CompositeSearch: A Generalized Network Approach for Composite Gene Families Detection

Identification and evolution of a diterpenoid phytoalexin oryzalactone biosynthetic gene in the genus Oryza

The natural variation of plant-specialized metabolites represents the evolutionary adaptation of plants to their environments. However, the molecular mechanisms that account for the diversification of the metabolic pathways have not been fully clarified. Rice plants resist attacks from pathogens by accumulating diterpenoid phytoalexins. It has been confirmed that the composition of rice phytoalexins exhibits numerous natural variations. Major rice phytoalexins (momilactones and phytocassanes) are accumulated in most cultivars, although oryzalactone is a cultivar-specific compound. Here, we attempted to reveal the evolutionary trajectory of the diversification of phytoalexins by analyzing the oryzalactone biosynthetic gene in Oryza species. The candidate gene, KSLX-OL, which accounts for oryzalactone biosynthesis, was found around the single-nucleotide polymorphisms specific to the oryzalactone-accumulating cultivars in the long arm of chromosome 11. The metabolite analyses in Nicotiana benthamiana and rice plants overexpressing KSLX-OL indicated that KSLX-OL is responsible for the oryzalactone biosynthesis. KSLX-OL is an allele of KSL8 that is involved in the biosynthesis of another diterpenoid phytoalexin, oryzalexin S and is specifically distributed in the AA genome species. KSLX-NOL and KSLX-bar, which encode similar enzymes but are not involved in oryzalactone biosynthesis, were also found in AA genome species. The phylogenetic analyses of KSLXs, KSL8s, and related pseudogenes (KSL9s) indicated that KSLX-OL was generated from a common ancestor with KSL8 and KSL9 via gene duplication, functional differentiation, and gene fusion. The wide distributions of KSLX-OL and KSL8 in AA genome species demonstrate their long-term coexistence beyond species differentiation, suggesting a balancing selection between the genes.

S. Andrade R, El-Hani C. On the origin of mitochondria: a multilayer network approach

Backgound

The endosymbiotic theory is widely accepted to explain the origin of mitochondria from a bacterial ancestor. While ample evidence supports the intimate connection of Alphaproteobacteria to the mitochondrial ancestor, pinpointing its closest relative within sampled Alphaproteobacteria is still an open evolutionary debate. Many different phylogenetic methods and approaches have been used to answer this challenging question, further compounded by the heterogeneity of sampled taxa, varying evolutionary rates of mitochondrial proteins, and the inherent biases in each method, all factors that can produce phylogenetic artifacts. By harnessing the simplicity and interpretability of protein similarity networks, herein we re-evaluated the origin of mitochondria within an enhanced multilayer framework, which is an extension and improvement of a previously developed method.

Methods

We used a dataset of eight proteins found in mitochondria (N = 6 organisms) and bacteria (N = 80 organisms). The sequences were aligned and resulting identity matrices were combined to generate an eight-layer multiplex network. Each layer corresponded to a protein network, where nodes represented organisms and edges were placed following mutual sequence identity. The Multi-Newman-Girvan algorithm was applied to evaluate community structure, and bifurcation events linked to network partition allowed to trace patterns of divergence between studied taxa.

Results

In our network-based analysis, we first examined the topology of the 8-layer multiplex when mitochondrial sequences disconnected from the main alphaproteobacterial cluster. The resulting topology lent firm support toward an Alphaproteobacteria-sister placement for mitochondria, reinforcing the hypothesis that mitochondria diverged from the common ancestor of all Alphaproteobacteria. Additionally, we observed that the divergence of Rickettsiales was an early event in the evolutionary history of alphaproteobacterial clades.

Conclusion

By leveraging complex networks methods to the challenging question of circumscribing mitochondrial origin, we suggest that the entire Alphaproteobacteria clade is the closest relative to mitochondria (Alphaproteobacterial-sister hypothesis), echoing recent findings based on different datasets and methodologies.

Divergent genomic trajectories predate the origin of animals and fungi

Animals and fungi have radically distinct morphologies, yet both evolved within the same eukaryotic supergroup: Opisthokonta^1,2. Here we reconstructed the trajectory of genetic changes that accompanied the origin of Metazoa and Fungi since the divergence of Opisthokonta with a dataset that includes four novel genomes from crucial positions in the Opisthokonta phylogeny. We show that animals arose only after the accumulation of genes functionally important for their multicellularity, a tendency that began in the pre-metazoan ancestors and later accelerated in the metazoan root. By contrast, the pre-fungal ancestors experienced net losses of most functional categories, including those gained in the path to Metazoa. On a broad-scale functional level, fungal genomes contain a higher proportion of metabolic genes and diverged less from the last common ancestor of Opisthokonta than did the gene repertoires of Metazoa. Metazoa and Fungi also show differences regarding gene gain mechanisms. Gene fusions are more prevalent in Metazoa, whereas a larger fraction of gene gains were detected as horizontal gene transfers in Fungi and protists, in agreement with the long-standing idea that transfers would be less relevant in Metazoa due to germline isolation^3-5. Together, our results indicate that animals and fungi evolved under two contrasting trajectories of genetic change that predated the origin of both groups. The gradual establishment of two clearly differentiated genomic contexts thus set the stage for the emergence of Metazoa and Fungi.

Hundreds of out-of-frame remodelled gene families in the E. coli pangenome

All genomes include gene families with very limited taxonomic distributions that potentially represent new genes and innovations in protein-coding sequence, raising questions on the origins of such genes. Some of these genes are hypothesized to have formed de novo, from noncoding sequences, and recent work has begun to elucidate the processes by which de novo gene formation can occur. A special case of de novo gene formation, overprinting, describes the origin of new genes from noncoding alternative reading frames of existing open reading frames (ORFs). We argue that additionally, out-of-frame gene fission/fusion events of alternative reading frames of ORFs and out-of-frame lateral gene transfers could contribute to the origin of new gene families. To demonstrate this, we developed an original pattern-search in sequence similarity networks, enhancing the use of these graphs, commonly used to detect in-frame remodeled genes. We applied this approach to gene families in 524 complete genomes of Escherichia coli. We identified 767 gene families whose evolutionary history likely included at least one out-of-frame remodeling event. These genes with out-of-frame components represent ∼2.5% of all genes in the E. coli pangenome, suggesting that alternative reading frames of existing ORFs can contribute to a significant proportion of de novo genes in bacteria.

Phylogenomic fingerprinting of tempo and functions of horizontal gene transfer within ochrophytes

Horizontal gene transfer (HGT) is an important source of novelty in eukaryotic genomes. This is particularly true for the ochrophytes, a diverse and important group of algae. Previous studies have shown that ochrophytes possess a mosaic of genes derived from bacteria and eukaryotic algae, acquired through chloroplast endosymbiosis and from HGTs, although understanding of the time points and mechanisms underpinning these transfers has been restricted by the depth of taxonomic sampling possible. We harness an expanded set of ochrophyte sequence libraries, alongside automated and manual phylogenetic annotation, in silico modeling, and experimental techniques, to assess the frequency and functions of HGT across this lineage. Through manual annotation of thousands of single-gene trees, we identify continuous bacterial HGT as the predominant source of recently arrived genes in the model diatom Phaeodactylum tricornutum Using a large-scale automated dataset, a multigene ochrophyte reference tree, and mathematical reconciliation of gene trees, we note a probable elevation of bacterial HGTs at foundational points in diatom evolution, following their divergence from other ochrophytes. Finally, we demonstrate that throughout ochrophyte evolutionary history, bacterial HGTs have been enriched in genes encoding secreted proteins. Our study provides insights into the sources and frequency of HGTs, and functional contributions that HGT has made to algal evolution.

Broccoli: Combining Phylogenetic and Network Analyses for Orthology Assignment

Orthology assignment is a key step of comparative genomic studies, for which many bioinformatic tools have been developed. However, all gene clustering pipelines are based on the analysis of protein distances, which are subject to many artifacts. In this article, we introduce Broccoli, a user-friendly pipeline designed to infer, with high precision, orthologous groups, and pairs of proteins using a phylogeny-based approach. Briefly, Broccoli performs ultrafast phylogenetic analyses on most proteins and builds a network of orthologous relationships. Orthologous groups are then identified from the network using a parameter-free machine learning algorithm. Broccoli is also able to detect chimeric proteins resulting from gene-fusion events and to assign these proteins to the corresponding orthologous groups. Tested on two benchmark data sets, Broccoli outperforms current orthology pipelines. In addition, Broccoli is scalable, with runtimes similar to those of recent distance-based pipelines. Given its high level of performance and efficiency, this new pipeline represents a suitable choice for comparative genomic studies. Broccoli is freely available at https://github.com/rderelle/Broccoli.

Ab Initio Construction and Evolutionary Analysis of Protein-Coding Gene Families with Partially Homologous Relationships: Closely Related Drosophila Genomes as a Case Study

How have genes evolved within a well-known genome phylogeny? Many protein-coding genes should have evolved as a whole at the gene level, and some should have evolved partly through fragments at the subgene level. To comprehensively explore such complex homologous relationships and better understand gene family evolution, here, with de novo-identified modules, the subgene units which could consecutively cover proteins within a set of closely related species, we applied a new phylogeny-based approach that considers evolutionary models with partial homology to classify all protein-coding genes in nine Drosophila genomes. Compared with two other popular methods for gene family construction, our approach improved practical gene family classifications with a more reasonable view of homology and provided a much more complete landscape of gene family evolution at the gene and subgene levels. In the case study, we found that most expanded gene families might have evolved mainly through module rearrangements rather than gene duplications and mainly generated single-module genes through partial gene duplication, suggesting that there might be pervasive subgene rearrangement in the evolution of protein-coding gene families. The use of a phylogeny-based approach with partial homology to classify and analyze protein-coding gene families may provide us with a more comprehensive landscape depicting how genes evolve within a well-known genome phylogeny.

Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach

The increasing interest in bioactive peptides with therapeutic potentials has been reflected in a large variety of biological databases published over the last years. However, the knowledge discovery process from these heterogeneous data sources is a nontrivial task, becoming the essence of our research endeavor. Therefore, we devise a unified data model based on molecular similarity networks for representing a chemical reference space of bioactive peptides, having an implicit knowledge that is currently not explicitly accessed in existing biological databases. Indeed, our main contribution is a novel workflow for the automatic construction of such similarity networks, enabling visual graph mining techniques to uncover new insights from the "ocean" of known bioactive peptides. The workflow presented here relies on the following sequential steps: (i) calculation of molecular descriptors by applying statistical and aggregation operators on amino acid property vectors; (ii) a two-stage unsupervised feature selection method to identify an optimized subset of descriptors using the concepts of entropy and mutual information; (iii) generation of sparse networks where nodes represent bioactive peptides, and edges between two nodes denote their pairwise similarity/distance relationships in the defined descriptor space; and (iv) exploratory analysis using visual inspection in combination with clustering and network science techniques. For practical purposes, the proposed workflow has been implemented in our visual analytics software tool ( http://mobiosd-hub.com/starpep/ ), to assist researchers in extracting useful information from an integrated collection of 45120 bioactive peptides, which is one of the largest and most diverse data in its field. Finally, we illustrate the applicability of the proposed workflow for discovering central nodes in molecular similarity networks that may represent a biologically relevant chemical space known to date.

Gene Similarity Networks Unveil a Potential Novel Unicellular Group Closely Related to Animals from the Tara Oceans Expedition

The Holozoa clade comprises animals and several unicellular lineages (choanoflagellates, filastereans, and teretosporeans). Understanding their full diversity is essential to address the origins of animals and other evolutionary questions. However, they are poorly known. To provide more insights into the real diversity of holozoans and check for undiscovered diversity, we here analyzed 18S rDNA metabarcoding data from the global Tara Oceans expedition. To overcome the low phylogenetic information contained in the metabarcoding data set (composed of sequences from the short V9 region of the gene), we used similarity networks by combining two data sets: unknown environmental sequences from Tara Oceans and known reference sequences from GenBank. We then calculated network metrics to compare environmental sequences with reference sequences. These metrics reflected the divergence between both types of sequences and provided an effective way to search for evolutionary relevant diversity, further validated by phylogenetic placements. Our results showed that the percentage of unicellular holozoan diversity remains hidden. We found novelties in several lineages, especially in Acanthoecida choanoflagellates. We also identified a potential new holozoan group that could not be assigned to any of the described extant clades. Data on geographical distribution showed that, although ubiquitous, each unicellular holozoan lineage exhibits a different distribution pattern. We also identified a positive association between new animal hosts and the ichthyosporean symbiont Creolimax fragrantissima, as well as for other holozoans previously reported as free-living. Overall, our analyses provide a fresh perspective into the diversity and ecology of unicellular holozoans, highlighting the amount of undescribed diversity.

Characterization of Burkholderia cepacia Complex Core Genome and the Underlying Recombination and Positive Selection

Recombination and positive selection are two key factors that play a vital role in pathogenic microorganisms’ population adaptation and diversification. The Burkholderia cepacia complex (Bcc) represents bacterial species with high similarity, which can cause severe infections among cases suffering from the chronic granulomatous disorder and cystic fibrosis (CF). At present, no genome-wide study has been carried out focusing on investigating the core genome of Bcc associated with the two evolutionary forces. The general characteristics of the core genome of Bcc species remain scarce as well. In this study, we explored the core orthologous genes of 116 Bcc strains using comparative genomic analysis and studied the two adaptive evolutionary forces: recombination and positive selection. We estimated 1005 orthogroups consisting entirely of single copy genes. These single copy orthologous genes in some Cluster of Orthologous Groups (COG) categories showed significant differences in the comparison of several evolutionary properties, and the encoding proteins were relatively simple and compact. Our findings showed that 5.8% of the core orthologous genes strongly supported recombination; in the meantime, 1.1% supported positive selection. We found that genes involved in protein synthesis as well as material transport and metabolism are favored by selection pressure. More importantly, homologous recombination contributed more genetic variation to a large number of genes and largely maintained the genetic cohesion in Bcc. This high level of recombination between Bcc species blurs their taxonomic boundaries, which leads Bcc species to be difficult or impossible to distinguish phenotypically and genotypically.

Transfer index, NetUniFrac and some useful shortest path-based distances for community analysis in sequence similarity networks

MOTIVATION

Phylogenetic trees and the methods for their analysis have played a key role in many evolutionary, ecological and bioinformatics studies. Alternatively, phylogenetic networks have been widely used to analyze and represent complex reticulate evolutionary processes which cannot be adequately studied using traditional phylogenetic methods. These processes include, among others, hybridization, horizontal gene transfer, and genetic recombination. Nowadays, sequence similarity and genome similarity networks have become an efficient tool for community analysis of large molecular datasets in comparative studies. These networks can be used for tackling a variety of complex evolutionary problems such as the identification of horizontal gene transfer events, the recovery of mosaic genes and genomes, and the study of holobionts.

RESULTS

The shortest path in a phylogenetic tree is used to estimate evolutionary distances between species. We show how the shortest path concept can be extended to sequence similarity networks by defining five new distances, NetUniFrac, Spp, Spep, Spelp and Spinp, and the Transfer index, between species communities present in the network. These new distances can be seen as network analogs of the traditional UniFrac distance used to assess dissimilarity between species communities in a phylogenetic tree, whereas the Transfer index is intended for estimating the rate and direction of gene transfers, or species dispersal, between different phylogenetic, or ecological, species communities. Moreover, NetUniFrac and the Transfer index can be computed in linear time with respect to the number of edges in the network. We show how these new measures can be used to analyze microbiota and antibiotic resistance gene similarity networks.

AVAILABILITY AND IMPLEMENTATION

Our NetFrac program, implemented in R and C, along with its source code, is freely available on Github at the following URL address: https://github.com/XPHenry/Netfrac.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Eukaryote Genes Are More Likely than Prokaryote Genes to Be Composites

The formation of new genes by combining parts of existing genes is an important evolutionary process. Remodelled genes, which we call composites, have been investigated in many species, however, their distribution across all of life is still unknown. We set out to examine the extent to which genomes from cells and mobile genetic elements contain composite genes. We identify composite genes as those that show partial homology to at least two unrelated component genes. In order to identify composite and component genes, we constructed sequence similarity networks (SSNs) of more than one million genes from all three domains of life, as well as viruses and plasmids. We identified non-transitive triplets of nodes in this network and explored the homology relationships in these triplets to see if the middle nodes were indeed composite genes. In total, we identified 221,043 (18.57%) composites genes, which were distributed across all genomic and functional categories. In particular, the presence of composite genes is statistically more likely in eukaryotes than prokaryotes.

Reticulate evolution in eukaryotes: Origin and evolution of the nitrate assimilation pathway

Genes and genomes can evolve through interchanging genetic material, this leading to reticular evolutionary patterns. However, the importance of reticulate evolution in eukaryotes, and in particular of horizontal gene transfer (HGT), remains controversial. Given that metabolic pathways with taxonomically-patchy distributions can be indicative of HGT events, the eukaryotic nitrate assimilation pathway is an ideal object of investigation, as previous results revealed a patchy distribution and suggested that the nitrate assimilation cluster of dikaryotic fungi (Opisthokonta) could have been originated and transferred from a lineage leading to Oomycota (Stramenopiles). We studied the origin and evolution of this pathway through both multi-scale bioinformatic and experimental approaches. Our taxon-rich genomic screening shows that nitrate assimilation is present in more lineages than previously reported, although being restricted to autotrophs and osmotrophs. The phylogenies indicate a pervasive role of HGT, with three bacterial transfers contributing to the pathway origin, and at least seven well-supported transfers between eukaryotes. In particular, we propose a distinct and more complex HGT path between Opisthokonta and Stramenopiles than the one previously suggested, involving at least two transfers of a nitrate assimilation gene cluster. We also found that gene fusion played an essential role in this evolutionary history, underlying the origin of the canonical eukaryotic nitrate reductase, and of a chimeric nitrate reductase in Ichthyosporea (Opisthokonta). We show that the ichthyosporean pathway, including this novel nitrate reductase, is physiologically active and transcriptionally co-regulated, responding to different nitrogen sources; similarly to distant eukaryotes with independent HGT-acquisitions of the pathway. This indicates that this pattern of transcriptional control evolved convergently in eukaryotes, favoring the proper integration of the pathway in the metabolic landscape. Our results highlight the importance of reticulate evolution in eukaryotes, by showing the crucial contribution of HGT and gene fusion in the evolutionary history of the nitrate assimilation pathway.

One of the most relevant findings in evolution was that lineages, either genes or genomes, can evolve through interchanging genetic material. For example, exon shuffling can lead to genes with complete novel functions, and genomes can acquire novel functionalities by means of horizontal gene transfer (HGT). Whereas HGT is known to be an important driver of metabolic remodelling and ecological adaptations in Bacteria, its importance and prevalence in eukaryotes remains controversial. We show that HGT played a major role in the origin and evolution of the eukaryotic nitrate assimilation pathway, with several bacteria-to-eukaryote and eukaryote-to-eukaryote transfers promoting the acquisition of this ecologically-relevant pathway to autotrophs and to distinct groups of osmotrophs. Moreover, we also show that gene fusion was important in this evolutionary history, underlying the origin of the canonical eukaryotic nitrate reductase, but also of a non-canonical nitrate reductase that we describe in Ichthyosporea, a poorly-characterized eukaryotic group that includes many parasitic species. In conclusion, our results highlight the importance of reticulate evolution in eukaryotes, by showing the contribution of HGT and gene fusion in the evolutionary history of the nitrate assimilation pathway.

The Methodology Behind Network Thinking: Graphs to Analyze Microbial Complexity and Evolution

In the post genomic era, large and complex molecular datasets from genome and metagenome sequencing projects expand the limits of what is possible for bioinformatic analyses. Network-based methods are increasingly used to complement phylogenetic analysis in studies in molecular evolution, including comparative genomics, classification, and ecological studies. Using network methods, the vertical and horizontal relationships between all genes or genomes, whether they are from cellular chromosomes or mobile genetic elements, can be explored in a single expandable graph. In recent years, development of new methods for the construction and analysis of networks has helped to broaden the availability of these approaches from programmers to a diversity of users. This chapter introduces the different kinds of networks based on sequence similarity that are already available to tackle a wide range of biological questions, including sequence similarity networks, gene-sharing networks and bipartite graphs, and a guide for their construction and analyses.

Ancestrality and Mosaicism of Giant Viruses Supporting the Definition of the Fourth TRUC of Microbes

Giant viruses of amoebae were discovered in 2003. Since then, their diversity has greatly expanded. They were suggested to form a fourth branch of life, collectively named ‘TRUC’ (for “Things Resisting Uncompleted Classifications”) alongside Bacteria, Archaea, and Eukarya. Their origin and ancestrality remain controversial. Here, we specify the evolution and definition of giant viruses. Phylogenetic and phenetic analyses of informational gene repertoires of giant viruses and selected bacteria, archaea and eukaryota were performed, including structural phylogenomics based on protein structural domains grouped into 289 universal fold superfamilies (FSFs). Hierarchical clustering analysis was performed based on a binary presence/absence matrix constructed using 727 informational COGs from cellular organisms. The presence/absence of ‘universal’ FSF domains was used to generate an unrooted maximum parsimony phylogenomic tree. Comparison of the gene content of a giant virus with those of a bacterium, an archaeon, and a eukaryote with small genomes was also performed. Overall, both cladistic analyses based on gene sequences of very central and ancient proteins and on highly conserved protein fold structures as well as phenetic analyses were congruent regarding the delineation of a fourth branch of microbes comprised by giant viruses. Giant viruses appeared as a basal group in the tree of all proteomes. A pangenome and core genome determined for Rickettsia bellii (bacteria), Methanomassiliicoccus luminyensis (archaeon), Encephalitozoon intestinalis (eukaryote), and Tupanvirus (giant virus) showed a substantial proportion of Tupanvirus genes that overlap with those of the cellular microbes. In addition, a substantial genome mosaicism was observed, with 51, 11, 8, and 0.2% of Tupanvirus genes best matching with viruses, eukaryota, bacteria, and archaea, respectively. Finally, we found that genes themselves may be subject to lateral sequence transfers. In summary, our data highlight the quantum leap between classical and giant viruses. Phylogenetic and phyletic analyses and the study of protein fold superfamilies confirm previous evidence of the existence of a fourth TRUC of life that includes giant viruses, and highlight its ancestrality and mosaicism. They also point out that best evolutionary representations for giant viruses and cellular microorganisms are rhizomes, and that sequence transfers rather than gene transfers have to be considered.

MultiTwin: A Software Suite to Analyze Evolution at Multiple Levels of Organization Using Multipartite Graphs

The inclusion of introgressive processes in evolutionary studies induces a less constrained view of evolution. Network-based methods (like large-scale similarity networks) allow to include in comparative genomics all extrachromosomic carriers (like viruses, the most abundant biological entities on the planet) with their cellular hosts. The integration of several levels of biological organization (genes, genomes, communities, environments) enables more comprehensive analyses of gene sharing and improved sequence-based classifications. However, the algorithmic tools for the analysis of such networks are usually restricted to people with high programming skills. We present an integrated suite of software tools named MultiTwin, aimed at the construction, structuring, and analysis of multipartite graphs for evolutionary biology. Typically, this kind of graph is useful for the comparative analysis of the gene content of genomes in microbial communities from the environment and for exploring patterns of gene sharing, for example between distantly related cellular genomes, pangenomes, or between cellular genomes and their mobile genetic elements. We illustrate the use of this tool with an application of the bipartite approach (using gene family-genome graphs) for the analysis of pathogenicity traits in prokaryotes.