The role of peptide motifs in the evolution of a protein network

Naturally occurring proteins in cellular networks often share peptide motifs. These motifs have been known to play a pivotal role in protein interactions among the components of a network. However, it remains unknown how these motifs have contributed to the evolution of the protein network. Here we addressed this issue by a synthetic biology approach. Through the motif programming method, we have constructed an artificial protein library by mixing four peptide motifs shared among the Bcl-2 family proteins that positively or negatively regulate the apoptosis networks. We found one strong pro-apoptotic protein, d29, and two proteins having moderate, but unambiguous anti-apoptotic functions, a10 and d16, from the 28 tested clones. Thus both the pro- and anti-apoptotic modulators were present in the library, demonstrating that functional proteins with opposing effects can emerge from a single pool prepared from common motifs. Motif programming studies have exhibited that the annotated function of the motifs were significantly influenced by the context that the motifs embedded. The results further revealed that reshuffling of a set of motifs realized the promiscuous state of protein, from which disparate functions could emerge. Our finding suggests that motifs contributed to the plastic evolvability of the protein network.     

Reverse engineering a gene network using an asynchronous parallel evolution strategy

Background  

The use of reverse engineering methods to infer gene regulatory networks by fitting mathematical models to gene expression data is becoming increasingly popular and successful. However, increasing model complexity means that more powerful global optimisation techniques are required for model fitting. The parallel Lam Simulated Annealing (pLSA) algorithm has been used in such approaches, but recent research has shown that island Evolutionary Strategies can produce faster, more reliable results. However, no parallel island Evolutionary Strategy (piES) has yet been demonstrated to be effective for this task.     

Deconvoluting lung evolution: from phenotypes to gene regulatory networks

Speakers in this symposium presented examples of respiratoryregulation that broadly illustrate principles of evolution fromwhole organ to genes. The swim bladder and lungs of aquaticand terrestrial organisms arose independently from a commonprimordial "respiratory pharynx" but not from each other. Pathwaysof lung evolution are similar between crocodiles and birds buta low compliance of mammalian lung may have driven the developmentof the diaphragm to permit lung inflation during inspiration.To meet the high oxygen demands of flight, bird lungs have evolvedseparate gas exchange and pump components to achieve unidirectionalventilation and minimize dead space. The process of "screening"(removal of oxygen from inspired air prior to entering the terminalunits) reduces effective alveolar oxygen tension and potentiallyexplains why nonathletic large mammals possess greater pulmonarydiffusing capacities than required by their oxygen consumption.The "primitive" central admixture of oxygenated and deoxygenatedblood in the incompletely divided reptilian heart is actuallyco-regulated with other autonomic cardiopulmonary responsesto provide flexible control of arterial oxygen tension independentof ventilation as well as a unique mechanism for adjusting metabolicrate. Some of the most ancient oxygen-sensing molecules, i.e.,hypoxia-inducible factor-1alpha and erythropoietin, are up-regulatedduring mammalian lung development and growth under apparentlynormoxic conditions, suggesting functional evolution. Normalalveolarization requires pleiotropic growth factors acting viahighly conserved cell–cell signal transduction, e.g.,parathyroid hormone-related protein transducing at least partlythrough the Wingless/int pathway. The latter regulates morphogenesisfrom nematode to mammal. If there is commonality among thesediverse respiratory processes, it is that all levels of organization,from molecular signaling to structure to function, co-evolveprogressively, and optimize an existing gas-exchange framework.     

Algebraic connectivity may explain the evolution of gene regulatory networks

Gene expression is a result of the interplay between the structure, type, kinetics, and specificity of gene regulatory interactions, whose diversity gives rise to the variety of life forms. As the dynamic behavior of gene regulatory networks depends on their structure, here we attempt to determine structural reasons which, despite the similarities in global network properties, may explain the large differences in organismal complexity. We demonstrate that the algebraic connectivity, the smallest non-trivial eigenvalue of the Laplacian, of the directed gene regulatory networks decreases with the increase of organismal complexity, and may therefore explain the difference between the variety of analyzed regulatory networks. In addition, our results point out that, for the species considered in this study, evolution favours decreasing concentration of strategically positioned feed forward loops, so that the network as a whole can increase the specificity towards changing environments. Moreover, contrary to the existing results, we show that the average degree, the length of the longest cascade, and the average cascade length of gene regulatory networks cannot recover the evolutionary relationships between organisms. Whereas the dynamical properties of special subnetworks are relatively well understood, there is still limited knowledge about the evolutionary reasons for the already identified design principles pertaining to these special subnetworks, underlying the global quantitative features of gene regulatory networks of different organisms. The behavior of the algebraic connectivity, which we show valid on gene regulatory networks extracted from curated databases, can serve as an additional evolutionary principle of organism-specific regulatory networks.     

Assessing the exceptionality of network motifs.

Getting and analyzing biological interaction networks is at the core of systems biology. To help understanding these complex networks, many recent works have suggested to focus on motifs which occur more frequently than expected in random. To identify such exceptional motifs in a given network, we propose a statistical and analytical method which does not require any simulation. For this, we first provide an analytical expression of the mean and variance of the count under any exchangeable random graph model. Then we approximate the motif count distribution by a compound Poisson distribution whose parameters are derived from the mean and variance of the count. Thanks to simulations, we show that the compound Poisson approximation outperforms the Gaussian approximation. The compound Poisson distribution can then be used to get an approximate p-value and to decide if an observed count is significantly high or not. Our methodology is applied on protein-protein interaction (PPI) networks, and statistical issues related to exceptional motif detection are discussed.     

Directed evolution to re-adapt a co-evolved network within an enzyme

We have previously used targeted active-site saturation mutagenesis to identify a number of transketolase single mutants that improved activity towards either glycolaldehyde (GA), or the non-natural substrate propionaldehyde (PA). Here, all attempts to recombine the singles into double mutants led to unexpected losses of specific activity towards both substrates. A typical trade-off occurred between soluble expression levels and specific activity for all single mutants, but many double mutants decreased both properties more severely suggesting a critical loss of protein stability or native folding. Statistical coupling analysis (SCA) of a large multiple sequence alignment revealed a network of nine co-evolved residues that affected all but one double mutant. Such networks maintain important functional properties such as activity, specificity, folding, stability, and solubility and may be rapidly disrupted by introducing one or more non-naturally occurring mutations. To identify variants of this network that would accept and improve upon our best D469 mutants for activity towards PA, we created a library of random single, double and triple mutants across seven of the co-evolved residues, combining our D469 variants with only naturally occurring mutations at the remaining sites. A triple mutant cluster at D469, E498 and R520 was found to behave synergistically for the specific activity towards PA. Protein expression was severely reduced by E498D and improved by R520Q, yet variants containing both mutations led to improved specific activity and enzyme expression, but with loss of solubility and the formation of inclusion bodies. D469S and R520Q combined synergistically to improve k_cat 20-fold for PA, more than for any previous transketolase mutant. R520Q also doubled the specific activity of the previously identified D469T to create our most active transketolase mutant to date. Our results show that recombining active-site mutants obtained by saturation mutagenesis can rapidly destabilise critical networks of co-evolved residues, whereas beneficial single mutants can be retained and improved upon by randomly recombining them with natural variants at other positions in the network.     

Evolving beyond perfection: an investigation of the effects of long-term evolution on fractal gene regulatory networks

This paper continues a theme of exploring algorithms based on principles of biological development for tasks such as pattern generation, machine learning and robot control. Previous work has investigated the use of genes expressed as fractal proteins to enable greater evolvability of gene regulatory networks (GRNs). Here, the evolution of such GRNs is investigated further to determine whether evolution exhibits natural tendencies towards efficiency and graceful degradation of developmental programs. Experiments where "perfect" GRNs are evolved for a further thousand generations without the addition of any further selection pressure, confirm this hypothesis. After further evolution, the perfect GRNs operate in a more efficient manner (using fewer proteins) and show an improved ability to function correctly with missing genes. When the algorithm is applied to applications (e.g. robot control) this equates to efficient and fault-tolerant controllers.     

Parasites lead to evolution of robustness against gene loss in host signaling networks

Many biological networks can maintain their function against single gene loss. However, the evolutionary mechanisms responsible for such robustness remain unclear. Here, we demonstrate that antagonistic host–parasite interactions can act as a selective pressure driving the emergence of robustness against gene loss. Using a model of host signaling networks and simulating their coevolution with parasites that interfere with network function, we find that networks evolve both redundancy and specific architectures that allow them to maintain their response despite removal of proteins. We show that when the parasite pressure is removed, subsequent evolution can lead to loss of redundancy while architecture‐based robustness is retained. Contrary to intuition, increased parasite virulence hampers evolution of robustness by limiting the generation of population level diversity in the host. However, when robustness emerges under high virulence, it tends to be stronger. These findings predict an increased presence of robustness mechanisms in biological networks operating under parasite interference. Conversely, the presence of such mechanisms could indicate current or past parasite interference.     

Modification of gene duplicability during the evolution of protein interaction network

Duplications of genes encoding highly connected and essential proteins are selected against in several species but not in human, where duplicated genes encode highly connected proteins. To understand when and how gene duplicability changed in evolution, we compare gene and network properties in four species (Escherichia coli, yeast, fly, and human) that are representative of the increase in evolutionary complexity, defined as progressive growth in the number of genes, cells, and cell types. We find that the origin and conservation of a gene significantly correlates with the properties of the encoded protein in the protein-protein interaction network. All four species preserve a core of singleton and central hubs that originated early in evolution, are highly conserved, and accomplish basic biological functions. Another group of hubs appeared in metazoans and duplicated in vertebrates, mostly through vertebrate-specific whole genome duplication. Such recent and duplicated hubs are frequently targets of microRNAs and show tissue-selective expression, suggesting that these are alternative mechanisms to control their dosage. Our study shows how networks modified during evolution and contributes to explaining the occurrence of somatic genetic diseases, such as cancer, in terms of network perturbations.     

Population genetics theory of concerted evolution and its application to the immunoglobulin V gene tree

Summary The previous simple model for treating concerted evolution of multigene families has been revised to be compatible with various new observations on the immunoglobulin variable region family and other families. In the previous model, gene conversion and unequal crossing-over were considered, and it was assumed that genes are randomly arranged on the chromosome; neither subdivision nor correlation of gene identity and chromosomal distance were considered. Although this model satisfactorily explains the observed amino acid diversity within and between species, it fails to predict the very ancient branching of the mouse immunoglobulin heavy chain V-gene family. By incorporating subdivided structure and genetic correlation with chromosomal distance into the simple model, the data of divergence may be satisfactorily explained, as well as the rate of nucleotide substitution and the amino acid diversity. The rate at which a V-gene is duplicated or deleted by conversion or by unequal crossing-over is estimated by the new model to be on the order of 10^–6 per year. The model may be applicable to other multigene families, such as those coding for silkmoth chorion or mammalian kallikrein.Contribution no. 1560 from the National Institute of Genetics, Mishima, 411 Japan     

A network perspective on the evolution of metabolism by gene duplication

Background  

Gene duplication followed by divergence is one of the main sources of metabolic versatility. The patchwork and stepwise models of metabolic evolution help us to understand these processes, but their assumptions are relatively simplistic. We used a network-based approach to determine the influence of metabolic constraints on the retention of duplicated genes.     

Detecting network communities: an application to phylogenetic analysis

This paper proposes a new method to identify communities in generally weighted complex networks and apply it to phylogenetic analysis. In this case, weights correspond to the similarity indexes among protein sequences, which can be used for network construction so that the network structure can be analyzed to recover phylogenetically useful information from its properties. The analyses discussed here are mainly based on the modular character of protein similarity networks, explored through the Newman-Girvan algorithm, with the help of the neighborhood matrix . The most relevant networks are found when the network topology changes abruptly revealing distinct modules related to the sets of organisms to which the proteins belong. Sound biological information can be retrieved by the computational routines used in the network approach, without using biological assumptions other than those incorporated by BLAST. Usually, all the main bacterial phyla and, in some cases, also some bacterial classes corresponded totally (100%) or to a great extent (>70%) to the modules. We checked for internal consistency in the obtained results, and we scored close to 84% of matches for community pertinence when comparisons between the results were performed. To illustrate how to use the network-based method, we employed data for enzymes involved in the chitin metabolic pathway that are present in more than 100 organisms from an original data set containing 1,695 organisms, downloaded from GenBank on May 19, 2007. A preliminary comparison between the outcomes of the network-based method and the results of methods based on Bayesian, distance, likelihood, and parsimony criteria suggests that the former is as reliable as these commonly used methods. We conclude that the network-based method can be used as a powerful tool for retrieving modularity information from weighted networks, which is useful for phylogenetic analysis.     

An approach to evaluate the topological significance of motifs and other patterns in regulatory networks

Background  

The identification of network motifs as statistically over-represented topological patterns has become one of the most promising topics in the analysis of complex networks. The main focus is commonly made on how they operate by means of their internal organization. Yet, their contribution to a network's global architecture is poorly understood. However, this requires switching from the abstract view of a topological pattern to the level of its instances. Here, we show how a recently proposed metric, the pairwise disconnectivity index, can be adapted to survey if and which kind of topological patterns and their instances are most important for sustaining the connectivity within a network.     

On the evolution of scale-free topologies with a gene regulatory network model

A novel approach to generating scale-free network topologies is introduced, based on an existing artificial gene regulatory network model. From this model, different interaction networks can be extracted, based on an activation threshold. By using an evolutionary computation approach, the model is allowed to evolve, in order to reach specific network statistical measures. The results obtained show that, when the model uses a duplication and divergence initialisation, such as seen in nature, the resulting regulation networks not only are closer in topology to scale-free networks, but also require only a few evolutionary cycles to achieve a satisfactory error value.     

Exploiting application buffer reuse to improve MPI small message transfer protocols over RDMA-enabled networks

To avoid the memory registration cost for small messages in MPI implementations over RDMA-enabled networks, message transfer protocols involve a copy to intermediate buffers at both sender and receiver. In this paper, we propose to eliminate the send-side copy when an application buffer is reused frequently. We show that it is more efficient to register the application buffer and use it for data transfer. The idea is examined for small message transfer protocols in MVAPICH2, including RDMA Write and Send/Receive based communications, one-sided communications and collectives. The proposed protocol adaptively falls back to the current protocol when the application does not frequently use its buffers. The performance results over InfiniBand indicate up to 14% improvement for single message latency, close to 20% improvement for one-sided operations and up to 25% improvement for collectives. In addition, the communication time in MPI applications with high buffer reuse is improved using this technique.