Predicting functional gene links from phylogenetic-statistical analyses of whole genomes

Accurate Detection of Convergent Mutations in Large Protein Alignments With ConDor

Evolutionary convergences are observed at all levels, from phenotype to DNA and protein sequences, and changes at these different levels tend to be correlated. Notably, convergent mutations can lead to convergent changes in phenotype, such as changes in metabolism, drug resistance, and other adaptations to changing environments. We propose a two-component approach to detect mutations subject to convergent evolution in protein alignments. The "Emergence" component selects mutations that emerge more often than expected, while the "Correlation" component selects mutations that correlate with the convergent phenotype under study. With regard to Emergence, a phylogeny deduced from the alignment is provided by the user and is used to simulate the evolution of each alignment position. These simulations allow us to estimate the expected number of mutations in a neutral model, which is compared to the observed number of mutations in the data studied. In Correlation, a comparative phylogenetic approach, is used to measure whether the presence of each of the observed mutations is correlated with the convergent phenotype. Each component can be used on its own, for example Emergence when no phenotype is available. Our method is implemented in a standalone workflow and a webserver, called ConDor. We evaluate the properties of ConDor using simulated data, and we apply it to three real datasets: sedge PEPC proteins, HIV reverse transcriptase, and fish rhodopsin. The results show that the two components of ConDor complement each other, with an overall accuracy that compares favorably to other available tools, especially on large datasets.

Identification of hidden associations among eukaryotic genes through statistical analysis of coevolutionary transitions

Coevolution at the gene level, as reflected by correlated events of gene loss or gain, can be revealed by phylogenetic profile analysis. The optimal method and metric for comparing phylogenetic profiles, especially in eukaryotic genomes, are not yet established. Here, we describe a procedure suitable for large-scale analysis, which can reveal coevolution based on the assessment of the statistical significance of correlated presence/absence transitions between gene pairs. This metric can identify coevolution in profiles with low overall similarities and is not affected by similarities lacking coevolutionary information. We applied the procedure to a large collection of 60,912 orthologous gene groups (orthogroups) in 1,264 eukaryotic genomes extracted from OrthoDB. We found significant cotransition scores for 7,825 orthogroups associated in 2,401 coevolving modules linking known and unknown genes in protein complexes and biological pathways. To demonstrate the ability of the method to predict hidden gene associations, we validated through experiments the involvement of vertebrate malate synthase-like genes in the conversion of (S)-ureidoglycolate into glyoxylate and urea, the last step of purine catabolism. This identification explains the presence of glyoxylate cycle genes in metazoa and suggests an anaplerotic role of purine degradation in early eukaryotes.

Molecular Phylogeny of Cimicoidea (Heteroptera: Cimicomorpha) Revisited: Increased Taxon Sampling Reveals Evolution of Traumatic Insemination and Paragenitalia

The molecular phylogeny of the Cimicoidea was reconstructed from an expanded sampling based on mitochondrial (16S, COI) and nuclear (18S, 28SD3) genes. The data were analyzed using maximum likelihood (ML), maximum parsimony (MP), and Bayesian inference (BI) phylogenetic frameworks. The phylogenetic relationships inferred by the model-based analyses (ML and BI) were largely congruent with those inferred by the MP analysis in terms of the monophyly of most of the higher taxonomic groups and the species-level relationships. The following clades were recovered in all analyses: Cimiciformes; Nabidae: Prostemmatinae; Nabidae: Nabinae; Plokiophilidae; Microphysidae; Lasiochilidae; Cimicidae: Cacodminae; Cimicidae; Lyctocoridae; Anthocoridae s. str.; Cardiastethini excluding Amphiareus; Almeidini; Scolopini; Anthocorini; Oriini; Curaliidae + Lasiochilidae; Almeidini + Xylocorini; Oriini + Cardiastethini; and Anthocorini + Amphiareus. Reconstructions of ancestral copulation states based on Bayesian and parsimony inference indicated that at least one shift from standard insemination (SI) to traumatic insemination (TI) occurred within Cimicoidea, and an investigation of the evolutionary correlation between TI and paragenitalia (PG) revealed that the acquisition of PG in cimicoid females was correlated with the TI habit. Additionally, our morphological examination of various types of PG suggested that even the same PG type may not constitute a homologous feature at various taxonomic levels, indicating the convergent evolution of female morphology to adapt to TI.

Computational models for prediction of protein-protein interaction in rice and Magnaporthe grisea

INTRODUCTION

Plant-microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein-protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease.

METHODS

In this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea in a whole-genome scale.

RESULTS AND DISCUSSION

A total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen-host datasets where less accuracy is obtained, which confirmed that the model is specific to O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.

ProteinPrompt: a webserver for predicting protein-protein interactions

Motivation

Protein-protein interactions (PPIs) play an essential role in a great variety of cellular processes and are therefore of significant interest for the design of new therapeutic compounds as well as the identification of side effects due to unexpected binding. Here, we present ProteinPrompt, a webserver that uses machine learning algorithms to calculate specific, currently unknown PPIs. Our tool is designed to quickly and reliably predict contact propensities based on an input sequence in order to scan large sequence libraries for potential binding partners, with the goal to accelerate and assure the quality of the laborious process of drug target identification.

Results

We collected and thoroughly filtered a comprehensive database of known binders from several sources, which is available as download. ProteinPrompt provides two complementary search methods of similar accuracy for comparison and consensus building. The default method is a random forest (RF) algorithm that uses the auto-correlations of seven amino acid scales. Alternatively, a graph neural network (GNN) implementation can be selected. Additionally, a consensus prediction is available. For each query sequence, potential binding partners are identified from a protein sequence database. The proteom of several organisms are available and can be searched for binders. To evaluate the predictive power of the algorithms, we prepared a test dataset that was rigorously filtered for redundancy. No sequence pairs similar to the ones used for training were included in this dataset. With this challenging dataset, the RF method achieved an accuracy rate of 0.88 and an area under the curve of 0.95. The GNN achieved an accuracy rate of 0.86 using the same dataset. Since the underlying learning approaches are unrelated, comparing the results of RF and GNNs reduces the likelihood of errors. The consensus reached an accuracy of 0.89.

Availability and implementation

ProteinPrompt is available online at: http://proteinformatics.org/ProteinPrompt, where training and test data used to optimize the methods are also available. The server makes it possible to scan the human proteome for potential binding partners of an input sequence within minutes. For local offline usage, we furthermore created a ProteinPrompt Docker image which allows for batch submission: https://gitlab.hzdr.de/proteinprompt/ProteinPrompt. In conclusion, we offer a fast, accurate, easy-to-use online service for predicting binding partners from an input sequence.

The Community Coevolution Model with Application to the Study of Evolutionary Relationships between Genes based on Phylogenetic Profiles

Organismal traits can evolve in a coordinated way, with correlated patterns of gains and losses reflecting important evolutionary associations. Discovering these associations can reveal important information about the functional and ecological linkages among traits. Phylogenetic profiles treat individual genes as traits distributed across sets of genomes and can provide a fine-grained view of the genetic underpinnings of evolutionary processes in a set of genomes. Phylogenetic profiling has been used to identify genes that are functionally linked, and to identify common patterns of lateral gene transfer in microorganisms. However, comparative analysis of phylogenetic profiles and other trait distributions should take into account the phylogenetic relationships among the organisms under consideration. Here we propose the Community Coevolution Model (CCM), a new coevolutionary model to analyze the evolutionary associations among traits, with a focus on phylogenetic profiles. In the CCM, traits are considered to evolve as a community with interactions, and the transition rate for each trait depends on the current states of other traits. Surpassing other comparative methods for pairwise trait analysis, CCM has the additional advantage of being able to examine multiple traits as a community to reveal more dependency relationships. We also develop a simulation procedure to generate phylogenetic profiles with correlated evolutionary patterns that can be used as benchmark data for evaluation purposes. A simulation study demonstrates that CCM is more accurate than other methods including the Jaccard Index and three tree-aware methods. The parameterization of CCM makes the interpretation of the relations between genes more direct, which leads to Darwin's scenario being identified easily based on the estimated parameters. We show that CCM is more efficient and fits real data better than other methods resulting in higher likelihood scores with fewer parameters. An examination of 3786 phylogenetic profiles across a set of 659 bacterial genomes highlights linkages between genes with common functions, including many patterns that would not have been identified under a non-phylogenetic model of common distribution. We also applied the CCM to 44 proteins in the well-studied Mitochondrial Respiratory Complex I and recovered associations that mapped well onto the structural associations that exist in the complex.

Comparative Genomics of Prunus-Associated Members of the Pseudomonas syringae Species Complex Reveals Traits Supporting Co-evolution and Host Adaptation

Members of the Pseudomonas syringae species complex cause symptoms that are ranging from leaf spots to cankers on a multitude of plant species, including some of the genus Prunus. To date, a total of two species of the P. syringae species complex and six different pathovars have been associated with diseases on Prunus spp., which were shown to belong to different phylogenetic units (phylogroups, PG) based on sequence similarity of housekeeping genes or whole genomes, suggesting that virulence to Prunus spp. may be the result of convergent pathoadaptation. In this study, a comparative genomics approach was used to determine genes significantly associated with strains isolated from Prunus spp. across a phylogeny of 97 strains belonging to the P. syringae species complex. Our study revealed the presence of a set of orthologous proteins which were significantly associated with strains isolated from Prunus spp. than in strains isolated from other hosts or from non-agricultural environments. Among them, the type III effector HopAY predicted to encode for a C58 cysteine protease was found to be highly associated with strains isolated from Prunus spp. and revealed patterns supporting co-evolution and host adaptation.

PRDM9 losses in vertebrates are coupled to those of paralogs ZCWPW1 and ZCWPW2

We take a phylogenetic approach to search for molecular partners of PRDM9, a key meiotic recombination gene, by leveraging the fact that the complete PRDM9 gene has been lost at least 13 times independently in vertebrates. We identify two genes, ZCWPW1 and its paralog ZCWPW2, whose presence or absence across vertebrates is coupled to that of PRDM9. ZCWPW1 was recently shown to be recruited to sites of PRDM9 binding and to aid in the repair of double strand breaks. ZCWPW2 is likely recruited to sites of PRDM9 binding as well; its tight coevolution with PRDM9 across vertebrates suggests that it too plays an important role in mammals and beyond, either in double strand break formation or repair.

In most mammals and likely throughout vertebrates, the gene PRDM9 specifies the locations of meiotic double strand breaks; in mice and humans at least, it also aids in their repair. For both roles, many of the molecular partners remain unknown. Here, we take a phylogenetic approach to identify genes that may be interacting with PRDM9 by leveraging the fact that PRDM9 arose before the origin of vertebrates but was lost many times, either partially or entirely—and with it, its role in recombination. As a first step, we characterize PRDM9 domain composition across 446 vertebrate species, inferring at least 13 independent losses. We then use the interdigitation of PRDM9 orthologs across vertebrates to test whether it coevolved with any of 241 candidate genes coexpressed with PRDM9 in mice or associated with recombination phenotypes in mammals. Accounting for the phylogenetic relationship among a subsample of 189 species, we find two genes whose presence and absence is unexpectedly coincident with that of PRDM9: ZCWPW1, which was recently shown to facilitate double strand break repair, and its paralog ZCWPW2, as well as, more tentatively, TEX15 and FBXO47. ZCWPW2 is expected to be recruited to sites of PRDM9 binding; its tight coevolution with PRDM9 across vertebrates suggests that it is a key interactor within mammals and beyond, with a role either in recruiting the recombination machinery or in double strand break repair.

In silico characterization, docking, and simulations to understand host-pathogen interactions in an effort to enhance crop production in date palms

Food safety remains a significant challenge despite the growth and development in agricultural research and the advent of modern biotechnological and agricultural tools. Though the agriculturist struggles to aid the growing population's needs, many pathogen-based plant diseases by their direct impact on cell division and tissue development have led to the loss of tons of food crops every year. Though there are many conventional and traditional methods to overcome this issue, the amount and time spend are huge. Scientists have developed systems biology tools to study the root cause of the problem and rectify it. Host-pathogen protein interactions (HPIs) have a promising role in identifying the pathogens' strategy to conquer the host organism. In this paper, the interactions between the host Rhynchophorus ferrugineus (an invasive wood-boring pest that destroys palm) and the pathogens Proteus mirabilis, Serratia marcescens, and Klebsiella pneumoniae are comprehensively studied using protein-protein interactions, molecular docking, and followed by 200 ns molecular dynamic simulations. This study elucidates the structural and functional basis of these proteins leading towards better plant health, production, and reliability.

GFICLEE: ultrafast tree-based phylogenetic profile method inferring gene function at the genomic-wide level

Background

Phylogenetic profiling is widely used to predict novel members of large protein complexes and biological pathways. Although methods combined with phylogenetic trees have significantly improved prediction accuracy, computational efficiency is still an issue that limits its genome-wise application.

Results

Here we introduce a new tree-based phylogenetic profiling algorithm named GFICLEE, which infers common single and continuous loss (SCL) events in the evolutionary patterns. We validated our algorithm with human pathways from three databases and compared the computational efficiency with current tree-based with 10 different scales genome dataset. Our algorithm has a better predictive performance with high computational efficiency.

Conclusions

The GFICLEE is a new method to infers genome-wide gene function. The accuracy and computational efficiency of GFICLEE make it possible to explore gene functions at the genome-wide level on a personal computer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08070-7.

An efficient, non-phylogenetic method for detecting genes sharing evolutionary signals in phylogenomic datasets

Assessing the compatibility between gene family phylogenies is a crucial and often computationally demanding step in many phylogenomic analyses. Here, we describe the Evolutionary Similarity Index (IES), a means to assess shared evolution between gene families using a weighted orthogonal distance regression model applied to sequence distances. The utilization of pairwise distance matrices circumvents comparisons between gene tree topologies, which are inherently uncertain and sensitive to evolutionary model choice, phylogenetic reconstruction artifacts, and other sources of error. Furthermore, IES enables the many-to-many pairing of multiple copies between similarly evolving gene families. This is done by selecting non-overlapping pairs of copies, one from each assessed family, and yielding the least sum of squared residuals. Analyses of simulated gene family data sets show that IES’s accuracy is on par with popular tree-based methods while also less susceptible to noise introduced by sequence alignment and evolutionary model fitting. Applying IES to an empirical data set of 1,322 genes from 42 archaeal genomes identified eight major clusters of gene families with compatible evolutionary trends. The most cohesive cluster consisted of 62 genes with compatible evolutionary signal, which occur as both single-copy and multiple homologs per genome; phylogenetic analysis of concatenated alignments from this cluster produced a tree closely matching previously published species trees for Archaea. Four other clusters are mainly composed of accessory genes with limited distribution among Archaea and enriched toward specific metabolic functions. Pairwise evolutionary distances obtained from these accessory gene clusters suggest patterns of interphyla horizontal gene transfer. An IES implementation is available at https://github.com/lthiberiol/evolSimIndex.

Identification and characterization of proteins of unknown function (PUFs) in Clostridium thermocellum DSM 1313 strains as potential genetic engineering targets

BACKGROUND

Mass spectrometry-based proteomics can identify and quantify thousands of proteins from individual microbial species, but a significant percentage of these proteins are unannotated and hence classified as proteins of unknown function (PUFs). Due to the difficulty in extracting meaningful metabolic information, PUFs are often overlooked or discarded during data analysis, even though they might be critically important in functional activities, in particular for metabolic engineering research.

RESULTS

We optimized and employed a pipeline integrating various "guilt-by-association" (GBA) metrics, including differential expression and co-expression analyses of high-throughput mass spectrometry proteome data and phylogenetic coevolution analysis, and sequence homology-based approaches to determine putative functions for PUFs in Clostridium thermocellum. Our various analyses provided putative functional information for over 95% of the PUFs detected by mass spectrometry in a wild-type and/or an engineered strain of C. thermocellum. In particular, we validated a predicted acyltransferase PUF (WP_003519433.1) with functional activity towards 2-phenylethyl alcohol, consistent with our GBA and sequence homology-based predictions.

CONCLUSIONS

This work demonstrates the value of leveraging sequence homology-based annotations with empirical evidence based on the concept of GBA to broadly predict putative functions for PUFs, opening avenues to further interrogation via targeted experiments.

PhyloCorrelate: inferring bacterial gene-gene functional associations through large-scale phylogenetic profiling

MOTIVATION

Statistical detection of co-occurring genes across genomes, known as "phylogenetic profiling", is a powerful bioinformatic technique for inferring gene-gene functional associations. However, this can be a challenging task given the size and complexity of phylogenomic databases, difficulty in accounting for phylogenetic structure, inconsistencies in genome annotation, and substantial computational requirements.

RESULTS

We introduce PhyloCorrelate-a computational framework for gene co-occurrence analysis across large phylogenomic datasets. PhyloCorrelate implements a variety of co-occurrence metrics including standard correlation metrics and model-based metrics that account for phylogenetic history. By combining multiple metrics, we developed an optimized score that exhibits a superior ability to link genes with overlapping GO terms and KEGG pathways, enabling gene function prediction. Using genomic and functional annotation data from the Genome Taxonomy Database and AnnoTree, we performed all-by-all comparisons of gene occurrence profiles across the bacterial tree of life, totaling 154,217,052 comparisons for 28,315 genes across 27,372 bacterial genomes. All predictions are available in an online database, which instantaneously returns the top correlated genes for any PFAM, TIGRFAM, or KEGG query. In total, PhyloCorrelate detected 29,762 high confidence associations between bacterial gene/protein pairs, and generated functional predictions for 834 DUFs and proteins of unknown function.

AVAILABILITY

PhyloCorrelate is available as a web-server at phylocorrelate.uwaterloo.ca as well as an R package for analysis of custom datasets. We anticipate that PhyloCorrelate will be broadly useful as a tool for predicting function and interactions for gene families.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Repeated gain and loss of a single gene modulates the evolution of vascular plant pathogen lifestyles

Vascular plant pathogens travel long distances through host veins, leading to life-threatening, systemic infections. In contrast, nonvascular pathogens remain restricted to infection sites, triggering localized symptom development. The contrasting features of vascular and nonvascular diseases suggest distinct etiologies, but the basis for each remains unclear. Here, we show that the hydrolase CbsA acts as a phenotypic switch between vascular and nonvascular plant pathogenesis. cbsA was enriched in genomes of vascular phytopathogenic bacteria in the family Xanthomonadaceae and absent in most nonvascular species. CbsA expression allowed nonvascular Xanthomonas to cause vascular blight, while cbsA mutagenesis resulted in reduction of vascular or enhanced nonvascular symptom development. Phylogenetic hypothesis testing further revealed that cbsA was lost in multiple nonvascular lineages and more recently gained by some vascular subgroups, suggesting that vascular pathogenesis is ancestral. Our results overall demonstrate how the gain and loss of single loci can facilitate the evolution of complex ecological traits.

Analysis of Protein-Protein Interaction Networks through Computational Approaches

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein-protein interaction prediction.

Gene expression of functionally-related genes coevolves across fungal species: detecting coevolution of gene expression using phylogenetic comparative methods

BACKGROUND

Researchers often measure changes in gene expression across conditions to better understand the shared functional roles and regulatory mechanisms of different genes. Analogous to this is comparing gene expression across species, which can improve our understanding of the evolutionary processes shaping the evolution of both individual genes and functional pathways. One area of interest is determining genes showing signals of coevolution, which can also indicate potential functional similarity, analogous to co-expression analysis often performed across conditions for a single species. However, as with any trait, comparing gene expression across species can be confounded by the non-independence of species due to shared ancestry, making standard hypothesis testing inappropriate.

RESULTS

We compared RNA-Seq data across 18 fungal species using a multivariate Brownian Motion phylogenetic comparative method (PCM), which allowed us to quantify coevolution between protein pairs while directly accounting for the shared ancestry of the species. Our work indicates proteins which physically-interact show stronger signals of coevolution than randomly-generated pairs. Interactions with stronger empirical and computational evidence also showing stronger signals of coevolution. We examined the effects of number of protein interactions and gene expression levels on coevolution, finding both factors are overall poor predictors of the strength of coevolution between a protein pair. Simulations further demonstrate the potential issues of analyzing gene expression coevolution without accounting for shared ancestry in a standard hypothesis testing framework. Furthermore, our simulations indicate the use of a randomly-generated null distribution as a means of determining statistical significance for detecting coevolving genes with phylogenetically-uncorrected correlations, as has previously been done, is less accurate than PCMs, although is a significant improvement over standard hypothesis testing. These methods are further improved by using a phylogenetically-corrected correlation metric.

CONCLUSIONS

Our work highlights potential benefits of using PCMs to detect gene expression coevolution from high-throughput omics scale data. This framework can be built upon to investigate other evolutionary hypotheses, such as changes in transcription regulatory mechanisms across species.

Logicome Profiler: Exhaustive detection of statistically significant logic relationships from comparative omics data

Logic relationship analysis is a data mining method that comprehensively detects item triplets that satisfy logic relationships from a binary matrix dataset, such as an ortholog table in comparative genomics. Thanks to recent technological advancements, many binary matrix datasets are now being produced in genomics, transcriptomics, epigenomics, metagenomics, and many other fields for comparative purposes. However, regardless of presumed interpretability and importance of logic relationships, existing data mining methods are not based on the framework of statistical hypothesis testing. That means, the type-1 and type-2 error rates are neither controlled nor estimated. Here, we developed Logicome Profiler, which exhaustively detects statistically significant triplet logic relationships from a binary matrix dataset (Logicome means ome of logics). To test all item triplets in a dataset while avoiding false positives, Logicome Profiler adjusts a significance level by the Bonferroni or Benjamini-Yekutieli method for the multiple testing correction. Its application to an ocean metagenomic dataset showed that Logicome Profiler can effectively detect statistically significant triplet logic relationships among environmental microbes and genes, which include those among urea transporter, urease, and photosynthesis-related genes. Beyond omics data analysis, Logicome Profiler is applicable to various binary matrix datasets in general for finding significant triplet logic relationships. The source code is available at https://github.com/fukunagatsu/LogicomeProfiler.

Novel phylogenetic methods are needed for understanding gene function in the era of mega-scale genome sequencing

Ongoing large-scale genome sequencing projects are forecasting a data deluge that will almost certainly overwhelm current analytical capabilities of evolutionary genomics. In contrast to population genomics, there are no standardized methods in evolutionary genomics for extracting evolutionary and functional (e.g. gene-trait association) signal from genomic data. Here, we examine how current practices of multi-species comparative genomics perform in this aspect and point out that many genomic datasets are under-utilized due to the lack of powerful methodologies. As a result, many current analyses emphasize gene families for which some functional data is already available, resulting in a growing gap between functionally well-characterized genes/organisms and the universe of unknowns. This leaves unknown genes on the 'dark side' of genomes, a problem that will not be mitigated by sequencing more and more genomes, unless we develop tools to infer functional hypotheses for unknown genes in a systematic manner. We provide an inventory of recently developed methods capable of predicting gene-gene and gene-trait associations based on comparative data, then argue that realizing the full potential of whole genome datasets requires the integration of phylogenetic comparative methods into genomics, a rich but underutilized toolbox for looking into the past.

PhySpeTree: an automated pipeline for reconstructing phylogenetic species trees

Background

Phylogenetic species trees are widely used in inferring evolutionary relationships. Existing software and algorithms mainly focus on phylogenetic inference. However, less attention has been paid to intermediate steps, such as processing extremely large sequences and preparing configure files to connect multiple software. When the species number is large, the intermediate steps become a bottleneck that may seriously affect the efficiency of tree building.

Results

Here, we present an easy-to-use pipeline named PhySpeTree to facilitate the reconstruction of species trees across bacterial, archaeal, and eukaryotic organisms. Users need only to input the abbreviations of species names; PhySpeTree prepares complex configure files for different software, then automatically downloads genomic data, cleans sequences, and builds trees. PhySpeTree allows users to perform critical steps such as sequence alignment and tree construction by adjusting advanced options. PhySpeTree provides two parallel pipelines based on concatenated highly conserved proteins and small subunit ribosomal RNA sequences, respectively. Accessory modules, such as those for inserting new species, generating visualization configurations, and combining trees, are distributed along with PhySpeTree.

Conclusions

Together with accessory modules, PhySpeTree significantly simplifies tree reconstruction. PhySpeTree is implemented in Python running on modern operating systems (Linux, macOS, and Windows). The source code is freely available with detailed documentation (https://github.com/yangfangs/physpetools).

Coupled Genomic Evolutionary Histories as Signatures of Organismal Innovations in Cephalopods: Co-evolutionary Signatures Across Levels of Genome Organization May Shed Light on Functional Linkage and Origin of Cephalopod Novelties

How genomic innovation translates into organismal organization remains largely unanswered. Possessing the largest invertebrate nervous system, in conjunction with many species-specific organs, coleoid cephalopods (octopuses, squids, cuttlefishes) provide exciting model systems to investigate how organismal novelties evolve. However, dissecting these processes requires novel approaches that enable deeper interrogation of genome evolution. Here, the existence of specific sets of genomic co-evolutionary signatures between expanded gene families, genome reorganization, and novel genes is posited. It is reasoned that their co-evolution has contributed to the complex organization of cephalopod nervous systems and the emergence of ecologically unique organs. In the course of reviewing this field, how the first cephalopod genomic studies have begun to shed light on the molecular underpinnings of morphological novelty is illustrated and their impact on directing future research is described. It is argued that the application and evolutionary profiling of evolutionary signatures from these studies will help identify and dissect the organismal principles of cephalopod innovations. By providing specific examples, the implications of this approach both within and beyond cephalopod biology are discussed.

Identification and characterization of NF-Y gene family in walnut (Juglans regia L.)

BACKGROUND

The eukaryotic transcription factor NF-Y (which consists of NF-YA, NF-YB and NF-YC subunits) is involved in many important plant development processes. There are many reports about the NF-Y family in Arabidopsis and other plant species. However, there are no reports about the NF-Y family in walnut (Juglans regia L.).

RESULTS

Thirty-three walnut NF-Y genes (JrNF-Ys) were identified and mapped on the walnut genome. The JrNF-Y gene family consisted of 17 NF-YA genes, 9 NF-YB genes, and 7 NF-YC genes. The structural features of the JrNF-Y genes were investigated by comparing their evolutionary relationship and motif distributions. The comparisons indicated the NF-Y gene structure was both conserved and altered during evolution. Functional prediction and protein interaction analysis were performed by comparing the JrNF-Y protein structure with that in Arabidopsis. Two differentially expressed JrNF-Y genes were identified. Their expression was compared with that of three JrCOs and two JrFTs using quantitative real-time PCR (qPCR). The results revealed that the expression of JrCO2 was positively correlated with the expression of JrNF-YA11 and JrNF-YA12. In contrast, JrNF-CO1 and JrNF-YA12 were negatively correlated.

CONCLUSIONS

Thirty-three JrNF-Ys were identified and their evolutionary, structure, biological function and expression pattern were analyzed. Two of the JrNF-Ys were screened out, their expression was differentially expressed in different development periods of female flower buds, and in different tissues (female flower buds and leaf buds). Based on prediction and experimental data, JrNF-Ys may be involved in flowering regulation by co-regulate the expression of flowering genes with other transcription factors (TFs). The results of this study may make contribution to the further investigation of JrNF-Y family.

Phylogenetic Clustering of Genes Reveals Shared Evolutionary Trajectories and Putative Gene Functions

Homologous genes in prokaryotes can be described using phylogenetic profiles which summarize their patterns of presence or absence across a set of genomes. Phylogenetic profiles have been used for nearly twenty years to cluster genes based on measures such as the Euclidean distance between profile vectors. However, most approaches do not take into account the phylogenetic relationships amongst the profiled genomes, and overrepresentation of certain taxonomic groups (i.e., pathogenic species with many sequenced representatives) can skew the interpretation of profiles. We propose a new approach that uses a coevolutionary method defined by Pagel to account for the phylogenetic relationships amongst target organisms, and a hierarchical-clustering approach to define sets of genes with common distributions across the organisms. The clusters we obtain using our method show greater evidence of phylogenetic and functional clustering than a recently published approach based on hidden Markov models. Our clustering method identifies sets of amino-acid biosynthesis genes that constitute cohesive pathways, and motility/chemotaxis genes with common histories of descent and lateral gene transfer.

Testing the Domino Theory of Gene Loss in Buchnera aphidicola: The Relevance of Epistatic Interactions

The domino theory of gene loss states that when some particular gene loses its function and cripples a cellular function, selection will relax in all functionally related genes, which may allow for the non-functionalization and loss of these genes. Here we study the role of epistasis in determining the pattern of gene losses in a set of genes participating in cell envelope biogenesis in the endosymbiotic bacteria Buchnera aphidicola. We provide statistical evidence indicating pairs of genes in B. aphidicola showing correlated gene loss tend to have orthologs in Escherichia coli known to have alleviating epistasis. In contrast, pairs of genes in B. aphidicola not showing correlated gene loss tend to have orthologs in E. coli known to have aggravating epistasis. These results suggest that during the process of genome reduction in B. aphidicola by gene loss, positive or alleviating epistasis facilitates correlated gene losses while negative or aggravating epistasis impairs correlated gene losses. We interpret this as evidence that the reduced proteome of B. aphidicola contains less pathway redundancy and more compensatory interactions, mimicking the situation of E. coli when grown under environmental constrains.

A phylogenetic method to perform genome-wide association studies in microbes that accounts for population structure and recombination

Genome-Wide Association Studies (GWAS) in microbial organisms have the potential to vastly improve the way we understand, manage, and treat infectious diseases. Yet, microbial GWAS methods established thus far remain insufficiently able to capitalise on the growing wealth of bacterial and viral genetic sequence data. Facing clonal population structure and homologous recombination, existing GWAS methods struggle to achieve both the precision necessary to reject spurious findings and the power required to detect associations in microbes. In this paper, we introduce a novel phylogenetic approach that has been tailor-made for microbial GWAS, which is applicable to organisms ranging from purely clonal to frequently recombining, and to both binary and continuous phenotypes. Our approach is robust to the confounding effects of both population structure and recombination, while maintaining high statistical power to detect associations. Thorough testing via application to simulated data provides strong support for the power and specificity of our approach and demonstrates the advantages offered over alternative cluster-based and dimension-reduction methods. Two applications to Neisseria meningitidis illustrate the versatility and potential of our method, confirming previously-identified penicillin resistance loci and resulting in the identification of both well-characterised and novel drivers of invasive disease. Our method is implemented as an open-source R package called treeWAS which is freely available at https://github.com/caitiecollins/treeWAS.

Measurable differences often exist within a microbial population, with important ecological or epidemiological consequences. Examples include differences in growth rates, host range, transmissibility, antimicrobial resistance, virulence, etc. Understanding the genetic factors involved in these phenotypic properties is a crucial aim in microbial genomics. A fundamental approach for doing so is to perform a Genome-Wide Association Study (GWAS), where genomes are compared to search for genetic markers systematically correlated with the property of interest. If this strategy were implemented naively in microbes, it could lead to spurious results due to the confounding effects of population structure and recombination. Here we present treeWAS, a new phylogenetic method to perform microbial GWAS that avoids these pitfalls. We show, using simulated datasets, that treeWAS is able to distinguish between genetic markers that are truly associated with the property of interest and those that are not. Furthermore, we demonstrate that treeWAS offers advantages in both sensitivity and specificity over alternative cluster-based and dimension-reduction techniques. We also showcase treeWAS in two applications to real datasets from N. meningitidis. We have developed an easy-to-use implementation of treeWAS in the R environment, which should be useful to a wide range of researchers in microbial genomics.

Pairwise comparisons across species are problematic when analyzing functional genomic data

There is considerable interest in comparing functional genomic data across species. One goal of such work is to provide an integrated understanding of genome and phenotype evolution. Most comparative functional genomic studies have relied on multiple pairwise comparisons between species, an approach that does not incorporate information about the evolutionary relationships among species. The statistical problems that arise from not considering these relationships can lead pairwise approaches to the wrong conclusions and are a missed opportunity to learn about biology that can only be understood in an explicit phylogenetic context. Here, we examine two recently published studies that compare gene expression across species with pairwise methods, and find reason to question the original conclusions of both. One study interpreted pairwise comparisons of gene expression as support for the ortholog conjecture, the hypothesis that orthologs tend to have more similar attributes (expression in this case) than paralogs. The other study interpreted pairwise comparisons of embryonic gene expression across distantly related animals as evidence for a distinct evolutionary process that gave rise to phyla. In each study, distinct patterns of pairwise similarity among species were originally interpreted as evidence of particular evolutionary processes, but instead, we find that they reflect species relationships. These reanalyses concretely show the inadequacy of pairwise comparisons for analyzing functional genomic data across species. It will be critical to adopt phylogenetic comparative methods in future functional genomic work. Fortunately, phylogenetic comparative biology is also a rapidly advancing field with many methods that can be directly applied to functional genomic data.

PrePhyloPro: phylogenetic profile-based prediction of whole proteome linkages

Direct and indirect functional links between proteins as well as their interactions as part of larger protein complexes or common signaling pathways may be predicted by analyzing the correlation of their evolutionary patterns. Based on phylogenetic profiling, here we present a highly scalable and time-efficient computational framework for predicting linkages within the whole human proteome. We have validated this method through analysis of 3,697 human pathways and molecular complexes and a comparison of our results with the prediction outcomes of previously published co-occurrency model-based and normalization methods. Here we also introduce PrePhyloPro, a web-based software that uses our method for accurately predicting proteome-wide linkages. We present data on interactions of human mitochondrial proteins, verifying the performance of this software. PrePhyloPro is freely available at http://prephylopro.org/phyloprofile/.

Ecology and Feeding Habits Drive Infection of Water Bugs with Mycobacterium ulcerans

Mycobacterium ulcerans (MU), the causative agent of Buruli ulcer, is present in a wide spectrum of environments, including terrestrial and aquatic ecosystems in tropical regions. The most promising studies on the epidemiological risk of this disease suggest that some ecological settings may favor infection of animals with MU including human. A species' needs and impacts on resources and the environment, i.e., its ecological niche, may influence its susceptibility to be infected by this microbial form. For example, some Naucoridae may dive in fresh waters to prey upon infected animals and thus may get infected with MU. However, these studies have rarely considered that inference on the ecological settings favoring infection and transmission may be confounded because host carrier sister species have similar ecological niches, and potentially the same host-microbe interactions. Hence, a relationship between the ecological niche of Naucoridae and its infection with MU may be due to a symbiotic relationship between the host and the pathogen, rather than its ecological niche. To account for this confounding effect, we investigated the relationships between surrogates of the ecological niche of water bug species and their susceptibility to MU, by performing phylogenetic comparative analyses on a large dataset of 11 families of water bugs collected in 10 different sites across Cameroon, central Africa. Our results indicate that MU circulates and infects a couple of host taxa, i.e., Belostomatidae, Naucoridae, living both in the aquatic vegetation and as predators inside the trophic network and sister species of water bugs have indeed similar host-microbe interactions with MU.

From Genomes to Phenotypes: Traitar, the Microbial Trait Analyzer

Bacteria are ubiquitous in our ecosystem and have a major impact on human health, e.g., by supporting digestion in the human gut. Bacterial communities can also aid in biotechnological processes such as wastewater treatment or decontamination of polluted soils. Diverse bacteria contribute with their unique capabilities to the functioning of such ecosystems, but lab experiments to investigate those capabilities are labor-intensive. Major advances in sequencing techniques open up the opportunity to study bacteria by their genome sequences. For this purpose, we have developed Traitar, software that predicts traits of bacteria on the basis of their genomes. It is applicable to studies with tens or hundreds of bacterial genomes. Traitar may help researchers in microbiology to pinpoint the traits of interest, reducing the amount of wet lab work required.

The number of sequenced genomes is growing exponentially, profoundly shifting the bottleneck from data generation to genome interpretation. Traits are often used to characterize and distinguish bacteria and are likely a driving factor in microbial community composition, yet little is known about the traits of most microbes. We describe Traitar, the microbial trait analyzer, which is a fully automated software package for deriving phenotypes from a genome sequence. Traitar provides phenotype classifiers to predict 67 traits related to the use of various substrates as carbon and energy sources, oxygen requirement, morphology, antibiotic susceptibility, proteolysis, and enzymatic activities. Furthermore, it suggests protein families associated with the presence of particular phenotypes. Our method uses L1-regularized L2-loss support vector machines for phenotype assignments based on phyletic patterns of protein families and their evolutionary histories across a diverse set of microbial species. We demonstrate reliable phenotype assignment for Traitar to bacterial genomes from 572 species of eight phyla, also based on incomplete single-cell genomes and simulated draft genomes. We also showcase its application in metagenomics by verifying and complementing a manual metabolic reconstruction of two novel Clostridiales species based on draft genomes recovered from commercial biogas reactors. Traitar is available at https://github.com/hzi-bifo/traitar.

IMPORTANCE Bacteria are ubiquitous in our ecosystem and have a major impact on human health, e.g., by supporting digestion in the human gut. Bacterial communities can also aid in biotechnological processes such as wastewater treatment or decontamination of polluted soils. Diverse bacteria contribute with their unique capabilities to the functioning of such ecosystems, but lab experiments to investigate those capabilities are labor-intensive. Major advances in sequencing techniques open up the opportunity to study bacteria by their genome sequences. For this purpose, we have developed Traitar, software that predicts traits of bacteria on the basis of their genomes. It is applicable to studies with tens or hundreds of bacterial genomes. Traitar may help researchers in microbiology to pinpoint the traits of interest, reducing the amount of wet lab work required.

The phylogeny of Heliconia (Heliconiaceae) and the evolution of floral presentation

Heliconia (Heliconiaceae, order Zingiberales) is among the showiest plants of the Neotropical rainforest and represent a spectacular co-evolutionary radiation with hummingbirds. Despite the attractiveness and ecological importance of many Heliconia, the genus has been the subject of limited molecular phylogenetic studies. We sample seven markers from the plastid and nuclear genomes for 202 samples of Heliconia. This represents ca. 75% of accepted species and includes coverage of all taxonomic subgenera and sections. We date this phylogeny using fossils associated with other families in the Zingiberales; in particular we review and evaluate the Eocene fossil Ensete oregonense. We use this dated phylogenetic framework to evaluate the evolution of two components of flower orientation that are hypothesized to be important for modulating pollinator discrimination and pollen placement: resupination and erect versus pendant inflorescence habit. Our phylogenetic results suggest that the monophyletic Melanesian subgenus Heliconiopsis and a small clade of Ecuadorian species are together the sister group to the rest of Heliconia. Extant diversity of Heliconia originated in the Late Eocene (39Ma) with rapid diversification through the Early Miocene, making it the oldest known clade of hummingbird-pollinated plants. Most described subgenera and sections are not monophyletic, though closely related groups of species, often defined by shared geography, mirror earlier morphological cladistic analyses. Evaluation of changes in resupination and inflorescence habit suggests that these characters are more homoplasious than expected, and this largely explains the non-monophyly of previously circumscribed subgenera, which were based on these characters. We also find strong evidence for the correlated evolution of resupination and inflorescence habit. The correlated model suggests that the most recent common ancestor of all extant Heliconia had resupinate flowers and erect inflorescences. Finally, we note our nearly complete species sampling and dated phylogeny allow for an assessment of taxonomic history in terms of phylogenetic diversity. We find approximately half of the currently recognized species, corresponding to half of the phylogenetic diversity, have been described since 1975, highlighting the continued importance of basic taxonomic research and conservation initiatives to preserve both described and undiscovered species of Heliconia.

Comparative genomics reveals genes significantly associated with woody hosts in the plant pathogen Pseudomonas syringae

The diversification of lineages within Pseudomonas syringae has involved a number of adaptive shifts from herbaceous hosts onto various species of tree, resulting in the emergence of highly destructive diseases such as bacterial canker of kiwi and bleeding canker of horse chestnut. This diversification has involved a high level of gene gain and loss, and these processes are likely to play major roles in the adaptation of individual lineages onto their host plants. In order to better understand the evolution of P. syringae onto woody plants, we have generated de novo genome sequences for 26 strains from the P. syringae species complex that are pathogenic on a range of woody species, and have looked for statistically significant associations between gene presence and host type (i.e. woody or herbaceous) across a phylogeny of 64 strains. We have found evidence for a common set of genes associated with strains that are able to colonize woody plants, suggesting that divergent lineages have acquired similarities in genome composition that may form the genetic basis of their adaptation to woody hosts. We also describe in detail the gain, loss and rearrangement of specific loci that may be functionally important in facilitating this adaptive shift. Overall, our analyses allow for a greater understanding of how gene gain and loss may contribute to adaptation in P. syringae.

Testing for Independence between Evolutionary Processes

Evolutionary events co-occurring along phylogenetic trees usually point to complex adaptive phenomena, sometimes implicating epistasis. While a number of methods have been developed to account for co-occurrence of events on the same internal or external branch of an evolutionary tree, there is a need to account for the larger diversity of possible relative positions of events in a tree. Here we propose a method to quantify to what extent two or more evolutionary events are associated on a phylogenetic tree. The method is applicable to any discrete character, like substitutions within a coding sequence or gains/losses of a biological function. Our method uses a general approach to statistically test for significant associations between events along the tree, which encompasses both events inseparable on the same branch, and events genealogically ordered on different branches. It assumes that the phylogeny and themapping of branches is known without errors. We address this problem from the statistical viewpoint by a linear algebra representation of the localization of the evolutionary events on the tree.We compute the full probability distribution of the number of paired events occurring in the same branch or in different branches of the tree, under a null model of independence where each type of event occurs at a constant rate uniformly inthephylogenetic tree. The strengths andweaknesses of themethodare assessed via simulations;we then apply the method to explore the loss of cell motility in intracellular pathogens.

Phylogenetic Profiling for Probing the Modular Architecture of the Human Genome

Information about functional connections between genes can be derived from patterns of coupled loss of their homologs across multiple species. This comparative approach, termed phylogenetic profiling, has been successfully used to infer genetic interactions in bacteria and eukaryotes. Rapid progress in sequencing eukaryotic species has enabled the recent phylogenetic profiling of the human genome, resulting in systematic functional predictions for uncharacterized human genes. Importantly, groups of co-evolving genes reveal widespread modularity in the underlying genetic network, facilitating experimental analyses in human cells as well as comparative studies of conserved functional modules across species. This strategy is particularly successful in identifying novel metabolic proteins and components of multi-protein complexes. The targeted sequencing of additional key eukaryotes and the incorporation of improved methods to generate and compare phylogenetic profiles will further boost the predictive power and utility of this evolutionary approach to the functional analysis of gene interaction networks.

Prediction of host - pathogen protein interactions between Mycobacterium tuberculosis and Homo sapiens using sequence motifs

Background

Emergence of multiple drug resistant strains of M. tuberculosis (MDR-TB) threatens to derail global efforts aimed at reigning in the pathogen. Co-infections of M. tuberculosis with HIV are difficult to treat. To counter these new challenges, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease.

Results

We report a systematic flow to predict the host pathogen interactions (HPIs) between M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs by ‘interolog’ method. HPIs were further filtered by prediction of domain-domain interactions (DDIs). Functional annotations of protein and publicly available experimental results were applied to filter the remaining HPIs. Using such a strategy, 118 pairs of HPIs were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. A biological interaction network between M. tuberculosis and Homo sapiens was then constructed using the predicted inter- and intra-species interactions based on the 118 pairs of HPIs. Finally, a web accessible database named PATH (Protein interactions of M. tuberculosis and Human) was constructed to store these predicted interactions and proteins.

Conclusions

This interaction network will facilitate the research on host-pathogen protein-protein interactions, and may throw light on how M. tuberculosis interacts with its host.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0535-y) contains supplementary material, which is available to authorized users.

Systematic Discovery of Human Gene Function and Principles of Modular Organization through Phylogenetic Profiling

Functional links between genes can be predicted using phylogenetic profiling, by correlating the appearance and loss of homologs in subsets of species. However, effective genome-wide phylogenetic profiling has been hindered by the large fraction of human genes related to each other through historical duplication events. Here, we overcame this challenge by automatically profiling over 30,000 groups of homologous human genes (orthogroups) representing the entire protein-coding genome across 177 eukaryotic species (hOP profiles). By generating a full pairwise orthogroup phylogenetic co-occurrence matrix, we derive unbiased genome-wide predictions of functional modules (hOP modules). Our approach predicts functions for hundreds of poorly characterized genes. The results suggest evolutionary constraints that lead components of protein complexes and metabolic pathways to co-evolve while genes in signaling and transcriptional networks do not. As a proof of principle, we validated two subsets of candidates experimentally for their predicted link to the actin-nucleating WASH complex and cilia/basal body function.

Expansion of biological pathways based on evolutionary inference

The availability of diverse genomes makes it possible to predict gene function based on shared evolutionary history. This approach can be challenging, however, for pathways whose components do not exhibit a shared history but rather consist of distinct "evolutionary modules." We introduce a computational algorithm, clustering by inferred models of evolution (CLIME), which inputs a eukaryotic species tree, homology matrix, and pathway (gene set) of interest. CLIME partitions the gene set into disjoint evolutionary modules, simultaneously learning the number of modules and a tree-based evolutionary history that defines each module. CLIME then expands each module by scanning the genome for new components that likely arose under the inferred evolutionary model. Application of CLIME to ∼1,000 annotated human pathways and to the proteomes of yeast, red algae, and malaria reveals unanticipated evolutionary modularity and coevolving components. CLIME is freely available and should become increasingly powerful with the growing wealth of eukaryotic genomes.

EvoCor: a platform for predicting functionally related genes using phylogenetic and expression profiles

The wealth of publicly available gene expression and genomic data provides unique opportunities for computational inference to discover groups of genes that function to control specific cellular processes. Such genes are likely to have co-evolved and be expressed in the same tissues and cells. Unfortunately, the expertise and computational resources required to compare tens of genomes and gene expression data sets make this type of analysis difficult for the average end-user. Here, we describe the implementation of a web server that predicts genes involved in affecting specific cellular processes together with a gene of interest. We termed the server 'EvoCor', to denote that it detects functional relationships among genes through evolutionary analysis and gene expression correlation. This web server integrates profiles of sequence divergence derived by a Hidden Markov Model (HMM) and tissue-wide gene expression patterns to determine putative functional linkages between pairs of genes. This server is easy to use and freely available at http://pilot-hmm.vbi.vt.edu/.

Practical aspects of protein co-evolution

Co-evolution is a fundamental aspect of Evolutionary Theory. At the molecular level, co-evolutionary linkages between protein families have been used as indicators of protein interactions and functional relationships from long ago. Due to the complexity of the problem and the amount of genomic data required for these approaches to achieve good performances, it took a relatively long time from the appearance of the first ideas and concepts to the quotidian application of these approaches and their incorporation to the standard toolboxes of bioinformaticians and molecular biologists. Today, these methodologies are mature (both in terms of performance and usability/implementation), and the genomic information that feeds them large enough to allow their general application. This review tries to summarize the current landscape of co-evolution-based methodologies, with a strong emphasis on describing interesting cases where their application to important biological systems, alone or in combination with other computational and experimental approaches, allowed getting new insight into these.

Computational prediction of the human-microbial oral interactome

BACKGROUND

The oral cavity is a complex ecosystem where human chemical compounds coexist with a particular microbiota. However, shifts in the normal composition of this microbiota may result in the onset of oral ailments, such as periodontitis and dental caries. In addition, it is known that the microbial colonization of the oral cavity is mediated by protein-protein interactions (PPIs) between the host and microorganisms. Nevertheless, this kind of PPIs is still largely undisclosed. To elucidate these interactions, we have created a computational prediction method that allows us to obtain a first model of the Human-Microbial oral interactome.

RESULTS

We collected high-quality experimental PPIs from five major human databases. The obtained PPIs were used to create our positive dataset and, indirectly, our negative dataset. The positive and negative datasets were merged and used for training and validation of a naïve Bayes classifier. For the final prediction model, we used an ensemble methodology combining five distinct PPI prediction techniques, namely: literature mining, primary protein sequences, orthologous profiles, biological process similarity, and domain interactions. Performance evaluation of our method revealed an area under the ROC-curve (AUC) value greater than 0.926, supporting our primary hypothesis, as no single set of features reached an AUC greater than 0.877. After subjecting our dataset to the prediction model, the classified result was filtered for very high confidence PPIs (probability ≥ 1-10-7), leading to a set of 46,579 PPIs to be further explored.

CONCLUSIONS

We believe this dataset holds not only important pathways involved in the onset of infectious oral diseases, but also potential drug-targets and biomarkers. The dataset used for training and validation, the predictions obtained and the network final network are available at http://bioinformatics.ua.pt/software/oralint.

Molecular evolution of Zika virus during its emergence in the 20(th) century

Zika virus (ZIKV) is a mosquito-borne flavivirus first isolated in Uganda in 1947. Although entomological and virologic surveillance have reported ZIKV enzootic activity in diverse countries of Africa and Asia, few human cases were reported until 2007, when a Zika fever epidemic took place in Micronesia. In the context of West Africa, the WHO Collaborating Centre for Arboviruses and Hemorrhagic Fever at Institut Pasteur of Dakar (http://www.pasteur.fr/recherche/banques/CRORA/) reports the periodic circulation of ZIKV since 1968. Despite several reports on ZIKV, the genetic relationships among viral strains from West Africa remain poorly understood. To evaluate the viral spread and its molecular epidemiology, we investigated 37 ZIKV isolates collected from 1968 to 2002 in six localities in Senegal and Côte d'Ivoire. In addition, we included strains from six other countries. Our results suggested that these two countries in West Africa experienced at least two independent introductions of ZIKV during the 20(th) century, and that apparently these viral lineages were not restricted by mosquito vector species. Moreover, we present evidence that ZIKV has possibly undergone recombination in nature and that a loss of the N154 glycosylation site in the envelope protein was a possible adaptive response to the Aedes dalzieli vector.

Reconciliation-based detection of co-evolving gene families

Background

Genes located in the same chromosome region share common evolutionary events more often than other genes (e.g. a segmental duplication of this region). Their evolution may also be related if they are involved in the same protein complex or biological process. Identifying co-evolving genes can thus shed light on ancestral genome structures and functional gene interactions.

Results

We devise a simple, fast and accurate probability method based on species tree-gene tree reconciliations to detect when two gene families have co-evolved. Our method observes the number and location of predicted macro-evolutionary events, and estimates the probability of having the observed number of common events by chance.

Conclusions

Simulation studies confirm that our method effectively identifies co-evolving families. This opens numerous perspectives on genome-scale analysis where this method could be used to pinpoint co-evolving gene families and thus help to unravel ancestral genome arrangements or undocumented gene interactions.

Advancing data reuse in phyloinformatics using an ontology-driven Semantic Web approach

Phylogenetic analyses can resolve historical relationships among genes, organisms or higher taxa. Understanding such relationships can elucidate a wide range of biological phenomena, including, for example, the importance of gene and genome duplications in the evolution of gene function, the role of adaptation as a driver of diversification, or the evolutionary consequences of biogeographic shifts. Phyloinformaticists are developing data standards, databases and communication protocols (e.g. Application Programming Interfaces, APIs) to extend the accessibility of gene trees, species trees, and the metadata necessary to interpret these trees, thus enabling researchers across the life sciences to reuse phylogenetic knowledge. Specifically, Semantic Web technologies are being developed to make phylogenetic knowledge interpretable by web agents, thereby enabling intelligently automated, high-throughput reuse of results generated by phylogenetic research. This manuscript describes an ontology-driven, semantic problem-solving environment for phylogenetic analyses and introduces artefacts that can promote phyloinformatic efforts to promote accessibility of trees and underlying metadata. PhylOnt is an extensible ontology with concepts describing tree types and tree building methodologies including estimation methods, models and programs. In addition we present the PhylAnt platform for annotating scientific articles and NeXML files with PhylOnt concepts. The novelty of this work is the annotation of NeXML files and phylogenetic related documents with PhylOnt Ontology. This approach advances data reuse in phyloinformatics.

FunCoup 3.0: database of genome-wide functional coupling networks

We present an update of the FunCoup database (http://FunCoup.sbc.su.se) of functional couplings, or functional associations, between genes and gene products. Identifying these functional couplings is an important step in the understanding of higher level mechanisms performed by complex cellular processes. FunCoup distinguishes between four classes of couplings: participation in the same signaling cascade, participation in the same metabolic process, co-membership in a protein complex and physical interaction. For each of these four classes, several types of experimental and statistical evidence are combined by Bayesian integration to predict genome-wide functional coupling networks. The FunCoup framework has been completely re-implemented to allow for more frequent future updates. It contains many improvements, such as a regularization procedure to automatically downweight redundant evidences and a novel method to incorporate phylogenetic profile similarity. Several datasets have been updated and new data have been added in FunCoup 3.0. Furthermore, we have developed a new Web site, which provides powerful tools to explore the predicted networks and to retrieve detailed information about the data underlying each prediction.