not by physical contact but by a common function like forming a binding pocket. Although a wealth of experimental data has been collected and carefully curated for protein-protein interactions, as of today little useful data is available from major databases with respect to relations on the domain level. This lack of data makes computational prediction of domain-domain interactions a very important endeavor. In this chapter, we discuss the available experimental data (iPfam) and describe some important approaches to the problem of identifying interacting and/or functionally linked domain pairs from different kinds of input data. Specifically, we will discuss phylogenetic profiling on the level of conserved protein domains on one hand and inference of domain-interactions from observed or predicted protein-protein interactions datasets on the other. We explore the predictive power of these predictions and point out the importance of deploying as many different methods as possible for the best results.

Background As the number of fully sequenced genome increases, the need is greater for bioinformatics to predict or annotate genes of a newly sequenced genome. Ever since Eisenberg and his colleagues introduced phylogenetic profiling for assigning or predicting protein functions using comparative genomic analysis, the approach has been used in predicting function of some prokaryotic genomes quite successfully. Very little work has been reported in functional prediction of eukaryotes such as mouse and Homo sapiens species from phylogenetic profiles. Results We have proposed a general methodology for validating the hypothesis underlying phylogenetic profiling techniques, and have demonstrated it using eukaryotic target genomes such as Homo sapiens and mouse. The gene ontology is used as the gold standard for validating functional similarity among the genes in each cluster. We compute the functional cohesiveness of each cluster and the results appeared to be not encouraging towards finding functionally cohesive phylogenetic profiles. This result complements one recent work on the poor performance on functional linkage in some eukaryotic genome using phylogenetic profiling techniques. If we introduce a broad interpretation for functionally related genes as functional sub-clustering within a phylogenetic profile, then we have a very strong support for the hypothesis as we have shown in the paper.

We have developed a computational technique refereed to as complex phylogenetic profiling. Our approach combines logic analyses of gene phylogenetic profiles and phenotype data. Logic analysis of phylogenetic profiles identifies sets of proteins whose presence or absence follows certain logic relationships. Our approach identifies phenotype specific logic, i.e. it identifies sets of proteins simultaneously present or absent only in genomes with a given phenotype. For example, for most genomes expressing phenotype A, the presence of protein C presumes the presence of protein B, while for other genomes (not expressing phenotype A) the presence of protein C presumes the absence of protein B. Application of complex phylogenetic profiling to bacterial data and several well studied phenotypes reveals genotype-phenotype associations on the level of fundamental biochemical pathways.

Mycolic acids, which render unique qualities to mycobacteria, are known to be important for mycobacterial growth, survival, and pathogenicity. It is of interest to understand the evolutionary origins of the mycolic acid pathway (MAP), as well as the common minimum principles critical for generating the capability of mycolic acid biosynthesis. The recent curation of a comprehensive model of the MAP in Mycobacterium tuberculosis and the availability of a large number of genome sequences make it feasible to carry out detailed sequence and phylogenetic analyses, to address these questions. A comprehensive phylogenetic pathway profile analysis was carried out for 318 fully sequenced bacterial genomes, for each of the proteins present in the MAP. The organisms were clustered on the basis of co-occurrence of the MAP proteins in their proteome, while the proteins were clustered on the basis of their phylogenetic profiles. The MAP proteins were also searched against the nonredundant sequence database, to identify similar proteins from other phyla. The pathway profiles indicate that four proteins and certain protein domains stand out as more characteristic to mycolate producing organisms. Further analysis leads to the identification of the desaturases DesA1 and DesA2 and certain domains of Fas and Pks13 as hallmarks of the pathway. The roles of these proteins in some other organisms, as well as the distribution of these proteins across all known genome sequences are also briefly discussed. The clustering of organisms, carried out to group organisms with similar profiles, provides a means of obtaining finer classification as compared to the standard taxonomic method. The results indicate that the MAP and hence the capacity of mycolic acid production in mycobacteria is an example of an emergent property that has come about by recruiting enzymes from unrelated pathways in plants, presumably through lateral gene transfer. The understanding of the hallmarks of mycolic acid biosynthesis will also find application in evaluating drug targets.

Background A widely-used approach for discovering functional and physical interactions among proteins involves phylogenetic profile comparisons (PPCs). Here, proteins with similar profiles are inferred to be functionally related under the assumption that proteins involved in the same metabolic pathway or cellular system are likely to have been co-inherited during evolution. Results Our experimentation with E. coli and yeast proteins with 16 different carefully composed reference sets of genomes revealed that the phyletic patterns of proteins in prokaryotes alone could be adequate enough to make reasonably accurate functional linkage predictions. A slight improvement in performance is observed on adding few eukaryotes into the reference set, but a noticeable drop-off in performance is observed with increased number of eukaryotes. Inclusion of most parasitic, pathogenic or vertebrate genomes and multiple strains of the same species into the reference set do not necessarily contribute to an improved sensitivity or accuracy. Interestingly, we also found that evolutionary histories of individual pathways have a significant affect on the performance of the PPC approach with respect to a particular reference set. For example, to accurately predict functional links in carbohydrate or lipid metabolism, a reference set solely composed of prokaryotic (or bacterial) genomes performed among the best compared to one composed of genomes from all three super-kingdoms; this is in contrast to predicting functional links in translation for which a reference set composed of prokaryotic (or bacterial) genomes performed the worst. We also demonstrate that the widely used random null model to quantify the statistical significance of profile similarity is incomplete, which could result in an increased number of false-positives. Conclusion Contrary to previous proposals, it is not merely the number of genomes but a careful selection of informative genomes in the reference set that influences the prediction accuracy of the PPC approach. We note that the predictive power of the PPC approach, especially in eukaryotes, is heavily influenced by the primary endosymbiosis and subsequent bacterial contributions. The over-representation of parasitic unicellular eukaryotes and vertebrates additionally make eukaryotes less useful in the reference sets. Reference sets composed of highly non-redundant set of genomes from all three super-kingdoms fare better with pathways showing considerable vertical inheritance and strong conservation (e.g. translation apparatus), while reference sets solely composed of prokaryotic genomes fare better for more variable pathways like carbohydrate metabolism. Differential performance of the PPC approach on various pathways, and a weak positive correlation between functional and profile similarities suggest that caution should be exercised while interpreting functional linkages inferred from genome-wide large-scale profile comparisons using a single reference set.

Phylogenetic profiling of amino acid substitution patterns in proteins has led many to conclude that most structural information is carried by interior core residues that are solvent inaccessible. This conclusion is based on the observation that buried residues generally tolerate only conserved sequence changes, while surface residues allow more diverse chemical substitutions. This notion is now changing as it has become apparent that both core and surface residues play important roles in protein folding and stability. Unfortunately, the ability to identify specific mutations that will lead to enhanced stability remains a challenging problem. Here we discuss two mutations that emerged from an in vitro selection experiment designed to improve the folding stability of a non-biological ATP binding protein. These mutations alter two solvent accessible residues, and dramatically enhance the expression, solubility, thermal stability, and ligand binding affinity of the protein. The significance of both mutations was investigated individually and together, and the X-ray crystal structures of the parent sequence and double mutant protein were solved to a resolution limit of 2.8 and 1.65 A, respectively. Comparative structural analysis of the evolved protein to proteins found in nature reveals that our non-biological protein evolved certain structural features shared by many thermophilic proteins. This experimental result suggests that protein fold optimization by in vitro selection offers a viable approach to generating stable variants of many naturally occurring proteins whose structures and functions are otherwise difficult to study.

Background Current advances in genomics, proteomics and other areas of molecular biology make the identification and reconstruction of novel pathways an emerging area of great interest. One such class of pathways is involved in the biogenesis of Iron-Sulfur Clusters (ISC). Results Our goal is the development of a new approach based on the use and combination of mathematical, theoretical and computational methods to identify the topology of a target network. In this approach, mathematical models play a central role for the evaluation of the alternative network structures that arise from literature data-mining, phylogenetic profiling, structural methods, and human curation. As a test case, we reconstruct the topology of the reaction and regulatory network for the mitochondrial ISC biogenesis pathway in S. cerevisiae. Predictions regarding how proteins act in ISC biogenesis are validated by comparison with published experimental results. For example, the predicted role of Arh1 and Yah1 and some of the interactions we predict for Grx5 both matches experimental evidence. A putative role for frataxin in directly regulating mitochondrial iron import is discarded from our analysis, which agrees with also published experimental results. Additionally, we propose a number of experiments for testing other predictions and further improve the identification of the network structure. Conclusion We propose and apply an iterative in silico procedure for predictive reconstruction of the network topology of metabolic pathways. The procedure combines structural bioinformatics tools and mathematical modeling techniques that allow the reconstruction of biochemical networks. Using the Iron Sulfur cluster biogenesis in S. cerevisiae as a test case we indicate how this procedure can be used to analyze and validate the network model against experimental results. Critical evaluation of the obtained results through this procedure allows devising new wet lab experiments to confirm its predictions or provide alternative explanations for further improving the models.

Contains fulltext : 36341.pdf (Publisher’s version ) (Closed access) The gap between the amount of genome information released by genome sequencing projects and our knowledge about the proteins' functions is rapidly increasing. To fill this gap, various 'genomic-context' methods have been proposed that exploit sequenced genomes to predict the functions of the encoded proteins. One class of methods, phylogenetic profiling, predicts protein function by correlating the phylogenetic distribution of genes with that of other genes or phenotypic characteristics. The functions of a number of proteins, including ones of medical relevance, have thus been predicted and subsequently confirmed experimentally. Additionally, various approaches to measure the similarity of phylogenetic profiles and to account for the phylogenetic bias in the data have been proposed. We review the successful applications of phylogenetic profiling and analyse the performance of various profile similarity measures with a set of one microsporidial and 25 fungal genomes. In the fungi, phylogenetic profiling yields high-confidence predictions for the highest and only the highest scoring gene pairs illustrating both the power and the limitations of the approach. Both practical examples and theoretical considerations suggest that in order to get a reliable and specific picture of a protein's function, results from phylogenetic profiling have to be combined with other sources of evidence.

Many newly identified gene products from completely sequenced genomes are difficult to characterize in the absence of sequence homology to known proteins. In such a scenario, the context of the proteins' functional associations can be used for annotation; overrepresented functional linkages with a certain class of proteins or members of a pathway allow putative function assignments based on the "guilt-by-association" principle. Two computational functional genomics methods, phylogenetic profiling and identification of Rosetta stone linkages, are described in this chapter, which allow assessment of functional linkages between proteins, consequently facilitating annotation. Phylogenetic profiling involves measuring similarity between profiles that describe the presence or absence of a protein in a set of reference genomes, whereas Rosetta stone fusion sequences help link two or more independently transcribed and translated proteins. Both methods can be applied to investigate functional associations between individual proteins, and can also be extended to reconstruct the genome-wide network of functional linkages by querying the entire protein complement of an organism.

BackgroundPhylogenetic analysis is emerging as one of the most informative computational methods for the annotation of genes and identification of evolutionary modules of functionally related genes. The effectiveness with which phylogenetic profiles can be utilized to assign genes to pathways depends on an appropriate measure of correlation between gene profiles, and an effective decision rule to use the correlate. Current methods, though useful, perform at a level well below what is possible, largely because performance of the latter deteriorates rapidly as coverage increases.ResultsWe introduce, test and apply a new decision rule, correlation enrichment (CE), for assigning genes to functional categories at various levels of resolution. Among the results are: (1) CE performs better than standard guilt by association (SGA, assignment to a functional category when a simple correlate exceeds a pre-specified threshold) irrespective of the number of genes assigned (i.e.coverage); improvement is greatest at high coverage where precision (positive predictive value) of CE is approximately 6-fold higher than that of SGA. (2) CE is estimated to allocate each of the 2918 unannotated orthologs to KEGG pathways with an average precision of 49% (approximately 7-fold higher than SGA) (3) An estimated 94% of the 1846 unannotated orthologs in the COG ontology can be assigned a function with an average precision of 0.4 or greater. (4) Dozens of functional and evolutionarily conserved cliques or quasi-cliques can be identified, many having previously unannotated genes.ConclusionThe method serves as a general computational tool for annotating large numbers of unknown genes, uncovering evolutionary and functional modules. It appears to perform substantially better than extant stand alone high throughout methods.

The predicted extracellular proteins of the bacterium Lactobacillus plantarum were analysed to gain insight into the mechanisms underlying interactions of this bacterium with its environment. Extracellular proteins play important roles in processes ranging from probiotic effects in the gastrointestinal tract to degradation of complex extracellular carbon sources such as those found in plant materials, and they have a primary role in the adaptation of a bacterium to changing environmental conditions. The functional annotation of extracellular proteins was improved using a wide variety of bioinformatics methods, including domain analysis and phylogenetic profiling. At least 12 proteins are predicted to be directly involved in adherence to host components such as collagen and mucin, and about 30 extracellular enzymes, mainly hydrolases and transglycosylases, might play a role in the degradation of substrates by L. plantarum to sustain its growth in different environmental niches. A comprehensive overview of all predicted extracellular proteins, their domains composition and their predicted function is provided through a database at http://www.cmbi.ru.nl/secretome, which could serve as a basis for targeted experimental studies into the function of extracellular proteins.

Micro-indels are small insertion or deletion events (indels) that occur during genome evolution. The study of micro-indels is important, both in order to better understand the underlying biological mechanisms, and also for improving the evolutionary models used in sequence alignment and phylogenetic analysis. The inference of micro-indels from multiple sequence alignments of related genomes poses a difficult computational problem, and is far more complicated than the related task of inferring the history of point mutations. We introduce a tree alignment based approach that is suitable for working with multiple genomes and that emphasizes the concept of indel history. By working with an appropriately restricted alignment model, we are able to propose an algorithm for inferring the optimal indel history of homologous sequences that is efficient for practical problems. Using data from the ENCODE project as well as related sequences from multiple primates, we are able to compare and contrast indel events in both coding and non-coding regions. The ability to work with multiple sequences allows us to refute a previous claim that indel rates are approximately fixed even when the mutation rate changes, and allows us to show that indel events are not neutral. In particular, we identify indel hotspots in the human genome.

Background Fungi can undergo autophagic- or apoptotic-type programmed cell death (PCD) on exposure to antifungal agents, developmental signals, and stress factors. Filamentous fungi can also exhibit a form of cell death called heterokaryon incompatibility (HI) triggered by fusion between two genetically incompatible individuals. With the availability of recently sequenced genomes of Aspergillus fumigatus and several related species, we were able to define putative components of fungi-specific death pathways and the ancestral core apoptotic machinery shared by all fungi and metazoa. Results Phylogenetic profiling of HI-associated proteins from four Aspergilli and seven other fungal species revealed lineage-specific protein families, orphan genes, and core genes conserved across all fungi and metazoa. The Aspergilli-specific domain architectures include NACHT family NTPases, which may function as key integrators of stress and nutrient availability signals. They are often found fused to putative effector domains such as Pfs, SesB/LipA, and a newly identified domain, HET-s/LopB. Many putative HI inducers and mediators are specific to filamentous fungi and not found in unicellular yeasts. In addition to their role in HI, several of them appear to be involved in regulation of cell cycle, development and sexual differentiation. Finally, the Aspergilli possess many putative downstream components of the mammalian apoptotic machinery including several proteins not found in the model yeast, Saccharomyces cerevisiae. Conclusion Our analysis identified more than 100 putative PCD associated genes in the Aspergilli, which may help expand the range of currently available treatments for aspergillosis and other invasive fungal diseases. The list includes species-specific protein families as well as conserved core components of the ancestral PCD machinery shared by fungi and metazoa.

Comparative genomics provides at least three methods beyond traditional sequence similarity for identifying functional links between genes: the examination of common phylogenetic distributions, the analysis of conserved proximity along the chromosomes of multiple genomes, and observations of fusions of genes into a multidomain gene in another organism. Keywords: comparative genomics; orthology; evolution; phylogenetic profiling; chromosomal proximity; domain fusion

Publicly available protein sequences represent only a small fraction of the full catalog of genes encoded by the genomes of different plants, such as green algae, mosses, gymnosperms, and angiosperms. By contrast, an enormous amount of expressed sequence tags (ESTs) exists for a wide variety of plant species, representing a substantial part of all transcribed plant genes. Integrating protein and EST sequences in comparative and evolutionary analyses is not straightforward because of the heterogeneous nature of both types of sequence data. By combining information from publicly available EST and protein sequences for 32 different plant species, we identified more than 250,000 plant proteins organized in more than 12,000 gene families. Approximately 60% of the proteins are absent from current sequence databases but provide important new information about plant gene families. Analysis of the distribution of gene families over different plant species through phylogenetic profiling reveals interesting insights into plant gene evolution, and identifies species- and lineage-specific gene families, orphan genes, and conserved core genes across the green plant lineage. We counted a similar number of approximately 9,500 gene families in monocotyledonous and eudicotyledonous plants and found strong evidence for the existence of at least 33,700 genes in rice (Oryza sativa). Interestingly, the larger number of genes in rice compared to Arabidopsis (Arabidopsis thaliana) can partially be explained by a larger amount of species-specific single-copy genes and species-specific gene families. In addition, a majority of large gene families, typically containing more than 50 genes, are bigger in rice than Arabidopsis, whereas the opposite seems true for small gene families.

Phylogenetic profiling is a well established method for predicting functional relations and physical interactions between proteins. We present a new method for finding such relations based on phylogenetic profiling of conserved domains rather than proteins, avoiding computationally expensive all versus all sequence comparisons among genomes. The resulting domain interaction map (DIMA) can be explored directly or mapped to a genome of interest. We demonstrate that the performance of DIMA is comparable to that of classical phylogenetic profiling and its predictions often yield information that cannot be detected by profiling of entire protein chains. We provide a list of novel domain associations predicted by our method.

Experimentally derived genome-wide protein interaction networks have been useful in the elucidation of functional information that is not evident from examining individual proteins but determination of these networks is complex and time consuming. To address this problem, several computational methods for predicting protein networks in novel genomes have been developed. A recent publication by Date and Marcotte describes the use of phylogenetic profiling for elucidating novel pathways in proteomes that have not been experimentally characterized. This method, in combination with other computational methods for generating protein-interaction networks, might help identify novel functional pathways and enhance functional annotation of individual proteins.

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. Contact: delisi@bu.edu

We introduce a general computational method, applicable on a genome-wide scale, for the systematic discovery of uncharacterized cellular systems. Quantitative analysis of the coinheritance of pairs of genes among different organisms, calculated using phylogenetic profiles, allows the prediction of thousands of functional linkages between the corresponding proteins. A comparison of these functional linkages to known pathways reveals that calculated linkages are comparable in accuracy to genome-wide yeast two-hybrid screens or mass spectrometry interaction assays. In aggregate, these linkages describe the structure of large-scale networks, with the resulting yeast network composed of 3,875 linkages among 804 proteins, and the resulting pathogenic Escherichia coli network composed of 2,043 linkages among 828 proteins. The search of such networks for groups of uncharacterized, linked proteins led to the identification of 27 novel cellular systems from one nonpathogenic and three pathogenic bacterial genomes.

Phylogenetic profiling is an ingenious computational method for identifying functional linkage between proteins. It is based on the assumption that a set of proteins involved in a common pathway or composing a structural complex tends to evolve in a correlated fashion. We present a rigorous statistical analysis of the success of this method in identifying proteins involved in the same metabolic pathway. We suggest several modifications for the use of phylogenetic profiles that improve the success measure significantly. The potential and the limitations of this method for protein functional predictions are discussed.

We introduce a computational method for identifying subcellular locations of proteins from the phylogenetic distribution of the homologs of organellar proteins. This method is based on the observation that proteins localized to a given organelle by experiments tend to share a characteristic phylogenetic distribution of their homologs—a phylogenetic profile. Therefore any other protein can be localized by its phylogenetic profile. Application of this method to mitochondrial proteins reveals that nucleus-encoded proteins previously known to be destined for mitochondria fall into three groups: prokaryote-derived, eukaryote-derived, and organism-specific (i.e., found only in the organism under study). Prokaryote-derived mitochondrial proteins can be identified effectively by their phylogenetic profiles. In the yeast Saccharomyces cerevisiae , 361 nucleus-encoded mitochondrial proteins can be identified at 50% accuracy with 58% coverage. From these values and the proportion of conserved mitochondrial genes, it can be inferred that ≈630 genes, or 10% of the nuclear genome, is devoted to mitochondrial function. In the worm Caenorhabditis elegans, we estimate that there are ≈660 nucleus-encoded mitochondrial genes, or 4% of its genome, with ≈400 of these genes contributed from the prokaryotic mitochondrial ancestor. The large fraction of organism-specific and eukaryote-derived genes suggests that mitochondria perform specialized roles absent from prokaryotic mitochondrial ancestors. We observe measurably distinct phylogenetic profiles among proteins from different subcellular compartments, allowing the general use of prokaryotic genomes in learning features of eukaryotic proteins.

Summary: Conserved domains represent essential building blocks of most known proteins. Owing to their role as modular components carrying out specific functions they form a network based both on functional relations and direct physical interactions. We have previously shown that domain interaction networks provide substantially novel information with respect to networks built on full-length protein chains. In this work we present a comprehensive web resource for exploring the Domain Interaction MAp (DIMA), interactively. The tool aims at integration of multiple data sources and prediction techniques, two of which have been implemented so far: domain phylogenetic profiling and experimentally demonstrated domain contacts from known three-dimensional structures. A powerful yet simple user interface enables the user to compute, visualize, navigate and download domain networks based on specific search criteria. Availability: Contact: p.pagel@gsf.de

In order to extract the maximum amount of information from the rapidly accumulating genome sequences, all conserved genes need to be classified according to their homologous relationships. Comparison of proteins encoded in seven complete genomes from five major phylogenetic lineages and elucidation of consistent patterns of sequence similarities allowed the delineation of 720 clusters of orthologous groups (COGs). Each COG consists of individual orthologous proteins or orthologous sets of paralogs from at least three lineages. Orthologs typically have the same function, allowing transfer of functional information from one member to an entire COG. This relation automatically yields a number of functional predictions for poorly characterized genomes. The COGs comprise a framework for functional and evolutionary genome analysis.

Annotating protein functions and linking proteins with similar functions are important in systems biology. The rapid growth rate of newly sequenced genomes calls for the development of computational methods to help experimental techniques. Phylogenetic profiling (PP) is a method that exploits the evolutionary co-occurrence pattern to identify functional related proteins. However, PP-based methods delivered satisfactory performance only on prokaryotes but not on eukaryotes. This study proposed a two-stage framework to predict protein functional linkages, which successfully enhances a PP-based method with machine learning. The experimental results show that the proposed two-stage framework achieved the best overall performance in comparison with three PP-based methods.

T o truly understand a protein’s function in either a cellular or developmental context requires an appreciation of the other proteins with which it interacts. A broad range of experimental techniques have been developed to address this aspect of protein behaviour, ranging from yeast two hybrid assays and classical genetic screens with available mutants, to affinity chromatography, coimmunoprecipitation, protein crosslinking and phage display. However, no one technique is either infallibly accurate or sufficiently sensitive to detect all true physiological interactors. Consequently, most discerning biologists will not usually be convinced of the significance of a protein interaction either in vitro or in vivo unless strong evidence can be produced using two or more independent approaches. As most available screening methods are also relatively labour intensive, it is of considerable interest to see if the growing wealth of evolutionary data emerging from our present genomics era can be exploited in computational searches for interacting proteins. A number of methods have previously been employed to identify protein interactions based primarily on sequence data, including phylogenetic profiling, defined by the simultaneous presence or absence of certain proteins in different genomes (Pellegrini et al, 1999), and the identification of proteins in some organisms in which the domain structure consists of fusions between separate corresponding proteins found in other species (Enright et al, 1999). In prokaryotes, possible physical interactions between proteins may be inferred from conserved gene pairs in operons. However, each of these prediction methods is limited in its applicability, so more general approaches to identify candidate protein–protein interactions have been developed based on correlated evolutionary constraints, as discussed in a recent paper by Fraser et al (2004). The notion that interacting proteins might evolve at similar rates can be understood in terms of complementary substitutions to maintain associations between them. This may involve either amino-acid residues that directly form intermolecular contacts or the indirect effects of neighbouring residues on domain conformation. Whereas mutations that perturb interactions would usually be removed by selection, this might be prevented by compensatory substitutions in interaction partners. Additionally, proteins that have many interactors generally evolve slowly as a greater proportion of their total length may be involved in functional interactions (Fraser et al, 2002). Although coevolution between interacting proteins may be inferred when proteins are simultaneously lost in the same species, specific interactions may also be detected based on statistically significant similarities between phylogenetic trees of paralogues (Pazos and Valencia, 2001). For example, the coevolution of a family of protein ligands and their receptors has been exploited to identify interacting pairs based on correlations between their phylogenetic distance matrices (Goh and Cohen, 2002). Correlated rates of substitution have also been demonstrated between regions of proteins that are either known or expected to interact with each other on the basis of biochemical or microscopy data (Cobbe and Heck, 2004), while empirical protein–protein interaction data have been used to deduce probable domain–domain interactions from which additional, previously unknown protein–protein interactions could then be inferred (Deng et al, 2002). Previously, Fraser and Hirsh had shown that coevolution is probably the most likely explanation for observed similarities in evolutionary rates between interacting proteins in yeast, rather than alternative explanations such as similar fitness effects or overall numbers of interactors (Fraser et al, 2002). In their recent paper (Fraser et al, 2004), these authors now demonstrate how independent evidence for protein coevolution may be provided by not just examining similarities in evolutionary rates but also evaluating the extent to which genes may resemble each other in their presumed average expression levels. Firstly, evolutionary distance was estimated by calculating the average ratio of amino-acid replacement or nonsynonymous nucleotide substitutions to silent or synonymous substitutions at each codon position of orthologues from four closely related yeast species of the genus Saccharomyces. As well as indicating the divergence between proteins, this ratio also provides a measure of the intensity of purifying or negative selection exerted on genes, which results in decreased rates of amino-acid replacement and thus substitution ratios less than one. Consequently, any observed correlations should also reflect sequence coevolution based on adaptive changes, rather than just similarities in divergence patterns. Using normalised values for orthologues displaying greater variance in their rates, it was found that more significant correlations were observed between the evolutionary rates of protein pairs known to interact than with random pairs, in agreement with previous studies. The extent of coevolution in gene expression was then examined, as previous studies have shown that functionally related or interacting proteins have a significantly higher correlation between their gene expression profiles than random protein pairs. Analysis of mRNA coexpression has also been used to detect functional relationships between proteins in combination with other evidence such as phylogenetic profiles or domain fusions (Marcotte et al, 1999). However, rather than using expression profiles derived from DNA microarrays, Fraser et al employed patterns of codon usage to estimate the average expression level of genes in the same four yeast species. In such species, a strong association between codon bias and expression levels is thought to reflect selection for increased translational efficiency of highly expressed genes (corresponding to the most abundant tRNA in a given species) in addition to requirements for increased translational accuracy of essential proteins. Moreover, codon bias relationships should provide a more robust indication of evolutionarily conserved trends in gene expression, as codon usage is heritably selected but expression profiles may be influenced by experimental conditions. In the current paper, the extent to which codon usage patterns resemble those of highly expressed genes was measured by calculating the codon adaptation index for each gene, a statistic indicating the extent to which the pattern of codon usage resembles that of genes known to Heredity (2004) 93, 523–524 & 2004 Nature Publishing Group All rights reserved 0018-067X/04 $30.00

BACKGROUND: Recent progress in computational methods for predicting physical and functional protein-protein interactions has provided new insights into the complexity of biological processes. Most of these methods assume that functionally interacting proteins are likely to have a shared evolutionary history. This history can be traced out for the protein pairs of a query genome by correlating different evolutionary aspects of their homologs in multiple genomes known as the reference genomes. These methods include phylogenetic profiling, gene neighborhood and co-occurrence of the orthologous protein coding genes in the same cluster or operon. These are collectively known as genomic context methods. On the other hand a method called mirrortree is based on the similarity of phylogenetic trees between two interacting proteins. Comprehensive performance analyses of these methods have been frequently reported in literature. However, very few studies provide insight into the effect of reference genome selection on detection of meaningful protein interactions. METHODS: We analyzed the performance of four methods and their variants to understand the effect of reference genome selection on prediction efficacy. We used six sets of reference genomes, sampled in accordance with phylogenetic diversity and relationship between organisms from 565 bacteria. We used Escherichia coli as a model organism and the gold standard datasets of interacting proteins reported in DIP, EcoCyc and KEGG databases to compare the performance of the prediction methods. CONCLUSIONS: Higher performance for predicting protein-protein interactions was achievable even with 100-150 bacterial genomes out of 565 genomes. Inclusion of archaeal genomes in the reference genome set improves performance. We find that in order to obtain a good performance, it is better to sample few genomes of related genera of prokaryotes from the large number of available genomes. Moreover, such a sampling allows for selecting 50-100 genomes for comparable accuracy of predictions when computational resources are limited.

Motivations. Prediction of protein-protein interactions (PPIs) is a crucial goal for bioinformatics and the increasing availability of sequenced genomes support this challenge. One of the computational tools mostly used for this aim is the phylogenetic profiling, based on the co-conserved proteins identification by local alignments. The master idea is that co-evolving proteins share the same phylogenetic profile and can be grouped functionally. The field of parasite biology received a powerful improvement from post-genomics data. Several approaches have been utilized to predict PPIs for Plasmodium falciparum, including phylogenetic profiling. In contrast, no information about this is available in the case of Plasmodium berghei, even though for this parasite a huge amount of data is now available especially for mutant phenotypes. Methods. A new strategy has been developed to derive phylogenetic profiling. The critical steps of this strategy are: 1) genomes selection; 2) global vs local alignments comparison; 3) mutual information vs correlation coefficients calculation. Agreeing with specific criteria, 774 reference organisms, on 1133 available on Eggnog database (January 2012), were included in the study, a global alignment algorithm, over the mostly used local one, was used to perform proteins identification across the genomes, the Mutual Information and the Correlation Coefficient were calculated and the results were compared. Escherichia coli K12 before, suitable for the assessment of the method, and P. berghei later, were used as target organisms. Results. This analysis offers a new bioinformatical strategy to derive phylogenetic profile of an organism, highlighting on guidelines for the genomes selection, on the performance of different alignment algorithms and mathematical procedures. Moreover, strongly improve the knowledge about P. berghei and offers a new tool for evolution understanding and functional grouping of the proteins of this important biological model.

Background Enzymes in the radical SAM (rSAM) domain family serve in a wide variety of biological processes, including RNA modification, enzyme activation, bacteriocin core peptide maturation, and cofactor biosynthesis. Evolutionary pressures and relationships to other cellular constituents impose recognizable grammars on each class of rSAM-containing system, shaping patterns in results obtained through various comparative genomics analyses. Results An uncharacterized gene cluster found in many Actinobacteria and sporadically in Firmicutes, Chloroflexi, Deltaproteobacteria, and one Archaeal plasmid contains a PqqE-like rSAM protein family that includes Rv0693 from Mycobacterium tuberculosis. Members occur clustered with a strikingly well-conserved small polypeptide we designate "mycofactocin," similar in size to bacteriocins and PqqA, precursor of pyrroloquinoline quinone (PQQ). Partial Phylogenetic Profiling (PPP) based on the distribution of these markers identifies the mycofactocin cluster, but also a second tier of high-scoring proteins. This tier, strikingly, is filled with up to thirty-one members per genome from three variant subfamilies that occur, one each, in three unrelated classes of nicotinoproteins. The pattern suggests these variant enzymes require not only NAD(P), but also the novel gene cluster. Further study was conducted using SIMBAL, a PPP-like tool, to search these nicotinoproteins for subsequences best correlated across multiple genomes to the presence of mycofactocin. For both the short chain dehydrogenase/reductase (SDR) and iron-containing dehydrogenase families, aligning SIMBAL's top-scoring sequences to homologous solved crystal structures shows signals centered over NAD(P)-binding sites rather than over substrate-binding or active site residues. Previous studies on some of these proteins have revealed a non-exchangeable NAD cofactor, such that enzymatic activity in vitro requires an artificial electron acceptor such as N,N-dimethyl-4-nitrosoaniline (NDMA) for the enzyme to cycle. Conclusions Taken together, these findings suggest that the mycofactocin precursor is modified by the Rv0693 family rSAM protein and other enzymes in its cluster. It becomes an electron carrier molecule that serves in vivo as NDMA and other artificial electron acceptors do in vitro. Subclasses from three different nicotinoprotein families show "only-if" relationships to mycofactocin because they require its presence. This framework suggests a segregated redox pool in which mycofactocin mediates communication among enzymes with non-exchangeable cofactors.

Background Comparative genomics methods such as phylogenetic profiling can mine powerful inferences from inherently noisy biological data sets. We introduce Sites Inferred by Metabolic Background Assertion Labeling (SIMBAL), a method that applies the Partial Phylogenetic Profiling (PPP) approach locally within a protein sequence to discover short sequence signatures associated with functional sites. The approach is based on the basic scoring mechanism employed by PPP, namely the use of binomial distribution statistics to optimize sequence similarity cutoffs during searches of partitioned training sets. Results Here we illustrate and validate the ability of the SIMBAL method to find functionally relevant short sequence signatures by application to two well-characterized protein families. In the first example, we partitioned a family of ABC permeases using a metabolic background property (urea utilization). Thus, the TRUE set for this family comprised members whose genome of origin encoded a urea utilization system. By moving a sliding window across the sequence of a permease, and searching each subsequence in turn against the full set of partitioned proteins, the method found which local sequence signatures best correlated with the urea utilization trait. Mapping of SIMBAL "hot spots" onto crystal structures of homologous permeases reveals that the significant sites are gating determinants on the cytosolic face rather than, say, docking sites for the substrate-binding protein on the extracellular face. In the second example, we partitioned a protein methyltransferase family using gene proximity as a criterion. In this case, the TRUE set comprised those methyltransferases encoded near the gene for the substrate RF-1. SIMBAL identifies sequence regions that map onto the substrate-binding interface while ignoring regions involved in the methyltransferase reaction mechanism in general. Neither method for training set construction requires any prior experimental characterization. Conclusions SIMBAL shows that, in functionally divergent protein families, selected short sequences often significantly outperform their full-length parent sequence for making functional predictions by sequence similarity, suggesting avenues for improved functional classifiers. When combined with structural data, SIMBAL affords the ability to localize and model functional sites.

BackgroundThe rapidly increasing speed with which genome sequence data can be generated will be accompanied by an exponential increase in the number of sequenced eukaryotes. With the increasing number of sequenced eukaryotic genomes comes a need for bioinformatic techniques to aid in functional annotation. Ideally, genome context based techniques such as proximity, fusion, and phylogenetic profiling, which have been so successful in prokaryotes, could be utilized in eukaryotes. Here we explore the application of phylogenetic profiling, a method that exploits the evolutionary co-occurrence of genes in the assignment of functional linkages, to eukaryotic genomes.ResultsIn order to evaluate the performance of phylogenetic profiling in eukaryotes, we assessed the relative performance of commonly used profile construction techniques and genome compositions in predicting functional linkages in both prokaryotic and eukaryotic organisms. When predicting linkages inE. coliwith a prokaryotic profile, the use of continuous values constructed from transformed BLAST bit-scores performed better than profiles composed of discretized E-values; the use of discretized E-values resulted in more accurate linkages when usingS. cerevisiaeas the query organism. Extending this analysis by incorporating several eukaryotic genomes in profiles containing a majority of prokaryotes resulted in similar overall accuracy, but with a surprising reduction in pathway diversity among the most significant linkages. Furthermore, the application of phylogenetic profiling using profiles composed of only eukaryotes resulted in the loss of the strong correlation between common KEGG pathway membership and profile similarity score. Profile construction methods, orthology definitions, ontology and domain complexity were explored as possible sources of the poor performance of eukaryotic profiles, but with no improvement in results.ConclusionGiven the current set of completely sequenced eukaryotic organisms, phylogenetic profiling using profiles generated from any of the commonly used techniques was found to yield extremely poor results. These findings imply genome-specific requirements for constructing functionally relevant phylogenetic profiles, and suggest that differences in the evolutionary history between different kingdoms might generally limit the usefulness of phylogenetic profiling in eukaryotes.

“Phylogenetic profiling” is based on the hypothesis that during evolution functionally or physically interacting genes are likely to be inherited or eliminated in a codependent manner. Creating presence–absence profiles of orthologous genes is now a common and powerful way of identifying functionally associated genes. In this approach, correctly determining orthology, as a means of identifying functional equivalence between two genes, is a critical and nontrivial step and largely explains why previous work in this area has mainly focused on using presence–absence profiles in prokaryotic species. Here, we demonstrate that eukaryotic genomes have a high proportion of multigene families whose phylogenetic profile distributions are poor in presence–absence information content. This feature makes them prone to orthology mis-assignment and unsuited to standard profile-based prediction methods. Using CATH structural domain assignments from the Gene3D database for 13 complete eukaryotic genomes, we have developed a novel modification of the phylogenetic profiling method that uses genome copy number of each domain superfamily to predict functional relationships. In our approach, superfamilies are subclustered at ten levels of sequence identity—from 30% to 100%—and phylogenetic profiles built at each level. All the profiles are compared using normalised Euclidean distances to identify those with correlated changes in their domain copy number. We demonstrate that two protein families will “auto-tune” with strong co-evolutionary signals when their profiles are compared at the similarity levels that capture their functional relationship. Our method finds functional relationships that are not detectable by the conventional presence–absence profile comparisons, and it does not require a priori any fixed criteria to define orthologous genes., Author Summary The vast number of protein sequences being determined by the international genomics projects means that it is not possible to functionally characterise all the proteins through direct experimentation. One of the more successful electronic methods for detecting functionally associated genes has been through the comparison of genes' phylogenetic profiles. This method is based on the hypothesis that two functionally related genes will show very similar presence–absence profile patterns throughout different organisms. Whilst these methods have grown increasingly sophisticated, they have largely been based on detecting functionally homologous genes in different species (technically known as orthologous genes) and thus better suited to prokaryotic genomes, where this can be done more easily. We have developed a new type of hierarchical phylogenetic profile by subdividing protein families into subclusters in different sequence identity levels. This new approach encapsulates a more realistic model of the functional variation that uneven natural selection pressure produces on different protein families and organisms, and it can detect functional relationships between protein families without the initial application of rigid sequence similarity thresholds or complex protocols for orthology assignment. These advantages are especially useful in eukaryotes since the larger average size of eukaryotic multigene families makes them more prone to orthology mis-assignment than in prokaryotes.

The availability of the complete genome sequence of Arabidopsis thaliana together with those of other organisms provides an opportunity to decipher the genetic factors that define plant form and function. To begin this task, we have classified the nuclear protein-coding genes of Arabidopsis thaliana on the basis of their pattern of sequence similarity to organisms across the three domains of life. We identified 3,848 Arabidopsis proteins that are likely to be found solely within the plant lineage. More than half of these plant-specific proteins are of unknown function, emphasizing the general lack of knowledge of processes unique to plants. Plant-specific proteins that are membrane-associated and/or targeted to the mitochondria or chloroplasts are the most poorly characterized. Analyses of microarray data indicate that genes coding for plant-specific proteins, but not evolutionarily conserved proteins, are more likely to be expressed in an organ-specific manner. A large proportion (13%) of plant-specific proteins are transcription factors, whereas other basic cellular processes are under-represented, suggesting that evolution of plant-specific control of gene expression contributed to making plants different from other eukaryotes. We identified and characterized the Arabidopsis proteins that are most likely to be plant-specific. Our results provide a genome-wide assessment that supports the hypothesis that evolution of higher plant complexity and diversity is related to the evolution of regulatory mechanisms. Because proteins that are unique to the green plant lineage will not be studied in other model systems, they should be attractive priorities for future studies.

Background Although baker's yeast is a primary model organism for research on eukaryotic ribosome assembly and nucleoli, the list of its proteins that are functionally associated with nucleoli or ribosomes is still incomplete. We trained a naive Bayesian classifier to predict novel proteins that are associated with yeast nucleoli or ribosomes based on parts lists of nucleoli in model organisms and large-scale protein interaction data sets. Phylogenetic profiling and gene expression analysis were carried out to shed light on evolutionary and regulatory aspects of nucleoli and ribosome assembly.

A number of bacterial species, mostly proteobacteria, possess monothiol glutaredoxins homologous to theSaccharomyces cerevisiaemitochondrial protein Grx5, which is involved in iron–sulphur cluster synthesis. Phylogenetic profiling is used to predict that bacterial monothiol glutaredoxins also participate in the iron–sulphur cluster (ISC) assembly machinery, because their phylogenetic profiles are similar to the profiles of the bacterial homologues of yeast ISC proteins. High evolutionary co-occurrence is observed between the Grx5 homologues and the homologues of the Yah1 ferredoxin, the scaffold proteins Isa1 and Isa2, the frataxin protein Yfh1 and the Nfu1 protein. This suggests that a specific functional interaction exists between these ISC machinery proteins. Physical interaction analyses using low-definition protein docking predict the formation of strong and specific complexes between Grx5 and several components of the yeast ISC machinery. Two-hybrid analysis has confirmed thein vivointeraction between Grx5 and Isa1. Sequence comparison techniques and cladistics indicate that the other two monothiol glutaredoxins ofS. cerevisiae, Grx3 and Grx4, have evolved from the fusion of a thioredoxin gene with a monothiol glutaredoxin gene early in the eukaryotic lineage, leading to differential functional specialization. While bacteria do not contain these chimaeric glutaredoxins, in many eukaryotic species Grx5 and Grx3/4-type monothiol glutaredoxins coexist in the cell.

Despite many developed experimental and computational approaches, functional gene annotation remains challenging. With the rapidly growing number of sequenced genomes, the concept of phylogenetic profiling, which predicts functional links between genes that share a common co-occurrence pattern across different genomes, has gained renewed attention as it promises to annotate gene functions based on presence/absence calls alone. We applied phylogenetic profiling to the problem of metabolic pathway assignments of plant genes with a particular focus on secondary metabolism pathways. We determined phylogenetic profiles for 40,960 metabolic pathway enzyme genes with assigned EC numbers from 24 plant species based on sequence and pathway annotation data from KEGG and Ensembl Plants. For gene sequence family assignments, needed to determine the presence or absence of particular gene functions in the given plant species, we included data of all 39 species available at the Ensembl Plants database and established gene families based on pairwise sequence identities and annotation information. Aside from performing profiling comparisons, we used machine learning approaches to predict pathway associations from phylogenetic profiles alone. Selected metabolic pathways were indeed found to be composed of gene families of greater than expected phylogenetic profile similarity. This was particularly evident for primary metabolism pathways, whereas for secondary pathways, both the available annotation in different species as well as the abstraction of functional association via distinct pathways proved limiting. While phylogenetic profile similarity was generally not found to correlate with gene co-expression, direct physical interactions of proteins were reflected by a significantly increased profile similarity suggesting an application of phylogenetic profiling methods as a filtering step in the identification of protein-protein interactions. This feasibility study highlights the potential and challenges associated with phylogenetic profiling methods for the detection of functional relationships between genes as well as the need to enlarge the set of plant genes with proven secondary metabolism involvement as well as the limitations of distinct pathways as abstractions of relationships between genes.

Motivation: Genomic context analysis, also known as phylogenetic profiling, is widely used to infer functional interactions between proteins but rarely applied to non-coding cis-regulatory DNA elements. We were wondering whether this approach could provide insights about utlraconserved non-coding elements (UCNEs). These elements are organized as large clusters, so-called gene regulatory blocks (GRBs) around key developmental genes. Their molecular functions and the reasons for their high degree of conservation remain enigmatic. Results: In a special setting of genomic context analysis, we analyzed the fate of GRBs after a whole-genome duplication event in five fish genomes. We found that in most cases all UCNEs were retained together as a single block, whereas the corresponding target genes were often retained in two copies, one completely devoid of UCNEs. This ‘winner-takes-all’ pattern suggests that UCNEs of a GRB function in a highly cooperative manner. We propose that the multitude of interactions between UCNEs is the reason for their extreme sequence conservation. Supplementary information: Supplementary data are available at Bioinformatics online and at http://ccg.vital-it.ch/ucne/

Interacting proteins and protein domains coevolve on multiple scales, from their correlated presence across species, to correlations in amino-acid usage. Genomic databases provide rapidly growing data for variability in genomic protein content and in protein sequences, calling for computational predictions of unknown interactions. We first introduce the concept of direct phyletic couplings, based on global statistical models of phylogenetic profiles. They strongly increase the accuracy of predicting pairs of related protein domains beyond simpler correlation-based approaches like phylogenetic profiling (80% vs. 30–50% positives out of the 1000 highest-scoring pairs). Combined with the direct coupling analysis of inter-protein residue-residue coevolution, we provide multi-scale evidence for direct but unknown interaction between protein families. An in-depth discussion shows these to be biologically sensible and directly experimentally testable. Negative phyletic couplings highlight alternative solutions for the same functionality, including documented cases of convergent evolution. Thereby our work proves the strong potential of global statistical modeling approaches to genome-wide coevolutionary analysis, far beyond the established use for individual protein complexes and domain-domain interactions.

Cilia (flagella) are important eukaryotic organelles, present in the Last Eukaryotic Common Ancestor, and are involved in cell motility and integration of extracellular signals. Ciliary dysfunction causes a class of genetic diseases, known as ciliopathies, however current knowledge of the underlying mechanisms is still limited and a better characterization of genes is needed. As cilia have been lost independently several times during evolution and they are subject to important functional variation between species, ciliary genes can be investigated through comparative genomics. We performed phylogenetic profiling by predicting orthologs of human protein-coding genes in 100 eukaryotic species. The analysis integrated three independent methods to predict a consensus set of 274 ciliary genes, including 87 new promising candidates. A fine-grained analysis of the phylogenetic profiles allowed a partitioning of ciliary genes into modules with distinct evolutionary histories and ciliary functions (assembly, movement, centriole, etc.) and thus propagation of potential annotations to previously undocumented genes. The cilia/basal body localization was experimentally confirmed for five of these previously unannotated proteins (LRRC23, LRRC34, TEX9, WDR27, and BIVM), validating the relevance of our approach. Furthermore, our multi-level analysis sheds light on the core gene sets retained in gamete-only flagellates or Ecdysozoa for instance. By combining gene-centric and species-oriented analyses, this work reveals new ciliary and ciliopathy gene candidates and provides clues about the evolution of ciliary processes in the eukaryotic domain. Additionally, the positive and negative reference gene sets and the phylogenetic profile of human genes constructed during this study can be exploited in future work.

The earliest evidences on circulation of Avian Influenza (AI) virus on the Iranian poultry farms date back to 1998. Great economic losses through dramatic drop in egg production and high mortality rates are characteristically attributed to H9N2 AI virus. In the present work non-structural (NS) genes of 10 Iranian H9N2 chicken AI viruses collected during 1998-2007 were fully sequenced and subjected to a phylogenetic analysis. The observations proved allele A was the single-detectable type of the NS gene within the studied isolates. All the examined Iranian isolates fell into the Korean sublineage with a relatively broad sequence homology (91.6-98%) in nucleotide construction of the NS genes. The motif for PDZ ligand recognition of the group one isolates was either EDEV (N=6) or ESEV (N=1) While all viruses as group two contained a PL motif “KSEV” (N=3). The present work provides useful epidemiological data at molecular level on source and contemporary evolution of H9N2 virus population in Iran.

Phylogenetic profiling is a well-established approach for predicting gene function based on patterns of gene presence and absence across species. Much of the recent developments have focused on methodological improvements, but relatively little is known about the effect of input data size on the quality of predictions. In this work, we ask: how many genomes and functional annotations need to be considered for phylogenetic profiling to be effective? Phylogenetic profiling generally benefits from an increased amount of input data. However, by decomposing this improvement in predictive accuracy in terms of the contribution of additional genomes and of additional annotations, we observed diminishing returns in adding more than ∼100 genomes, whereas increasing the number of annotations remained strongly beneficial throughout. We also observed that maximising phylogenetic diversity within a clade of interest improves predictive accuracy, but the effect is small compared to changes in the number of genomes under comparison. Finally, we show that these findings are supported in light of the Open World Assumption, which posits that functional annotation databases are inherently incomplete. All the tools and data used in this work are available for reuse from http://lab.dessimoz.org/14_phylprof. Scripts used to analyse the data are available on request from the authors.

In the multiomics era, comparative genomics studies based on gene repertoire comparison are increasingly used to investigate evolutionary histories of species, to study genotype–phenotype relations, species adaptation to various environments, or to predict gene function using phylogenetic profiling. However, comparisons of orthologs have highlighted the prevalence of sequence plasticity among species, showing the benefits of combining protein and subprotein levels of analysis to allow for a more comprehensive study of genotype/phenotype correlations. In this article, we introduce a new approach called BLUR (BLAST Unexpected Ranking), capable of detecting genotype divergence or specialization between two related clades at different levels: gain/loss of proteins but also of subprotein regions. These regions can correspond to known domains, uncharacterized regions, or even small motifs. Our method was created to allow two types of research strategies: 1) the comparison of two groups of species with no previous knowledge, with the aim of predicting phenotype differences or specializations between close species or 2) the study of specific phenotypes by comparing species that present the phenotype of interest with species that do not. We designed a website to facilitate the use of BLUR with a possibility of in-depth analysis of the results with various tools, such as functional enrichments, protein–protein interaction networks, and multiple sequence alignments. We applied our method to the study of two different biological pathways and to the comparison of several groups of close species, all with very promising results. BLUR is freely available at http://lbgi.fr/blur/.

OMA is an established resource to elucidate evolutionary relationships among genes from currently 2326 genomes covering all domains of life. OMA provides pairwise and groupwise orthologs, functional annotations, local and global gene order conservation (synteny) information, among many other functions. This update paper describes the reorganisation of the database into gene-, group- and genome-centric pages. Other new and improved features are detailed, such as reporting of the evolutionarily best conserved isoforms of alternatively spliced genes, the inferred local order of ancestral genes, phylogenetic profiling, better cross-references, fast genome mapping, semantic data sharing via RDF, as well as a special coronavirus OMA with 119 viruses from the Nidovirales order, including SARS-CoV-2, the agent of the COVID-19 pandemic. We conclude with improvements to the documentation of the resource through primers, tutorials and short videos. OMA is accessible at https://omabrowser.org.

Ribosome assembly is an essential and carefully choreographed cellular process. In eukaryotes, several 100 proteins, distributed across the nucleolus, nucleus, and cytoplasm, co-ordinate the step-wise assembly of four ribosomal RNAs (rRNAs) and approximately 80 ribosomal proteins (RPs) into the mature ribosomal subunits. Due to the inherent complexity of the assembly process, functional studies identifying ribosome biogenesis factors and, more importantly, their precise functions and interplay are confined to a few and very well-established model organisms. Although best characterized in yeast (Saccharomyces cerevisiae), emerging links to disease and the discovery of additional layers of regulation have recently encouraged deeper analysis of the pathway in human cells. In archaea, ribosome biogenesis is less well-understood. However, their simpler sub-cellular structure should allow a less elaborated assembly procedure, potentially providing insights into the functional essentials of ribosome biogenesis that evolved long before the diversification of archaea and eukaryotes. Here, we use a comprehensive phylogenetic profiling setup, integrating targeted ortholog searches with automated scoring of protein domain architecture similarities and an assessment of when search sensitivity becomes limiting, to trace 301 curated eukaryotic ribosome biogenesis factors across 982 taxa spanning the tree of life and including 727 archaea. We show that both factor loss and lineage-specific modifications of factor function modulate ribosome biogenesis, and we highlight that limited sensitivity of the ortholog search can confound evolutionary conclusions. Projecting into the archaeal domain, we find that only few factors are consistently present across the analyzed taxa, and lineage-specific loss is common. While members of the Asgard group are not special with respect to their inventory of ribosome biogenesis factors (RBFs), they unite the highest number of orthologs to eukaryotic RBFs in one taxon. Using large ribosomal subunit maturation as an example, we demonstrate that archaea pursue a simplified version of the corresponding steps in eukaryotes. Much of the complexity of this process evolved on the eukaryotic lineage by the duplication of ribosomal proteins and their subsequent functional diversification into ribosome biogenesis factors. This highlights that studying ribosome biogenesis in archaea provides fundamental information also for understanding the process in eukaryotes.

Feathers are the most complex skin appendages of vertebrates. Mature feathers consist of interconnected dead keratinocytes that are filled with heavily cross-linked proteins. Although the molecular architecture determines essential functions of feathers, only few feather proteins have been characterized with regard to their amino acid sequences and evolution. Here, we identify Epidermal Differentiation protein containing DPCC Motifs (EDDM) as a cysteine-rich protein that has co-evolved with other feather proteins. The EDDM gene is located within the avian epidermal differentiation complex (EDC), a cluster of genes that has originated and diversified in amniotes. EDDM shares the exon-intron organization with EDC genes of other amniotes, including humans, and a gene encoding an EDDM-like protein is present in crocodilians, suggesting that avian EDDM arose by sequence modification of an epidermal differentiation gene present in a common ancestor of archosaurs. The EDDM protein contains multiple sequence repeats and a higher number of cysteine residues than any other protein encoded in the EDC. Immunohistochemical analysis of chicken skin and skin appendages showed expression of EDDM in barb and barbules of feathers as well as in the subperiderm on embryonic scutate scales. These results suggest that the diversification and differential expression of EDDM, besides other EDC genes, was instrumental in facilitating the evolution of the most complex molecular architecture of feathers.