Modular organization in the reductive evolution of protein-protein interaction networks

Chance Favors the Prepared Genomes: Horizontal Transfer Shapes the Emergence of Antibiotic Resistance Mutations in Core Genes

Bacterial lineages acquire novel traits at diverse rates in part because the genetic background impacts the successful acquisition of novel genes by horizontal transfer. Yet, how horizontal transfer affects the subsequent evolution of core genes remains poorly understood. Here, we studied the evolution of resistance to quinolones in Escherichia coli accounting for population structure. We found 60 groups of genes whose gain or loss induced an increase in the probability of subsequently becoming resistant to quinolones by point mutations in the gyrase and topoisomerase genes. These groups include functions known to be associated with direct mitigation of the effect of quinolones, with metal uptake, cell growth inhibition, biofilm formation, and sugar metabolism. Many of them are encoded in phages or plasmids. Although some of the chronologies may reflect epidemiological trends, many of these groups encoded functions providing latent phenotypes of antibiotic low-level resistance, tolerance, or persistence under quinolone treatment. The mutations providing resistance were frequent and accumulated very quickly. Their emergence was found to increase the rate of acquisition of other antibiotic resistances setting the path for multidrug resistance. Hence, our findings show that horizontal gene transfer shapes the subsequent emergence of adaptive mutations in core genes. In turn, these mutations further affect the subsequent evolution of resistance by horizontal gene transfer. Given the substantial gene flow within bacterial genomes, interactions between horizontal transfer and point mutations in core genes may be a key to the success of adaptation processes.

Networks Consolidate the Core Concepts of Evolution by Natural Selection

Microbiology has unraveled rich evidence of ongoing reticulate evolutionary processes and complex interactions both within and between cells. These phenomena feature real biological networks, which can logically be analyzed using network-based tools. It is thus not surprising that network sciences, a field independent from evolutionary biology and microbiology, have recently pervasively infused their methods into both fields. Importantly, network tools bring forward observations enhancing the understanding of three core evolutionary concepts: variation, fitness, and heredity. Consequently, our work shows how network sciences can enhance evolutionary theory by explaining the evolution by natural selection of a broad diversity of units of selection, while updating the popular figure of Darwin's tree of life with a comprehensive sketch of the networks of evolution.

Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory

The classic Darwinian theory and the Synthetic evolutionary theory and their linear models, while invaluable to study the origins and evolution of species, are not primarily designed to model the evolution of organisations, typically that of ecosystems, nor that of processes. How could evolutionary theory better explain the evolution of biological complexity and diversity? Inclusive network-based analyses of dynamic systems could retrace interactions between (related or unrelated) components. This theoretical shift from a Tree of Life to a Dynamic Interaction Network of Life, which is supported by diverse molecular, cellular, microbiological, organismal, ecological and evolutionary studies, would further unify evolutionary biology.

Synthetic Biology: Engineering Living Systems from Biophysical Principles

Synthetic biology was founded as a biophysical discipline that sought explanations for the origins of life from chemical and physical first principles. Modern synthetic biology has been reinvented as an engineering discipline to design new organisms as well as to better understand fundamental biological mechanisms. However, success is still largely limited to the laboratory and transformative applications of synthetic biology are still in their infancy. Here, we review six principles of living systems and how they compare and contrast with engineered systems. We cite specific examples from the synthetic biology literature that illustrate these principles and speculate on their implications for further study. To fully realize the promise of synthetic biology, we must be aware of life's unique properties.

Spectral properties of the hierarchical product of graphs

The hierarchical product of two graphs represents a natural way to build a larger graph out of two smaller graphs with less regular and therefore more heterogeneous structure than the Cartesian product. Here we study the eigenvalue spectrum of the adjacency matrix of the hierarchical product of two graphs. Introducing a coupling parameter describing the relative contribution of each of the two smaller graphs, we perform an asymptotic analysis for the full spectrum of eigenvalues of the adjacency matrix of the hierarchical product. Specifically, we derive the exact limit points for each eigenvalue in the limits of small and large coupling, as well as the leading-order relaxation to these values in terms of the eigenvalues and eigenvectors of the two smaller graphs. Given its central roll in the structural and dynamical properties of networks, we study in detail the Perron-Frobenius, or largest, eigenvalue. Finally, as an example application we use our theory to predict the epidemic threshold of the susceptible-infected-susceptible model on a hierarchical product of two graphs.

A Quantitative Approach to Analyzing Genome Reductive Evolution Using Protein-Protein Interaction Networks: A Case Study of Mycobacterium leprae

The advance in high-throughput sequencing technologies has yielded complete genome sequences of several organisms, including complete bacterial genomes. The growing number of these available sequenced genomes has enabled analyses of their dynamics, as well as the molecular and evolutionary processes which these organisms are under. Comparative genomics of different bacterial genomes have highlighted their genome size and gene content in association with lifestyles and adaptation to various environments and have contributed to enhancing our understanding of the mechanisms of their evolution. Protein–protein functional interactions mediate many essential processes for maintaining the stability of the biological systems under changing environmental conditions. Thus, these interactions play crucial roles in the evolutionary processes of different organisms, especially for obligate intracellular bacteria, proven to generally have reduced genome sizes compared to their nearest free-living relatives. In this study, we used the approach based on the Renormalization Group (RG) analysis technique and the Maximum-Excluded-Mass-Burning (MEMB) model to investigate the evolutionary process of genome reduction in relation to the organization of functional networks of two organisms. Using a Mycobacterium leprae (MLP) network in comparison with a Mycobacterium tuberculosis (MTB) network as a case study, we show that reductive evolution in MLP was as a result of removal of important proteins from neighbors of corresponding orthologous MTB proteins. While each orthologous MTB protein had an increase in number of interacting partners in most instances, the corresponding MLP protein had lost some of them. This work provides a quantitative model for mapping reductive evolution and protein–protein functional interaction network organization in terms of roles played by different proteins in the network structure.

What Symbionts Teach us about Modularity

The main goal of Synthetic Biology (SB) is to apply engineering principles to biotechnology in order to make life easier to engineer. These engineering principles include modularity: decoupling of complex systems into smaller, orthogonal sub-systems that can be used in a range of different applications. The successful use of modules in engineering is expected to be reproduced in synthetic biological systems. But the difficulties experienced up to date with SB approaches question the short-term feasibility of designing life. Considering the “engineerable” nature of life, here we discuss the existence of modularity in natural living systems, particularly in symbiotic interactions, and compare the behavior of such systems, with those of engineered modules. We conclude that not only is modularity present but it is also common among living structures, and that symbioses are a new example of module-like sub-systems having high similarity with modularly designed ones. However, we also detect and stress fundamental differences between man-made and biological modules. Both similarities and differences should be taken into account in order to adapt SB design to biological laws.

A kinetic model of the evolution of a protein interaction network

Background

Known protein interaction networks have very particular properties. Old proteins tend to have more interactions than new ones. One of the best statistical representatives of this property is the node degree distribution (distribution of proteins having a given number of interactions). It has previously been shown that this distribution is very close to the sum of two distinct exponential components. In this paper, we asked: What are the possible mechanisms of evolution for such types of networks? To answer this question, we tested a kinetic model for simplified evolution of a protein interactome. Our proposed model considers the emergence of new genes and interactions and the loss of old ones. We assumed that there are generally two coexisting classes of proteins. Proteins constituting the first class are essential only for ecological adaptations and are easily lost when ecological conditions change. Proteins of the second class are essential for basic life processes and, hence, are always effectively protected against deletion. All proteins can transit between the above classes in both directions. We also assumed that the phenomenon of gene duplication is always related to ecological adaptation and that a new copy of a duplicated gene is not essential. According to this model, all proteins gain new interactions with a rate that preferentially increases with the number of interactions (the rich get richer). Proteins can also gain interactions because of duplication. Proteins lose their interactions both with and without the loss of partner genes.

Results

The proposed model reproduces the main properties of protein-protein interaction networks very well. The connectivity of the oldest part of the interaction network is densest, and the node degree distribution follows the sum of two shifted power-law functions, which is a theoretical generalization of the previous finding. The above distribution covers the wide range of values of node degrees very well, much better than a power law or generalized power law supplemented with an exponential cut-off. The presented model also relates the total number of interactome links to the total number of interacting proteins. The theoretical results were for the interactomes of A. thaliana, B. taurus, C. elegans, D. melanogaster, E. coli, H. pylori, H. sapiens, M. musculus, R. norvegicus and S. cerevisiae.

Conclusions

Using these approaches, the kinetic parameters could be estimated. Finally, the model revealed the evolutionary kinetics of proteome formation, the phenomenon of protein differentiation and the process of gaining new interactions.

Genome reduction promotes increase in protein functional complexity in bacteria

Obligate pathogenic and endosymbiotic bacteria typically experience gene loss due to functional redundancy, asexuality, and genetic drift. We hypothesize that reduced genomes increase their functional complexity through protein multitasking, in which many genes adopt new roles to counteract gene loss. Comparisons of interaction networks among six bacteria that have varied genome sizes (Mycoplasma pneumoniae, Treponema pallidum, Helicobacter pylori, Campylobacter jejuni, Synechocystis sp., and Mycobacterium tuberculosis) reveal that proteins in small genomes interact with proteins from a wider range of functions than do their orthologs in larger genomes. This suggests that surviving proteins form increasingly complex functional relationships to compensate for genes that are lost.

ModuLand plug-in for Cytoscape: determination of hierarchical layers of overlapping network modules and community centrality

The ModuLand plug-in provides Cytoscape users an algorithm for determining extensively overlapping network modules. Moreover, it identifies several hierarchical layers of modules, where meta-nodes of the higher hierarchical layer represent modules of the lower layer. The tool assigns module cores, which predict the function of the whole module, and determines key nodes bridging two or multiple modules. The plug-in has a detailed JAVA-based graphical interface with various colouring options. The ModuLand tool can run on Windows, Linux or Mac OS. We demonstrate its use on protein structure and metabolic networks.

AVAILABILITY

The plug-in and its user guide can be downloaded freely from: http://www.linkgroup.hu/modules.php.

Organizational structure and the periphery of the gene regulatory network in B-cell lymphoma

Background

The physical periphery of a biological cell is mainly described by signaling pathways which are triggered by transmembrane proteins and receptors that are sentinels to control the whole gene regulatory network of a cell. However, our current knowledge about the gene regulatory mechanisms that are governed by extracellular signals is severely limited.

Results

The purpose of this paper is three fold. First, we infer a gene regulatory network from a large-scale B-cell lymphoma expression data set using the C3NET algorithm. Second, we provide a functional and structural analysis of the largest connected component of this network, revealing that this network component corresponds to the peripheral region of a cell. Third, we analyze the hierarchical organization of network components of the whole inferred B-cell gene regulatory network by introducing a new approach which exploits the variability within the data as well as the inferential characteristics of C3NET. As a result, we find a functional bisection of the network corresponding to different cellular components.

Conclusions

Overall, our study allows to highlight the peripheral gene regulatory network of B-cells and shows that it is centered around hub transmembrane proteins located at the physical periphery of the cell. In addition, we identify a variety of novel pathological transmembrane proteins such as ion channel complexes and signaling receptors in B-cell lymphoma.

Microbial lifestyle and genome signatures

Microbes are known for their unique ability to adapt to varying lifestyle and environment, even to the extreme or adverse ones. The genomic architecture of a microbe may bear the signatures not only of its phylogenetic position, but also of the kind of lifestyle to which it is adapted. The present review aims to provide an account of the specific genome signatures observed in microbes acclimatized to distinct lifestyles or ecological niches. Niche-specific signatures identified at different levels of microbial genome organization like base composition, GC-skew, purine-pyrimidine ratio, dinucleotide abundance, codon bias, oligonucleotide composition etc. have been discussed. Among the specific cases highlighted in the review are the phenomena of genome shrinkage in obligatory host-restricted microbes, genome expansion in strictly intra-amoebal pathogens, strand-specific codon usage in intracellular species, acquisition of genome islands in pathogenic or symbiotic organisms, discriminatory genomic traits of marine microbes with distinct trophic strategies, and conspicuous sequence features of certain extremophiles like those adapted to high temperature or high salinity.

Metabolic modeling of endosymbiont genome reduction on a temporal scale

This study explores the order in which individual metabolic genes are lost in an in silico evolutionary process leading from the metabolic network of Eschericia coli to that of the genome-reduced endosymbiont Buchnera aphidicola.

Simulating the reductive evolutionary process under several growth conditions, a remarkable correlation between in silico and phylogenetically reconstructed gene loss time is obtained.

A gene's k-robustness (its depth of backups) is prime determinant of its loss time.

In silico gene loss time is a better predictor of their actual loss times than genomic features and network properties.

Simulating the reductive evolutionary process by the loss of large blocks followed by single-gene deletions, as known to occur in evolution, yields a remarkable correspondence with the phylogenetic reconstruction and the block loss reported in the literature.

An open fundamental challenge in Systems Biology is whether a genome-scale model can predict patterns of genome evolution by realistically accounting for the associated biochemical constraints. In this study, we explore the order in which individual genes are lost in an in silico evolutionary process, leading from the metabolic network of Eschericia coli to that of the endosymbiont Buchnera aphidicola.

To evaluate the in silico gene loss time, we repeated the reductive evolutionary process introduced by Pál et al (2006), denoting the in silico deletion time of a gene in a single run of the reductive evolutionary process as the number of genes deleted before its own deletion occurred. By comparing the in silico evaluations of the gene loss time to that obtained by a phylogenetic reconstruction (Figure 1), we could evaluate the ability of an in silico process to predict temporal patterns of genome reduction. Applying this procedure on a literature-based viable media, we obtained a mean Spearman's correlation of 0.46 (53% of the maximal correlation, empirical P-value <9.9e−4) between in silico and phylogenetically reconstructed loss times. In order to provide an upper bound on evolutionary necessity stemming from metabolic constraints, we searched the space of potential growth media and biomass functions via a simulated annealing search algorithm aimed at identifying an environment/biomass function that maximizes the target correlation between in silico and reconstructed loss times. Simulating the reductive evolutionary process under the growth conditions and biomass function obtained in this process, we managed to improve the correlation between in silico and reconstructed loss times to a mean Spearman's correlation of 0.54 (63% of the maximal correlation, empirical P-value <9.9e−4, Figure 3).

Examining the dependency of the predicted loss time of each gene on its intrinsic network-level properties we find a very strong inverse Spearman's correlation of −0.84 (empirical P-value <9.9e−4) between the order of gene loss predicted in silico and the k-robustness levels of the genes, the latter denoting the depth of their functional backups in the network (Deutscher et al, 2006). Moreover, in order to examine whether the relative loss time of a gene is influenced by its functional dependencies with other genes, we performed a flux-coupling analysis and identified pairs of reactions whose activities asymmetrically depend on each other, i.e., are directionally coupled (Burgard et al, 2004). We find that genes encoding reactions whose activity is needed for activating the other reaction (and not vice versa) have a tendency to be lost later, as one would expect (binomial P-value <1e−14).

To assess the scale of these results, we examined as a control how well genomic features and network properties predict the phylogenetically reconstructed gene loss times. We examined the dependency of the latter on several factors that are known be inversely correlated with the propensity of a gene to be lost (Brinza et al, 2009; Delmotte et al, 2006; Tamames et al, 2007), including the genes' mRNA levels, tAI values (Covert et al, 2004; Reis et al, 2004; Sharp and Li, 1987; Tuller et al, 2010a) and the number of partners the gene products have in a protein–protein interaction network. Remarkably, these genomic features yield considerably lower Spearman's correlation than that obtained by the in silico simulations. Moreover, multiply regressing the loss times from the phylogenetic reconstruction on the in silico gene loss time predictions and the genomic and network variables, we found that the (normalized) coefficient of the in silico predictions in the regression is much higher than those of the genomic features, further testifying to the considerable independent predictive power of the metabolic model.

Finally, simulating the evolutionary process as large block deletions at first followed by single-gene deletions as is thought to occur in evolution (Moran and Mira, 2001; van Ham et al, 2003), a remarkable correspondence with the phylogenetic reconstruction was found. Namely, we find that after a certain amount of genes are deleted from the genome, no further block deletions can occur due to the increasing density of essential genes. Notably, the maximum amount of genes that can be deleted in blocks (i.e., until no more blocks can be deleted) corresponds to the number of genes appearing in our phylogenetic reconstruction from the LCA (last common ancestor of Buchnera and E. coli) to the LCSA (last common symbiotic ancestor, nodes 1–3 in Figure 1A), as described in the literature.

A fundamental challenge in Systems Biology is whether a cell-scale metabolic model can predict patterns of genome evolution by realistically accounting for associated biochemical constraints. Here, we study the order in which genes are lost in an in silico evolutionary process, leading from the metabolic network of Eschericia coli to that of the endosymbiont Buchnera aphidicola. We examine how this order correlates with the order by which the genes were actually lost, as estimated from a phylogenetic reconstruction. By optimizing this correlation across the space of potential growth and biomass conditions, we compute an upper bound estimate on the model's prediction accuracy (R=0.54). The model's network-based predictive ability outperforms predictions obtained using genomic features of individual genes, reflecting the effect of selection imposed by metabolic stoichiometric constraints. Thus, while the timing of gene loss might be expected to be a completely stochastic evolutionary process, remarkably, we find that metabolic considerations, on their own, make a marked 40% contribution to determining when such losses occur.

Loss of genetic redundancy in reductive genome evolution. PLoS Comput Biol. 2011;7:e1001082. [PMID: 21379323 PMCID: PMC3040653 DOI: 10.1371/journal.pcbi.1001082]

Biological systems evolved to be functionally robust in uncertain environments, but also highly adaptable. Such robustness is partly achieved by genetic redundancy, where the failure of a specific component through mutation or environmental challenge can be compensated by duplicate components capable of performing, to a limited extent, the same function. Highly variable environments require very robust systems. Conversely, predictable environments should not place a high selective value on robustness. Here we test this hypothesis by investigating the evolutionary dynamics of genetic redundancy in extremely reduced genomes, found mostly in intracellular parasites and endosymbionts. By combining data analysis with simulations of genome evolution we show that in the extensive gene loss suffered by reduced genomes there is a selective drive to keep the diversity of protein families while sacrificing paralogy. We show that this is not a by-product of the known drivers of genome reduction and that there is very limited convergence to a common core of families, indicating that the repertoire of protein families in reduced genomes is the result of historical contingency and niche-specific adaptations. We propose that our observations reflect a loss of genetic redundancy due to a decreased selection for robustness in a predictable environment.

Structure and dynamics of the operon map of Buchnera aphidicola sp. strain APS

BACKGROUND

Gene expression regulation is still poorly documented in bacteria with highly reduced genomes. Understanding the evolution and mechanisms underlying the regulation of gene transcription in Buchnera aphidicola, the primary endosymbiont of aphids, is expected both to enhance our understanding of this nutritionally based association and to provide an intriguing case-study of the evolution of gene expression regulation in a reduced bacterial genome.

RESULTS

A Bayesian predictor was defined to infer the B. aphidicola transcription units, which were further validated using transcriptomic data and RT-PCR experiments. The characteristics of B. aphidicola predicted transcription units (TUs) were analyzed in order to evaluate the impact of operon map organization on the regulation of gene transcription.On average, B. aphidicola TUs contain more genes than those of E. coli. The global layout of B. aphidicola operon map was mainly shaped by the big reduction and the rearrangements events, which occurred at the early stage of the symbiosis. Our analysis suggests that this operon map may evolve further only by small reorganizations around the frontiers of B. aphidicola TUs, through promoter and/or terminator sequence modifications and/or by pseudogenization events. We also found that the need for specific transcription regulation exerts some pressure on gene conservation, but not on gene assembling in the operon map in Buchnera. Our analysis of the TUs spacing pointed out that a selection pressure is maintained on the length of the intergenic regions between divergent adjacent gene pairs.

CONCLUSIONS

B. aphidicola can seemingly only evolve towards a more polycistronic operon map. This implies that gene transcription regulation is probably subject to weak selection pressure in Buchnera conserving operons composed of genes with unrelated functions.

Module detection in complex networks using integer optimisation

BACKGROUND

The detection of modules or community structure is widely used to reveal the underlying properties of complex networks in biology, as well as physical and social sciences. Since the adoption of modularity as a measure of network topological properties, several methodologies for the discovery of community structure based on modularity maximisation have been developed. However, satisfactory partitions of large graphs with modest computational resources are particularly challenging due to the NP-hard nature of the related optimisation problem. Furthermore, it has been suggested that optimising the modularity metric can reach a resolution limit whereby the algorithm fails to detect smaller communities than a specific size in large networks.

RESULTS

We present a novel solution approach to identify community structure in large complex networks and address resolution limitations in module detection. The proposed algorithm employs modularity to express network community structure and it is based on mixed integer optimisation models. The solution procedure is extended through an iterative procedure to diminish effects that tend to agglomerate smaller modules (resolution limitations).

CONCLUSIONS

A comprehensive comparative analysis of methodologies for module detection based on modularity maximisation shows that our approach outperforms previously reported methods. Furthermore, in contrast to previous reports, we propose a strategy to handle resolution limitations in modularity maximisation. Overall, we illustrate ways to improve existing methodologies for community structure identification so as to increase its efficiency and applicability.

Regulatory exaptation of the catabolite repression protein (Crp)-cAMP system in Pseudomonas putida

The genome of the soil bacterium Pseudomonas putida KT2440 encodes singular orthologues of genes crp (encoding the catabolite repression protein, Crp) and cyaA (adenylate cyclase) of Escherichia coli. The levels of cAMP formed by P. putida cells were below detection with a Dictyostelium biosensor in vivo. The cyaA(P. putida) gene was transcribed in vivo but failed to complement the lack of maltose consumption of a cyaA mutant of E. coli, thereby indicating that cyaA(P. putida) was poorly translated or rendered non-functional in the heterologous host. Yet, generation of cAMP by CyaA(P. putida) could be verified by expressing the cyaA(P. putida) gene in a hypersensitive E. coli strain. On the other hand, the crp(P. putida) gene restored the metabolic capacities of an equivalent crp mutant of E. coli, but not in a double crp/cyaA strain, suggesting that the ability to regulate such functions required cAMP. In order to clarify the breadth of the Crp/cAMP system in P. putida, crp and cyaA mutants were generated and passed through a battery of phenotypic tests for recognition of gross metabolic properties and stress-endurance abilities. These assays revealed that the loss of each gene led in most (but not all) cases to the same phenotypic behaviour, indicating a concerted functionality. Unexpectedly, none of the mutations affected the panel of carbon compounds that can be used by P. putida as growth substrates, the mutants being impaired only in the use of various dipeptides as N sources. Furthermore, the lack of crp or cyaA had little influence on the gross growth fingerprinting of the cells. The poor physiological profile of the Crp-cAMP system of P. putida when compared with E. coli exposes a case of regulatory exaptation, i.e. the process through which a property evolved for a particular function is co-opted for a new use.

The GimA locus of extraintestinal pathogenic E. coli: does reductive evolution correlate with habitat and pathotype?

IbeA (invasion of brain endothelium), which is located on a genomic island termed GimA, is involved in the pathogenesis of several extraintestinal pathogenic E. coli (ExPEC) pathotypes, including newborn meningitic E. coli (NMEC) and avian pathogenic E. coli (APEC). To unravel the phylogeny of GimA and to investigate its island character, the putative insertion locus of GimA was determined via Long Range PCR and DNA-DNA hybridization in 410 E. coli isolates, including APEC, NMEC, uropathogenic (UPEC), septicemia-associated E. coli (SEPEC), and human and animal fecal isolates as well as in 72 strains of the E. coli reference (ECOR) collection. In addition to a complete GimA (∼20.3 kb) and a locus lacking GimA we found a third pattern containing a 342 bp remnant of GimA in this strain collection. The presence of GimA was almost exclusively detected in strains belonging to phylogenetic group B2. In addition, the complete GimA was significantly more frequent in APEC and NMEC strains while the GimA remnant showed a higher association with UPEC strains. A detailed analysis of the ibeA sequences revealed the phylogeny of this gene to be consistent with that obtained by Multi Locus Sequence Typing of the strains. Although common criteria for genomic islands are partially fulfilled, GimA rather seems to be an ancestral part of phylogenetic group B2, and it would therefore be more appropriate to term this genomic region GimA locus instead of genomic island. The existence of two other patterns reflects a genomic rearrangement in a reductive evolution-like manner.

Comparative evolutionary analysis of protein complexes in E. coli and yeast

Background

Proteins do not act in isolation; they frequently act together in protein complexes to carry out concerted cellular functions. The evolution of complexes is poorly understood, especially in organisms other than yeast, where little experimental data has been available.

Results

We generated accurate, high coverage datasets of protein complexes for E. coli and yeast in order to study differences in the evolution of complexes between these two species. We show that substantial differences exist in how complexes have evolved between these organisms. A previously proposed model of complex evolution identified complexes with cores of interacting homologues. We support findings of the relative importance of this mode of evolution in yeast, but find that it is much less common in E. coli. Additionally it is shown that those homologues which do cluster in complexes are involved in eukaryote-specific functions. Furthermore we identify correlated pairs of non-homologous domains which occur in multiple protein complexes. These were identified in both yeast and E. coli and we present evidence that these too may represent complex cores in yeast but not those of E. coli.

Conclusions

Our results suggest that there are differences in the way protein complexes have evolved in E. coli and yeast. Whereas some yeast complexes have evolved by recruiting paralogues, this is not apparent in E. coli. Furthermore, such complexes are involved in eukaryotic-specific functions. This implies that the increase in gene family sizes seen in eukaryotes in part reflects multiple family members being used within complexes. However, in general, in both E. coli and yeast, homologous domains are used in different complexes.

Bacteria as computers making computers

Various efforts to integrate biological knowledge into networks of interactions have produced a lively microbial systems biology. Putting molecular biology and computer sciences in perspective, we review another trend in systems biology, in which recursivity and information replace the usual concepts of differential equations, feedback and feedforward loops and the like. Noting that the processes of gene expression separate the genome from the cell machinery, we analyse the role of the separation between machine and program in computers. However, computers do not make computers. For cells to make cells requires a specific organization of the genetic program, which we investigate using available knowledge. Microbial genomes are organized into a paleome (the name emphasizes the role of the corresponding functions from the time of the origin of life), comprising a constructor and a replicator, and a cenome (emphasizing community-relevant genes), made up of genes that permit life in a particular context. The cell duplication process supposes rejuvenation of the machine and replication of the program. The paleome also possesses genes that enable information to accumulate in a ratchet-like process down the generations. The systems biology must include the dynamics of information creation in its future developments.

Modularity of cellular networks shows general center-periphery polarization

The modular biology is supposed to be a bridge from the molecular to the systems biology. Using a new approach, it is shown here that the protein interaction networks of yeast Saccharomyces cerevisiae and bacteria Escherichia coli consist of two large-scale modularity layers, central and peripheral, separated by a zone of depressed modularity. This finding based on the analysis of network topology is further supported by the discovery that there are many more Gene Ontology categories (terms) and KEGG biochemical pathways that are overrepresented in the central and peripheral layers than in the intermediate zone. The categories of the central layer are mostly related to nuclear information processing, regulation and cell cycle, whereas the peripheral layer is dealing with various metabolic and energetic processes, transport and cell communication. A similar center-periphery polarization of modularity is found in the protein domain networks ('built-in interactome') and in a powergrid (as a non-biological example). These data suggest a 'polarized modularity' model of cellular networks where the central layer seems to be regulatory and to use information storage of the nucleus, whereas the peripheral layer seems devoted to more specialized tasks and environmental interactions, with a complex 'bus' between the layers.

Architectures and functional coverage of protein-protein interfaces

The diverse range of cellular functions is performed by a limited number of protein folds existing in nature. One may similarly expect that cellular functional diversity would be covered by a limited number of protein-protein interface architectures. Here, we present 8205 interface clusters, each representing a unique interface architecture. This data set of protein-protein interfaces is analyzed and compared with older data sets. We observe that the number of both biological and crystal interfaces increases significantly compared to the number of Protein Data Bank entries. Furthermore, we find that the number of distinct interface architectures grows at a much faster rate than the number of folds and is yet to level off. We further analyze the growth trend of the functional coverage by constructing functional interaction networks from interfaces. The functional coverage is also found to steadily increase. Interestingly, we also observe that despite the diversity of interface architectures, some are more favorable and frequently used, and of particular interest, are the ones that are also preferred in single chains.

Occurrence of cancer at multiple sites: towards distinguishing multigenesis from metastasis

Background

Occurrence of tumors at multiple sites is a hallmark of malignant cancers and contributes to the high mortality of cancers. The formation of multi-site cancers (MSCs) has conventionally been regarded as a result of hematogenous metastasis. However, some MSCs may appear as unusual in the sense of vascular dissemination pattern and therefore be explained by alternative metastasis models or even by non-metastatic independent formation mechanisms.

Results

Through literature review and incorporation of recent advance in understanding aging and development, we identified two alternative mechanisms for the independent formation of MSCs: 1) formation of separate tumors from cancer-initiating cells (CICs) mutated at an early stage of development and then diverging as to their physical locations upon further development, 2) formation of separate tumors from different CICs that contain mutations in some convergent ways. Either of these processes does not require long-distance migration and/or vascular dissemination of cancer cells from a primary site to a secondary site. Thus, we classify the formation of these MSCs from indigenous CICs (iCICs) into a new mechanistic category of tumor formation – multigenesis.

Conclusion

A multigenesis view on multi-site cancer (MSCs) may offer explanations for some "unusual metastasis" and has important implications for designing expanded strategies for the diagnosis and treatment of cancers.

Reviewers

This article was reviewed by Carlo C. Maley nominated by Laura F. Landweber and Razvan T. Radulescu nominated by David R. Kaplan. For the full reviews, please go to the Reviewers' comments section.