Co-evolution of proteins with their interaction partners

Protein-Protein Interaction Prediction Model Based on ProtBert-BiGRU-Attention

The physiological activities within cells are mainly regulated through protein-protein interactions (PPI). Therefore, studying protein interactions has become an essential part of researching protein function and mechanisms. Traditional biological experiments required for PPI prediction are expensive and time consuming. For this reason, many methods based on predicting PPI from protein sequences have been proposed in recent years. However, existing computational methods usually require the combination of evolutionary feature information of proteins to predict PPI docking situations. Because different relevant features of selected proteins are chosen, there may be differences in the predicted results for PPI. This article proposes a PPI prediction method based on the pretrained protein sequence model ProtBert, combined with the Bidirectional Gated Recurrent Unit (BiGRU) and attention mechanism. Only using protein sequence information and leveraging ProtBert's powerful ability to capture amino acid feature information, BiGRU is used for further feature extraction of the amino acid vectors output by ProtBert. The attention mechanism is then applied to enhance the focus on different amino acid features and improve the expression ability of protein sequence features, ultimately obtaining binary classification results for protein interactions. Experimental results show that our proposed ProtBert-BiGRU-Attention model has good predictive performance for PPI. Through relevant comparative experiments, it has been proven that our model performs well in protein binary prediction. Furthermore, through the ablation experiment of the model, different deep learning modules' contributions to the prediction have been demonstrated.

Evolutionary rate covariation is a reliable predictor of co-functional interactions but not necessarily physical interactions

Co-functional proteins tend to have rates of evolution that covary over time. This correlation between evolutionary rates can be measured over the branches of a phylogenetic tree through methods such as evolutionary rate covariation (ERC), and then used to construct gene networks by the identification of proteins with functional interactions. The cause of this correlation has been hypothesized to result from both compensatory coevolution at physical interfaces and nonphysical forces such as shared changes in selective pressure. This study explores whether coevolution due to compensatory mutations has a measurable effect on the ERC signal. We examined the difference in ERC signal between physically interacting protein domains within complexes compared to domains of the same proteins that do not physically interact. We found no generalizable relationship between physical interaction and high ERC, although a few complexes ranked physical interactions higher than nonphysical interactions. Therefore, we conclude that coevolution due to physical interaction is weak, but present in the signal captured by ERC, and we hypothesize that the stronger signal instead comes from selective pressures on the protein as a whole and maintenance of the general function.

Co-evolution of gene transfer agents and their alphaproteobacterial hosts

Gene transfer agents (GTAs) are enigmatic elements that resemble small viruses and are known to be produced during nutritional stress by some bacteria and archaea. The production of GTAs is regulated by quorum sensing, under which a small fraction of the population acts as GTA producers, while the rest becomes GTA recipients. In contrast to canonical viruses, GTAs cannot propagate themselves because they package pieces of the producing cell's genome. In alphaproteobacteria, GTAs are mostly vertically inherited and reside in their hosts' genomes for hundreds of millions of years. While GTAs' ability to transfer genetic material within a population and their long-term preservation suggest an increased fitness of GTA-producing microbes, the associated benefits and type of selection that maintains GTAs are poorly understood. By comparing rates of evolutionary change in GTA genes to the rates in gene families abundantly present across 293 alphaproteobacterial genomes, we detected 59 gene families that likely co-evolve with GTA genes. These gene families are predominantly involved in stress response, DNA repair, and biofilm formation. We hypothesize that biofilm formation enables the physical proximity of GTA-producing cells, limiting GTA-derived benefits only to a group of closely related cells. We further conjecture that the population structure of biofilm-forming sub-populations ensures that the trait of GTA production is maintained despite the inevitable rise of "cheating" genotypes. Because release of GTA particles kills the producing cell, maintenance of GTAs is an exciting example of social evolution in a microbial population.IMPORTANCEGene transfer agents (GTAs) are viruses domesticated by some archaea and bacteria as vehicles for carrying pieces of the host genome. Produced under certain environmental conditions, GTA particles can deliver DNA to neighboring, closely related cells. The function of GTAs remains uncertain. While making GTAs is suicidal for a cell, GTA-encoding genes are widespread in genomes of alphaproteobacteria. Such GTA persistence implies functional benefits but raises questions about how selection maintains this lethal trait. By showing that GTA genes co-evolve with genes involved in stress response, DNA repair, and biofilm formation, we provide support for the hypothesis that GTAs facilitate DNA exchange during the stress conditions and present a model for how GTAs persist in biofilm-forming bacterial populations despite being lethal.

In silico protein function prediction: the rise of machine learning-based approaches

Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.

Molecular Evolution of Aryl Hydrocarbon Receptor Signaling Pathway Genes

The Aryl hydrocarbon receptor is an ancient transcriptional factor originally discovered as a sensor of dioxin. In addition to its function as a receptor of environmental toxicants, it plays an important role in development. Although a significant amount of research has been carried out to understand the AHR signal transduction pathway and its involvement in species' susceptibility to environmental toxicants, none of them to date has comprehensively studied its evolutionary origins. Studying the evolutionary origins of molecules can inform ancestral relationships of genes. The vertebrate genome has been shaped by two rounds of whole-genome duplications (WGD) at the base of vertebrate evolution approximately 600 million years ago, followed by lineage-specific gene losses, which often complicate the assignment of orthology. It is crucial to understand the evolutionary origins of this transcription factor and its partners, to distinguish orthologs from ancient non-orthologous homologs. In this study, we have investigated the evolutionary origins of proteins involved in the AHR pathway. Our results provide evidence of gene loss and duplications, crucial for understanding the functional connectivity of humans and model species. Multiple studies have shown that 2R-ohnologs (genes and proteins that have survived from the 2R-WGD) are enriched in signaling components relevant to developmental disorders and cancer. Our findings provide a link between the AHR pathway's evolutionary trajectory and its potential mechanistic involvement in pathogenesis.

The Connection of the Generalized Robinson-Foulds Metric with Partial Wiener Indices

In this work we propose the partial Wiener index as one possible measure of branching in phylogenetic evolutionary trees. We establish the connection between the generalized Robinson-Foulds (RF) metric for measuring the similarity of phylogenetic trees and partial Wiener indices by expressing the number of conflicting pairs of edges in the generalized RF metric in terms of partial Wiener indices. To do so we compute the minimum and maximum value of the partial Wiener index [Formula: see text], where [Formula: see text] is a binary rooted tree with root [Formula: see text] and [Formula: see text] leaves. Moreover, under the Yule probabilistic model, we show how to compute the expected value of [Formula: see text]. As a direct consequence, we give exact formulas for the upper bound and the expected number of conflicting pairs. By doing so we provide a better theoretical understanding of the computational complexity of the generalized RF metric.

Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences

Inferring protein-protein interactions from sequences is an important task in computational biology. Recent methods based on Direct Coupling Analysis (DCA) or Mutual Information (MI) allow to find interaction partners among paralogs of two protein families. Does successful inference mainly rely on correlations from structural contacts or from phylogeny, or both? Do these two types of signal combine constructively or hinder each other? To address these questions, we generate and analyze synthetic data produced using a minimal model that allows us to control the amounts of structural constraints and phylogeny. We show that correlations from these two sources combine constructively to increase the performance of partner inference by DCA or MI. Furthermore, signal from phylogeny can rescue partner inference when signal from contacts becomes less informative, including in the realistic case where inter-protein contacts are restricted to a small subset of sites. We also demonstrate that DCA-inferred couplings between non-contact pairs of sites improve partner inference in the presence of strong phylogeny, while deteriorating it otherwise. Moreover, restricting to non-contact pairs of sites preserves inference performance in the presence of strong phylogeny. In a natural data set, as well as in realistic synthetic data based on it, we find that non-contact pairs of sites contribute positively to partner inference performance, and that restricting to them preserves performance, evidencing an important role of phylogeny.

An orthologous gene coevolution network provides insight into eukaryotic cellular and genomic structure and function

The evolutionary rates of functionally related genes often covary. We present a gene coevolution network inferred from examining nearly 3 million orthologous gene pairs from 332 budding yeast species spanning ~400 million years of evolution. Network modules provide insight into cellular and genomic structure and function. Examination of the phenotypic impact of network perturbation using deletion mutant data from the baker's yeast Saccharomyces cerevisiae, which were obtained from previously published studies, suggests that fitness in diverse environments is affected by orthologous gene neighborhood and connectivity. Mapping the network onto the chromosomes of S. cerevisiae and Candida albicans revealed that coevolving orthologous genes are not physically clustered in either species; rather, they are often located on different chromosomes or far apart on the same chromosome. The coevolution network captures the hierarchy of cellular structure and function, provides a roadmap for genotype-to-phenotype discovery, and portrays the genome as a linked ensemble of genes.

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

The Influence of Codon Usage, Protein Abundance, and Protein Stability on Protein Evolution Vary by Evolutionary Distance and the Type of Protein

In general, the evolutionary rate of proteins is not primarily related to protein and amino acid functions, and factors such as protein abundance, codon usage, and the protein's T_M are more important. To better understand the factors that affect protein evolution, E. coli MG1655 orthologs were compared to those in closely related bacteria and to more distantly related prokaryotes, eukaryotes, and archaea. Also, the evolution of different types of proteins was studied. The analyses indicate that the amino acid conservation of enzymes that do not use macromolecules (e.g. DNA, RNA, and proteins) as substrates and that carry out metabolic processes involving small molecules (i.e. small molecule enzymes) is different than other enzymes. For example, the small molecule enzymes have a lower percent identity than other enzymes when sequences from closely related bacteria are compared. Analyses indicate the lower percent identity is not a result of the amino acid or codon usage of the small molecule enzymes. The small molecule enzymes also don't have a significantly lower protein abundance indicating that is also not likely an important factor driving differences in amino acid conservation. Analyses indicate different methods to measure the T_M of proteins have different relationships between amino acid conservation over different evolutionary distances. In totality, the results demonstrate that the relationship between the factors thought to affect protein evolution (protein abundance, codon usage, and proteins T_Ms) and protein evolution are complex and depend on the factor, the organisms, and the type of proteins being analyzed.

The coevolution of large and small terminases of bacteriophages is a result of purifying selection leading to phenotypic stabilization

Genome packaging in many dsDNA phages requires a series of precisely coordinated actions of two phage-coded proteins, namely, large terminase (TerL) and small terminase (TerS) with DNA and ATP, and with each other. Despite the strict functional conservation, TerL and TerS homologs exhibit large sequence variations. We investigated the sequence variability across eight phage types and observed a coevolutionary framework wherein the genealogy of TerL homologs mirrored that of the corresponding TerS homologs. Furthermore, a high purifying selection observed (dN/dS«1) indicated strong structural constraints on both TerL and TerS, and identify coevolving residues in TerL and TerS of phage T4 and lambda. Using the highly coevolving (correlation coefficient of 0.99) TerL and TerS of phage N4, we show that their biochemical features are similar to the phylogenetically divergent phage λ terminases. We also demonstrate using the Surface Plasma Resonance (SPR) technique that phage N4 TerL transiently interacts with TerS.

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.

Genome-wide signatures of plastid-nuclear coevolution point to repeated perturbations of plastid proteostasis systems across angiosperms

Nuclear and plastid (chloroplast) genomes experience different mutation rates, levels of selection, and transmission modes, yet key cellular functions depend on their coordinated interactions. Functionally related proteins often show correlated changes in rates of sequence evolution across a phylogeny [evolutionary rate covariation (ERC)], offering a means to detect previously unidentified suites of coevolving and cofunctional genes. We performed phylogenomic analyses across angiosperm diversity, scanning the nuclear genome for genes that exhibit ERC with plastid genes. As expected, the strongest hits were highly enriched for genes encoding plastid-targeted proteins, providing evidence that cytonuclear interactions affect rates of molecular evolution at genome-wide scales. Many identified nuclear genes functioned in post-transcriptional regulation and the maintenance of protein homeostasis (proteostasis), including protein translation (in both the plastid and cytosol), import, quality control, and turnover. We also identified nuclear genes that exhibit strong signatures of coevolution with the plastid genome, but their encoded proteins lack organellar-targeting annotations, making them candidates for having previously undescribed roles in plastids. In sum, our genome-wide analyses reveal that plastid-nuclear coevolution extends beyond the intimate molecular interactions within chloroplast enzyme complexes and may be driven by frequent rewiring of the machinery responsible for maintenance of plastid proteostasis in angiosperms.

Coevolutionary and Phylogenetic Analysis of Mimiviral Replication Machinery Suggest the Cellular Origin of Mimiviruses

Mimivirus is one of the most complex and largest viruses known. The origin and evolution of Mimivirus and other giant viruses have been a subject of intense study in the last two decades. The two prevailing hypotheses on the origin of Mimivirus and other viruses are the reduction hypothesis, which posits that viruses emerged from modern unicellular organisms; whereas the virus-first hypothesis proposes viruses as relics of precellular forms of life. In this study, to gain insights into the origin of Mimivirus, we have carried out extensive phylogenetic, correlation, and multidimensional scaling analyses of the putative proteins involved in the replication of its 1.2-Mb large genome. Correlation analysis and multidimensional scaling methods were validated using bacteriophage, bacteria, archaea, and eukaryotic replication proteins before applying to Mimivirus. We show that a large fraction of mimiviral replication proteins, including polymerase B, clamp, and clamp loaders are of eukaryotic origin and are coevolving. Although phylogenetic analysis places some components along the lineages of phage and bacteria, we show that all the replication-related genes have been homogenized and are under purifying selection. Collectively our analysis supports the idea that Mimivirus originated from a complex cellular ancestor. We hypothesize that Mimivirus has largely retained complex replication machinery reminiscent of its progenitor while losing most of the other genes related to processes such as metabolism and translation.

Trivial and nontrivial error sources account for misidentification of protein partners in mutual information approaches

The problem of finding the correct set of partners for a given pair of interacting protein families based on multi-sequence alignments (MSAs) has received great attention over the years. Recently, the native contacts of two interacting proteins were shown to store the strongest mutual information (MI) signal to discriminate MSA concatenations with the largest fraction of correct pairings. Although that signal might be of practical relevance in the search for an effective heuristic to solve the problem, the number of MSA concatenations with near-native MI is large, imposing severe limitations. Here, a Genetic Algorithm that explores possible MSA concatenations according to a MI maximization criteria is shown to find degenerate solutions with two error sources, arising from mismatches among (i) similar and (ii) non-similar sequences. If mistakes made among similar sequences are disregarded, type-(i) solutions are found to resolve correct pairings at best true positive (TP) rates of 70%—far above the very same estimates in type-(ii) solutions. A machine learning classification algorithm helps to show further that differences between optimized solutions based on TP rates are not artificial and may have biological meaning associated with the three-dimensional distribution of the MI signal. Type-(i) solutions may therefore correspond to reliable results for predictive purposes, found here to be more likely obtained via MI maximization across protein systems having a minimum critical number of amino acid contacts on their interaction surfaces (N > 200).

Inter-protein residue covariation information unravels physically interacting protein dimers

BACKGROUND

Predicting physical interaction between proteins is one of the greatest challenges in computational biology. There are considerable various protein interactions and a huge number of protein sequences and synthetic peptides with unknown interacting counterparts. Most of co-evolutionary methods discover a combination of physical interplays and functional associations. However, there are only a handful of approaches which specifically infer physical interactions. Hybrid co-evolutionary methods exploit inter-protein residue coevolution to unravel specific physical interacting proteins. In this study, we introduce a hybrid co-evolutionary-based approach to predict physical interplays between pairs of protein families, starting from protein sequences only.

RESULTS

In the present analysis, pairs of multiple sequence alignments are constructed for each dimer and the covariation between residues in those pairs are calculated by CCMpred (Contacts from Correlated Mutations predicted) and three mutual information based approaches for ten accessible surface area threshold groups. Then, whole residue couplings between proteins of each dimer are unified into a single Frobenius norm value. Norms of residue contact matrices of all dimers in different accessible surface area thresholds are fed into support vector machine as single or multiple feature models. The results of training the classifiers by single features show no apparent different accuracies in distinct methods for different accessible surface area thresholds. Nevertheless, mutual information product and context likelihood of relatedness procedures may roughly have an overall higher and lower performances than other two methods for different accessible surface area cut-offs, respectively. The results also demonstrate that training support vector machine with multiple norm features for several accessible surface area thresholds leads to a considerable improvement of prediction performance. In this context, CCMpred roughly achieves an overall better performance than mutual information based approaches. The best accuracy, sensitivity, specificity, precision and negative predictive value for that method are 0.98, 1, 0.962, 0.96, and 0.962, respectively.

CONCLUSIONS

In this paper, by feeding norm values of protein dimers into support vector machines in different accessible surface area thresholds, we demonstrate that even small number of proteins in pairs of multiple alignments could allow one to accurately discriminate between positive and negative dimers.

Comparative Genomics within and across Bilaterians Illuminates the Evolutionary History of ALK and LTK Proto-Oncogene Origination and Diversification

Comparative genomic analyses have enormous potential for identifying key genes central to human health phenotypes, including those that promote cancers. In particular, the successful development of novel therapeutics using model species requires phylogenetic analyses to determine molecular homology. Accordingly, we investigate the evolutionary histories of anaplastic lymphoma kinase (ALK)—which can underlie tumorigenesis in neuroblastoma, nonsmall cell lung cancer, and anaplastic large-cell lymphoma—its close relative leukocyte tyrosine kinase (LTK) and their candidate ligands. Homology of ligands identified in model organisms to those functioning in humans remains unclear. Therefore, we searched for homologs of the human genes across metazoan genomes, finding that the candidate ligands Jeb and Hen-1 were restricted to nonvertebrate species. In contrast, the ligand augmentor (AUG) was only identified in vertebrates. We found two ALK-like and four AUG-like protein-coding genes in lamprey. Of these six genes, only one ALK-like and two AUG-like genes exhibited early embryonic expression that parallels model mammal systems. Two copies of AUG are present in nearly all jawed vertebrates. Our phylogenetic analysis strongly supports the presence of previously unrecognized functional convergences of ALK and LTK between actinopterygians and sarcopterygians—despite contemporaneous, highly conserved synteny of ALK and LTK. These findings provide critical guidance regarding the propriety of fish and mammal models with regard to model organism-based investigation of these medically important genes. In sum, our results provide the phylogenetic context necessary for effective investigations of the functional roles and biology of these critically important receptors.

Coevolutionary Analysis Reveals a Conserved Dual Binding Interface between Extracytoplasmic Function σ Factors and Class I Anti-σ Factors

Extracytoplasmic function σ factors (ECFs) belong to the most abundant signal transduction mechanisms in bacteria. Among the diverse regulators of ECF activity, class I anti-σ factors are the most important signal transducers in response to internal and external stress conditions. Despite the conserved secondary structure of the class I anti-σ factor domain (ASDI) that binds and inhibits the ECF under noninducing conditions, the binding interface between ECFs and ASDIs is surprisingly variable between the published cocrystal structures. In this work, we provide a comprehensive computational analysis of the ASDI protein family and study the different contact themes between ECFs and ASDIs. To this end, we harness the coevolution of these diverse protein families and predict covarying amino acid residues as likely candidates of an interaction interface. As a result, we find two common binding interfaces linking the first alpha-helix of the ASDI to the DNA-binding region in the σ4 domain of the ECF, and the fourth alpha-helix of the ASDI to the RNA polymerase (RNAP)-binding region of the σ2 domain. The conservation of these two binding interfaces contrasts with the apparent quaternary structure diversity of the ECF/ASDI complexes, partially explaining the high specificity between cognate ECF and ASDI pairs. Furthermore, we suggest that the dual inhibition of RNAP- and DNA-binding interfaces is likely a universal feature of other ECF anti-σ factors, preventing the formation of nonfunctional trimeric complexes between σ/anti-σ factors and RNAP or DNA.IMPORTANCE In the bacterial world, extracytoplasmic function σ factors (ECFs) are the most widespread family of alternative σ factors, mediating many cellular responses to environmental cues, such as stress. This work uses a computational approach to investigate how these σ factors interact with class I anti-σ factors-the most abundant regulators of ECF activity. By comprehensively classifying the anti-σs into phylogenetic groups and by comparing this phylogeny to the one of the cognate ECFs, the study shows how these protein families have coevolved to maintain their interaction over evolutionary time. These results shed light on the common contact residues that link ECFs and anti-σs in different phylogenetic families and set the basis for the rational design of anti-σs to specifically target certain ECFs. This will help to prevent the cross talk between heterologous ECF/anti-σ pairs, allowing their use as orthogonal regulators for the construction of genetic circuits in synthetic biology.

Statistical physics of interacting proteins: Impact of dataset size and quality assessed in synthetic sequences

Identifying protein-protein interactions is crucial for a systems-level understanding of the cell. Recently, algorithms based on inverse statistical physics, e.g., direct coupling analysis (DCA), have allowed to use evolutionarily related sequences to address two conceptually related inference tasks: finding pairs of interacting proteins and identifying pairs of residues which form contacts between interacting proteins. Here we address two underlying questions: How are the performances of both inference tasks related? How does performance depend on dataset size and the quality? To this end, we formalize both tasks using Ising models defined over stochastic block models, with individual blocks representing single proteins and interblock couplings protein-protein interactions; controlled synthetic sequence data are generated by Monte Carlo simulations. We show that DCA is able to address both inference tasks accurately when sufficiently large training sets of known interaction partners are available and that an iterative pairing algorithm allows to make predictions even without a training set. Noise in the training data deteriorates performance. In both tasks we find a quadratic scaling relating dataset quality and size that is consistent with noise adding in square-root fashion and signal adding linearly when increasing the dataset. This implies that it is generally good to incorporate more data even if their quality are imperfect, thereby shedding light on the empirically observed performance of DCA applied to natural protein sequences.

Identifying the drivers of computationally detected correlated evolution among sites under antibiotic selection

The ultimate causes of correlated evolution among sites in a genome remain difficult to tease apart. To address this problem directly, we performed a high-throughput search for correlated evolution among sites associated with resistance to a fluoroquinolone antibiotic using whole-genome data from clinical strains of Pseudomonas aeruginosa, before validating our computational predictions experimentally. We show that for at least two sites, this correlation is underlain by epistasis. Our analysis also revealed eight additional pairs of synonymous substitutions displaying correlated evolution underlain by physical linkage, rather than selection associated with antibiotic resistance. Our results provide direct evidence that both epistasis and physical linkage among sites can drive the correlated evolution identified by high-throughput computational tools. In other words, the observation of correlated evolution is not by itself sufficient evidence to guarantee that the sites in question are epistatic; such a claim requires additional evidence, ideally coming from direct estimates of epistasis, based on experimental evidence.

Coevolutive, evolutive and stochastic information in protein-protein interactions

Here, we investigate the contributions of coevolutive, evolutive and stochastic information in determining protein-protein interactions (PPIs) based on primary sequences of two interacting protein families A and B. Specifically, under the assumption that coevolutive information is imprinted on the interacting amino acids of two proteins in contrast to other (evolutive and stochastic) sources spread over their sequences, we dissect those contributions in terms of compensatory mutations at physically-coupled and uncoupled amino acids of A and B. We find that physically-coupled amino-acids at short range distances store the largest per-contact mutual information content, with a significant fraction of that content resulting from coevolutive sources alone. The information stored in coupled amino acids is shown further to discriminate multi-sequence alignments (MSAs) with the largest expectation fraction of PPI matches – a conclusion that holds against various definitions of intermolecular contacts and binding modes. When compared to the informational content resulting from evolution at long-range interactions, the mutual information in physically-coupled amino-acids is the strongest signal to distinguish PPIs derived from cospeciation and likely, the unique indication in case of molecular coevolution in independent genomes as the evolutive information must vanish for uncorrelated proteins.

Phylogenetic correlations can suffice to infer protein partners from sequences

Determining which proteins interact together is crucial to a systems-level understanding of the cell. Recently, algorithms based on Direct Coupling Analysis (DCA) pairwise maximum-entropy models have allowed to identify interaction partners among paralogous proteins from sequence data. This success of DCA at predicting protein-protein interactions could be mainly based on its known ability to identify pairs of residues that are in contact in the three-dimensional structure of protein complexes and that coevolve to remain physicochemically complementary. However, interacting proteins possess similar evolutionary histories. What is the role of purely phylogenetic correlations in the performance of DCA-based methods to infer interaction partners? To address this question, we employ controlled synthetic data that only involve phylogeny and no interactions or contacts. We find that DCA accurately identifies the pairs of synthetic sequences that share evolutionary history. While phylogenetic correlations confound the identification of contacting residues by DCA, they are thus useful to predict interacting partners among paralogs. We find that DCA performs as well as phylogenetic methods to this end, and slightly better than them with large and accurate training sets. Employing DCA or phylogenetic methods within an Iterative Pairing Algorithm (IPA) allows to predict pairs of evolutionary partners without a training set. We further demonstrate the ability of these various methods to correctly predict pairings among real paralogous proteins with genome proximity but no known direct physical interaction, illustrating the importance of phylogenetic correlations in natural data. However, for physically interacting and strongly coevolving proteins, DCA and mutual information outperform phylogenetic methods. We finally discuss how to distinguish physically interacting proteins from proteins that only share a common evolutionary history.

Many biologically important protein-protein interactions are conserved over evolutionary time scales. This leads to two different signals that can be used to computationally predict interactions between protein families and to identify specific interaction partners. First, the shared evolutionary history leads to highly similar phylogenetic relationships between interacting proteins of the two families. Second, the need to keep the interaction surfaces of partner proteins biophysically compatible causes a correlated amino-acid usage of interface residues. Employing simulated data, we show that the shared history alone can be used to detect partner proteins. Similar accuracies are achieved by algorithms comparing phylogenetic relationships and by methods based on Direct Coupling Analysis (DCA), which are primarily known for their ability to detect the second type of signal. Using natural sequence data, we show that in cases with shared evolutionary history but without known physical interactions, both methods work with similar accuracy, while for some physically interacting systems, DCA and mutual information outperform phylogenetic methods. We propose methods allowing both to predict interactions between protein families and to find interacting partners among paralogs.

Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique

MOTIVATION

The prediction of protein-protein interaction (PPI) sites is a key to mutation design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task considering the significant abundant sequences and the imbalance issue in samples.

RESULTS

A new ensemble learning-based method, Ensemble Learning of synthetic minority oversampling technique (SMOTE) for Unbalancing samples and RF algorithm (EL-SMURF), was proposed for PPI sites prediction in this study. The sequence profile feature and the residue evolution rates were combined for feature extraction of neighboring residues using a sliding window, and the SMOTE was applied to oversample interface residues in the feature space for the imbalance problem. The Multi-dimensional Scaling feature selection method was implemented to reduce feature redundancy and subset selection. Finally, the Random Forest classifiers were applied to build the ensemble learning model, and the optimal feature vectors were inserted into EL-SMURF to predict PPI sites. The performance validation of EL-SMURF on two independent validation datasets showed 77.1% and 77.7% accuracy, which were 6.2-15.7% and 6.1-18.9% higher than the other existing tools, respectively.

AVAILABILITY AND IMPLEMENTATION

The source codes and data used in this study are publicly available at http://github.com/QUST-AIBBDRC/EL-SMURF/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational identification of protein-protein interactions in model plant proteomes

Protein-protein interactions (PPIs) play essential roles in many biological processes. A PPI network provides crucial information on how biological pathways are structured and coordinated from individual protein functions. In the past two decades, large-scale PPI networks of a handful of organisms were determined by experimental techniques. However, these experimental methods are time-consuming, expensive, and are not easy to perform on new target organisms. Large-scale PPI data is particularly sparse in plant organisms. Here, we developed a computational approach for detecting PPIs trained and tested on known PPIs of Arabidopsis thaliana and applied to three plants, Arabidopsis thaliana, Glycine max (soybean), and Zea mays (maize) to discover new PPIs on a genome-scale. Our method considers a variety of features including protein sequences, gene co-expression, functional association, and phylogenetic profiles. This is the first work where a PPI prediction method was developed for is the first PPI prediction method applied on benchmark datasets of Arabidopsis. The method showed a high prediction accuracy of over 90% and very high precision of close to 1.0. We predicted 50,220 PPIs in Arabidopsis thaliana, 13,175,414 PPIs in corn, and 13,527,834 PPIs in soybean. Newly predicted PPIs were classified into three confidence levels according to the availability of existing supporting evidence and discussed. Predicted PPIs in the three plant genomes are made available for future reference.

Pathogenicity of the H1N1 influenza virus enhanced by functional synergy between the NPV100I and NAD248N pair

By comparing and measuring covariations of viral protein sequences from isolates of the 2009 pH1N1 influenza A virus (IAV), specific substitutions that co-occur in the NP-NA pair were identified. To investigate the effect of these co-occurring substitution pairs, the V100I substitution in NP and the D248N substitution in NA were introduced into laboratory-adapted WSN IAVs. The recombinant WSN with the covarying NP^V100I-NA^D248N pair exhibited enhanced pathogenicity, as characterized by increased viral production, increased death and inflammation of host cells, and high mortality in infected mice. Although direct interactions between the NP^V100I and NA^D248N proteins were not detected, the RNA-binding ability of NP^V100I was increased, which was further strengthened by NA^D248N, in expression-plasmid-transfected cells. Additionally, the NA^D248N protein was frequently recruited within lipid rafts, indirectly affecting the RNA-binding ability of NP as well as viral release. Altogether, our data indicate that the covarying NP^V100I-NA^D248N pair obtained from 2009 pH1N1 IAV sequence information function together to synergistically augment viral assembly and release, which may explain the observed enhanced viral pathogenicity.

Structural modulation of a periplasmic sugar-binding protein probes into its evolutionary ancestry

Substrate-binding proteins (SBPs) are periplasmic proteins consisting of two α/β domains joined by a hinge region with specificity towards cognate ligands. Based on three-dimensional fold, sugar-specific SBPs have been classified into cluster B and cluster D-I. The analysis of sequences and structures of sugar-binding pocket of cluster D-I SBPs revealed the presence of extra residues on two loops (L1, L2) and a helix (H1) in few members of this family, that binds specifically to monosaccharides. Presence of conserved histidine in L2 and tryptophan in H1 can be considered as the identity marks for the cluster D-I monosaccharide-binding SBPs. A glucose binding protein (ppGBP) from Pseudomonas putida CSV86 was found to contain a structural fold similar to oligosaccharide-binding cluster D-I SBPs, but functionally binds to only glucose due to constriction of its binding pocket mainly by L2 (375-382). ppGBP with partial deletion of L2 (ppGBPΔL2) was created, crystallized and biochemical characterization was performed. Compared to wild type ppGBP, the ppGBPΔL2 structure showed widening of the glucose-binding pocket with ∼80% lower glucose binding. Our results show that the substrate specificity of SBPs can be altered by modulating the size of the binding pocket. Based on this, we propose a sub classification of cluster D-I SBPs into (i) cluster D-I(a)-monosaccharide-binding SBPs and (ii) cluster D-I(b)-oligosaccharide-binding SBPs. This study also provides the direct structural and functional correlation indicating that divergence of proteins may occur through insertions or deletions of sequences in the already existing SBPs leading to evolution at the functional level.

A role for the CXCR4-CXCL12 axis in the little skate, Leucoraja erinacea

The interaction between C-X-C chemokine receptor type 4 (CXCR4) and its cognate ligand C-X-C motif chemokine ligand 12 (CXCL12) plays a critical role in regulating hematopoietic stem cell activation and subsequent cellular mobilization. Extensive studies of these genes have been conducted in mammals, but much less is known about the expression and function of CXCR4 and CXCL12 in non-mammalian vertebrates. In the present study, we identify simultaneous expression of CXCR4 and CXCL12 orthologs in the epigonal organ (the primary hematopoietic tissue) of the little skate, Leucoraja erinacea. Genetic and phylogenetic analyses were functionally supported by significant mobilization of leukocytes following administration of Plerixafor, a CXCR4 antagonist and clinically important drug. Our results provide evidence that, as in humans, Plerixafor disrupts CXCR4/CXCL12 binding in the little skate, facilitating release of leukocytes into the bloodstream. Our study illustrates the value of the little skate as a model organism, particularly in studies of hematopoiesis and potentially for preclinical research on hematological and vascular disorders.

Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features

Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms. However, results cover only a part of PPIs of organisms; moreover, there are many organisms whose PPIs have not yet been investigated. To complement experimental methods, many computational methods have been developed that predict PPIs from various characteristics of proteins. Here we provide an overview of literature reports to classify computational PPI prediction methods that consider different features of proteins, including protein sequence, genomes, protein structure, function, PPI network topology, and those which integrate multiple methods. © 2018 by John Wiley & Sons, Inc.

The nanos1 gene was duplicated in early Vertebrates and the two paralogs show different gonadal expression profiles in a shark

Nanos are RNA-binding proteins playing crucial roles in germ cell development and maintenance. Based on phylogenetic and synteny analyses, this study reveals that nanos1 gene has undergone multiple duplications and gene copies losses in Vertebrates. Chondrichthyan species display two nanos1 genes (named nanos1A/1B), which were both retrieved in some Osteichthyes at basal positions in Sarcopterygii and Actinopterygii lineages. In contrast, Teleosts have lost nanos1A but duplicated nanos1B leading to the emergence of two ohnologs (nanos1Ba/1Bb), whereas Tetrapods have lost nanos1B gene. The two successive nanos gene duplications may result from the second and third whole genome duplication events at the basis of Vertebrates and Teleosts respectively. The expression profiles of nanos1A and nanos1B paralogs were characterized in the dogfish, Scyliorhinus canicula. Nanos1A was strongly expressed in brain and also localized in all germ cell types in the polarized testis. In contrast, nanos1B was detected in testis with the highest expression in the germinative zone. In addition, Nanos1B protein was predominantly located in the nuclei of male germinal cells. In the ovary, both paralogs were detected in germinal and somatic cells. Our study opens new perspectives concerning the complex evolution of nanos1 paralogs and their potential distinct roles in Vertebrates gonads.

Evolutionary conservation and functional divergence of the LFK gene family play important roles in the photoperiodic flowering pathway of land plants

ZEITLUPE (ZTL), LOV KELCH PROTEIN 2 (LKP2), and FLAVIN-BINDING KELCH REPEAT F-BOX 1 (FKF1)-blue-light photoreceptors-play important roles in regulating the circadian clock and photoperiodic flowering pathway in plants. In this study, phylogenetic analysis revealed that the LOV (Light, Oxygen, or Voltage) and Kelch repeat-containing F-box (LFK) gene family can be classified into two clades, ZTL/LKP2 and FKF1, with clear differentiation between monocots and dicots within each clade. The LFK family genes underwent strong purifying selection; however, signatures of positive selection to adapt to local conditions still existed in 18 specific codons. In 87 diverse maize inbred lines, significant differences were identified (P ≤ 0.01) for days to female flowering between the haplotypes consisting of eight positive selection sites at ZmFKF1b corresponding to tropical and temperate maize groups of the phylogenetic tree, indicating a key role of ZmFKF1b in maize adaptive evolution. In addition, positive coevolution was detected in the domains of the LFK family for long-term cooperation to targets. The Type-I and Type-II functional divergence analysis revealed subfunctionalization or neofunctionalization of the LFKs, and the ZTL subfamily is most likely to maintain the ancestral function of LFKs. Over 50% of critical amino acid sites involved in the functional divergence were identified in the Kelch repeat domain, resulting in the distinction of substrates for ubiquitination and degradation. These results suggest that evolutionary conservation contributes to the maintenance of critical physiological functions, whereas functional divergence after duplication helps to generate diverse molecular regulation mechanisms.

The Polyphyletic Origins of Primase-Helicase Bifunctional Proteins

We studied the evolutionary relationships of different primase-helicase bifunctional proteins, found mostly in viruses, virophages, plasmids, and organellar genomes, by phylogeny and correlation analysis. Our study suggests independent origins of primase-helicase bifunctional proteins resulting from multiple fusion events between genes encoding primase and helicase domains of different families. The correlation analysis further indicated strong functional dependencies of domains in the bifunctional proteins that are part of smaller genomes and plasmids. Bifunctional proteins found in some bacterial genomes exhibited weak coevolution probably suggesting that these are the non-functional remnants of the proteins acquired via horizontal transfer. We have put forward possible scenarios for the origin of primase-helicase bifunctional proteins in large eukaryotic DNA viruses and virophages.

Evolution of Proteins and Proteomes: A Phylogenetics Approach

The study of evolutionary relationships among protein sequences was one of the first applications of bioinformatics. Since then, and accompanying the wealth of biological data produced by genome sequencing and other high-throughput techniques, the use of bioinformatics in general and phylogenetics in particular has been gaining ground in the study of protein and proteome evolution. Nowadays, the use of phylogenetics is instrumental not only to infer the evolutionary relationships among species and their genome sequences, but also to reconstruct ancestral states of proteins and proteomes and hence trace the paths followed by evolution. Here I survey recent progress in the elucidation of mechanisms of protein and proteome evolution in which phylogenetics has played a determinant role.

Protein complex analysis: From raw protein lists to protein interaction networks

The elucidation of molecular interaction networks is one of the pivotal challenges in the study of biology. Affinity purification-mass spectrometry and other co-complex methods have become widely employed experimental techniques to identify protein complexes. These techniques typically suffer from a high number of false negatives and false positive contaminants due to technical shortcomings and purification biases. To support a diverse range of experimental designs and approaches, a large number of computational methods have been proposed to filter, infer and validate protein interaction networks from experimental pull-down MS data. Nevertheless, this expansion of available methods complicates the selection of the most optimal ones to support systems biology-driven knowledge extraction. In this review, we give an overview of the most commonly used computational methods to process and interpret co-complex results, and we discuss the issues and unsolved problems that still exist within the field. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:600-614, 2017.

High-Throughput Sequencing and Co-Expression Network Analysis of lncRNAs and mRNAs in Early Brain Injury Following Experimental Subarachnoid Haemorrhage

Subarachnoid haemorrhage (SAH) is a fatal neurovascular disease following cerebral aneurysm rupture with high morbidity and mortality rates. Long non-coding RNAs (lncRNAs) are a type of mammalian genome transcript, are abundantly expressed in the brain and are involved in many nervous system diseases. However, little is currently known regarding the influence of lncRNAs in early brain injury (EBI) after SAH. This study analysed the expression profiles of lncRNAs and mRNAs in SAH brain tissues of mice using high-throughput sequencing. The results showed a remarkable difference in lncRNA and mRNA transcripts between SAH and control brains. Approximately 617 lncRNA transcripts and 441 mRNA transcripts were aberrantly expressed at 24 hours after SAH. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that the differentially expressed mRNAs were mostly involved in inflammation. Based on the lncRNA/mRNA co-expression network, knockdown of fantom3_F730004F19 reduced the mRNA and protein levels of CD14 and toll-like receptor 4 (TLR4) and attenuated inflammation in BV-2 microglia cells. These results indicate that lncRNA fantom3_F730004F19 may be associated with microglia induced inflammation via the TLR signaling pathway in EBI following SAH. LncRNA represent a potential therapeutic target for the prognosis, diagnosis, and treatment of SAH.

Protein sites with more coevolutionary connections tend to evolve slower, while more variable protein families acquire higher coevolutionary connections

Background: Amino acid exchanges within proteins sometimes compensate for one another and could therefore be co-evolved. It is essential to investigate the intricate relationship between the extent of coevolution and the evolutionary variability exerted at individual protein sites, as well as the whole protein. Methods: In this study, we have used a reliable set of coevolutionary connections (sites within 10Å spatial distance) and investigated their correlation with the evolutionary diversity within the respective protein sites. Results: Based on our observations, we propose an interesting hypothesis that higher numbers of coevolutionary connections are associated with lesser evolutionary variable protein sites, while higher numbers of the coevolutionary connections can be observed for a protein family that has higher evolutionary variability. Our findings also indicate that highly coevolved sites located in a solvent accessible state tend to be less evolutionary variable. This relationship reverts at the whole protein level where cytoplasmic and extracellular proteins show moderately higher anti-correlation between the number of coevolutionary connections and the average evolutionary conservation of the whole protein. Conclusions: Observations and hypothesis presented in this study provide intriguing insights towards understanding the critical relationship between coevolutionary and evolutionary changes observed within proteins. Our observations encourage further investigation to find out the reasons behind subtle variations in the relationship between coevolutionary connectivity and evolutionary diversity for proteins located at various cellular localizations and/or involved in different molecular-biological functions.

Extracting Coevolutionary Features from Protein Sequences for Predicting Protein-Protein Interactions

Knowing the ways proteins interact with each other are crucial to our understanding of the functional mechanisms of proteins. It is for this reason that different approaches have been developed in attempts to predict protein-protein interactions (PPIs) computationally. Among them, the sequence-based approaches are preferred to the others as they do not require any information about protein properties to perform their tasks. Instead, most sequence-based approaches make use of feature extraction methods to extract features directly from protein sequences so that for each protein sequence, we can construct a feature vector. The feature vectors of every pair of proteins are then concatenated to form two classes of interacting and non-interacting proteins. The prediction of whether or not two proteins interact with each other is then formulated as a classification problem. How accurate PPI predictions can be made therefore depends on how good the features are that can be extracted from the protein sequences to allow interacting or non-interacting to be best distinguished. To do so, instead of extracting such features from individual protein sequences independently of the other protein in the same pair, we propose to jointly consider features from both sequences in a protein pair during the feature extraction process through using a novel coevolutionary feature extraction approach called CoFex. Coevolutionary features extracted by CoFex refer to the covariations found at coevolving positions. Based on the presence and absence of these coevolutionary features in the sequences of two proteins, feature vectors can be composed for pairs of proteins rather than individual proteins. The experiment results show that CoFex is a promising feature extraction approach and can improve the performance of PPI prediction.

Origin of the plant Tm-1-like gene via two independent horizontal transfer events and one gene fusion event

The Tomato mosaic virus (ToMV) resistance gene Tm-1 encodes a direct inhibitor of ToMV RNA replication to protect tomato from infection. The plant Tm-1-like (Tm-1L) protein is predicted to contain an uncharacterized N-terminal UPF0261 domain and a C-terminal TIM-barrel signal transduction (TBST) domain. Homologous searches revealed that proteins containing both of these two domains are mainly present in charophyte green algae and land plants but absent from glaucophytes, red algae and chlorophyte green algae. Although Tm-1 homologs are widely present in bacteria, archaea and fungi, UPF0261- and TBST-domain-containing proteins are generally encoded by different genes in these linages. A co-evolution analysis also suggested a putative interaction between UPF0261- and TBST-domain-containing proteins. Phylogenetic analyses based on homologs of these two domains revealed that plants have acquired UPF0261- and TBST-domain-encoding genes through two independent horizontal gene transfer (HGT) events before the origin of land plants from charophytes. Subsequently, gene fusion occurred between these two horizontally acquired genes and resulted in the origin of the Tm-1L gene in streptophytes. Our results demonstrate a novel evolutionary mechanism through which the recipient organism may acquire genes with functional interaction through two different HGT events and further fuse them into one functional gene.

Altered expression of long non-coding RNA and mRNA in mouse cortex after traumatic brain injury

BACKGROUND AND OBJECTIVE

The present study aims to detect the altered lncRNA expression in the mouse cortex after traumatic brain injury (TBI). We also simultaneously detected the altered mRNA profile to further analyze the possible function of lncRNA.

METHOD

C57BL/6 mice (n=18) were used to construct a controlled cortical impact model. At 24h post-TBI, the cortex around injury site was collected and the total RNA was extracted to construct the cDNA library. RNA sequencing (RNA-seq) was carried out followed by RT-PCR for confirmation. Bioinformatic analysis (including GO analysis, KEGG pathway and co-expression analysis) also were performed.

RESULTS

A total of 64,530 transcripts were detected in the current sequencing study, in which 27,457 transcripts were identified as mRNA and 37,073 transcripts as lncRNA. A total of 1580 mRNAs (1430 up-regulated and 150 down-regulated) and 823 lncRNAs (667 up-regulated and 156 down-regulated) were significantly changed according to the criteria ( (|)log2((fold change))|>1 and P<0.05). These altered mRNAs were mainly related to inflammatory and immunological activity, metabolism, neuronal and vascular network. The expression of single lncRNA may be related with several mRNAs, and so was the mRNA. Also, a total of 360 new mRNAs and 8041 new lncRNAs were identified. The good reproducibility and reliability of RNA-seq were confirmed by RT-PCR.

CONCLUSION

Numerous lncRNAs and mRNAs were significantly altered in mouse cortex around the injury site 24h after TBI. Our present data may provide a promising approach for further study about TBI.

Prediction of protein-protein interaction network using a multi-objective optimization approach

Protein-Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score.

Target-Driven Evolution of Scorpion Toxins

It is long known that peptide neurotoxins derived from a diversity of venomous animals evolve by positive selection following gene duplication, yet a force that drives their adaptive evolution remains a mystery. By using maximum-likelihood models of codon substitution, we analyzed molecular adaptation in scorpion sodium channel toxins from a specific species and found ten positively selected sites, six of which are located at the core-domain of scorpion α-toxins, a region known to interact with two adjacent loops in the voltage-sensor domain (DIV) of sodium channels, as validated by our newly constructed computational model of toxin-channel complex. Despite the lack of positive selection signals in these two loops, they accumulated extensive sequence variations by relaxed purifying selection in prey and predators of scorpions. The evolutionary variability in the toxin-bound regions of sodium channels indicates that accelerated substitutions in the multigene family of scorpion toxins is a consequence of dealing with the target diversity. This work presents an example of atypical co-evolution between animal toxins and their molecular targets, in which toxins suffered from more prominent selective pressure from the channels of their competitors. Our discovery helps explain the evolutionary rationality of gene duplication of toxins in a specific venomous species.

New Measurement for Correlation of Co-evolution Relationship of Subsequences in Protein

Many computational tools have been developed to measure the protein residues co-evolution. Most of them only focus on co-evolution for pairwise residues in a protein sequence. However, number of residues participate in co-evolution might be multiple. And some co-evolved residues are clustered in several distinct regions in primary structure. Therefore, the co-evolution among the adjacent residues and the correlation between the distinct regions offer insights into function and evolution of the protein and residues. Subsequence is used to represent the adjacent multiple residues in one distinct region. In the paper, co-evolution relationship in each subsequence is represented by mutual information matrix (MIM). Then, Pearson's correlation coefficient: R value is developed to measure the similarity correlation of two MIMs. MSAs from Catalytic Data Base (Catalytic Site Atlas, CSA) are used for testing. R value characterizes a specific class of residues. In contrast to individual pairwise co-evolved residues, adjacent residues without high individual MI values are found since the co-evolved relationship among them is similar to that among another set of adjacent residues. These subsequences possess some flexibility in the composition of side chains, such as the catalyzed environment.

Phylogenetic Co-Occurrence of ExoR, ExoS, and ChvI, Components of the RSI Bacterial Invasion Switch, Suggests a Key Adaptive Mechanism Regulating the Transition between Free-Living and Host-Invading Phases in Rhizobiales

Both bacterial symbionts and pathogens rely on their host-sensing mechanisms to activate the biosynthetic pathways necessary for their invasion into host cells. The Gram-negative bacterium Sinorhizobium meliloti relies on its RSI (ExoR-ExoS-ChvI) Invasion Switch to turn on the production of succinoglycan, an exopolysaccharide required for its host invasion. Recent whole-genome sequencing efforts have uncovered putative components of RSI-like invasion switches in many other symbiotic and pathogenic bacteria. To explore the possibility of the existence of a common invasion switch, we have conducted a phylogenomic survey of orthologous ExoR, ExoS, and ChvI tripartite sets in more than ninety proteobacterial genomes. Our analyses suggest that functional orthologs of the RSI invasion switch co-exist in Rhizobiales, an order characterized by numerous invasive species, but not in the order’s close relatives. Phylogenomic analyses and reconstruction of orthologous sets of the three proteins in Alphaproteobacteria confirm Rhizobiales-specific gene synteny and congruent RSI evolutionary histories. Evolutionary analyses further revealed site-specific substitutions correlated specifically to either animal-bacteria or plant-bacteria associations. Lineage restricted conservation of any one specialized gene is in itself an indication of species adaptation. However, the orthologous phylogenetic co-occurrence of all interacting partners within this single signaling pathway strongly suggests that the development of the RSI switch was a key adaptive mechanism. The RSI invasion switch, originally found in S. meliloti, is a characteristic of the Rhizobiales, and potentially a conserved crucial activation step that may be targeted to control host invasion by pathogenic bacterial species.

Molecular Physiology of an Extra-renal Cl(-) Uptake Mechanism for Body Fluid Cl(-) Homeostasis

The development of an ion regulatory mechanism for body fluid homeostasis was an important trait for vertebrates during the evolution from aquatic to terrestrial life. The homeostatic mechanism of Cl^- in aquatic fish appears to be similar to that of terrestrial vertebrates; however, the mechanism in non-mammalian vertebrates is poorly understood. Unlike in mammals, in which the kidney plays a central role, in most fish species, the gill is responsible for the maintenance of Cl^- homeostasis via Cl^- transport uptake mechanisms. Previous studies in zebrafish identified Na⁺-Cl^- cotransporter (NCC) 2b-expressing cells in the gills and skin as the major ionocytes responsible for Cl^- uptake, similar to distal convoluted tubular cells in mammalian kidney. However, the mechanism by which basolateral ions exit from NCC cells is still unclear.

Of the in situ hybridization signals of twelve members of the clc Cl^- channel family, only that of clc-2c exhibited an ionocyte pattern in the gill and embryonic skin. Double in situ hybridization/immunocytochemistry confirmed colocalization of apical NCC2b with basolateral CLC-2c. Acclimation to a low Cl^- environment increased mRNA expression of both clc-2c and ncc2b, and also the protein expression of CLC-2c in embryos and adult gills. Loss-of-function of clc-2c resulted in a significant decrease in whole body Cl^- content in zebrafish embryos, a phenotype similar to that of ncc2b mutants; this finding suggests a role for CLC-2c in Cl^- uptake. Translational knockdown of clc-2c stimulated ncc2b mRNA expression and vice versa, revealing cooperation between these two transporters in the context of zebrafish Cl^- homeostasis. Further comparative genomic and phylogenetic analyses revealed that zebrafish CLC-2c is a fish-specific isoform that diverged from a kidney-predominant homologue, in the same manner as NCC2b and its counterparts (NCCs).

Several lines of molecular and cellular physiological evidences demonstrated the cofunctional role of apical NCC2b and basolateral CLC-2c in the gill/skin Cl^- uptake pathway. Taking the phylogenetic evidence into consideration, fish-specific NCC2b and CLC-2c may have coevolved to perform extra-renal Cl^- uptake during the evolution of vertebrates in an aquatic environment.

A draft network of ligand-receptor-mediated multicellular signalling in human

Cell-to-cell communication across multiple cell types and tissues strictly governs proper functioning of metazoans and extensively relies on interactions between secreted ligands and cell-surface receptors. Herein, we present the first large-scale map of cell-to-cell communication between 144 human primary cell types. We reveal that most cells express tens to hundreds of ligands and receptors to create a highly connected signalling network through multiple ligand-receptor paths. We also observe extensive autocrine signalling with approximately two-thirds of partners possibly interacting on the same cell type. We find that plasma membrane and secreted proteins have the highest cell-type specificity, they are evolutionarily younger than intracellular proteins, and that most receptors had evolved before their ligands. We provide an online tool to interactively query and visualize our networks and demonstrate how this tool can reveal novel cell-to-cell interactions with the prediction that mast cells signal to monoblastic lineages via the CSF1-CSF1R interacting pair.

Mac-1 Regulates IL-13 Activity in Macrophages by Directly Interacting with IL-13Rα1

Mac-1 exhibits a unique inhibitory activity toward IL-13-induced JAK/STAT activation and thereby regulates macrophage to foam cell transformation. However, the underlying molecular mechanism is unknown. In this study, we report the identification of IL-13Rα1, a component of the IL-13 receptor (IL-13R), as a novel ligand of integrin Mac-1, using a co-evolution-based algorithm. Biochemical analyses demonstrated that recombinant IL-13Rα1 binds Mac-1 in a purified system and supports Mac-1-mediated cell adhesion. Co-immunoprecipitation experiments revealed that endogenous Mac-1 forms a complex with IL-13Rα1 in solution, and confocal fluorescence microscopy demonstrated that these two receptors co-localize with each other on the surface of macrophages. Moreover, we found that genetic inactivation of Mac-1 promotes IL-13-induced JAK/STAT activation in macrophages, resulting in enhanced polarization along the alternative activation pathway. Importantly, we observed that Mac-1(-/-) macrophages exhibit increased expression of foam cell differentiation markers including 15-lipoxygenase and lectin-type oxidized LDL receptor-1 both in vitro and in vivo. Indeed, we found that Mac-1(-/-)LDLR(-/-) mice develop significantly more foam cells than control LDLR(-/-) mice, using an in vivo model of foam cell formation. Together, our data establish for the first time a molecular mechanism by which Mac-1 regulates the signaling activity of IL-13 in macrophages. This newly identified IL-13Rα1/Mac-1-dependent pathway may offer novel targets for therapeutic intervention in the future.