Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks

Intraspecies characterization of bacteria via evolutionary modeling of protein domains

The ability to detect and characterize bacteria within a biological sample is crucial for the monitoring of infections and epidemics, as well as for the study of human health and its relationship with commensal microorganisms. To this aim, a commonly used technique is the 16S rRNA gene targeted sequencing. PCR-amplified 16S sequences derived from the sample of interest are usually clustered into the so-called Operational Taxonomic Units (OTUs) based on pairwise similarities. Then, representative OTU sequences are compared with reference (human-made) databases to derive their phylogeny and taxonomic classification. Here, we propose a new reference-free approach to define the phylogenetic distance between bacteria based on protein domains, which are the evolving units of proteins. We extract the protein domain profiles of 3368 bacterial genomes and we use an ecological approach to model their Relative Species Abundance distribution. Based on the model parameters, we then derive a new measurement of phylogenetic distance. Finally, we show that such model-based distance is capable of detecting differences between bacteria in cases in which the 16S rRNA-based method fails, providing a possibly complementary approach , which is particularly promising for the analysis of bacterial populations measured by shotgun sequencing.

Evolution of networks of protein domain organization

Domains are the structural, functional and evolutionary units of proteins. They combine to form multidomain proteins. The evolutionary history of this molecular combinatorics has been studied with phylogenomic methods. Here, we construct networks of domain organization and explore their evolution. A time series of networks revealed two ancient waves of structural novelty arising from ancient 'p-loop' and 'winged helix' domains and a massive 'big bang' of domain organization. The evolutionary recruitment of domains was highly modular, hierarchical and ongoing. Domain rearrangements elicited non-random and scale-free network structure. Comparative analyses of preferential attachment, randomness and modularity showed yin-and-yang complementary transition and biphasic patterns along the structural chronology. Remarkably, the evolving networks highlighted a central evolutionary role of cofactor-supporting structures of non-ribosomal peptide synthesis pathways, likely crucial to the early development of the genetic code. Some highly modular domains featured dual response regulation in two-component signal transduction systems with DNA-binding activity linked to transcriptional regulation of responses to environmental change. Interestingly, hub domains across the evolving networks shared the historical role of DNA binding and editing, an ancient protein function in molecular evolution. Our investigation unfolds historical source-sink patterns of evolutionary recruitment that further our understanding of protein architectures and functions.

Grammar of protein domain architectures

Genomes appear similar to natural language texts, and protein domains can be treated as analogs of words. To investigate the linguistic properties of genomes further, we calculated the complexity of the “protein languages” in all major branches of life and identified a nearly universal value of information gain associated with the transition from a random domain arrangement to the current protein domain architecture. An exploration of the evolutionary relationship of the protein languages identified the domain combinations that discriminate between the major branches of cellular life. We conclude that there exists a “quasi-universal grammar” of protein domains and that the nearly constant information gain we identified corresponds to the minimal complexity required to maintain a functional cell.

From an abstract, informational perspective, protein domains appear analogous to words in natural languages in which the rules of word association are dictated by linguistic rules, or grammar. Such rules exist for protein domains as well, because only a small fraction of all possible domain combinations is viable in evolution. We employ a popular linguistic technique, n-gram analysis, to probe the “proteome grammar”—that is, the rules of association of domains that generate various domain architectures of proteins. Comparison of the complexity measures of “protein languages” in major branches of life shows that the relative entropy difference (information gain) between the observed domain architectures and random domain combinations is highly conserved in evolution and is close to being a universal constant, at ∼1.2 bits. Substantial deviations from this constant are observed in only two major groups of organisms: a subset of Archaea that appears to be cells simplified to the limit, and animals that display extreme complexity. We also identify the n-grams that represent signatures of the major branches of cellular life. The results of this analysis bolster the analogy between genomes and natural language and show that a “quasi-universal grammar” underlies the evolution of domain architectures in all divisions of cellular life. The nearly universal value of information gain by the domain architectures could reflect the minimum complexity of signal processing that is required to maintain a functioning cell.

Evolution of Protein Domain Architectures

This chapter reviews current research on how protein domain architectures evolve. We begin by summarizing work on the phylogenetic distribution of proteins, as this will directly impact which domain architectures can be formed in different species. Studies relating domain family size to occurrence have shown that they generally follow power law distributions, both within genomes and larger evolutionary groups. These findings were subsequently extended to multi-domain architectures. Genome evolution models that have been suggested to explain the shape of these distributions are reviewed, as well as evidence for selective pressure to expand certain domain families more than others. Each domain has an intrinsic combinatorial propensity, and the effects of this have been studied using measures of domain versatility or promiscuity. Next, we study the principles of protein domain architecture evolution and how these have been inferred from distributions of extant domain arrangements. Following this, we review inferences of ancestral domain architecture and the conclusions concerning domain architecture evolution mechanisms that can be drawn from these. Finally, we examine whether all known cases of a given domain architecture can be assumed to have a single common origin (monophyly) or have evolved convergently (polyphyly). We end by a discussion of some available tools for computational analysis or exploitation of protein domain architectures and their evolution.

MOCASSIN-prot: a multi-objective clustering approach for protein similarity networks

Motivation

Proteins often include multiple conserved domains. Various evolutionary events including duplication and loss of domains, domain shuffling, as well as sequence divergence contribute to generating complexities in protein structures, and consequently, in their functions. The evolutionary history of proteins is hence best modeled through networks that incorporate information both from the sequence divergence and the domain content. Here, a game-theoretic approach proposed for protein network construction is adapted into the framework of multi-objective optimization, and extended to incorporate clustering refinement procedure.

Results

The new method, MOCASSIN-prot, was applied to cluster multi-domain proteins from ten genomes. The performance of MOCASSIN-prot was compared against two protein clustering methods, Markov clustering (TRIBE-MCL) and spectral clustering (SCPS). We showed that compared to these two methods, MOCASSIN-prot, which uses both domain composition and quantitative sequence similarity information, generates fewer false positives. It achieves more functionally coherent protein clusters and better differentiates protein families.

Availability and implementation

MOCASSIN-prot, implemented in Perl and Matlab, is freely available at http://bioinfolab.unl.edu/emlab/MOCASSINprot.

Contact

emoriyama2@unl.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Dynamics of genomic innovation in the unicellular ancestry of animals

Which genomic innovations underpinned the origin of multicellular animals is still an open debate. Here, we investigate this question by reconstructing the genome architecture and gene family diversity of ancestral premetazoans, aiming to date the emergence of animal-like traits. Our comparative analysis involves genomes from animals and their closest unicellular relatives (the Holozoa), including four new genomes: three Ichthyosporea and Corallochytrium limacisporum. Here, we show that the earliest animals were shaped by dynamic changes in genome architecture before the emergence of multicellularity: an early burst of gene diversity in the ancestor of Holozoa, enriched in transcription factors and cell adhesion machinery, was followed by multiple and differently-timed episodes of synteny disruption, intron gain and genome expansions. Thus, the foundations of animal genome architecture were laid before the origin of complex multicellularity – highlighting the necessity of a unicellular perspective to understand early animal evolution.

DOI:http://dx.doi.org/10.7554/eLife.26036.001

Hundreds of millions of years ago, some single-celled organisms gained the ability to work together and form multicellular organisms. This transition was a major step in evolution and took place at separate times in several parts of the tree of life, including in animals, plants, fungi and algae.

Animals are some of the most complex organisms on Earth. Their single-celled ancestors were also quite genetically complex themselves and their genomes (the complete set of the organism’s DNA) already contained many genes that now coordinate the activity of the cells in a multicellular organism.

The genome of an animal typically has certain features: it is large, diverse and contains many segments (called introns) that are not genes. By seeing if the single-celled relatives of animals share these traits, it is possible to learn more about when specific genetic features first evolved, and whether they are linked to the origin of animals.

Now, Grau-Bové et al. have studied the genomes of several of the animal kingdom’s closest single-celled relatives using a technique called whole genome sequencing. This revealed that there was a period of rapid genetic change in the single-celled ancestors of animals during which their genes became much more diverse. Another ‘explosion’ of diversity happened after animals had evolved. Furthermore, the overall amount of the genomic content inside cells and the number of introns found in the genome rapidly increased in separate, independent events in both animals and their single-celled ancestors.

Future research is needed to investigate whether other multicellular life forms – such as plants, fungi and algae – originated in the same way as animal life. Understanding how the genetic material of animals evolved also helps us to understand the genetic structures that affect our health. For example, genes that coordinate the behavior of cells (and so are important for multicellular organisms) also play a role in cancer, where cells break free of this regulation to divide uncontrollably.

DOI:http://dx.doi.org/10.7554/eLife.26036.002

Measuring climate change on Twitter using Google’s algorithm: perception and events

Purpose

– The purpose of this paper is to test the hypothesis of whether more complex and emergent hashtags can be sufficient pointers to climate change events. Human-induced climate change is one of this century’s greatest unbalancing forces to have affected our planet. Capturing the public awareness of climate change on Twitter has proven to be significant. In a previous research, it was demonstrated by the authors that public awareness is prominently expressed in the form of hashtags that uses more than one bigram (i.e. a climate change term). The research finding showed that this awareness is expressed by more complex terms (e.g. “climate change”). It was learned that the awareness was dominantly expressed using the hashtag: #ClimateChange.

Design/methodology/approach

– The methods demonstrated here use objective computational approaches [i.e. Google’s ranking algorithm and Information Retrieval measures (e.g. TFIDF)] to detect and rank the emerging events.

Findings

– The results shows a clear significant evidence for the events signaled using emergent hashtags and how globally influential they are. The research detected the Earth Day, 2015, which was signaled using the hashtag #EarthDay. Clearly, this is a day that is globally observed by the worldwide population.

Originality/value

– It was proven that these computational methods eliminate the subjectivity errors associated with humans and provide inexpensive solution for event detection on Twitter. Indeed, the approach used here can also be applicable to other types of event detections, beyond climate change, and surely applicable to other social media platforms that support the use of hashtags (e.g. Facebook). The paper explains, in great detail, the methods and all the numerous events detected.

The language of the protein universe

Proteins, the main cell machinery which play a major role in nearly every cellular process, have always been a central focus in biology. We live in the post-genomic era, and inferring information from massive data sets is a steadily growing universal challenge. The increasing availability of fully sequenced genomes can be regarded as the 'Rosetta Stone' of the protein universe, allowing the understanding of genomes and their evolution, just as the original Rosetta Stone allowed Champollion to decipher the ancient Egyptian hieroglyphics. In this review, we consider aspects of the protein domain architectures repertoire that are closely related to those of human languages and aim to provide some insights about the language of proteins.

SuSPect: enhanced prediction of single amino acid variant (SAV) phenotype using network features

Whole-genome and exome sequencing studies reveal many genetic variants between individuals, some of which are linked to disease. Many of these variants lead to single amino acid variants (SAVs), and accurate prediction of their phenotypic impact is important. Incorporating sequence conservation and network-level features, we have developed a method, SuSPect (Disease-Susceptibility-based SAV Phenotype Prediction), for predicting how likely SAVs are to be associated with disease. SuSPect performs significantly better than other available batch methods on the VariBench benchmarking dataset, with a balanced accuracy of 82%. SuSPect is available at www.sbg.bio.ic.ac.uk/suspect. The Web site has been implemented in Perl and SQLite and is compatible with modern browsers. An SQLite database of possible missense variants in the human proteome is available to download at www.sbg.bio.ic.ac.uk/suspect/download.html.

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Bioinformatics approaches are key for identification of disease-causing variants.

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SAV phenotype prediction can be improved using network information.

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A method including these features, SuSPect, outperforms tested methods.

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SuSPect is available to use at www.sbg.bio.ic.ac.uk/suspect.

Plant science and agricultural productivity: why are we hitting the yield ceiling?

Trends in conventional plant breeding and in biotechnology research are analyzed with a focus on production and productivity of individual organisms. Our growing understanding of the productive/adaptive potential of (crop) plants is a prerequisite to increasing this potential and also its expression under environmental constraints. This review concentrates on growth rate, ribosome activity, and photosynthetic rate to link these key cellular processes to plant productivity. Examples of how they may be integrated in heterosis, organ growth control, and responses to abiotic stresses are presented. The yield components in rice are presented as a model. The ultimate goal of research programs, that concentrate on yield and productivity and integrating the panoply of systems biology tools, is to achieve "low input, high output" agriculture, i.e. shifting from a conventional "productivist" agriculture to an efficient sustainable agriculture. This is of critical, strategic importance, because the extent to which we, both locally and globally, secure and manage the long-term productive potential of plant resources will determine the future of humanity.

Proteins and domains vary in their tolerance of non-synonymous single nucleotide polymorphisms (nsSNPs)

The widespread application of whole-genome sequencing is identifying numerous non-synonymous single nucleotide polymorphisms (nsSNPs), many of which are associated with disease. We analyzed nsSNPs from Humsavar and the 1000 Genomes Project to investigate why some proteins and domains are more tolerant of mutations than others. We identified 311 proteins and 112 Pfam families, corresponding to 2910 domains, as diseasesusceptible and 32 proteins and 67 Pfam families (10,783 domains) as diseaseresistant based on the relative numbers of disease-associated and neutral polymorphisms. Proteins with no significant difference from expected numbers of disease and polymorphism nsSNPs are classified as other. This classification takes into account the phenotypes of all known mutations in the protein or domain rather than simply classifying based on the presence or absence of disease nsSNPs. Of the two hypotheses suggested, our results support the model that disease-resistant domains and proteins are more able to tolerate mutations rather than having more lethal mutations that are not observed. Disease-resistant proteins and domains show significantly higher mutation rates and lower sequence conservation than disease-susceptible proteins and domains. Disease-susceptible proteins are more likely to be encoded by essential genes, are more central in protein-protein interaction networks and are less likely to contain loss-of-function mutations in healthy individuals. We use this classification for nsSNP phenotype prediction, predicting nsSNPs in disease-susceptible domains to be disease and those in disease-resistant domains to be polymorphism. In this way, we achieve higher accuracy than SIFT, a state-of-the-art algorithm.

Modular evolution of phosphorylation-based signalling systems

Phosphorylation sites are formed by protein kinases ('writers'), frequently exert their effects following recognition by phospho-binding proteins ('readers') and are removed by protein phosphatases ('erasers'). This writer-reader-eraser toolkit allows phosphorylation events to control a broad range of regulatory processes, and has been pivotal in the evolution of new functions required for the development of multi-cellular animals. The proteins that comprise this system of protein kinases, phospho-binding targets and phosphatases are typically modular in organization, in the sense that they are composed of multiple globular domains and smaller peptide motifs with binding or catalytic properties. The linkage of these binding and catalytic modules in new ways through genetic recombination, and the selection of particular domain combinations, has promoted the evolution of novel, biologically useful processes. Conversely, the joining of domains in aberrant combinations can subvert cell signalling and be causative in diseases such as cancer. Major inventions such as phosphotyrosine (pTyr)-mediated signalling that flourished in the first multi-cellular animals and their immediate predecessors resulted from stepwise evolutionary progression. This involved changes in the binding properties of interaction domains such as SH2 and their linkage to new domain types, and alterations in the catalytic specificities of kinases and phosphatases. This review will focus on the modular aspects of signalling networks and the mechanism by which they may have evolved.