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

Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory population and quantitative genetics, a theoretical frame The classic Darwinian theory and the Synthetic that corroborated Darwin’s hypothesis of the paramount evolutionary theory and their linear models, while power of selection for driving adaptive evolution [2]. invaluable to study the origins and evolution of species, This framework progressively aggregated multiple disci- are not primarily designed to model the evolution of plines: behavioural ecology, microbiology, paleobiology, organisations, typically that of ecosystems, nor that of etc. Overall, this classic framework considers that the processes. How could evolutionary theory better principal agency of evolution is natural selection of explain the evolution of biological complexity and favourable variations, and that those variations are con- diversity? Inclusive network-based analyses of dynamic stituted by random mutations and recombination in a systems could retrace interactions between (related or Mendelian population. The processes of microevolution, unrelated) components. This theoretical shift from a modelled by population and quantitative genetics, are Tree of Life to a Dynamic Interaction Network of Life, likely to be extrapolated to macroevolution [3]. To this which is supported by diverse molecular, cellular, extent, models that focus on one or two loci are able to microbiological, organismal, ecological and evolutionary capture much of the evolutionary dynamics of an organ- studies, would further unify evolutionary biology. ism, even though in reality many interdependencies be- tween thousands of loci (epistasis, dominance, etc.) Keywords: Evolutionary biology, Interactions, occur as the basis of the production and functioning of a Theoretical biology, Tree of Life, Web of Life phenotypic trait. Among forces acting on populations and modelled by population geneticists, natural selection Deciphering diversity through evolution is the one that shapes traits as adaptations and the de- The living world is nested and multilevel, involves mul- sign of organisms; adaptive radiation then explains much tiple agents and changes at different timescales. Evolu- of the diversity; and common descent from adapted tionary biology tries to characterize the dynamics organisms explains most of the commonalities across responsible for such complexity to decipher the pro- living forms (labelled homologies), and allows for classi- cesses accounting for the past and extant diversity ob- fying living beings into phylogenetic trees. Evolution is served in molecules (namely, genes, RNA, proteins), gradual because the effects of mutations are generally cellular machineries, unicellular and multi-cellular or- small, large ones being most likely to be deleterious as ganisms, species, communities and ecosystems. In the theorized by Fisher’s geometric model [4]. 1930s and 1940s, a unified framework to handle this task Many theoretical divergences surround this core view: was built under the name of Modern Synthesis [1]. It not everyone agrees that evolution is change in allele fre- encompassed Darwin’s idea of evolution by natural selec- quencies, or that population genetics captures the whole tion as an explanation for diversity and adaptation and of the evolutionary process, or that the genotypic view- point — tracking the dynamics of genes as ‘replicators’ [5] or the strategy ‘choices’ of organisms as fitness maximiz- * Correspondence: eric.bapteste@upmc.fr ing agents [6] — should be favoured to understand evolu- Sorbonne Universités, UPMC Université Paris 06, Institut de Biologie Paris-Seine (IBPS), F-75005 Paris, France tion. Nevertheless, it has been a powerful enough CNRS, UMR7138, Institut de Biologie Paris-Seine, F-75005 Paris, France framework to drive successful research programs on Full list of author information is available at the end of the article © Bapteste et al. 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Bapteste and Huneman BMC Biology (2018) 16:56 Page 2 of 16 speciation, adaptation, phylogenies, evolution of sex, we argue that evolutionary biology could become a sci- cooperation altruism, mutualism, etc., and incorporate ap- ence of evolving networks, which would allow biologists parent challenges such as neutral evolution [7], acknow- to explain organisational complexity, while providing a ledgement of constraints on variation [8], or the recent novel way to reframe and to unify evolutionary biology. theoretical turn from genetics to genomics following the achievement of the Human Genome Program [9]. Caus- Biology is regulated by networks ation is here overall conceived of as a linear causal relation Networks at the molecular level of a twofold nature: from the genotype to the phenotype Although numerous studies have focused on the functions (assuming of course environmental parameters), and from of individual genes, proteins and other molecules, it is in- the environment to the shaping of organisms via natural creasingly clear that each of these functions belongs to selection. For instance, in the classic case of evolution of complex networks of interactions. Starting at the molecu- peppered moths in urban forests at the time of the indus- lar scale, the importance of a diversity of molecular agents, trial revolution, trees became darkened with soot, and such as (DNA-based) genes and their regulatory se- then natural selection favored darker morphs as ‘fitter’ quences, RNAs and proteins, is well recognized. Import- ones, due to their being less easily detected by predator antly, in terms of their origins and modes of evolution, birds, resulting in a relative increase in frequency of the these agents are diverse. Genes are replicated across gen- darker morphs in the population [10]. erations, via the recruitment of bases along a DNA tem- Yet in the last 15 years biologists and philosophers of plate, thereby forming continuous lineages, affected by biology have regularly questioned the genuinely unifying Darwinian evolution. By contrast, proteins are recon- character of this Synthesis, as well as its explanatory ac- structed by recruitment of amino acids at the ribosomal curacy [11]. Those criticisms questioned notably the set machinery. There is no physical continuity between gener- of objects privileged by the Modern Synthesis, arguably ations of proteins, and thus no possibility for these agents too gene-centered [12], and its key explanatory to directly accumulate beneficial mutations [25]. processes, since niche construction [13], lateral gene Moreover, all these molecular entities are compositionally transfer [14, 15], phenotypic plasticity [16, 17], and mass complex, in the sense that they are made of inherited or extinction [18] could, for example, be added [11]. reassembled parts. E pluribus unum: genes and proteins Usually these critiques emphasize aspects rooted in a are (often) conglomerates of exons, introns [26–28], and particular biological discipline: lateral gene transfer from domains [29–31]. Similar claims can be made about com- microbiology, plasticity from developmental biology, posite molecular systems, such as CRISPR and Casposons mass extinction from paleobiology, ecosystem engineer- [32, 33], etc. This modular organisation has numerous ing from functional ecology, etc. There were also consequences: among them, genes can be nested within recurring claims for novel transdisciplinary fields: evo- genes [34]; proteins congregate in larger complexes [35]. eco-devo [19], investigating the evolutionary dynamics Importantly, this modularity is not the mere result of a of host and microbe associations (forming combinations divergence from a single ancestral form, but also involves often referred to as holobionts), evolutionary cell biology combinatorial processes and molecular tinkering of avail- [20], or microbial endocrinology [21], among others. able genetic material [36–38]. The coupling and decoup- This latter discipline aims at understanding the evolved ling of molecular components can operate randomly, as in interactions between microbial signals and host develop- cases of presuppression proposed to neutrally lead to large ment. Indeed, it is compelling for evolutionary biologists molecular complexes [39–41]. Presuppression, also known to decipher how such multi-species interactions became as constructive neutralism, is a process that generates established (namely, whether they involved specific mi- complexity by mechanically increasing dependencies crobial species and molecules, and whether they evolved between interacting molecules, in the absence of positive independently in different host lineages). selection. When a deleterious mutation affects one mo- Evolutionary biology is thus currently undergoing vari- lecular partner, existing properties of another molecule ous theoretical debates concerning the proper frame to with which the mutated molecule already interacted can formulate it [11, 22–24]. Here, we introduce an original compensate for its partner defect. Presuppression operates solution which moves this debate forward, acknowledg- like a ratchet, since the likelihood to restore the original ing that nothing on Earth evolves and makes sense in independency between molecules (by reverting the dele- isolation, thereby challenging the key assumption of the terious mutation) is lower than the likelihood to move Modern Synthesis framework that targeting the individ- away from this original state (by accumulating other mu- ual gene or organism (even when in principle knowing tations). Molecular associations can also evolve under that it is part of a set of complex interactions) allows us constraints [42], eventually reinforcing the relationships to capture evolution in all its dimensions. Since the liv- between molecular partners, as suggested for some op- ing world evolves as a dynamic network of interactions, erons [43] and fused genes [44, 45]. Bapteste and Huneman BMC Biology (2018) 16:56 Page 3 of 16 Consistently, interconnectedness is a striking feature At the molecular level, it is reasonable to assume that of the molecular world [46, 47]. Genes belong to regula- processes resulting from interactions of a diversity of tory networks with feedback loops [48]. Proteins belong intertwined agents offer a crucial explanans of biological to protein–protein interaction networks. This systemic complexity. Rather than ‘one agent, one action’, it would view contrasts with former atomistic views assigning one be more accurate to consider ‘a relationship between function to one gene. First, it is not always correct that a agents, one action’ as the modus operandi of life. gene produces only a protein, in the case of alternative Multiple drivers, of different nature, contribute to the splicing. Second, it is also unlikely that a protein evolution of these interactions: among others, gene co- performs one function, because no protein acts alone. expression/co-regulation [57], sometimes mediated by Rather, biological traits result from co-production pro- transposons [58–61]; the evolutionary origin of the cesses. This is nicely illustrated by the actual process of genes [62]; and also physical and chemical laws, as well translation, during which both proteins and DNA neces- as the presence of targeting machineries that constrain sarily interact, allowing for the collective reproduction of and regulate diffusion processes in the cell. These types these two types of molecular agents. How these different of relationships described at the molecular level are also components became so tightly integrated is a central recovered at other levels of biological organisations. issue for explaining evolution. Understanding how the molecular world functions and evolves therefore re- Networks at the cellular level quires analysing molecular organisation and the evolu- Similar conclusions have been reached at the cellular tion of the architecture of interaction networks, level, also crucial for understanding life history. All pro- especially since this structure can partly explain karyotes and protists are unicellular organisations, and molecular reactions [46, 47, 49, 50]. Thus, systems the cell is a fundamental building block of multicellular biologists search for common motifs in molecular inter- organisms. Cells must constantly evaluate the states of action networks from different organisms, such as feed- their inner and outer environments, i.e. to adjust their forward loops, assuming that some of these recurring gene expression and react accordingly [46]. This involves patterns, because they affect different gene or protein sets, regulatory, transduction, developmental, and protein may reflect general rules and constraints affecting the con- interaction networks, etc. Cells are built upon inner net- struction and evolution of biological organisations [46]. works of interacting components, and involved in or af- Focusing evolutionary explanations on the structure of fected by a diversity of exchanges, influences and modes the interactions between genes rather than on the pri- of communications (namely, genetic, energetic, chemical mary sequence of the genes is fundamentally different and electrical modes). Microbiology has gone a long way from sequencing genes and inferring history from their toward unraveling these processes since its heyday of sequences alone. One could think here of the case of pure culture studies, a fruitful reductionist approach explaining gene activation/repression. Comparative now complemented by environmental studies. These lat- works on molecular interaction networks show that in- ter further unraveled that cells compete and cooperate teractions affect the evolution of the molecules compos- with, and even compensate for each other, within mono- ing networks, which means that beyond compositional or multispecific microbiomes [63, 64]. Both types of complexity, organisational complexity must be modeled microbiomes have a fundamental commonality: they to understand biological evolution [46, 51–54]. Before produce collective properties and co-constructed pheno- the analysis of complex networks, compensatory sets of types (Fig. 1) evolving at the interface between cells. elements, such as groups of sub-functional paralogous Such properties cannot be understood without consider- genes [55], or groups of genes with pressupressed muta- ing networks of influences: the oscillatory growth of bio- tions [39, 40], already stressed the evolutionary inter- films of Bacillus subtilis cannot be deduced from the dependence of molecules. However, compensatory analyses of the complete genomes of these clones, but interactions between agents, each of them being by requires modeling metabolic co-dependence within a themselves poorly adapted, ran counter to the intuition monogenic community affected by a delayed feedback that natural selection will eliminate dysfunctional indi- loop, involving chemical and electrical signals [65, 66]. vidual entities. Their recognition invites one to consider Furthermore, many cellular agents show a relative lack Earth as possibly populated by unions of individually of autonomy. In nature, some groups of prokaryotes dis- dysfunctional agents rather than by the fittest survivors play complementary genomes with incomplete metabolic within individual lineages, possibly since early life, ac- pathways, consistent with the black queen hypothesis, cording to Woese’s theory on progenotes, namely com- which predicts that our planet is populated by groups of munities of interacting protocells unable to sustain (inter)dependent microbes [67, 68]. More precisely, this themselves alone, evolving via massive lateral genetic ex- hypothesis predicts the loss of a costly function, encoded changes [56]. by a gene or a set of genes, in individuals, when this Bapteste and Huneman BMC Biology (2018) 16:56 Page 4 of 16 of components from different lineages, no complete pic- ture of evolution can be provided without these jumps, which are naturally modeled by networks. Indeed, gen- etic information has been flowing both vertically and horizontally between prokaryotes for over 3.5 billion years [71–77], and possibly earlier, according to Woese, who proposed that our universal ancestor was not an entity but a process, that is, genetic and energetic ex- changes within protocellular communities [56]. Remark- ably, this latter case indicates that network modeling could help to tackle a fundamental issue in evolutionary biology: modeling the evolution of biological processes that emerge from interactions between biological en- tities. Since these interactions can be represented by a Fig. 1. An example of co-construction, the case of holobionts. The left network, the evolution of these interactions, describing circle represents the set of traits associated with a host, the right circle represents the set of traits associated with its microbial communities; the evolution of biological processes, can then be repre- the intersected area represents traits that are produced jointly as a sented by dynamic networks. Likewise, eukaryogenesis result of the interaction between hosts and microbes. When this area rested on the co-construction of a novel type of cell, as a becomes large or when co-constructed traits are remarkable, they result of the endosymbiosis of a bacteria within an cannot be correctly explained under a simple model treating hosts and archaeon [78–80]. Later, the evolution of photosynthetic microbes in isolation. This scheme holds for different types of partners protists emerged from endosymbioses involving unicel- lular eukaryotes and cyanobacteria, or various lineages function becomes dispensable at the individual level, of protists, namely in secondary and tertiary endosymbi- since it is achieved by other individuals that produce oses [81]. Such endosymbioses, and their outcomes as (usually leaky) public goods in sufficient amount to sup- illustrated in our work [82, 83], are also naturally mod- port the equilibrium of the community. Thus, gene eled using networks. losses in some cells are compensated by leaks of sub- Moreover, the long-term impact of these introgressive strates from other cells, formerly encoded by the lost processes on cellular evolution should not be underesti- genes. Some microbes experience labor division [69]. mated. For instance, endosymbiosis does not merely intro- Symbionts and endosymbionts depend on their hosts. duce new cellular lineages, it also favors the evolution of The ‘kill the winner’ theory [70] further challenges the chimeric structures and chimeric processes within cells notion that the microbial world is a world of fit cellular [83–91]. Such intertwining cannot be modeled using a individuals. This theory stresses a collective process via single genealogical tree, which only recapitulates cellular which viruses mechanically mostly attack cells that re- divergence from a last common ancestor. Even though produce faster and thus regulate bacterial populations, cells always derive from other cells, a full cellular history these latter sustaining their diversity because these pop- cannot be reduced to the history of some cellular compo- ulations are comprised of individual prokaryotic cells nents that are assumed to track the history of cellular that make a suboptimal use of a diversity of resources. division [92]. In particular, phylogenetic analyses of infor- Thus, cells belong to networks that affect their growth mational genes cannot be the only clue to understanding and survival, which might explain why most bacteria the origins of cellular diversity, since these genes do not cannot be grown in pure culture. They only truly thrive reflect how cells are organized, how they gather their within communities, whose global genetic instructions energy, and how they interact with each other. Analyzing are spread over several genetically incomplete microbes. the co-construction side of evolution requires enhanced Accounting for these internal and external cellular models: understanding eukaryotic evolution requires networks requires considering processes that are not mixed considerations of cellular architecture, population central in the synthetic evolutionary theory. Typically, genetics and energetics, which go beyond classic phylo- the notion that cellular evolution makes jumps, because genetic models, which not so long ago were still prone to new components and processes (such as metabolic considering three primary domains of life [93–95]. pathways) are acquired from outside a given cellular Although invoking multiple agents rather than a single lineage, contrasts with more gradual accounts of bio- ancestor in evolutionary explanations might appear to logical change, like accounts based on point mutations contradict the famous Ockham’s razor [96], it does so affecting genes already present in the lineage. Because only superficially when it is likely that many cells are saltations (macromutations) are essential evolutionary co-constructed, especially in the context of a web of life. outcomes of introgressive processes, via the combination Enhanced models including intra- and extracellular Bapteste and Huneman BMC Biology (2018) 16:56 Page 5 of 16 interactions appear necessary to understand cellular recruit environmental microbes and transmit them (with a complexity, including the predictable disappearance of non-null heritability [113]) to their progeny. Therefore, traits (and processes), namely the convergent gene loss nuclear gene inheritance alone may provide too narrow a of mitochondria and plastids [97] by a process called perspective to account for the evolution of all animal dedarwinification [98, 99]. traits; as an example, aphid body color depends on animal genetics and the presence of Rickettsiella [114]. Population Networks beyond the cellular level genetics gets included in a broader community genetics, Studies of multicellular organisms—we will focus on ani- which also considers transmission of microbes and their mals—have led to similar general findings. Understand- genes [108, 114]. The use of gnotobiotic and transbiotic ing animal traits and their evolution requires analyzing animals becomes a new experimental standard to analyze the relationships between a multiplicity of agents be- multigenomic collectives without counterparts in modern longing to different levels of biological organisation, synthesis theories. These collectives harbor morphological, eventually nested, some of which co-constructs animals physiological, developmental, ecological, behavioral and and guarantees their complete lifecycle [100]. Because evolutionary features [115–119] that are not purely con- no sterile organism lives on Earth, animal development, structed by animal genes, but rather appear to be co- health and survival depend on microbes. Granted, bac- constructed at the genetic and metabolic interface teria can often legitimately be seen as part of the envir- between the microbial and macrobial worlds, while the onmental demands in an evolutionary model focused on content of the respective animal genomes only provides the host’s lineage; or sometimes bacteria and host could incomplete instructions. Understanding animal evolution also be considered as part of a coevolution process, with requires understanding the interaction networks between no need to posit the whole as a unit of selection [101]. components from which these taxa evolved, and the net- However, asking ‘who is the beneficiary of the symbiosis works to which these taxa still belong. as the result of evolution?’ may in some cases lead to the In ecology, an analogous turn towards network think- recognition that bacteria and host evolved together and ing has been promoted since the 1990s with the general were selected together [102]. More generally, while some acceptance of the notions of metapopulations [120] and microbes contribute to animals’ lives possibly as a result then metacommunities [121]. These views suggest that of host-derived selection, others contribute as a result of the dynamics of ecological biodiversity is not so much selectively neutral processes (like microbial priming located within a community of species but rather in a [103]) [101, 104]. These interactions produce communi- metacommunity, which can be thought of as a network cation networks within the animal body: chemical infor- of communities exchanging species, while targeting one mation circulates between the animal brain and the gut community blinds one to what genuinely accounts for microbiome. These interactions also result in communi- biodiversity and ecosystem functioning [122]. cation and interaction networks between individuals. In This quick overview provides evidence that networks some animal lineages, the microbiome affects social be- are at the origin of the genes of unicellular and multicel- haviors, for instance fermenting microbes inform about lular organisms and central for their functions. The the gender and reproductive status in hyena [105]. Com- living world is a world of ‘and’ and ‘co-’. From division ponents of the microbiome also affect mating choice of labor and compensations, to dependencies and co- [106], reproductive isolation and possibly speciation. constructions, etc.: interactions (which only begin to be Consequently, the microbiome now appears as an essen- deciphered) are everywhere in biology. Thus, explaining tial component of animal studies [107]. Microbiome the actual features of biodiversity requires explaining studies, the significance of which is overstated in some how multiple processes, interface phenomena (co-con- respects, nevertheless have shown that the evolutionary struction of biological features, niche construction, intertwining between many metazoa and commensal or metabolic cooperation, co-infection and co-evolution) symbiotic bacteria could not be neglected anymore and and organisations (for instance, from molecular path- black-boxed in favor of purely host gene-centered evolu- ways to organisms and ecosystems) arose from interact- tionary models. And the associations between hosts and ing components, and how these processes, phenomena microbes do not need to be units of selection to be part and organisations may have been sustained and trans- of the recent insights that support the novel theoretical formed on Earth. framework proposed here. Their interplay imposes reconfigurations of practices, theories and disciplines Reframing evolutionary explanations from the [108]. As a result of our improved insight into evolution, scaffolded evolution perspective zoology and immunology [109] become theaters of new Introducing a classification of interacting components ecological considerations [110], sometimes strangely While classic evolutionary models, prompted by qualified as Lamarckian [111, 112], because animals can Darwin’s famous tree [123], mostly stress how related Bapteste and Huneman BMC Biology (2018) 16:56 Page 6 of 16 entities diverge in relative independence, it appears im- alter the evolution of the biotic components, for ex- portant to show how a diversity of components, which ample, environmental change can drive genetic and may not be related, interact and produce various evolu- organismal evolution and selection. The history of life tionary patterns. clearly depends on the interplay of both types of compo- The notion of scaffolding [124], which describes how nents. Biotic components, however, deserve a specific one entity continues an event initiated by another entity, focus. Some of them form lineages (for instance, genes and relies on it up to the point that at some timescale it replicate), while others do not (for instance, proteins are becomes dependent upon it for further evolution, ap- reconstructed). Finally, interacting replicated compo- pears as a fundamental relationship to describe the evo- nents can be further classified into fraternal components lution of life. We propose scaffolding should become when they share a close last common ancestor (e.g. in more central in explanations of evolution because no kin selection cases), and egalitarian components, when components from the biological world are actually able they belong to distinct lineages (as an example, think of to reproduce, or persist, alone (Fig. 2). Each entity influ- the evolution of chimeric genes by fusion and shuffling ences or is influenced by something external to it, and is [29, 45, 126]) [63]. consequently part of a process. Scaffolding thus defines the causal backbone of collective evolution. It describes Introducing dynamic interaction networks the historical continuity between temporal slices of Biodiversity usually evolves from interactions between interaction networks, since any evolutionary stage relies the diverse types of components described above. For on previously achieved networks and organisations. example, metalloproteases emerge from the interaction Therefore, describing the evolution of interactions re- between reconstructed biotic components (proteins) and quires explanations to address the following issues: what a metal ion. Regulatory networks involve biotic compo- scaffolds what, what transforms the environment of what, nents that can be either replicated (i.e. genes and pro- and are these influences reciprocal? Characterizing the moters) or reconstructed (i.e. proteins). Protein types of components that, together, have evolutionary interaction networks intertwine reconstructed egalitarian importance through their potential interaction is therefore biotic components, which means proteins that are not a central step to expanding evolutionary theory. homologous. Evolutionary transitions such as eukaryo- We propose that a first distinction can be made be- genesis result from the interweaving of biotic compo- tween obligate and facultative components. Suppressing nents (cells) from multiple lineages. Holobionts evolve the former impacts the course and eventually the from interactions between egalitarian biotic components reproduction of the process to which they contribute (macrobial hosts and microbial communities) and pos- (Fig. 3), whereas facultative components do not hold sibly abiotic components, such as the mineral termite such a crucial role, and may simply be involved by mounds, or the volatile chemicals produced by the chance. A second distinction is whether the components microbial communities of hyenas [105]. are biotic (genes, proteins, organisms…) or abiotic (such Taking collectives of interacting components as central as minerals, environmental, cultural artefacts). Abiotic objects of study in evolutionary biology invites us to ex- components can be recruited from the environment or pand the methods of this field. It encourages developing be shaped by biological processes [125]. They can also statistical approaches or inference methods beyond those ab c d Fig. 2. Different types of scaffolding, at four levels of biological organisations. a Functional interactions at the molecular level. b Introgression and vertical descent at the cellular level. c Co-construction at the multicellular level. d Niche-construction and physico-chemical interactions at the eco-systemic level Bapteste and Huneman BMC Biology (2018) 16:56 Page 7 of 16 Fig. 3. Classification of major types of components in evolving systems. A process/collective cannot be completed in the absence of obligate components, whereas facultative components do not affect the outcome of the process/function of the collective. Biotic components are biological, material products, whereas abiotic components are environmental, geological, chemical, physical or cultural artefacts. Replicated components are produced by replication, which implies a physical continuity between ancestral and descendent components; they undergo a paradigmatic Darwinian evolution. Reconstructed components are reproduced without direct physical continuity, and cannot directly accumulate beneficial mutations. Fraternal components belong to the same lineage, whereas egalitarian components belong to different lineages operating under the very common assumption that bio- recognized as a more inclusive object of study (Fig. 4). logical components are independent. Therefore, we Where phylogenies describe relationships, networks can propose to represent interactions between components describe organisations. How such organisations evolve in the form of networks in which components are nodes could for example be described by identifying evolution- and their interactions (of various sorts) are edges. These ary stages, that is, sets of components and of their inter- networks are conceptually simple objects. They can be actions simultaneously present in the network (Fig. 4). described as adjacency lists of interactions, in the form Investigating the evolution of an ecosystem corresponds ‘component A interacts with component B, at time t to studying the succession of evolutionary stages in such (when such a temporal precision is known)’. Such dy- networks and detecting possible regularities—in the namic interaction networks could become more central sense that some evolutionary stages would fully or partly representations and analytical frameworks, and serve as reiterate over time—or hinting at rules or constraints a common explanans for various disciplines in an ex- (like architectural contingencies [127, 128] or principles panded evolutionary theory. Importantly, because these of organisations [46]) on the recruitment, reproduction networks embed both abiotic and biotic, related and un- and heritability of their components. related components (like viruses, cells and rocks), they Thus, we suggest that evolutionary biology could be should not be conflated with phylogenetic networks, but reframed as a science of evolving networks, because Fig. 4. An evolving interaction network. Nodes are components (circles are full when the component is biotic). Thick black edges represent interactions between these components. The network topology evolves as nodes or their connection change. Dashed edges represent the phylogenetic ancestry of lineage-forming components Bapteste and Huneman BMC Biology (2018) 16:56 Page 8 of 16 such a shift would allow inclusive, multilevel studies of a reveal conservation and divergence in gene regulation larger body of biological and abiotic data, via approaches [138]. GCNs are already used for micro-evolution stud- from network sciences. ies, as in the case of fine-grained comparisons of expres- sion variations between orthologous genes across closely Concrete strategies to enhance network-based related species, and for the analysis of minor evolution- evolutionary analyses ary and ecological transitions, such as changes of ploidy Enhancing network-based evolutionary analyses, beyond [139, 140], adaptation to salty environments [141]or the now classic research program of phylogenetic net- drugs [142], or the effects of plant domestication works, could consolidate comparative analyses in the [143, 144]. Likewise, GRNs are starting to be used in nascent field of evolutionary systems biology [129, 130], micro-evolution and phenotypic plasticity studies as illustrated by examples based on molecular networks. [145]. Understanding the dynamics of GRNs appears Network construction/gathering constitutes the first step critical to inferring the evolution of organismal traits, of such analyses. This involves first defining nodes of the in particular during metazoan [146–148], plant [149]and network, namely components suspected to be involved fungal [150] evolution. We suggest that PPI, GCN and in a given system, and edges, namely qualitative (or GRN studies could become mainstream and also be quantitative, when weighted) interactions between these conducted at (much) larger evolutionary and temporal entities. Many biological interaction networks (gene co- scales, to analyze additional, major, transitions. expression networks (GCNs), gene regulatory networks Based on these established networks, two major types (GRNs), metabolic networks, protein–protein interaction of evolutionary analyses (network-decomposition and networks (PPIs), etc. [46]) are already known for some graph-matching; Fig. 5) can be easily further developed species, or can be inferred [131–136]. For example, by evolutionary biologists. More precisely, the above- GCNs offer an increasingly popular resource to study mentioned kinds of biological networks could be system- the evolution of biological pathways [137], as well as to atically turned into what we call evolutionary colored Fig. 5. Workflow of the evolutionary analysis of interaction networks. From left to right: triangles represent components of interaction networks, edges between triangles represent interactions between these components. Interaction networks are first constructed/inferred, then their nodes and edges are colored to produce evolutionary colored networks (ECNs) that represent both the topological and the evolutionary properties of the networks. ECNs can be investigated individually by graph decomposition and centrality analyses, or several ECNs can be compared by graph alignment. The two types of comparisons can return conserved subgraphs that allow understanding of the dynamics of interaction networks, meaning when different sets of interactions (hence processes) evolved, and whether these interactions were evolutionarily stable. Ancient and Contemporary refer to the relative age of the sub-graphs, identifying new clade-specific relationships (here called refinement); introgression indicates that a component, and the relationship it entertains with the rest of the network, was inferred to result from a lateral acquisition Bapteste and Huneman BMC Biology (2018) 16:56 Page 9 of 16 biological networks (ECNs). In ECNs, each node of a This focus would complement a classic tree-based given biological network is colored to reflect one or sev- view. For instance, under the reasonable working hy- eral evolutionary properties. For example, in molecular pothesis that pairs of connected nodes of a given age re- networks, nodes correspond to molecular sequences flect an interaction between nodes that may have arisen (genes, RNA, proteins) that belong to homologous at that time [154, 171], ECNs can easily be easily decom- families that phylogenetic distribution across host spe- posed into sub-networks, featuring processes of different cies allows us to date [137, 151–156]. The ‘age’ of the ages (that is, sets of nodes of a given age, e.g. sets of family at the node can thus become one evolutionary interacting genes). This strategy allows identification of color (Fig. 5). Likewise, several processes affecting the conserved network patterns, possibly under strong se- evolution of a molecular family (selection, duplication, lective pressure [159]. Constructing and exploiting ECNs transfer, and divergence in primary sequence) can be from bacteria, archaea, and eukaryotes thus has the po- inferred by classic phylogenetic analyses or, as we pro- tential to define conserved ancestral sets of relationships posed, by analyses of sequence similarity networks [157]. between components, allowing evolutionary biologists to Such studies provide additional evolutionary colors (like infer aspects of the early biological networks of the last quantitative measures: intensity of selection, rates of common ancestor of eukaryotes, archaea and bacteria duplication, transfer, and percentage of divergence), which and even of the last universal common ancestor of cells. can be associated with nodes in ECNs [139, 149, 154, Assuming that some of these topological units corres- 158–161]. Thus, ECNs contain both topological informa- pond to functional units [172], especially for broadly tion, characteristic of the biological network under investi- conserved subgraphs [138, 149, 152, 166, 173–182], gation, as well as evolutionary information: what node would allow network decompositions to propose sets of belongs to a family prone to duplication, divergence, or important processes associated with the emergence of lateral transfer, as well as when this family arose. Combin- major lineages. ing these two types of information in a single graph allows Moreover, graph-matching of ECNs allows several us to test specific hypotheses regarding evolution. complementary analyses. First, for interaction networks, Using ECNs, it is first fruitful to test whether (or such as GRNs, whose sets of components and edges which of) these evolutionary colors correlates with evolve rapidly [183–185], it becomes relevant to analyze topological properties of the ECNs [162–164]. The null where in the network such changes occur in addition to hypothesis that nodes’ centrality, e.g. nodes’ positions in (simply) tracking conserved sets of components and the network, is neither correlated with the age nor with edges. Whereas the latter can test to what extent conser- the duplicability, transferability or divergence of the vation of the interaction networks across higher taxa molecular entities represented by these nodes can be supports generalizations made from a limited number of tested. Rejection of this hypothesis would hint at pro- model species [186], the former allows us to test a gen- cesses that affect the topology of biological networks or eral hypothesis: are there repeated types of network are affected by the network topology. For example, con- changes? For example, does network modification sidering degree in networks, proteins with more neigh- primarily affect nodes with particular centralities, as bors are less easily transferred [163], highly expressed exemplified by terminal processes [187], or modules? genes, more connected in GCNs, evolve slower than Systematizing these analyses would provide new insights weakly expressed genes [165], and genes with lower into whether the organisation principles of biological degrees have higher duplicability in yeast, worm and flies networks changed when major lineages evolved or [166]. Considering position in networks, node centrality remained conserved. In terms of the ECN, can the same correlates with evolutionary conservation [136], gene model of graph evolution explain the topology of ECNs eccentricity correlates with level of gene expression and from different lineages? The null hypothesis would be dispensability [167], and proteins interacting with the that these major transitions left no common traces in external environment have higher average duplicability biological networks. An alternative hypothesis would be than proteins localized within intracellular compart- that the biological networks convergently became more ments [168]. Additionally, network structure gives a clue complex (more connected and larger) during these tran- to evolution since old proteins have more interactions sitions to novel life forms. Indeed, analyses conducted than new ones [169, 170]. Generalizing these disparate on a few taxa have reported quantifiable and qualifiable studies could help to understand the dynamics of bio- modifications in biological networks (in response to logical networks, in other words how the architecture, environmental challenges [188], during ecological transi- the nodes and edges of present day networks, evolved tions [189] or as niche specific adaptations [190]). More and whether their changes involved random or biased systematic graph-matching [191–193] and motif ana- sets of nodes and edges or follow general models of lyses, comparing the topology of ECNs from multiple network growth with detectable drivers. species, could likewise be used to test the hypothesis Bapteste and Huneman BMC Biology (2018) 16:56 Page 10 of 16 that major lineages are enriched in particular motifs Further justifications for a shift toward network (either modules of colored nodes and edges, or specific thinking topological features, such as feed-forward loops [46]or Enlargement of evolutionary biology bow-ties [194]). It would also allow identification of Focusing evolutionary explanations and theories on collec- functionally equivalent components across species, tives of interacting components, which may be under namely different genes with similar neighbors in differ- selection, facilitate selection, or condition arrangements ent species [176]. through neutral processes [39, 40, 202], and representing While inferences on conserved sets of nodes and edges these scaffolding relationships using networks with biotic in ECNs are likely to be robust (since the patterns are and abiotic components and a diversity of edges represent- observed in multiple species), missing data (missing ing a diversity of interaction types would be an enlarge- nodes and edges) constitute a recognized challenge, ment. Enlargements, as expressing the need to consider especially for the interpretation of what will appear in structures that are more general than what already exists, ECN studies as the most versatile (least conserved) parts have already occurred within evolutionary theory, when of the biological networks. The issue of missing data, simplifications from population genetics were relaxed with however, is not specific to network-based evolutionary respect to the original formalization in the Modern analyses, and should be tackled, as with other compara- Synthesis [203], to account for within-genome interaction tive approaches, by the development and testing of [9], gene–environment covariance [204], parental effects imputation methods [195–197]. Moreover, issues of [205], and extended fitness though generations [206]. It missing data can also be addressed by the production of also occurred when reticulations representing introgres- high coverage -omics datasets in simple systems, allow- sions were added to the evolutionary tree. ing for (nearly) exhaustive representations of the entities Interestingly, replacing standard linear models in evolu- and their interactions (i.e. PPIs, GCNs and GRNs within tionary theory with network approaches would transcend a cell, or metabolic networks within a species poor eco- several traditional axes structuring the debates in evolu- system). This kind of data would allow testing for the tionary biology. For instance, scaffolded evolution, the idea existence of selected emergent ecosystemic properties that evolution relies on what came before, is orthogonal to (like carbon fixation), as stated by the ITSNTS hypoth- the distinction between vertical and horizontal descent, esis [198]. For instance, deep coverage time series of since both tree-like and introgressive evolution are metagenomic/metatranscriptomic data coupled with en- particular cases of scaffolding. Scaffolded evolution is also vironmental measures from a simple microbial ecosys- orthogonal to the distinction between gradual and salta- tem, such as carbon fixation, could produce enough data tional evolution. Likewise, scaffolded evolution is orthog- to allow the evolutionary coloring of nodes of metabolic onal to the debates between the actual role of adaptations networks. Comparing ECNs representing, at each time vs neutral processes. Selection is a key mode of evolution point, the origin and abundance of the lineages hosting of collectives but not the only one. The processes involved the enzymes involved in carbon fixation could test in the forming and evolution of collectives are not even whether some combinations of lineages are repeated restricted to the key processes of the Modern Synthesis over time, and whether the components (e.g. genes and (drift, selection, mutation and migration) but embrace in- lineages) vary, whereas carbon fixation is maintained in teractions such as facilitation—namely antagonistic inter- the ecosystem, which would suggest that this process actions between two species that allow a third species to evolves irrespective of the nature of the interacting prosper by restraining one of its predators or parasites components. [207], presuppression [39, 40], etc. Consequently, some Finally, entities from different levels of biological or- evolutionary concepts may become more important than ganisation (domains, genes, genomes, lineages, etc.) they currently are to explain evolution. For example, con- could also be studied together in a single network frame- tingency, which means the dependence of an evolutionary work, by integrating them into multipartite networks chain of events upon an event that itself is contingent, in [199]. Recently, our studies and others (see [200] and the sense that it can’t be understood as a selective re- references therein) have demonstrated that various pat- sponse to environmental changes [18, 208, 209], is often terns in multipartite graphs can be used to detect and associated with extraordinary events, like mass decima- test combinatorial (introgressive) and gradual evolution tion. Contingency could come to be seen as a less extraor- (by vertical descent) affecting genes and genomes. dinary mode of evolution in the history of life, since the Decomposing multipartite networks into twins and ordinary course of evolution might include many cases of articulation points could for example then be used to contingent events, that is, associations of entities in a tran- represent and analyze the evolution of complex compos- sient collective, including any scaffolds—associations that ite molecular systems, such as CRISPR, or the dynamics are not necessarily selective responses or the outcomes of of invasions of hairpins in genomes [201]. processes modeled in population genetics. Bapteste and Huneman BMC Biology (2018) 16:56 Page 11 of 16 Likewise, adopting a broader ontology could affect edges and components). For example, the fermentation how evolutionary theorists think about evolution. Popu- hypothesis for mammalian chemical communication lation thinking and tree-thinking came after essentialist could be analyzed in a multipartite network framework, conceptions of the living words, when populations and which would involve nodes corresponding to individual lineages were recognized as central objects of evolution- mammals, nodes corresponding to microbes, and nodes ary studies [210]. A shift towards collectives and scaf- corresponding to odorous metabolites. Nodes corre- folded evolution might encourage a similar development: sponding to mammals could either be colored to reflect the emergence of an openly pluralistic processual think- an individual’s properties (its lineage, social position, ing, consistent with Carl Woese’sproposal to reformulate gender, sexual availability), or these nodes could be con- our view of evolution in terms of complex dynamic sys- nected by edges that reflect these shared properties, tems [211]. which defines a first host subnetwork. This host subnet- work can itself be further connected to a second subnet- Further unifying the evolutionary theory work, namely the microbial subnetwork in which nodes Using a network-based approach to analyse dynamic representing microbes, colored by phylogenetic origins, systems also permits explanations that rely purely on could be connected to reflect microbial interactions statistical properties [212] or on topological or graph (gene transfer, competition, metabolic cooperation, etc.). theoretical properties [213, 214] besides standard expla- Connections between the host and microbial subnet- nations devoted to unravelling mechanisms responsible works could simply be made by drawing edges between for a phenomenon. Moreover, because of the inclusive- nodes representing individual mammals hosting mi- ness of the network model, disciplines already recog- crobes, and nodes representing these microbes. More- nized for their contribution to evolutionary theory over, nodes representing mammals and nodes (microbiology, ecology, cell biology, genetics, etc.) could representing microbes could be connected to nodes become even more part of an interdisciplinary research representing odorous metabolites to show what odours program on evolution, effectively addressing current is- are associated with what combinations of hosts and mi- sues, consistent with the repeated calls for transdis- crobes. Elaborating this network in a piecemeal fashion ciplinary collaborations [19–21, 215]. Disciplines that would involve cooperation between chemists, microbiol- were not central in the Modern Synthesis—chemistry, ogists, zoologists and evolutionary biologists. physics, geology, oceanography, cybernetics or linguis- Of note, the use of integrated networks could prag- tics—could aggregate with evolutionary biology. Since a matically address a deep concern for evolutionary stud- diversity of components gets connected by a diversity of ies, by connecting phenomena that occur at different edges in networks featuring collectives, as a result of a timescales: development and evolution [219] or ecology diversity of drivers, several explanatory strategies could and evolution [220]. Considering transient collectives be combined to analyze evolution. This extension to (thus processes) as stable entities at a given time-scale, seemingly foreign fields makes sense when the compo- when these collectives change much more slowly than nents/processes studied by these other disciplines are the process in which they take part, amounts to a focus evolutionarily or functionally related to biotic compo- on interactions occurring at a given time scale by treat- nents and processes (either as putative ancestors of ing the slower dynamics as stable edges/nodes. Then, biological components and processes, like the use of a various parts of the networks embody distinct time- proton gradient in cells, which possibly derived from scales, which may provide a new form of timescale inte- geological processes affecting early life [216], or as gration, working out the merging of timescales from the descendants of biological systems, e.g. technically syn- viewpoint of the model, and with resources intrinsic to thesized life forms, which have a potential to alter the the model itself. The reason for this is that a node in an future course of standard biological evolution). interaction network Ni, describing processes relevant at Remarkably, this mode of unification of diverse scien- a time scale i, can itself be seen as the outcome of tific disciplines would be original: the integration would another (embedded) interaction network Nj, unfolding not be a unification in the sense of logical positivism at a time scale j. This nestedness typically occurs when [217]—namely reducing a theory to a theory with more the node in Ni represents a collective process, involving basic laws, or a theory with a larger extension. It would components that evolve sufficiently slowly with respect be a piecemeal [218] unification. Some aspects would be to the system considered at the time scale i to figure as unified through a specific kind of graph modeling an entity, a node in Ni. In the case of a PPI network Ni, (because some interactions, namely mechanical, chem- each node conventionally represents a protein, but the ical, ecological ones, and a range of time scales are privi- evolution of each protein could be further analysed as leged in a set of theories), while other theories might be the result of mutation, duplication, fusion and shuffling unified by other graph properties (like different types of events affecting the gene family coding the proteins over Bapteste and Huneman BMC Biology (2018) 16:56 Page 12 of 16 time; for instance, each protein could thus be repre- descendants and those of other life forms will be pro- sented as the outcome of interaction between domains cesses too. Some one hundred and fifty years after On in a domain–domain interaction network Nj. Consider- the Origin of Species, which started a great evolutionary ing these two time-scales, it becomes apparent that gene inquiry, evolutionists should prepare to face a larger families enriched in exon shuffling events, a process challenge: expanding evolutionary theory to study the directly analysable in Nj, have a higher degree in PPI evolution of processes. With the development of -omics networks represented at the time-scale Ni [221]. and network sciences, the concepts, data and tools for this research program are increasingly available. Predictions: discovery of co-constructed phenotypes Acknowledgements What possible findings may result from this perspective This manuscript is dedicated to the memory of Jean Gayon, a great shift? One can only speculate, but the nature of the historian and philosopher of biology, and a great friend. We thank Ford Doolittle, P. Lopez, G. Bernard, JL Martin, A. Watson, FJ Lapointe potential discoveries is exciting. At the molecular level, for critical reading and discussion. the structure and composition of regulatory networks and protein interaction networks could be substantially Funding EB is funded by the European Research Council under the European enhanced to scaffolding elements. Currently, these net- Community’s Seventh Framework Program FP7 (grant agreement number works represent interactions within a single individual/ 615274). PH is funded by the ANR (ANR 13 BSH3 0007 ‘Explabio’) and the LIA species. Yet, viruses are everywhere, viral genes and pro- CNRS Montréal-Paris ECIEB. teins clearly influence the networks of their hosts, and Authors’ contributions likely constitute an actual part of their evolution. Thus, EB and PH wrote, read and approved the final manuscript. virogenetics, a novel transdiscipline, may prosper in an expanded evolutionary theory to show how and to what Competing interests The authors declare that they have no competing interests. extent viruses co-construct their hosts, including perhaps reproductive-viruses, allowing their hosts to Publisher’sNote complete their lifecycles. At the cellular level, new Springer Nature remains neutral with regard to jurisdictional claims in modes of communication [222, 223] could be discov- published maps and institutional affiliations. ered, as possible viral and microbial languages and com- Author details munication networks in biofilms would exemplify. At Sorbonne Universités, UPMC Université Paris 06, Institut de Biologie the level of multicellular organisms and holobionts, ‘sym- Paris-Seine (IBPS), F-75005 Paris, France. CNRS, UMR7138, Institut de Biologie biotic codes’, guiding the preferential association between Paris-Seine, F-75005 Paris, France. Institut d’Histoire et de Philosophie des Sciences et des Techniques (CNRS / Paris I Sorbonne), F-75006 Paris, France. hosts and symbionts, could be identified. At the level of phyla, hidden evolutionary transitions may be unraveled. While secondary (and tertiary) acquisitions of plastids have been documented [81], it might be shown that References 1. Huxley J. Evolution: the modern synthesis. Princeton: Princeton University mitochondria too have been so acquired in some Press; 1942. eukaryotic lineages (alongside the plastid or independ- 2. Gayon J. Darwinism's struggle for survival: heredity and the hypothesis of ently). Secondarily acquired mitochondria may provide natural selection. Cambridge: Cambridge University Press; 1998. 3. Simpson GG. Tempo and mode in evolution. New York: Columbia University their new hosts with additional compartments, where Press; 1944. chimeric proteomes could assemble [91, 224] and per- 4. Martin G, Lenormand T. The distribution of beneficial and fixed mutation form original physiological processes. At the ecosystemic fitness effects close to an optimum. Genetics. 2008;179:907–16. 5. Dawkins R. The extended phenotype. New-York: Oxford University Press; 1982. level, evolving networks could be used to model the 6. Grafen A. A first formal link between the Price equation and an optimisation changes and stases of our planet, grounding biotic line- program. J Theor Biol. 2002;217:75–91. ages and processes in their environment, while highlight- 7. Kimura M. The neutral theory of molecular evolution. Cambridge: Cambridge University Press; 1983. ing potential regularities in the organisations and 8. Maynard Smith J, Burian R, Kauffman S, Alberch P, Campbell J, Goodwin B, dynamics of ecosystems. What affects the stability of et al. Developmental constraints and evolution. Q Rev Biol. 1985;60:265–87. what over the course of evolution could thus become a 9. Griffiths P, Stotz S. Genetics and philosophy: an introduction. Cambridge: Cambridge University Press; 2013. central theme of an expanded evolutionary theory. 10. Kettlewell HDB. Selection experiments on industrial melanism in the Lepidoptera. Heredity. 1955;9:323–42. Concluding remarks and open questions 11. Laland K, Uller T, Feldman M, Sterelny K, Muller GB, Moczek A, et al. Does evolutionary theory need a rethink? Nature. 2014;514:161–4. Interactions are not merely a part of biological history, 12. Bateson P. The return of the whole organism. J Biosci. 2005;30:31–9. they are what made this history. But evolutionary biolo- 13. Odling-Smee J, Laland K, Feldman M. Niche construction: the neglected gists have certainly not reconstructed the Dynamic process in evolution. Princeton: Princeton University Press; 2003. 14. Doolittle WF, Bapteste E. Pattern pluralism and the Tree of Life hypothesis. Interaction Network of Life (DINol) yet. Undertaking Proc Natl Acad Sci U S A. 2007;104:2043–9. this endeavor, however, would emphasize the importance 15. Sapp J. The new foundations of evolution: on the Tree of Life. New-York: of processes. Our ancestors were processes. Our Oxford University Press; 2009. Bapteste and Huneman BMC Biology (2018) 16:56 Page 13 of 16 16. Walsh DM. Organisms, agency, and evolution. Cambridge: Cambridge 48. Britten RJ, Davidson EH. Gene regulation for higher cells: a theory. Science. University Press; 2015. 1969;165:349–57. 17. West-Eberhard MJ. Developmental plasticity and evolution. Oxford: Oxford 49. Artzy-Randrup Y, Fleishman SJ, Ben-Tal N, Stone L. Comment on "Network University Press; 2003. motifs: simple building blocks of complex networks" and "Superfamilies of 18. Gould SJ. Wonderful life. The Burgess shale and the nature of history. New evolved and designed networks". Science. 2004;305:1107. author reply York: Norton; 1989. 50. Sorrells TR, Johnson AD. Making sense of transcription networks. Cell. 2015; 161:714–23. 19. Gilbert SF, Bosch TC, Ledon-Rettig C. Eco-evo-devo: developmental symbiosis and developmental plasticity as evolutionary agents. Nat Rev 51. Carroll SB. Evolution at two levels: on genes and form. PLoS Biol. 2005;3:e245. Genet. 2015;16:611–22. 52. Mallarino R, Grant PR, Grant BR, Herrel A, Kuo WP, Abzhanov A. Two 20. Lynch M, Field MC, Goodson HV, Malik HS, Pereira-Leal JB, Roos DS, et al. developmental modules establish 3D beak-shape variation in Darwin's Evolutionary cell biology: two origins, one objective. Proc Natl Acad Sci U S finches. Proc Natl Acad Sci U S A. 2011;108:4057–62. A. 2014;111:16990–4. 53. Peter IS, Davidson EH. Implications of developmental gene regulatory 21. Lyte M. Probiotics function mechanistically as delivery vehicles for networks inside and outside developmental biology. Curr Topics Dev Biol. neuroactive compounds: Microbial endocrinology in the design and use of 2016;117:237–51. probiotics. BioEssays. 2011;33:574–81. 54. Prud'homme B, Gompel N, Carroll SB. Emerging principles of regulatory 22. Huneman P, Walsh D. Challenging the modern synthesis: Development, evolution. Proc Natl Acad Sci U S A. 2007;104(Suppl 1):8605–12. adaptation and inheritance. New York: Oxford University Press; 2017. 55. Force A, Lynch M, Pickett FB, Amores A, Yan YL, Postlethwait J. Preservation 23. Pigliucci M, Müller G. Evolution: the extended synthesis. Cambridge, MA: of duplicate genes by complementary, degenerative mutations. Genetics. MIT Press; 2011. 1999;151:1531–45. 24. Wray GA, Hoekstra HE, Futuyma DJ, Lenski RE, Mackay TFC, Schluter D, et al. Does 56. Woese C. The universal ancestor. Proc Natl Acad Sci U S A. 1998;95:6854–9. evolutionary theory need a rethink? No, all is well. Nature. 2014;514:161–4. 57. Coulombe-Huntington J, Xia Y. Network centrality analysis in fungi reveals 25. Eigen M, Schuster P. The hypercycle. A principle of natural self-organization. complex regulation of lost and gained genes. PLoS One. 2017;12:e0169459. Part A: Emergence of the hypercycle. Die Naturwissenschaften. 1977;64:541–65. 58. Chuong EB, Elde NC, Feschotte C. Regulatory activities of transposable 26. Doolittle WF. Genes in pieces: Were they ever together? Nature. 1978;272: elements: from conflicts to benefits. Nat Rev Genet. 2017;18:71–86. 581–2. 59. Garcia-Perez JL, Widmann TJ, Adams IR. The impact of transposable 27. Gilbert W. Why genes in pieces? Nature. 1978;271:501. elements on mammalian development. Development. 2016;143:4101–14. 28. Irimia M, Roy SW. Origin of spliceosomal introns and alternative splicing. 60. Imbeault M, Helleboid P-Y, Trono D. KRAB zinc-finger proteins contribute to Cold Spring Harb Perspec Biol. 2014;6:a016071 the evolution of gene regulatory networks. Nature. 2017;543:550–4. 29. de Souza SJ. Domain shuffling and the increasing complexity of biological 61. Sundaram V, Wang T. Transposable element mediated innovation in gene networks. BioEssays. 2012;34:655–7. regulatory landscapes of cells: re-visiting the "gene-battery" model. 30. Marsh JA, Teichmann SA. How do proteins gain new domains? Genome BioEssays. 2018;40:1700155. Biol. 2010;11:126. 62. Ispolatov I, Yuryev A, Mazo I, Maslov S. Binding properties and evolution of 31. Wang M, Caetano-Anolles G. The evolutionary mechanics of domain homodimers in protein-protein interaction networks. Nucleic Acids Res. organization in proteomes and the rise of modularity in the protein world. 2005;33:3629–35. Structure. 2009;17:66–78. 63. Bapteste E. The origins of microbial adaptations: how introgressive descent, 32. Koonin EV, Makarova KS. Mobile genetic elements and evolution of CRISPR- egalitarian evolutionary transitions and expanded kin selection shape the Cas systems: all the way there and back. Genome Biol Evol. 2017;9:2812–25. network of life. Front Microbiol. 2014;5:83. 33. Krupovic M, Béguin P, Koonin EV. Casposons: mobile genetic elements that 64. Bapteste E, Lopez P, Bouchard F, Baquero F, McInerney JO, Burian RM. gave rise to the CRISPR-Cas adaptation machinery. Curr Opin Microbiol. Evolutionary analyses of non-genealogical bonds produced by introgressive 2017;38:36–43. descent. Proc Natl Acad Sci U S A. 2012;109:18266–72. 34. Assis R, Kondrashov AS, Koonin EV, Kondrashov FA. Nested genes and 65. Liu J, Prindle A, Humphries J, Gabalda-Sagarra M, Asally M, Lee DY, et al. increasing organizational complexity of metazoan genomes. Trends Genet. Metabolic co-dependence gives rise to collective oscillations within 2008;24:475–8. biofilms. Nature. 2015;523:550–4. 35. Lynch M. Evolutionary diversification of the multimeric states of proteins. 66. Nunes-Alves C. Biofilms: Electrifying long-range signalling. Nat Rev Microbiol. Proc Natl Acad Sci U S A. 2013;110:E2821–8. 2015;13:737. 36. Duboule D, Wilkins AS. The evolution of 'bricolage'. Trends Genet. 1998;14: 67. Morris JJ, Lenski RE, Zinser ER. The Black Queen Hypothesis: evolution of 54–9. dependencies through adaptive gene loss. MBio. 2012;3:e00036–12. 37. Jacob F. Evolution and tinkering. Science. 1977;196:1162. 68. Sachs JL, Hollowell AC. The origins of cooperative bacterial communities. 38. Wilkins A. Between "design" and "bricolage": genetic networks, levels of MBio. 2012;3:e00099–12. selection, and adaptive evolution. Proc Natl Acad Sci U S A. 2007;104:8590–6. 69. Bonner JT. The origins of multicellularity. Integr Biol. 1998;1:27–36. 39. Doolittle WF, Lukes J, Archibald JM, Keeling PJ, Gray MW. Comment on 70. Rodriguez-Valera F, Martin-Cuadrado AB, Rodriguez-Brito B, Pasic L, "Does constructive neutral evolution play an important role in the origin of Thingstad TF, Rohwer F, et al. Explaining microbial population genomics cellular complexity?". BioEssays. 2011;33:427–9. through phage predation. Nat Rev Microbiol. 2009;7:828–36. 40. Gray MW, Lukes J, Archibald JM, Keeling PJ, Doolittle WF. Cell biology. 71. Archibald JM. One plus one equals one: symbiosis and the evolution of Irremediable complexity? Science. 2010;330:920–1. complex life. Eur J Phycol. 2015;50:18. 41. Lukes J, Archibald JM, Keeling PJ, Doolittle WF, Gray MW. How a neutral 72. Bapteste E, Anderson G. Intersecting processes are necessary explanans for evolutionary ratchet can build cellular complexity. IUBMB Life. 2011;63:528–37. evolutionary biology, but challenge retrodiction. In: Nicholson DJD, editor. 42. Jain R, Rivera MC, Lake JA. Horizontal gene transfer among genomes: the Everything flows: Towards a processual philosophy of biology. Oxford: complexity hypothesis. Proc Natl Acad Sci U S A. 1999;96:3801–6. Oxford University Press. in press. 43. Lawrence JG, Roth JR. Selfish operons: horizontal transfer may drive the 73. Koonin EV. The turbulent network dynamics of microbial evolution and the evolution of gene clusters. Genetics. 1996;143:1843–60. statistical Tree of Life. J Mol Evol. 2015;80:244–50. 44. Promponas VJ, Ouzounis CA, Iliopoulos I. Experimental evidence validating 74. Lopez-Garcia P, Zivanovic Y, Deschamps P, Moreira D. Bacterial gene the computational inference of functional associations from gene fusion import and mesophilic adaptation in archaea. Nat Rev Microbiol. events: a critical survey. Brief Bioinformatics. 2014;15:443–54. 2015;13:447–56. 45. Tsoka S, Ouzounis CA. Prediction of protein interactions: metabolic enzymes 75. Nelson-Sathi S, Dagan T, Landan G, Janssen A, Steel M, McInerney JO, et al. are frequently involved in gene fusion. Nat Genet. 2000;26:141–2. Acquisition of 1,000 eubacterial genes physiologically transformed a 46. Alon U. An introduction to systems biology: design principles of biological methanogen at the origin of Haloarchaea. Proc Natl Acad Sci U S A. 2012; circuits. Florida: Chapman and Hall/CRC; 2006. 109:20537–42. 47. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U. Network 76. Nelson-Sathi S, Sousa FL, Roettger M, Lozada-Chavez N, Thiergart T, Janssen motifs: simple building blocks of complex networks. Science. 2002;298: A, et al. Origins of major archaeal clades correspond to gene acquisitions 824–7. from bacteria. Nature. 2015;517:77–80. Bapteste and Huneman BMC Biology (2018) 16:56 Page 14 of 16 77. Levasseur A, Merhej V, Baptiste E, Sharma V, Pontarotti P, Raoult D. The 104. Theis KR, Dheilly NM, Klassen JL, Brucker RM, Baines JF, Bosch TC, et al. rhizome of Lokiarchaeota illustrates the mosaicity of archaeal genomes. Getting the hologenome concept right: an eco-evolutionary framework for Genome Biol Evol. 2017;9:2635–9. hosts and their microbiomes. mSystems. 2016;1:e00028–16. 78. Akanni WA, Siu-Ting K, Creevey CJ, McInerney JO, Wilkinson M, Foster PG, 105. Theis KR, Venkataraman A, Dycus JA, Koonter KD, Schmitt-Matzen EN, et al. Horizontal gene flow from Eubacteria to Archaebacteria and what it Wagner AP, et al. Symbiotic bacteria appear to mediate hyena social odors. means for our understanding of eukaryogenesis. Philos Trans R Soc Lond Proc Natl Acad Sci U S A. 2013;110:19832–7. Ser B Biol Sci. 2015;370:20140337. 106. Sharon G, Segal D, Ringo JM, Hefetz A, Zilber-Rosenberg I, Rosenberg E. 79. Martin WF, Garg S, Zimorski V. Endosymbiotic theories for eukaryote origin. Commensal bacteria play a role in mating preference of Drosophila Philos Trans R Soc Lond Ser B Biol Sci. 2015;370:20140330. melanogaster. Proc Natl Acad Sci U S A. 2010;107:20051–6. 80. Pittis AA, Gabaldon T. Late acquisition of mitochondria by a host with 107. Bosch TC, McFall-Ngai MJ. Metaorganisms as the new frontier. Zoology. chimaeric prokaryotic ancestry. Nature. 2016;531:101–4. 2011;114:185–90. 81. Archibald JM. Genomic perspectives on the birth and spread of plastids. 108. Bordenstein SR, Theis KR. Host biology in light of the microbiome: ten Proc Natl Acad Sci U S A. 2015;112:10147–53. principles of holobionts and hologenomes. PLoS Biol. 2015;13:e1002226. 82. Méheust R, Bhattacharya D, Pathmanathan JS, McInerney JO, Lopez P, 109. Bosch TC. Rethinking the role of immunity: lessons from Hydra. Trends Bapteste E. Formation of chimeric genes with essential functions at the Immunol. 2014;35:495–502. origin of eukaryotes. BMC Biol. 2018;16:30. 110. Costello EK, Stagaman K, Dethlefsen L, Bohannan BJ, Relman DA. The 83. Meheust R, Zelzion E, Bhattacharya D, Lopez P, Bapteste E. Protein networks application of ecological theory toward an understanding of the human identify novel symbiogenetic genes resulting from plastid endosymbiosis. microbiome. Science. 2012;336:1255–62. Proc Natl Acad Sci U S A. 2016;113:3579–84. 111. Barr JJ, Auro R, Furlan M, Whiteson KL, Erb ML, Pogliano J, et al. Bacteriophage adhering to mucus provide a non-host-derived immunity. 84. Bailleul B, Berne N, Murik O, Petroutsos D, Prihoda J, Tanaka A, et al. Proc Natl Acad Sci U S A. 2013;110:10771–6. Energetic coupling between plastids and mitochondria drives CO2 assimilation in diatoms. Nature. 2015;524:366–9. 112. Rosenberg E, Sharon G, Zilber-Rosenberg I. The hologenome theory of 85. Bogumil D, Alvarez-Ponce D, Landan G, McInerney JO, Dagan T. Integration of evolution contains Lamarckian aspects within a Darwinian framework. two ancestral chaperone systems into one: the evolution of eukaryotic Environmental Microbiol. 2009;11:2959–62. molecular chaperones in light of eukaryogenesis. Mol Biol Evol. 2014;31:410–8. 113. Ley RE. The gene-microbe link. Nature. 2015;518:S7. 86. Dorrell RG, Howe CJ. Functional remodeling of RNA processing in 114. Tsuchida T, Koga R, Horikawa M, Tsunoda T, Maoka T, Matsumoto S, et al. replacement chloroplasts by pathways retained from their predecessors. Symbiotic bacterium modifies aphid body color. Science. 2010;330:1102–4. Proc Natl Acad Sci U S A. 2012;109:18879–84. 115. Bravo JA, Forsythe P, Chew MV, Escaravage E, Savignac HM, Dinan TG, et al. 87. Gavelis GS, Hayakawa S, White RA 3rd, Gojobori T, Suttle CA, Keeling PJ, Ingestion of Lactobacillus strain regulates emotional behavior and central et al. Eye-like ocelloids are built from different endosymbiotically acquired GABA receptor expression in a mouse via the vagus nerve. Proc Natl Acad components. Nature. 2015;523:204–7. Sci U S A. 2011;108:16050–5. 88. Husnik F, Nikoh N, Koga R, Ross L, Duncan RP, Fujie M, et al. Horizontal 116. Dupressoir A, Lavialle C, Heidmann T. From ancestral infectious retroviruses gene transfer from diverse bacteria to an insect genome enables a tripartite to bona fide cellular genes: role of the captured syncytins in placentation. nested mealybug symbiosis. Cell. 2013;153:1567–78. Placenta. 2012;33:663–71. 89. Martin W, Koonin EV. Introns and the origin of nucleus-cytosol 117. Emera D, Casola C, Lynch VJ, Wildman DE, Agnew D, Wagner GP. compartmentalization. Nature. 2006;440:41–5. Convergent evolution of endometrial prolactin expression in primates, mice, 90. Nowack EC, Price DC, Bhattacharya D, Singer A, Melkonian M, Grossman AR. and elephants through the independent recruitment of transposable Gene transfers from diverse bacteria compensate for reductive genome elements. Mol Biol Evol. 2012;29:239–47. evolution in the chromatophore of Paulinella chromatophora. Proc Natl 118. Ezenwa VO, Gerardo NM, Inouye DW, Medina M, Xavier JB. Microbiology. Acad Sci U S A. 2016;113:12214–9. Animal behavior and the microbiome. Science. 2012;338:198–9. 91. Stairs CW, Leger MM, Roger AJ. Diversity and origins of anaerobic 119. Scarborough CL, Ferrari J, Godfray HC. Aphid protected from pathogen by metabolism in mitochondria and related organelles. Philos Trans R Soc endosymbiont. Science. 2005;310:1781. Lond Ser B Biol Sci. 2015;370:20140326. 120. Hanski I. Metapopulation ecology. Oxford: Oxford University Press; 1999. 92. Bapteste E, O'Malley MA, Beiko RG, Ereshefsky M, Gogarten JP, Franklin-Hall 121. Leibold MA, Holyoak M, Mouquet N, Amarasekare P, Chase JM, Hoopes MF, L, et al. Prokaryotic evolution and the tree of life are two different things. et al. The metacommunity concept: a framework for multi-scale community Biol Direct. 2009;4:34. ecology. Ecol Lett. 2004;7:601–13. 93. Koonin EV. Energetics and population genetics at the root of eukaryotic 122. Ricklefs RE. Disintegration of the ecological community. Am Nat. 2008;172: cellular and genomic complexity. Proc Natl Acad Sci U S A. 2015;112:15777–8. 741–50. 94. Martin W, Muller M. The hydrogen hypothesis for the first eukaryote. Nature. 123. Darwin CA. On the origin of species by means of natural selection. London: 1998;392:37–41. John Murray; 1859. 95. Williams TA, Embley TM. Changing ideas about eukaryotic origins. Philos 124. Caporael L, Griesemer J, Wimsatt W. Scaffolding in evolution, culture, and Trans R Soc Lond Ser B Biol Sci. 2015;370:20140318. cognition. Cambridge: MIT Press; 2013. 96. Simplicity BA. The Stanford Encyclopedia of Philosophy: Metaphysics 125. Laland K, Matthews B, Feldman MW. An introduction to niche construction Research Lab, Stanford University; 2016. https://plato.stanford.edu/archives/ theory. Evol Ecol. 2016;30:191–202. win2016/entries/simplicity/ 126. Rogers RL, Hartl DL. Chimeric genes as a source of rapid evolution in 97. Allen JF. Why chloroplasts and mitochondria retain their own genomes and Drosophila melanogaster. Mol Biol Evol. 2012;29:517–29. genetic systems: Colocation for redox regulation of gene expression. Proc 127. Gould SJ, Lewontin RC. The spandrels of San Marco and the Panglossian Natl Acad Sci U S A. 2015;112:10231–8. paradigm: a critique of the adaptationist programme. Proc R Soc Lond B 98. Godfrey-Smith P. Reproduction, symbiosis, and the eukaryotic cell. Proc Natl Biol Sci. 1979;205:581–98. Acad Sci U S A. 2015;112:10120–5. 128. Sole RV, Valverde S. Are network motifs the spandrels of cellular complexity? 99. Karnkowska A, Vacek V, Zubacova Z, Treitli SC, Petrzelkova R, Eme L, et al. A Trends Ecol Evol. 2006;21:419–22. eukaryote without a mitochondrial organelle. Curr Biol. 2016;26:1274–84. 129. O'Malley MA, Soyer OS, Siegal ML. A philosophical perspective on 100. Gilbert SF, Sapp J, Tauber AI. A symbiotic view of life: we have never been evolutionary systems biology. Biol Theory. 2015;10:6–17. individuals. Q Rev Biol. 2012;87:325–41. 130. Soyer OS, O'Malley MA. Evolutionary systems biology: what it is and why it 101. Moran NA, Sloan DB. The hologenome concept: helpful or hollow? PLoS matters. BioEssays. 2013;35:696–705. Biol. 2015;13:e1002311. 131. Karimpour-Fard A, Hunter L, Gill RT. Investigation of factors affecting 102. Bouchard F. Understanding colonial traits using symbiosis research and prediction of protein-protein interaction networks by phylogenetic profiling. ecosystem ecology. Biol Theory. 2009;4:240–6. BMC Genomics. 2007;8:393. 103. Selosse MA, Bessis A, Pozo MJ. Microbial priming of plant and animal 132. Koch C, Konieczka J, Delorey T, Lyons A, Socha A, Davis K, et al. Inference immunity: symbionts as developmental signals. Trends Microbiol. 2014;22: and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large 607–13. Phylogenies. Cell Syst. 2017;4:543–58. e8 Bapteste and Huneman BMC Biology (2018) 16:56 Page 15 of 16 133. Shahdoust M, Pezeshk H, Mahjub H, Sadeghi M. F-MAP: a Bayesian 157. Corel E, Lopez P, Méheust R, Bapteste E. Network-thinking: graphs to approach to infer the gene regulatory network using external hints. PLoS analyze microbial complexity and evolution. Trends Microbiol. 2016;24: One. 2017;12:e0184795. 224–37. 134. Simonsen M, Maetschke SR, Ragan MA. Automatic selection of reference 158. Friedlander T, Prizak R, Barton NH, Tkačik G. Evolution of new regulatory taxa for protein-protein interaction prediction with phylogenetic profiling. functions on biophysically realistic fitness landscapes. Nat Commun. Bioinformatics. 2012;28:851–7. 2017;8:216. 135. Spanier KI, Jansen M, Decaestecker E, Hulselmans G, Becker D, Colbourne JK, 159. Gouy A, Daub JT, Excoffier L. Detecting gene subnetworks under selection et al. Conserved transcription factors steer growth-related genomic in biological pathways. Nucleic Acids Res. 2017;45:e149. programs in Daphnia. Genome Biol Evol. 2017;9:1821–42. 160. MacKintosh C, DEK F. Recent advances in understanding the roles of whole 136. Wang P, Yu X, Lü J. Identification and evolution of structurally dominant genome duplications in evolution. F1000Res. 2017;6:1623. nodes in protein-protein interaction networks. IEEE Trans Biomed Circuits 161. Nguyen Ba AN, Strome B, Osman S, Legere E-A, Zarin T, Moses AM. Parallel Syst. 2014;8:87–97. reorganization of protein function in the spindle checkpoint pathway 137. Ruprecht C, Vaid N, Proost S, Persson S, Mutwil M. Beyond genomics: through evolutionary paths in the fitness landscape that appear neutral in studying evolution with gene coexpression networks. Trends Plant Sci. 2017; laboratory experiments. PLoS Genet. 2017;13:e1006735. 22:298–307. 162. Alvarez-Ponce D, Feyertag F, Chakraborty S. Position matters: network 138. Netotea S, Sundell D, Street NR, Hvidsten TR. ComPlEx: conservation and centrality considerably impacts rates of protein evolution in the human divergence of co-expression networks in A. thaliana, Populus and O. sativa. protein-protein interaction network. Genome Biol Evol. 2017;9:1742–56. BMC Genomics. 2014;15:106. 163. Cohen O, Gophna U, Pupko T. The complexity hypothesis revisited: connectivity rather than function constitutes a barrier to horizontal gene 139. Conant GC, Wolfe KH. Functional partitioning of yeast co-expression transfer. Mol Biol Evol. 2011;28:1481–9. networks after genome duplication. PLoS Biol. 2006;4:e109. 140. You Q, Xu W, Zhang K, Zhang L, Yi X, Yao D, et al. ccNET: Database of co- 164. Raymond J, Segrè D. The effect of oxygen on biochemical networks and expression networks with functional modules for diploid and polyploid the evolution of complex life. Science. 2006;311:1764–7. Gossypium. Nucleic Acids Res. 2017;45:D1090–D9. 165. Wolf YI, Carmel L, Koonin EV. Unifying measures of gene function and 141. Tang J, Lin J, Li H, Li X, Yang Q, Cheng Z-M, et al. Characterization of CIPK evolution. Proc Biol Sci. 2006;273:1507–15. family in Asian Pear (Pyrus bretschneideri Rehd) and co-expression analysis 166. Hase T, Niimura Y, Tanaka H. Difference in gene duplicability may explain related to salt and osmotic stress responses. Front Plant Sci. 2016;7:1361. the difference in overall structure of protein-protein interaction networks 142. Siwo GH, Tan A, Button-Simons KA, Samarakoon U, Checkley LA, Pinapati RS, among eukaryotes. BMC Evol Biol. 2010;10:358. et al. Predicting functional and regulatory divergence of a drug resistance 167. Xu K, Bezakova I, Bunimovich L, Yi SV. Path lengths in protein-protein transporter gene in the human malaria parasite. BMC Genomics. 2015;16:115. interaction networks and biological complexity. Proteomics. 2011;11:1857–67. 143. Hu G, Hovav R, Grover CE, Faigenboim-Doron A, Kadmon N, Page JT, et al. 168. Prachumwat A, Li W-H. Protein function, connectivity, and duplicability in Evolutionary conservation and divergence of gene coexpression networks yeast. Mol Biol Evol. 2006;23:30–9. in Gossypium (cotton) seeds. Genome Biol Evol. 2016;8:3765–83. 169. Peterson GJ, Pressé S, Peterson KS, Dill KA. Simulated evolution of protein- 144. Lu X, Li Q-T, Xiong Q, Li W, Bi Y-D, Lai Y-C, et al. The transcriptomic protein interaction networks with realistic topology. PLoS One. 2012;7: signature of developing soybean seeds reveals the genetic basis of seed e39052. trait adaptation during domestication. Plant J. 2016;86:530–44. 170. Pawlowski PH, Kaczanowski S, Zielenkiewicz P. A kinetic model of the 145. Zandveld J, van den Heuvel J, Mulder M, Brakefield PM, Kirkwood TBL, Shanley evolution of a protein interaction network. BMC Genomics. 2013;14:172. DP, et al. Pervasive gene expression responses to a fluctuating diet in Drosophila 171. Ruprecht C, Proost S, Hernandez-Coronado M, Ortiz-Ramirez C, Lang D, melanogaster: The importance of measuring multiple traits to decouple Rensing SA, et al. Phylogenomic analysis of gene co-expression networks potential mediators of life span and reproduction. Evolution. 2017;71:2572–83. reveals the evolution of functional modules. Plant J. 2017;90:447–65. 146. Halfon MS. Perspectives on gene regulatory network evolution. Trends 172. Zhao Y, Mooney SD. Functional organization and its implication in Genet. 2017;33:436–47. evolution of the human protein-protein interaction network. BMC 147. Simakov O, Kawashima T. Independent evolution of genomic characters Genomics. 2012;13:150. during major metazoan transitions. Dev Biol. 2017;427:179–92. 173. Akinola RO, Mazandu GK, Mulder NJ. A quantitative approach to analyzing 148. Wang P, Zhao D, Rockowitz S, Zheng D. Divergence and rewiring of genome reductive evolution using protein-protein interaction networks: a regulatory networks for neural development between human and other case study of Mycobacterium leprae. Front Genet. 2016;7:39. species. Neurogenesis (Austin). 2016;3:e1231495. 174. Briones-Moreno A, Hernández-García J, Vargas-Chávez C, Romero-Campero 149. Masalia RR, Bewick AJ, Burke JM. Connectivity in gene coexpression FJ, Romero JM, Valverde F, et al. Evolutionary analysis of DELLA-associated networks negatively correlates with rates of molecular evolution in transcriptional networks. Front Plant Sci. 2017;8:626. flowering plants. PLoS One. 2017;12:e0182289. 175. Hahn MW, Kern AD. Comparative genomics of centrality and essentiality in 150. Muñoz A, Santos Muñoz D, Zimin A, Yorke JA. Evolution of transcriptional three eukaryotic protein-interaction networks. Mol Biol Evol. 2005;22:803–6. networks in yeast: alternative teams of transcriptional factors for different 176. Hansen BO, Vaid N, Musialak-Lange M, Janowski M, Mutwil M. Elucidating species. BMC Genomics. 2016;17:826. gene function and function evolution through comparison of co-expression 151. Kacharia FR, Millar JA, Raghavan R. Emergence of New sRNAs in Enteric networks of plants. Front Plant Sci. 2014;5:394. Bacteria is Associated with Low Expression and Rapid Evolution. J Mol Evol. 177. Kelliher CM, Leman AR, Sierra CS, Haase SB. Investigating conservation of 2017;84:204–13. the cell-cycle-regulated transcriptional program in the fungal pathogen, 152. Kim HS, Mittenthal JE, Caetano-Anollés G. MANET: tracing evolution of Cryptococcus neoformans. PLoS Genet. 2016;12:e1006453. protein architecture in metabolic networks. BMC Bioinformatics. 2006;7:351. 178. Martinez-Pastor M, Tonner PD, Darnell CL, Schmid AK. Transcriptional 153. Leyn SA, Suvorova IA, Kazakov AE, Ravcheev DA, Stepanova VV, Novichkov regulation in Archaea: from individual genes to global regulatory networks. PS, et al. Comparative genomics and evolution of transcriptional regulons Annu Rev Genet. 2017;51:143–70. inProteobacteria. Microb Genom. 2016;2:e000061. 179. Phan HTT, Sternberg MJE. PINALOG: a novel approach to align protein 154. Liang C, Luo J, Song D. Network simulation reveals significant contribution interaction networks–implications for complex detection and function of network motifs to the age-dependency of yeast protein-protein prediction. Bioinformatics. 2012;28:1239–45. interaction networks. Mol BioSyst. 2014;10:2277–88. 180. Romero-Campero FJ, Perez-Hurtado I, Lucas-Reina E, Romero JM, Valverde F. ChlamyNET: a Chlamydomonas gene co-expression network reveals global 155. Mustafin ZS, Lashin SA, Matushkin YG, Gunbin KV, Afonnikov DA. properties of the transcriptome and the early setup of key co-expression Orthoscape: a cytoscape application for grouping and visualization KEGG patterns in the green lineage. BMC Genomics. 2016;17:227. based gene networks by taxonomy and homology principles. BMC Bioinformatics. 2017;18:1427. 181. Tamames J, Moya A, Valencia A. Modular organization in the reductive 156. Thompson JR, Erkenbrack EM, Hinman VF, McCauley BS, Petsios E, Bottjer evolution of protein-protein interaction networks. Genome Biol. 2007;8:R94. DJ. Paleogenomics of echinoids reveals an ancient origin for the double- 182. Wang D, He F, Maslov S, Gerstein M. DREISS: using state-space models to negative specification of micromeres in sea urchins. Proc Natl Acad Sci U S infer the dynamics of gene expression driven by external and internal A. 2017;114:5870–7. regulatory networks. PLoS Comput Biol. 2016;12:e1005146. Bapteste and Huneman BMC Biology (2018) 16:56 Page 16 of 16 183. Aguilera F, McDougall C, Degnan BM. Co-option and de novo gene 210. O'Hara RJ. Population thinking and tree thinking in systematics. Zool Scr. evolution underlie molluscan shell diversity. Mol Biol Evol. 2017;34:779–92. 1997;26:323–9. 184. Auman T, Chipman AD. The evolution of gene regulatory networks that 211. Woese CR. A new biology for a new century. Microbiol Mol Biol Rev. 2004; define arthropod body plans. Integr Comp Biol. 2017;57:523–32. 68:173–86. 185. Mateos JL, Tilmes V, Madrigal P, Severing E, Richter R, Rijkenberg CWM, et al. 212. Lange M. Really statistical explanations and genetic drift. Philosophy Sci. Divergence of regulatory networks governed by the orthologous 2013;80:169–88. transcription factors FLC and PEP1 in Brassicaceae species. Proc Natl Acad 213. Huneman P. Diversifying the picture of explanations in biological sciences: Sci U S A. 2017;114:E11037–E46. Ways of combining topology with mechanisms. Synthese. 2018;195:115–46. 214. Jones N. Bowtie structures, pathway diagrams, and topological explanation. 186. Renvoisé E, Kavanagh KD, Lazzari V, Häkkinen TJ, Rice R, Pantalacci S, et al. Erkenntnis. 2014;89:1355–555. Mechanical constraint from growing jaw facilitates mammalian dental 215. Lane N, Martin WF, Raven JA, Allen JF. Energy, genes and evolution: diversity. Proc Natl Acad Sci U S A. 2017;114:9403–8. introduction to an evolutionary synthesis. Philos Trans R Soc Lond Ser B Biol 187. Cary GA, Cheatle Jarvela AM, Francolini RD, Hinman VF. Genome-wide use Sci. 2013;368:20120253. of high- and low-affinity Tbrain transcription factor binding sites during 216. Sousa FL, Thiergart T, Landan G, Nelson-Sathi S, Pereira IA, Allen JF, et al. echinoderm development. Proc Natl Acad Sci U S A. 2017;114:5854–61. Early bioenergetic evolution. Philos Trans R Soc Lond Ser B Biol Sci. 2013; 188. Noble R, Noble D. Was the watchmaker blind? Or was she one-eyed? 368:20130088. Biology (Basel). 2017;6:47. 217. Nagel E. The structure of science. Harcourt, Brace & World: New-York; 1961. 189. Capela D, Marchetti M, Clérissi C, Perrier A, Guetta D, Gris C, et al. 218. Booth A, Mariscal C, Doolittle WF. The Modern Synthesis in the light of Recruitment of a lineage-specific virulence regulatory pathway promotes microbial genomics. Annu Rev Microbiol. 2016;70:279–97. intracellular infection by a plant pathogen experimentally evolved into a 219. Gilbert SF, Opitz JM, Raff RA. Resynthesizing evolutionary and legume symbiont. Mol Biol Evol. 2017;34:2503–21. developmental biology. Dev Biol. 1996;173:357–72. 190. Orsini L, Brown JB, Shams Solari O, Li D, He S, Podicheti R, et al. Early 220. Kokko H, Chaturvedi A, Croll D, Fischer MC, Guillaume F, Karrenberg S, et al. transcriptional response pathways in Daphnia magna are coordinated in Can evolution supply what ecology demands? Trends Ecol Evol. 2017;32: networks of crustacean-specific genes. Mol Ecol. 2018;27:886–97. 187–97. 191. Dohrmann J, Puchin J, Singh R. Global multiple protein-protein interaction 221. Cancherini DV, França GS, de Souza SJ. The role of exon shuffling in shaping network alignment by combining pairwise network alignments. BMC protein-protein interaction networks. BMC Genomics. 2010;11(Suppl 5):S11. Bioinformatics. 2015;16(Suppl 13):S11. 222. Amin SA, Hmelo LR, van Tol HM, Durham BP, Carlson LT, Heal KR, et al. 192. Emmert-Streib F, Dehmer M, Shi Y. Fifty years of graph matching, network Interaction and signalling between a cosmopolitan phytoplankton and alignment and network comparison. Inf Sci. 2016;346:180–97. associated bacteria. Nature. 2015;522:98–101. 193. Kelley BP, Yuan B, Lewitter F, Sharan R, Stockwell BR, Ideker T. PathBLAST: a 223. Erez Z, Steinberger-Levy I, Shamir M, Doron S, Stokar-Avihail A, Peleg Y, tool for alignment of protein interaction networks. Nucleic Acids Res. 2004; et al. Communication between viruses guides lysis-lysogeny decisions. 32:W83–8. Nature. 2017;541:488–93. 194. Ni B, Ghosh B, Paldy FS, Colin R, Heimerl T, Sourjik V. Evolutionary remodeling 224. Dorrell RG, Gile G, McCallum G, Meheust R, Bapteste EP, Klinger CM, et al. of bacterial motility checkpoint control. Cell Rep. 2017;18:866–77. Chimeric origins of ochrophytes and haptophytes revealed through an 195. Huisman M. Imputation of missing network data: some simple procedures. ancient plastid proteome. elife. 2017;6:e23717. In: Alhajj R, Rokne J, editors. Encyclopedia of Social Network Analysis and Mining. New York: Springer New York; 2014. p. 707–15. 196. Ogundijo OE, Elmas A, Wang X. Reverse engineering gene regulatory networks from measurement with missing values. EURASIP J Bioinform Syst Biol. 2016;2017:2. 197. Shao M, Zhou S, Guan J. Revisiting topological properties and models of protein-protein interaction networks from the perspective of dataset evolution. IET Syst Biol. 2015;9:113–9. 198. Doolittle WF, Inkpen SA. Processes and patterns of interaction as units of selection: An introduction to ITSNTS thinking. Proc Natl Acad Sci U S A. 2018;115:4006–14. 199. Wong DCJ, Matus JT. Constructing integrated networks for identifying new secondary metabolic pathway regulators in grapevine: recent applications and future opportunities. Front Plant Sci. 2017;8:505. 200. Corel E, Méheust R, Watson AK, McInerney JO, Lopez P, Bapteste E. Bipartite network analysis of gene sharings in the microbial world. Mol Biol Evol. 2017;35:899–913. 201. Seligmann H, Raoult D. Unifying view of stem–loop hairpin RNA as origin of current and ancient parasitic and non-parasitic RNAs, including in giant viruses. Curr Opin Microbiol. 2016;31:1–8. 202. Stoltzfus A. Constructive neutral evolution: exploring evolutionary theory's curious disconnect. Biol Direct. 2012;7:35. 203. Gillespie JH. Population genetics: A concise guide. Baltimore: The Johns Hopkins University Press; 2004. 204. Barker G, Desjardins E, Pearce T. Entangled Life: Organism and Environment in the Biological and Social Sciences. Dordrecht: Springer; 2013. 205. Bonduriansky R. Rethinking heredity, again. Trends Ecol Evol. 2012;27:330–6. 206. Lehmann L. The adaptive dynamics of niche constructing traits in spatially subdivided populations: evolving posthumous extended phenotypes. Evolution. 2008;62:549–66. 207. Bruno JF, Stachowicz JJ, Bertness MD. Inclusion of facilitation into ecological theory. Trends Ecol Evol. 2003;18:119–25. 208. Beatty J. Replaying life’s tape. J Philosophy. 2006;103:336–62. 209. Turner D. Historical contingency and the explanation of evolutionary trends. In: Malaterre C, Braillard PA, editors. Biological explanation: An enquiry into the diversity of explanatory patterns in the life sciences. Dordrecht: Springer; 2015. p. 73–90. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BMC Biology Springer Journals

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

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Abstract

population and quantitative genetics, a theoretical frame The classic Darwinian theory and the Synthetic that corroborated Darwin’s hypothesis of the paramount evolutionary theory and their linear models, while power of selection for driving adaptive evolution [2]. invaluable to study the origins and evolution of species, This framework progressively aggregated multiple disci- are not primarily designed to model the evolution of plines: behavioural ecology, microbiology, paleobiology, organisations, typically that of ecosystems, nor that of etc. Overall, this classic framework considers that the processes. How could evolutionary theory better principal agency of evolution is natural selection of explain the evolution of biological complexity and favourable variations, and that those variations are con- diversity? Inclusive network-based analyses of dynamic stituted by random mutations and recombination in a systems could retrace interactions between (related or Mendelian population. The processes of microevolution, unrelated) components. This theoretical shift from a modelled by population and quantitative genetics, are Tree of Life to a Dynamic Interaction Network of Life, likely to be extrapolated to macroevolution [3]. To this which is supported by diverse molecular, cellular, extent, models that focus on one or two loci are able to microbiological, organismal, ecological and evolutionary capture much of the evolutionary dynamics of an organ- studies, would further unify evolutionary biology. ism, even though in reality many interdependencies be- tween thousands of loci (epistasis, dominance, etc.) Keywords: Evolutionary biology, Interactions, occur as the basis of the production and functioning of a Theoretical biology, Tree of Life, Web of Life phenotypic trait. Among forces acting on populations and modelled by population geneticists, natural selection Deciphering diversity through evolution is the one that shapes traits as adaptations and the de- The living world is nested and multilevel, involves mul- sign of organisms; adaptive radiation then explains much tiple agents and changes at different timescales. Evolu- of the diversity; and common descent from adapted tionary biology tries to characterize the dynamics organisms explains most of the commonalities across responsible for such complexity to decipher the pro- living forms (labelled homologies), and allows for classi- cesses accounting for the past and extant diversity ob- fying living beings into phylogenetic trees. Evolution is served in molecules (namely, genes, RNA, proteins), gradual because the effects of mutations are generally cellular machineries, unicellular and multi-cellular or- small, large ones being most likely to be deleterious as ganisms, species, communities and ecosystems. In the theorized by Fisher’s geometric model [4]. 1930s and 1940s, a unified framework to handle this task Many theoretical divergences surround this core view: was built under the name of Modern Synthesis [1]. It not everyone agrees that evolution is change in allele fre- encompassed Darwin’s idea of evolution by natural selec- quencies, or that population genetics captures the whole tion as an explanation for diversity and adaptation and of the evolutionary process, or that the genotypic view- point — tracking the dynamics of genes as ‘replicators’ [5] or the strategy ‘choices’ of organisms as fitness maximiz- * Correspondence: eric.bapteste@upmc.fr ing agents [6] — should be favoured to understand evolu- Sorbonne Universités, UPMC Université Paris 06, Institut de Biologie Paris-Seine (IBPS), F-75005 Paris, France tion. Nevertheless, it has been a powerful enough CNRS, UMR7138, Institut de Biologie Paris-Seine, F-75005 Paris, France framework to drive successful research programs on Full list of author information is available at the end of the article © Bapteste et al. 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Bapteste and Huneman BMC Biology (2018) 16:56 Page 2 of 16 speciation, adaptation, phylogenies, evolution of sex, we argue that evolutionary biology could become a sci- cooperation altruism, mutualism, etc., and incorporate ap- ence of evolving networks, which would allow biologists parent challenges such as neutral evolution [7], acknow- to explain organisational complexity, while providing a ledgement of constraints on variation [8], or the recent novel way to reframe and to unify evolutionary biology. theoretical turn from genetics to genomics following the achievement of the Human Genome Program [9]. Caus- Biology is regulated by networks ation is here overall conceived of as a linear causal relation Networks at the molecular level of a twofold nature: from the genotype to the phenotype Although numerous studies have focused on the functions (assuming of course environmental parameters), and from of individual genes, proteins and other molecules, it is in- the environment to the shaping of organisms via natural creasingly clear that each of these functions belongs to selection. For instance, in the classic case of evolution of complex networks of interactions. Starting at the molecu- peppered moths in urban forests at the time of the indus- lar scale, the importance of a diversity of molecular agents, trial revolution, trees became darkened with soot, and such as (DNA-based) genes and their regulatory se- then natural selection favored darker morphs as ‘fitter’ quences, RNAs and proteins, is well recognized. Import- ones, due to their being less easily detected by predator antly, in terms of their origins and modes of evolution, birds, resulting in a relative increase in frequency of the these agents are diverse. Genes are replicated across gen- darker morphs in the population [10]. erations, via the recruitment of bases along a DNA tem- Yet in the last 15 years biologists and philosophers of plate, thereby forming continuous lineages, affected by biology have regularly questioned the genuinely unifying Darwinian evolution. By contrast, proteins are recon- character of this Synthesis, as well as its explanatory ac- structed by recruitment of amino acids at the ribosomal curacy [11]. Those criticisms questioned notably the set machinery. There is no physical continuity between gener- of objects privileged by the Modern Synthesis, arguably ations of proteins, and thus no possibility for these agents too gene-centered [12], and its key explanatory to directly accumulate beneficial mutations [25]. processes, since niche construction [13], lateral gene Moreover, all these molecular entities are compositionally transfer [14, 15], phenotypic plasticity [16, 17], and mass complex, in the sense that they are made of inherited or extinction [18] could, for example, be added [11]. reassembled parts. E pluribus unum: genes and proteins Usually these critiques emphasize aspects rooted in a are (often) conglomerates of exons, introns [26–28], and particular biological discipline: lateral gene transfer from domains [29–31]. Similar claims can be made about com- microbiology, plasticity from developmental biology, posite molecular systems, such as CRISPR and Casposons mass extinction from paleobiology, ecosystem engineer- [32, 33], etc. This modular organisation has numerous ing from functional ecology, etc. There were also consequences: among them, genes can be nested within recurring claims for novel transdisciplinary fields: evo- genes [34]; proteins congregate in larger complexes [35]. eco-devo [19], investigating the evolutionary dynamics Importantly, this modularity is not the mere result of a of host and microbe associations (forming combinations divergence from a single ancestral form, but also involves often referred to as holobionts), evolutionary cell biology combinatorial processes and molecular tinkering of avail- [20], or microbial endocrinology [21], among others. able genetic material [36–38]. The coupling and decoup- This latter discipline aims at understanding the evolved ling of molecular components can operate randomly, as in interactions between microbial signals and host develop- cases of presuppression proposed to neutrally lead to large ment. Indeed, it is compelling for evolutionary biologists molecular complexes [39–41]. Presuppression, also known to decipher how such multi-species interactions became as constructive neutralism, is a process that generates established (namely, whether they involved specific mi- complexity by mechanically increasing dependencies crobial species and molecules, and whether they evolved between interacting molecules, in the absence of positive independently in different host lineages). selection. When a deleterious mutation affects one mo- Evolutionary biology is thus currently undergoing vari- lecular partner, existing properties of another molecule ous theoretical debates concerning the proper frame to with which the mutated molecule already interacted can formulate it [11, 22–24]. Here, we introduce an original compensate for its partner defect. Presuppression operates solution which moves this debate forward, acknowledg- like a ratchet, since the likelihood to restore the original ing that nothing on Earth evolves and makes sense in independency between molecules (by reverting the dele- isolation, thereby challenging the key assumption of the terious mutation) is lower than the likelihood to move Modern Synthesis framework that targeting the individ- away from this original state (by accumulating other mu- ual gene or organism (even when in principle knowing tations). Molecular associations can also evolve under that it is part of a set of complex interactions) allows us constraints [42], eventually reinforcing the relationships to capture evolution in all its dimensions. Since the liv- between molecular partners, as suggested for some op- ing world evolves as a dynamic network of interactions, erons [43] and fused genes [44, 45]. Bapteste and Huneman BMC Biology (2018) 16:56 Page 3 of 16 Consistently, interconnectedness is a striking feature At the molecular level, it is reasonable to assume that of the molecular world [46, 47]. Genes belong to regula- processes resulting from interactions of a diversity of tory networks with feedback loops [48]. Proteins belong intertwined agents offer a crucial explanans of biological to protein–protein interaction networks. This systemic complexity. Rather than ‘one agent, one action’, it would view contrasts with former atomistic views assigning one be more accurate to consider ‘a relationship between function to one gene. First, it is not always correct that a agents, one action’ as the modus operandi of life. gene produces only a protein, in the case of alternative Multiple drivers, of different nature, contribute to the splicing. Second, it is also unlikely that a protein evolution of these interactions: among others, gene co- performs one function, because no protein acts alone. expression/co-regulation [57], sometimes mediated by Rather, biological traits result from co-production pro- transposons [58–61]; the evolutionary origin of the cesses. This is nicely illustrated by the actual process of genes [62]; and also physical and chemical laws, as well translation, during which both proteins and DNA neces- as the presence of targeting machineries that constrain sarily interact, allowing for the collective reproduction of and regulate diffusion processes in the cell. These types these two types of molecular agents. How these different of relationships described at the molecular level are also components became so tightly integrated is a central recovered at other levels of biological organisations. issue for explaining evolution. Understanding how the molecular world functions and evolves therefore re- Networks at the cellular level quires analysing molecular organisation and the evolu- Similar conclusions have been reached at the cellular tion of the architecture of interaction networks, level, also crucial for understanding life history. All pro- especially since this structure can partly explain karyotes and protists are unicellular organisations, and molecular reactions [46, 47, 49, 50]. Thus, systems the cell is a fundamental building block of multicellular biologists search for common motifs in molecular inter- organisms. Cells must constantly evaluate the states of action networks from different organisms, such as feed- their inner and outer environments, i.e. to adjust their forward loops, assuming that some of these recurring gene expression and react accordingly [46]. This involves patterns, because they affect different gene or protein sets, regulatory, transduction, developmental, and protein may reflect general rules and constraints affecting the con- interaction networks, etc. Cells are built upon inner net- struction and evolution of biological organisations [46]. works of interacting components, and involved in or af- Focusing evolutionary explanations on the structure of fected by a diversity of exchanges, influences and modes the interactions between genes rather than on the pri- of communications (namely, genetic, energetic, chemical mary sequence of the genes is fundamentally different and electrical modes). Microbiology has gone a long way from sequencing genes and inferring history from their toward unraveling these processes since its heyday of sequences alone. One could think here of the case of pure culture studies, a fruitful reductionist approach explaining gene activation/repression. Comparative now complemented by environmental studies. These lat- works on molecular interaction networks show that in- ter further unraveled that cells compete and cooperate teractions affect the evolution of the molecules compos- with, and even compensate for each other, within mono- ing networks, which means that beyond compositional or multispecific microbiomes [63, 64]. Both types of complexity, organisational complexity must be modeled microbiomes have a fundamental commonality: they to understand biological evolution [46, 51–54]. Before produce collective properties and co-constructed pheno- the analysis of complex networks, compensatory sets of types (Fig. 1) evolving at the interface between cells. elements, such as groups of sub-functional paralogous Such properties cannot be understood without consider- genes [55], or groups of genes with pressupressed muta- ing networks of influences: the oscillatory growth of bio- tions [39, 40], already stressed the evolutionary inter- films of Bacillus subtilis cannot be deduced from the dependence of molecules. However, compensatory analyses of the complete genomes of these clones, but interactions between agents, each of them being by requires modeling metabolic co-dependence within a themselves poorly adapted, ran counter to the intuition monogenic community affected by a delayed feedback that natural selection will eliminate dysfunctional indi- loop, involving chemical and electrical signals [65, 66]. vidual entities. Their recognition invites one to consider Furthermore, many cellular agents show a relative lack Earth as possibly populated by unions of individually of autonomy. In nature, some groups of prokaryotes dis- dysfunctional agents rather than by the fittest survivors play complementary genomes with incomplete metabolic within individual lineages, possibly since early life, ac- pathways, consistent with the black queen hypothesis, cording to Woese’s theory on progenotes, namely com- which predicts that our planet is populated by groups of munities of interacting protocells unable to sustain (inter)dependent microbes [67, 68]. More precisely, this themselves alone, evolving via massive lateral genetic ex- hypothesis predicts the loss of a costly function, encoded changes [56]. by a gene or a set of genes, in individuals, when this Bapteste and Huneman BMC Biology (2018) 16:56 Page 4 of 16 of components from different lineages, no complete pic- ture of evolution can be provided without these jumps, which are naturally modeled by networks. Indeed, gen- etic information has been flowing both vertically and horizontally between prokaryotes for over 3.5 billion years [71–77], and possibly earlier, according to Woese, who proposed that our universal ancestor was not an entity but a process, that is, genetic and energetic ex- changes within protocellular communities [56]. Remark- ably, this latter case indicates that network modeling could help to tackle a fundamental issue in evolutionary biology: modeling the evolution of biological processes that emerge from interactions between biological en- tities. Since these interactions can be represented by a Fig. 1. An example of co-construction, the case of holobionts. The left network, the evolution of these interactions, describing circle represents the set of traits associated with a host, the right circle represents the set of traits associated with its microbial communities; the evolution of biological processes, can then be repre- the intersected area represents traits that are produced jointly as a sented by dynamic networks. Likewise, eukaryogenesis result of the interaction between hosts and microbes. When this area rested on the co-construction of a novel type of cell, as a becomes large or when co-constructed traits are remarkable, they result of the endosymbiosis of a bacteria within an cannot be correctly explained under a simple model treating hosts and archaeon [78–80]. Later, the evolution of photosynthetic microbes in isolation. This scheme holds for different types of partners protists emerged from endosymbioses involving unicel- lular eukaryotes and cyanobacteria, or various lineages function becomes dispensable at the individual level, of protists, namely in secondary and tertiary endosymbi- since it is achieved by other individuals that produce oses [81]. Such endosymbioses, and their outcomes as (usually leaky) public goods in sufficient amount to sup- illustrated in our work [82, 83], are also naturally mod- port the equilibrium of the community. Thus, gene eled using networks. losses in some cells are compensated by leaks of sub- Moreover, the long-term impact of these introgressive strates from other cells, formerly encoded by the lost processes on cellular evolution should not be underesti- genes. Some microbes experience labor division [69]. mated. For instance, endosymbiosis does not merely intro- Symbionts and endosymbionts depend on their hosts. duce new cellular lineages, it also favors the evolution of The ‘kill the winner’ theory [70] further challenges the chimeric structures and chimeric processes within cells notion that the microbial world is a world of fit cellular [83–91]. Such intertwining cannot be modeled using a individuals. This theory stresses a collective process via single genealogical tree, which only recapitulates cellular which viruses mechanically mostly attack cells that re- divergence from a last common ancestor. Even though produce faster and thus regulate bacterial populations, cells always derive from other cells, a full cellular history these latter sustaining their diversity because these pop- cannot be reduced to the history of some cellular compo- ulations are comprised of individual prokaryotic cells nents that are assumed to track the history of cellular that make a suboptimal use of a diversity of resources. division [92]. In particular, phylogenetic analyses of infor- Thus, cells belong to networks that affect their growth mational genes cannot be the only clue to understanding and survival, which might explain why most bacteria the origins of cellular diversity, since these genes do not cannot be grown in pure culture. They only truly thrive reflect how cells are organized, how they gather their within communities, whose global genetic instructions energy, and how they interact with each other. Analyzing are spread over several genetically incomplete microbes. the co-construction side of evolution requires enhanced Accounting for these internal and external cellular models: understanding eukaryotic evolution requires networks requires considering processes that are not mixed considerations of cellular architecture, population central in the synthetic evolutionary theory. Typically, genetics and energetics, which go beyond classic phylo- the notion that cellular evolution makes jumps, because genetic models, which not so long ago were still prone to new components and processes (such as metabolic considering three primary domains of life [93–95]. pathways) are acquired from outside a given cellular Although invoking multiple agents rather than a single lineage, contrasts with more gradual accounts of bio- ancestor in evolutionary explanations might appear to logical change, like accounts based on point mutations contradict the famous Ockham’s razor [96], it does so affecting genes already present in the lineage. Because only superficially when it is likely that many cells are saltations (macromutations) are essential evolutionary co-constructed, especially in the context of a web of life. outcomes of introgressive processes, via the combination Enhanced models including intra- and extracellular Bapteste and Huneman BMC Biology (2018) 16:56 Page 5 of 16 interactions appear necessary to understand cellular recruit environmental microbes and transmit them (with a complexity, including the predictable disappearance of non-null heritability [113]) to their progeny. Therefore, traits (and processes), namely the convergent gene loss nuclear gene inheritance alone may provide too narrow a of mitochondria and plastids [97] by a process called perspective to account for the evolution of all animal dedarwinification [98, 99]. traits; as an example, aphid body color depends on animal genetics and the presence of Rickettsiella [114]. Population Networks beyond the cellular level genetics gets included in a broader community genetics, Studies of multicellular organisms—we will focus on ani- which also considers transmission of microbes and their mals—have led to similar general findings. Understand- genes [108, 114]. The use of gnotobiotic and transbiotic ing animal traits and their evolution requires analyzing animals becomes a new experimental standard to analyze the relationships between a multiplicity of agents be- multigenomic collectives without counterparts in modern longing to different levels of biological organisation, synthesis theories. These collectives harbor morphological, eventually nested, some of which co-constructs animals physiological, developmental, ecological, behavioral and and guarantees their complete lifecycle [100]. Because evolutionary features [115–119] that are not purely con- no sterile organism lives on Earth, animal development, structed by animal genes, but rather appear to be co- health and survival depend on microbes. Granted, bac- constructed at the genetic and metabolic interface teria can often legitimately be seen as part of the envir- between the microbial and macrobial worlds, while the onmental demands in an evolutionary model focused on content of the respective animal genomes only provides the host’s lineage; or sometimes bacteria and host could incomplete instructions. Understanding animal evolution also be considered as part of a coevolution process, with requires understanding the interaction networks between no need to posit the whole as a unit of selection [101]. components from which these taxa evolved, and the net- However, asking ‘who is the beneficiary of the symbiosis works to which these taxa still belong. as the result of evolution?’ may in some cases lead to the In ecology, an analogous turn towards network think- recognition that bacteria and host evolved together and ing has been promoted since the 1990s with the general were selected together [102]. More generally, while some acceptance of the notions of metapopulations [120] and microbes contribute to animals’ lives possibly as a result then metacommunities [121]. These views suggest that of host-derived selection, others contribute as a result of the dynamics of ecological biodiversity is not so much selectively neutral processes (like microbial priming located within a community of species but rather in a [103]) [101, 104]. These interactions produce communi- metacommunity, which can be thought of as a network cation networks within the animal body: chemical infor- of communities exchanging species, while targeting one mation circulates between the animal brain and the gut community blinds one to what genuinely accounts for microbiome. These interactions also result in communi- biodiversity and ecosystem functioning [122]. cation and interaction networks between individuals. In This quick overview provides evidence that networks some animal lineages, the microbiome affects social be- are at the origin of the genes of unicellular and multicel- haviors, for instance fermenting microbes inform about lular organisms and central for their functions. The the gender and reproductive status in hyena [105]. Com- living world is a world of ‘and’ and ‘co-’. From division ponents of the microbiome also affect mating choice of labor and compensations, to dependencies and co- [106], reproductive isolation and possibly speciation. constructions, etc.: interactions (which only begin to be Consequently, the microbiome now appears as an essen- deciphered) are everywhere in biology. Thus, explaining tial component of animal studies [107]. Microbiome the actual features of biodiversity requires explaining studies, the significance of which is overstated in some how multiple processes, interface phenomena (co-con- respects, nevertheless have shown that the evolutionary struction of biological features, niche construction, intertwining between many metazoa and commensal or metabolic cooperation, co-infection and co-evolution) symbiotic bacteria could not be neglected anymore and and organisations (for instance, from molecular path- black-boxed in favor of purely host gene-centered evolu- ways to organisms and ecosystems) arose from interact- tionary models. And the associations between hosts and ing components, and how these processes, phenomena microbes do not need to be units of selection to be part and organisations may have been sustained and trans- of the recent insights that support the novel theoretical formed on Earth. framework proposed here. Their interplay imposes reconfigurations of practices, theories and disciplines Reframing evolutionary explanations from the [108]. As a result of our improved insight into evolution, scaffolded evolution perspective zoology and immunology [109] become theaters of new Introducing a classification of interacting components ecological considerations [110], sometimes strangely While classic evolutionary models, prompted by qualified as Lamarckian [111, 112], because animals can Darwin’s famous tree [123], mostly stress how related Bapteste and Huneman BMC Biology (2018) 16:56 Page 6 of 16 entities diverge in relative independence, it appears im- alter the evolution of the biotic components, for ex- portant to show how a diversity of components, which ample, environmental change can drive genetic and may not be related, interact and produce various evolu- organismal evolution and selection. The history of life tionary patterns. clearly depends on the interplay of both types of compo- The notion of scaffolding [124], which describes how nents. Biotic components, however, deserve a specific one entity continues an event initiated by another entity, focus. Some of them form lineages (for instance, genes and relies on it up to the point that at some timescale it replicate), while others do not (for instance, proteins are becomes dependent upon it for further evolution, ap- reconstructed). Finally, interacting replicated compo- pears as a fundamental relationship to describe the evo- nents can be further classified into fraternal components lution of life. We propose scaffolding should become when they share a close last common ancestor (e.g. in more central in explanations of evolution because no kin selection cases), and egalitarian components, when components from the biological world are actually able they belong to distinct lineages (as an example, think of to reproduce, or persist, alone (Fig. 2). Each entity influ- the evolution of chimeric genes by fusion and shuffling ences or is influenced by something external to it, and is [29, 45, 126]) [63]. consequently part of a process. Scaffolding thus defines the causal backbone of collective evolution. It describes Introducing dynamic interaction networks the historical continuity between temporal slices of Biodiversity usually evolves from interactions between interaction networks, since any evolutionary stage relies the diverse types of components described above. For on previously achieved networks and organisations. example, metalloproteases emerge from the interaction Therefore, describing the evolution of interactions re- between reconstructed biotic components (proteins) and quires explanations to address the following issues: what a metal ion. Regulatory networks involve biotic compo- scaffolds what, what transforms the environment of what, nents that can be either replicated (i.e. genes and pro- and are these influences reciprocal? Characterizing the moters) or reconstructed (i.e. proteins). Protein types of components that, together, have evolutionary interaction networks intertwine reconstructed egalitarian importance through their potential interaction is therefore biotic components, which means proteins that are not a central step to expanding evolutionary theory. homologous. Evolutionary transitions such as eukaryo- We propose that a first distinction can be made be- genesis result from the interweaving of biotic compo- tween obligate and facultative components. Suppressing nents (cells) from multiple lineages. Holobionts evolve the former impacts the course and eventually the from interactions between egalitarian biotic components reproduction of the process to which they contribute (macrobial hosts and microbial communities) and pos- (Fig. 3), whereas facultative components do not hold sibly abiotic components, such as the mineral termite such a crucial role, and may simply be involved by mounds, or the volatile chemicals produced by the chance. A second distinction is whether the components microbial communities of hyenas [105]. are biotic (genes, proteins, organisms…) or abiotic (such Taking collectives of interacting components as central as minerals, environmental, cultural artefacts). Abiotic objects of study in evolutionary biology invites us to ex- components can be recruited from the environment or pand the methods of this field. It encourages developing be shaped by biological processes [125]. They can also statistical approaches or inference methods beyond those ab c d Fig. 2. Different types of scaffolding, at four levels of biological organisations. a Functional interactions at the molecular level. b Introgression and vertical descent at the cellular level. c Co-construction at the multicellular level. d Niche-construction and physico-chemical interactions at the eco-systemic level Bapteste and Huneman BMC Biology (2018) 16:56 Page 7 of 16 Fig. 3. Classification of major types of components in evolving systems. A process/collective cannot be completed in the absence of obligate components, whereas facultative components do not affect the outcome of the process/function of the collective. Biotic components are biological, material products, whereas abiotic components are environmental, geological, chemical, physical or cultural artefacts. Replicated components are produced by replication, which implies a physical continuity between ancestral and descendent components; they undergo a paradigmatic Darwinian evolution. Reconstructed components are reproduced without direct physical continuity, and cannot directly accumulate beneficial mutations. Fraternal components belong to the same lineage, whereas egalitarian components belong to different lineages operating under the very common assumption that bio- recognized as a more inclusive object of study (Fig. 4). logical components are independent. Therefore, we Where phylogenies describe relationships, networks can propose to represent interactions between components describe organisations. How such organisations evolve in the form of networks in which components are nodes could for example be described by identifying evolution- and their interactions (of various sorts) are edges. These ary stages, that is, sets of components and of their inter- networks are conceptually simple objects. They can be actions simultaneously present in the network (Fig. 4). described as adjacency lists of interactions, in the form Investigating the evolution of an ecosystem corresponds ‘component A interacts with component B, at time t to studying the succession of evolutionary stages in such (when such a temporal precision is known)’. Such dy- networks and detecting possible regularities—in the namic interaction networks could become more central sense that some evolutionary stages would fully or partly representations and analytical frameworks, and serve as reiterate over time—or hinting at rules or constraints a common explanans for various disciplines in an ex- (like architectural contingencies [127, 128] or principles panded evolutionary theory. Importantly, because these of organisations [46]) on the recruitment, reproduction networks embed both abiotic and biotic, related and un- and heritability of their components. related components (like viruses, cells and rocks), they Thus, we suggest that evolutionary biology could be should not be conflated with phylogenetic networks, but reframed as a science of evolving networks, because Fig. 4. An evolving interaction network. Nodes are components (circles are full when the component is biotic). Thick black edges represent interactions between these components. The network topology evolves as nodes or their connection change. Dashed edges represent the phylogenetic ancestry of lineage-forming components Bapteste and Huneman BMC Biology (2018) 16:56 Page 8 of 16 such a shift would allow inclusive, multilevel studies of a reveal conservation and divergence in gene regulation larger body of biological and abiotic data, via approaches [138]. GCNs are already used for micro-evolution stud- from network sciences. ies, as in the case of fine-grained comparisons of expres- sion variations between orthologous genes across closely Concrete strategies to enhance network-based related species, and for the analysis of minor evolution- evolutionary analyses ary and ecological transitions, such as changes of ploidy Enhancing network-based evolutionary analyses, beyond [139, 140], adaptation to salty environments [141]or the now classic research program of phylogenetic net- drugs [142], or the effects of plant domestication works, could consolidate comparative analyses in the [143, 144]. Likewise, GRNs are starting to be used in nascent field of evolutionary systems biology [129, 130], micro-evolution and phenotypic plasticity studies as illustrated by examples based on molecular networks. [145]. Understanding the dynamics of GRNs appears Network construction/gathering constitutes the first step critical to inferring the evolution of organismal traits, of such analyses. This involves first defining nodes of the in particular during metazoan [146–148], plant [149]and network, namely components suspected to be involved fungal [150] evolution. We suggest that PPI, GCN and in a given system, and edges, namely qualitative (or GRN studies could become mainstream and also be quantitative, when weighted) interactions between these conducted at (much) larger evolutionary and temporal entities. Many biological interaction networks (gene co- scales, to analyze additional, major, transitions. expression networks (GCNs), gene regulatory networks Based on these established networks, two major types (GRNs), metabolic networks, protein–protein interaction of evolutionary analyses (network-decomposition and networks (PPIs), etc. [46]) are already known for some graph-matching; Fig. 5) can be easily further developed species, or can be inferred [131–136]. For example, by evolutionary biologists. More precisely, the above- GCNs offer an increasingly popular resource to study mentioned kinds of biological networks could be system- the evolution of biological pathways [137], as well as to atically turned into what we call evolutionary colored Fig. 5. Workflow of the evolutionary analysis of interaction networks. From left to right: triangles represent components of interaction networks, edges between triangles represent interactions between these components. Interaction networks are first constructed/inferred, then their nodes and edges are colored to produce evolutionary colored networks (ECNs) that represent both the topological and the evolutionary properties of the networks. ECNs can be investigated individually by graph decomposition and centrality analyses, or several ECNs can be compared by graph alignment. The two types of comparisons can return conserved subgraphs that allow understanding of the dynamics of interaction networks, meaning when different sets of interactions (hence processes) evolved, and whether these interactions were evolutionarily stable. Ancient and Contemporary refer to the relative age of the sub-graphs, identifying new clade-specific relationships (here called refinement); introgression indicates that a component, and the relationship it entertains with the rest of the network, was inferred to result from a lateral acquisition Bapteste and Huneman BMC Biology (2018) 16:56 Page 9 of 16 biological networks (ECNs). In ECNs, each node of a This focus would complement a classic tree-based given biological network is colored to reflect one or sev- view. For instance, under the reasonable working hy- eral evolutionary properties. For example, in molecular pothesis that pairs of connected nodes of a given age re- networks, nodes correspond to molecular sequences flect an interaction between nodes that may have arisen (genes, RNA, proteins) that belong to homologous at that time [154, 171], ECNs can easily be easily decom- families that phylogenetic distribution across host spe- posed into sub-networks, featuring processes of different cies allows us to date [137, 151–156]. The ‘age’ of the ages (that is, sets of nodes of a given age, e.g. sets of family at the node can thus become one evolutionary interacting genes). This strategy allows identification of color (Fig. 5). Likewise, several processes affecting the conserved network patterns, possibly under strong se- evolution of a molecular family (selection, duplication, lective pressure [159]. Constructing and exploiting ECNs transfer, and divergence in primary sequence) can be from bacteria, archaea, and eukaryotes thus has the po- inferred by classic phylogenetic analyses or, as we pro- tential to define conserved ancestral sets of relationships posed, by analyses of sequence similarity networks [157]. between components, allowing evolutionary biologists to Such studies provide additional evolutionary colors (like infer aspects of the early biological networks of the last quantitative measures: intensity of selection, rates of common ancestor of eukaryotes, archaea and bacteria duplication, transfer, and percentage of divergence), which and even of the last universal common ancestor of cells. can be associated with nodes in ECNs [139, 149, 154, Assuming that some of these topological units corres- 158–161]. Thus, ECNs contain both topological informa- pond to functional units [172], especially for broadly tion, characteristic of the biological network under investi- conserved subgraphs [138, 149, 152, 166, 173–182], gation, as well as evolutionary information: what node would allow network decompositions to propose sets of belongs to a family prone to duplication, divergence, or important processes associated with the emergence of lateral transfer, as well as when this family arose. Combin- major lineages. ing these two types of information in a single graph allows Moreover, graph-matching of ECNs allows several us to test specific hypotheses regarding evolution. complementary analyses. First, for interaction networks, Using ECNs, it is first fruitful to test whether (or such as GRNs, whose sets of components and edges which of) these evolutionary colors correlates with evolve rapidly [183–185], it becomes relevant to analyze topological properties of the ECNs [162–164]. The null where in the network such changes occur in addition to hypothesis that nodes’ centrality, e.g. nodes’ positions in (simply) tracking conserved sets of components and the network, is neither correlated with the age nor with edges. Whereas the latter can test to what extent conser- the duplicability, transferability or divergence of the vation of the interaction networks across higher taxa molecular entities represented by these nodes can be supports generalizations made from a limited number of tested. Rejection of this hypothesis would hint at pro- model species [186], the former allows us to test a gen- cesses that affect the topology of biological networks or eral hypothesis: are there repeated types of network are affected by the network topology. For example, con- changes? For example, does network modification sidering degree in networks, proteins with more neigh- primarily affect nodes with particular centralities, as bors are less easily transferred [163], highly expressed exemplified by terminal processes [187], or modules? genes, more connected in GCNs, evolve slower than Systematizing these analyses would provide new insights weakly expressed genes [165], and genes with lower into whether the organisation principles of biological degrees have higher duplicability in yeast, worm and flies networks changed when major lineages evolved or [166]. Considering position in networks, node centrality remained conserved. In terms of the ECN, can the same correlates with evolutionary conservation [136], gene model of graph evolution explain the topology of ECNs eccentricity correlates with level of gene expression and from different lineages? The null hypothesis would be dispensability [167], and proteins interacting with the that these major transitions left no common traces in external environment have higher average duplicability biological networks. An alternative hypothesis would be than proteins localized within intracellular compart- that the biological networks convergently became more ments [168]. Additionally, network structure gives a clue complex (more connected and larger) during these tran- to evolution since old proteins have more interactions sitions to novel life forms. Indeed, analyses conducted than new ones [169, 170]. Generalizing these disparate on a few taxa have reported quantifiable and qualifiable studies could help to understand the dynamics of bio- modifications in biological networks (in response to logical networks, in other words how the architecture, environmental challenges [188], during ecological transi- the nodes and edges of present day networks, evolved tions [189] or as niche specific adaptations [190]). More and whether their changes involved random or biased systematic graph-matching [191–193] and motif ana- sets of nodes and edges or follow general models of lyses, comparing the topology of ECNs from multiple network growth with detectable drivers. species, could likewise be used to test the hypothesis Bapteste and Huneman BMC Biology (2018) 16:56 Page 10 of 16 that major lineages are enriched in particular motifs Further justifications for a shift toward network (either modules of colored nodes and edges, or specific thinking topological features, such as feed-forward loops [46]or Enlargement of evolutionary biology bow-ties [194]). It would also allow identification of Focusing evolutionary explanations and theories on collec- functionally equivalent components across species, tives of interacting components, which may be under namely different genes with similar neighbors in differ- selection, facilitate selection, or condition arrangements ent species [176]. through neutral processes [39, 40, 202], and representing While inferences on conserved sets of nodes and edges these scaffolding relationships using networks with biotic in ECNs are likely to be robust (since the patterns are and abiotic components and a diversity of edges represent- observed in multiple species), missing data (missing ing a diversity of interaction types would be an enlarge- nodes and edges) constitute a recognized challenge, ment. Enlargements, as expressing the need to consider especially for the interpretation of what will appear in structures that are more general than what already exists, ECN studies as the most versatile (least conserved) parts have already occurred within evolutionary theory, when of the biological networks. The issue of missing data, simplifications from population genetics were relaxed with however, is not specific to network-based evolutionary respect to the original formalization in the Modern analyses, and should be tackled, as with other compara- Synthesis [203], to account for within-genome interaction tive approaches, by the development and testing of [9], gene–environment covariance [204], parental effects imputation methods [195–197]. Moreover, issues of [205], and extended fitness though generations [206]. It missing data can also be addressed by the production of also occurred when reticulations representing introgres- high coverage -omics datasets in simple systems, allow- sions were added to the evolutionary tree. ing for (nearly) exhaustive representations of the entities Interestingly, replacing standard linear models in evolu- and their interactions (i.e. PPIs, GCNs and GRNs within tionary theory with network approaches would transcend a cell, or metabolic networks within a species poor eco- several traditional axes structuring the debates in evolu- system). This kind of data would allow testing for the tionary biology. For instance, scaffolded evolution, the idea existence of selected emergent ecosystemic properties that evolution relies on what came before, is orthogonal to (like carbon fixation), as stated by the ITSNTS hypoth- the distinction between vertical and horizontal descent, esis [198]. For instance, deep coverage time series of since both tree-like and introgressive evolution are metagenomic/metatranscriptomic data coupled with en- particular cases of scaffolding. Scaffolded evolution is also vironmental measures from a simple microbial ecosys- orthogonal to the distinction between gradual and salta- tem, such as carbon fixation, could produce enough data tional evolution. Likewise, scaffolded evolution is orthog- to allow the evolutionary coloring of nodes of metabolic onal to the debates between the actual role of adaptations networks. Comparing ECNs representing, at each time vs neutral processes. Selection is a key mode of evolution point, the origin and abundance of the lineages hosting of collectives but not the only one. The processes involved the enzymes involved in carbon fixation could test in the forming and evolution of collectives are not even whether some combinations of lineages are repeated restricted to the key processes of the Modern Synthesis over time, and whether the components (e.g. genes and (drift, selection, mutation and migration) but embrace in- lineages) vary, whereas carbon fixation is maintained in teractions such as facilitation—namely antagonistic inter- the ecosystem, which would suggest that this process actions between two species that allow a third species to evolves irrespective of the nature of the interacting prosper by restraining one of its predators or parasites components. [207], presuppression [39, 40], etc. Consequently, some Finally, entities from different levels of biological or- evolutionary concepts may become more important than ganisation (domains, genes, genomes, lineages, etc.) they currently are to explain evolution. For example, con- could also be studied together in a single network frame- tingency, which means the dependence of an evolutionary work, by integrating them into multipartite networks chain of events upon an event that itself is contingent, in [199]. Recently, our studies and others (see [200] and the sense that it can’t be understood as a selective re- references therein) have demonstrated that various pat- sponse to environmental changes [18, 208, 209], is often terns in multipartite graphs can be used to detect and associated with extraordinary events, like mass decima- test combinatorial (introgressive) and gradual evolution tion. Contingency could come to be seen as a less extraor- (by vertical descent) affecting genes and genomes. dinary mode of evolution in the history of life, since the Decomposing multipartite networks into twins and ordinary course of evolution might include many cases of articulation points could for example then be used to contingent events, that is, associations of entities in a tran- represent and analyze the evolution of complex compos- sient collective, including any scaffolds—associations that ite molecular systems, such as CRISPR, or the dynamics are not necessarily selective responses or the outcomes of of invasions of hairpins in genomes [201]. processes modeled in population genetics. Bapteste and Huneman BMC Biology (2018) 16:56 Page 11 of 16 Likewise, adopting a broader ontology could affect edges and components). For example, the fermentation how evolutionary theorists think about evolution. Popu- hypothesis for mammalian chemical communication lation thinking and tree-thinking came after essentialist could be analyzed in a multipartite network framework, conceptions of the living words, when populations and which would involve nodes corresponding to individual lineages were recognized as central objects of evolution- mammals, nodes corresponding to microbes, and nodes ary studies [210]. A shift towards collectives and scaf- corresponding to odorous metabolites. Nodes corre- folded evolution might encourage a similar development: sponding to mammals could either be colored to reflect the emergence of an openly pluralistic processual think- an individual’s properties (its lineage, social position, ing, consistent with Carl Woese’sproposal to reformulate gender, sexual availability), or these nodes could be con- our view of evolution in terms of complex dynamic sys- nected by edges that reflect these shared properties, tems [211]. which defines a first host subnetwork. This host subnet- work can itself be further connected to a second subnet- Further unifying the evolutionary theory work, namely the microbial subnetwork in which nodes Using a network-based approach to analyse dynamic representing microbes, colored by phylogenetic origins, systems also permits explanations that rely purely on could be connected to reflect microbial interactions statistical properties [212] or on topological or graph (gene transfer, competition, metabolic cooperation, etc.). theoretical properties [213, 214] besides standard expla- Connections between the host and microbial subnet- nations devoted to unravelling mechanisms responsible works could simply be made by drawing edges between for a phenomenon. Moreover, because of the inclusive- nodes representing individual mammals hosting mi- ness of the network model, disciplines already recog- crobes, and nodes representing these microbes. More- nized for their contribution to evolutionary theory over, nodes representing mammals and nodes (microbiology, ecology, cell biology, genetics, etc.) could representing microbes could be connected to nodes become even more part of an interdisciplinary research representing odorous metabolites to show what odours program on evolution, effectively addressing current is- are associated with what combinations of hosts and mi- sues, consistent with the repeated calls for transdis- crobes. Elaborating this network in a piecemeal fashion ciplinary collaborations [19–21, 215]. Disciplines that would involve cooperation between chemists, microbiol- were not central in the Modern Synthesis—chemistry, ogists, zoologists and evolutionary biologists. physics, geology, oceanography, cybernetics or linguis- Of note, the use of integrated networks could prag- tics—could aggregate with evolutionary biology. Since a matically address a deep concern for evolutionary stud- diversity of components gets connected by a diversity of ies, by connecting phenomena that occur at different edges in networks featuring collectives, as a result of a timescales: development and evolution [219] or ecology diversity of drivers, several explanatory strategies could and evolution [220]. Considering transient collectives be combined to analyze evolution. This extension to (thus processes) as stable entities at a given time-scale, seemingly foreign fields makes sense when the compo- when these collectives change much more slowly than nents/processes studied by these other disciplines are the process in which they take part, amounts to a focus evolutionarily or functionally related to biotic compo- on interactions occurring at a given time scale by treat- nents and processes (either as putative ancestors of ing the slower dynamics as stable edges/nodes. Then, biological components and processes, like the use of a various parts of the networks embody distinct time- proton gradient in cells, which possibly derived from scales, which may provide a new form of timescale inte- geological processes affecting early life [216], or as gration, working out the merging of timescales from the descendants of biological systems, e.g. technically syn- viewpoint of the model, and with resources intrinsic to thesized life forms, which have a potential to alter the the model itself. The reason for this is that a node in an future course of standard biological evolution). interaction network Ni, describing processes relevant at Remarkably, this mode of unification of diverse scien- a time scale i, can itself be seen as the outcome of tific disciplines would be original: the integration would another (embedded) interaction network Nj, unfolding not be a unification in the sense of logical positivism at a time scale j. This nestedness typically occurs when [217]—namely reducing a theory to a theory with more the node in Ni represents a collective process, involving basic laws, or a theory with a larger extension. It would components that evolve sufficiently slowly with respect be a piecemeal [218] unification. Some aspects would be to the system considered at the time scale i to figure as unified through a specific kind of graph modeling an entity, a node in Ni. In the case of a PPI network Ni, (because some interactions, namely mechanical, chem- each node conventionally represents a protein, but the ical, ecological ones, and a range of time scales are privi- evolution of each protein could be further analysed as leged in a set of theories), while other theories might be the result of mutation, duplication, fusion and shuffling unified by other graph properties (like different types of events affecting the gene family coding the proteins over Bapteste and Huneman BMC Biology (2018) 16:56 Page 12 of 16 time; for instance, each protein could thus be repre- descendants and those of other life forms will be pro- sented as the outcome of interaction between domains cesses too. Some one hundred and fifty years after On in a domain–domain interaction network Nj. Consider- the Origin of Species, which started a great evolutionary ing these two time-scales, it becomes apparent that gene inquiry, evolutionists should prepare to face a larger families enriched in exon shuffling events, a process challenge: expanding evolutionary theory to study the directly analysable in Nj, have a higher degree in PPI evolution of processes. With the development of -omics networks represented at the time-scale Ni [221]. and network sciences, the concepts, data and tools for this research program are increasingly available. Predictions: discovery of co-constructed phenotypes Acknowledgements What possible findings may result from this perspective This manuscript is dedicated to the memory of Jean Gayon, a great shift? One can only speculate, but the nature of the historian and philosopher of biology, and a great friend. We thank Ford Doolittle, P. Lopez, G. Bernard, JL Martin, A. Watson, FJ Lapointe potential discoveries is exciting. At the molecular level, for critical reading and discussion. the structure and composition of regulatory networks and protein interaction networks could be substantially Funding EB is funded by the European Research Council under the European enhanced to scaffolding elements. Currently, these net- Community’s Seventh Framework Program FP7 (grant agreement number works represent interactions within a single individual/ 615274). PH is funded by the ANR (ANR 13 BSH3 0007 ‘Explabio’) and the LIA species. Yet, viruses are everywhere, viral genes and pro- CNRS Montréal-Paris ECIEB. teins clearly influence the networks of their hosts, and Authors’ contributions likely constitute an actual part of their evolution. Thus, EB and PH wrote, read and approved the final manuscript. virogenetics, a novel transdiscipline, may prosper in an expanded evolutionary theory to show how and to what Competing interests The authors declare that they have no competing interests. extent viruses co-construct their hosts, including perhaps reproductive-viruses, allowing their hosts to Publisher’sNote complete their lifecycles. At the cellular level, new Springer Nature remains neutral with regard to jurisdictional claims in modes of communication [222, 223] could be discov- published maps and institutional affiliations. ered, as possible viral and microbial languages and com- Author details munication networks in biofilms would exemplify. At Sorbonne Universités, UPMC Université Paris 06, Institut de Biologie the level of multicellular organisms and holobionts, ‘sym- Paris-Seine (IBPS), F-75005 Paris, France. CNRS, UMR7138, Institut de Biologie biotic codes’, guiding the preferential association between Paris-Seine, F-75005 Paris, France. Institut d’Histoire et de Philosophie des Sciences et des Techniques (CNRS / Paris I Sorbonne), F-75006 Paris, France. hosts and symbionts, could be identified. At the level of phyla, hidden evolutionary transitions may be unraveled. While secondary (and tertiary) acquisitions of plastids have been documented [81], it might be shown that References 1. Huxley J. Evolution: the modern synthesis. Princeton: Princeton University mitochondria too have been so acquired in some Press; 1942. eukaryotic lineages (alongside the plastid or independ- 2. Gayon J. Darwinism's struggle for survival: heredity and the hypothesis of ently). Secondarily acquired mitochondria may provide natural selection. Cambridge: Cambridge University Press; 1998. 3. Simpson GG. Tempo and mode in evolution. New York: Columbia University their new hosts with additional compartments, where Press; 1944. chimeric proteomes could assemble [91, 224] and per- 4. Martin G, Lenormand T. The distribution of beneficial and fixed mutation form original physiological processes. At the ecosystemic fitness effects close to an optimum. Genetics. 2008;179:907–16. 5. Dawkins R. The extended phenotype. New-York: Oxford University Press; 1982. level, evolving networks could be used to model the 6. Grafen A. A first formal link between the Price equation and an optimisation changes and stases of our planet, grounding biotic line- program. J Theor Biol. 2002;217:75–91. ages and processes in their environment, while highlight- 7. Kimura M. The neutral theory of molecular evolution. Cambridge: Cambridge University Press; 1983. ing potential regularities in the organisations and 8. Maynard Smith J, Burian R, Kauffman S, Alberch P, Campbell J, Goodwin B, dynamics of ecosystems. What affects the stability of et al. Developmental constraints and evolution. Q Rev Biol. 1985;60:265–87. what over the course of evolution could thus become a 9. Griffiths P, Stotz S. Genetics and philosophy: an introduction. Cambridge: Cambridge University Press; 2013. central theme of an expanded evolutionary theory. 10. Kettlewell HDB. Selection experiments on industrial melanism in the Lepidoptera. Heredity. 1955;9:323–42. Concluding remarks and open questions 11. Laland K, Uller T, Feldman M, Sterelny K, Muller GB, Moczek A, et al. Does evolutionary theory need a rethink? Nature. 2014;514:161–4. Interactions are not merely a part of biological history, 12. Bateson P. The return of the whole organism. J Biosci. 2005;30:31–9. they are what made this history. But evolutionary biolo- 13. Odling-Smee J, Laland K, Feldman M. Niche construction: the neglected gists have certainly not reconstructed the Dynamic process in evolution. Princeton: Princeton University Press; 2003. 14. Doolittle WF, Bapteste E. Pattern pluralism and the Tree of Life hypothesis. Interaction Network of Life (DINol) yet. Undertaking Proc Natl Acad Sci U S A. 2007;104:2043–9. this endeavor, however, would emphasize the importance 15. Sapp J. The new foundations of evolution: on the Tree of Life. New-York: of processes. Our ancestors were processes. Our Oxford University Press; 2009. Bapteste and Huneman BMC Biology (2018) 16:56 Page 13 of 16 16. Walsh DM. Organisms, agency, and evolution. Cambridge: Cambridge 48. Britten RJ, Davidson EH. Gene regulation for higher cells: a theory. Science. University Press; 2015. 1969;165:349–57. 17. West-Eberhard MJ. Developmental plasticity and evolution. Oxford: Oxford 49. Artzy-Randrup Y, Fleishman SJ, Ben-Tal N, Stone L. Comment on "Network University Press; 2003. motifs: simple building blocks of complex networks" and "Superfamilies of 18. Gould SJ. Wonderful life. The Burgess shale and the nature of history. New evolved and designed networks". Science. 2004;305:1107. author reply York: Norton; 1989. 50. Sorrells TR, Johnson AD. Making sense of transcription networks. Cell. 2015; 161:714–23. 19. Gilbert SF, Bosch TC, Ledon-Rettig C. Eco-evo-devo: developmental symbiosis and developmental plasticity as evolutionary agents. Nat Rev 51. Carroll SB. Evolution at two levels: on genes and form. PLoS Biol. 2005;3:e245. Genet. 2015;16:611–22. 52. Mallarino R, Grant PR, Grant BR, Herrel A, Kuo WP, Abzhanov A. Two 20. Lynch M, Field MC, Goodson HV, Malik HS, Pereira-Leal JB, Roos DS, et al. developmental modules establish 3D beak-shape variation in Darwin's Evolutionary cell biology: two origins, one objective. Proc Natl Acad Sci U S finches. Proc Natl Acad Sci U S A. 2011;108:4057–62. A. 2014;111:16990–4. 53. Peter IS, Davidson EH. Implications of developmental gene regulatory 21. Lyte M. Probiotics function mechanistically as delivery vehicles for networks inside and outside developmental biology. Curr Topics Dev Biol. neuroactive compounds: Microbial endocrinology in the design and use of 2016;117:237–51. probiotics. BioEssays. 2011;33:574–81. 54. Prud'homme B, Gompel N, Carroll SB. Emerging principles of regulatory 22. Huneman P, Walsh D. Challenging the modern synthesis: Development, evolution. Proc Natl Acad Sci U S A. 2007;104(Suppl 1):8605–12. adaptation and inheritance. New York: Oxford University Press; 2017. 55. Force A, Lynch M, Pickett FB, Amores A, Yan YL, Postlethwait J. Preservation 23. Pigliucci M, Müller G. Evolution: the extended synthesis. Cambridge, MA: of duplicate genes by complementary, degenerative mutations. Genetics. MIT Press; 2011. 1999;151:1531–45. 24. Wray GA, Hoekstra HE, Futuyma DJ, Lenski RE, Mackay TFC, Schluter D, et al. Does 56. Woese C. The universal ancestor. Proc Natl Acad Sci U S A. 1998;95:6854–9. evolutionary theory need a rethink? No, all is well. Nature. 2014;514:161–4. 57. Coulombe-Huntington J, Xia Y. Network centrality analysis in fungi reveals 25. Eigen M, Schuster P. The hypercycle. A principle of natural self-organization. complex regulation of lost and gained genes. PLoS One. 2017;12:e0169459. Part A: Emergence of the hypercycle. Die Naturwissenschaften. 1977;64:541–65. 58. Chuong EB, Elde NC, Feschotte C. Regulatory activities of transposable 26. Doolittle WF. Genes in pieces: Were they ever together? Nature. 1978;272: elements: from conflicts to benefits. Nat Rev Genet. 2017;18:71–86. 581–2. 59. Garcia-Perez JL, Widmann TJ, Adams IR. The impact of transposable 27. Gilbert W. Why genes in pieces? Nature. 1978;271:501. elements on mammalian development. Development. 2016;143:4101–14. 28. Irimia M, Roy SW. Origin of spliceosomal introns and alternative splicing. 60. Imbeault M, Helleboid P-Y, Trono D. KRAB zinc-finger proteins contribute to Cold Spring Harb Perspec Biol. 2014;6:a016071 the evolution of gene regulatory networks. Nature. 2017;543:550–4. 29. de Souza SJ. Domain shuffling and the increasing complexity of biological 61. Sundaram V, Wang T. Transposable element mediated innovation in gene networks. BioEssays. 2012;34:655–7. regulatory landscapes of cells: re-visiting the "gene-battery" model. 30. Marsh JA, Teichmann SA. How do proteins gain new domains? Genome BioEssays. 2018;40:1700155. Biol. 2010;11:126. 62. Ispolatov I, Yuryev A, Mazo I, Maslov S. Binding properties and evolution of 31. Wang M, Caetano-Anolles G. The evolutionary mechanics of domain homodimers in protein-protein interaction networks. Nucleic Acids Res. organization in proteomes and the rise of modularity in the protein world. 2005;33:3629–35. Structure. 2009;17:66–78. 63. Bapteste E. The origins of microbial adaptations: how introgressive descent, 32. Koonin EV, Makarova KS. Mobile genetic elements and evolution of CRISPR- egalitarian evolutionary transitions and expanded kin selection shape the Cas systems: all the way there and back. Genome Biol Evol. 2017;9:2812–25. network of life. Front Microbiol. 2014;5:83. 33. Krupovic M, Béguin P, Koonin EV. Casposons: mobile genetic elements that 64. Bapteste E, Lopez P, Bouchard F, Baquero F, McInerney JO, Burian RM. gave rise to the CRISPR-Cas adaptation machinery. Curr Opin Microbiol. Evolutionary analyses of non-genealogical bonds produced by introgressive 2017;38:36–43. descent. Proc Natl Acad Sci U S A. 2012;109:18266–72. 34. Assis R, Kondrashov AS, Koonin EV, Kondrashov FA. Nested genes and 65. Liu J, Prindle A, Humphries J, Gabalda-Sagarra M, Asally M, Lee DY, et al. increasing organizational complexity of metazoan genomes. Trends Genet. Metabolic co-dependence gives rise to collective oscillations within 2008;24:475–8. biofilms. Nature. 2015;523:550–4. 35. Lynch M. Evolutionary diversification of the multimeric states of proteins. 66. Nunes-Alves C. Biofilms: Electrifying long-range signalling. Nat Rev Microbiol. Proc Natl Acad Sci U S A. 2013;110:E2821–8. 2015;13:737. 36. Duboule D, Wilkins AS. The evolution of 'bricolage'. Trends Genet. 1998;14: 67. Morris JJ, Lenski RE, Zinser ER. The Black Queen Hypothesis: evolution of 54–9. dependencies through adaptive gene loss. MBio. 2012;3:e00036–12. 37. Jacob F. Evolution and tinkering. Science. 1977;196:1162. 68. Sachs JL, Hollowell AC. The origins of cooperative bacterial communities. 38. Wilkins A. Between "design" and "bricolage": genetic networks, levels of MBio. 2012;3:e00099–12. selection, and adaptive evolution. Proc Natl Acad Sci U S A. 2007;104:8590–6. 69. Bonner JT. The origins of multicellularity. Integr Biol. 1998;1:27–36. 39. Doolittle WF, Lukes J, Archibald JM, Keeling PJ, Gray MW. Comment on 70. Rodriguez-Valera F, Martin-Cuadrado AB, Rodriguez-Brito B, Pasic L, "Does constructive neutral evolution play an important role in the origin of Thingstad TF, Rohwer F, et al. Explaining microbial population genomics cellular complexity?". BioEssays. 2011;33:427–9. through phage predation. Nat Rev Microbiol. 2009;7:828–36. 40. Gray MW, Lukes J, Archibald JM, Keeling PJ, Doolittle WF. Cell biology. 71. Archibald JM. One plus one equals one: symbiosis and the evolution of Irremediable complexity? Science. 2010;330:920–1. complex life. Eur J Phycol. 2015;50:18. 41. Lukes J, Archibald JM, Keeling PJ, Doolittle WF, Gray MW. How a neutral 72. Bapteste E, Anderson G. Intersecting processes are necessary explanans for evolutionary ratchet can build cellular complexity. IUBMB Life. 2011;63:528–37. evolutionary biology, but challenge retrodiction. In: Nicholson DJD, editor. 42. Jain R, Rivera MC, Lake JA. Horizontal gene transfer among genomes: the Everything flows: Towards a processual philosophy of biology. Oxford: complexity hypothesis. Proc Natl Acad Sci U S A. 1999;96:3801–6. Oxford University Press. in press. 43. Lawrence JG, Roth JR. Selfish operons: horizontal transfer may drive the 73. Koonin EV. The turbulent network dynamics of microbial evolution and the evolution of gene clusters. Genetics. 1996;143:1843–60. statistical Tree of Life. J Mol Evol. 2015;80:244–50. 44. Promponas VJ, Ouzounis CA, Iliopoulos I. Experimental evidence validating 74. Lopez-Garcia P, Zivanovic Y, Deschamps P, Moreira D. Bacterial gene the computational inference of functional associations from gene fusion import and mesophilic adaptation in archaea. Nat Rev Microbiol. events: a critical survey. Brief Bioinformatics. 2014;15:443–54. 2015;13:447–56. 45. Tsoka S, Ouzounis CA. Prediction of protein interactions: metabolic enzymes 75. Nelson-Sathi S, Dagan T, Landan G, Janssen A, Steel M, McInerney JO, et al. are frequently involved in gene fusion. Nat Genet. 2000;26:141–2. Acquisition of 1,000 eubacterial genes physiologically transformed a 46. Alon U. An introduction to systems biology: design principles of biological methanogen at the origin of Haloarchaea. Proc Natl Acad Sci U S A. 2012; circuits. Florida: Chapman and Hall/CRC; 2006. 109:20537–42. 47. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U. Network 76. Nelson-Sathi S, Sousa FL, Roettger M, Lozada-Chavez N, Thiergart T, Janssen motifs: simple building blocks of complex networks. Science. 2002;298: A, et al. Origins of major archaeal clades correspond to gene acquisitions 824–7. from bacteria. Nature. 2015;517:77–80. Bapteste and Huneman BMC Biology (2018) 16:56 Page 14 of 16 77. Levasseur A, Merhej V, Baptiste E, Sharma V, Pontarotti P, Raoult D. The 104. Theis KR, Dheilly NM, Klassen JL, Brucker RM, Baines JF, Bosch TC, et al. rhizome of Lokiarchaeota illustrates the mosaicity of archaeal genomes. Getting the hologenome concept right: an eco-evolutionary framework for Genome Biol Evol. 2017;9:2635–9. hosts and their microbiomes. mSystems. 2016;1:e00028–16. 78. Akanni WA, Siu-Ting K, Creevey CJ, McInerney JO, Wilkinson M, Foster PG, 105. Theis KR, Venkataraman A, Dycus JA, Koonter KD, Schmitt-Matzen EN, et al. Horizontal gene flow from Eubacteria to Archaebacteria and what it Wagner AP, et al. Symbiotic bacteria appear to mediate hyena social odors. means for our understanding of eukaryogenesis. Philos Trans R Soc Lond Proc Natl Acad Sci U S A. 2013;110:19832–7. Ser B Biol Sci. 2015;370:20140337. 106. Sharon G, Segal D, Ringo JM, Hefetz A, Zilber-Rosenberg I, Rosenberg E. 79. Martin WF, Garg S, Zimorski V. Endosymbiotic theories for eukaryote origin. Commensal bacteria play a role in mating preference of Drosophila Philos Trans R Soc Lond Ser B Biol Sci. 2015;370:20140330. melanogaster. Proc Natl Acad Sci U S A. 2010;107:20051–6. 80. Pittis AA, Gabaldon T. Late acquisition of mitochondria by a host with 107. Bosch TC, McFall-Ngai MJ. Metaorganisms as the new frontier. Zoology. chimaeric prokaryotic ancestry. Nature. 2016;531:101–4. 2011;114:185–90. 81. Archibald JM. Genomic perspectives on the birth and spread of plastids. 108. Bordenstein SR, Theis KR. Host biology in light of the microbiome: ten Proc Natl Acad Sci U S A. 2015;112:10147–53. principles of holobionts and hologenomes. PLoS Biol. 2015;13:e1002226. 82. Méheust R, Bhattacharya D, Pathmanathan JS, McInerney JO, Lopez P, 109. Bosch TC. Rethinking the role of immunity: lessons from Hydra. Trends Bapteste E. Formation of chimeric genes with essential functions at the Immunol. 2014;35:495–502. origin of eukaryotes. BMC Biol. 2018;16:30. 110. Costello EK, Stagaman K, Dethlefsen L, Bohannan BJ, Relman DA. The 83. Meheust R, Zelzion E, Bhattacharya D, Lopez P, Bapteste E. Protein networks application of ecological theory toward an understanding of the human identify novel symbiogenetic genes resulting from plastid endosymbiosis. microbiome. Science. 2012;336:1255–62. Proc Natl Acad Sci U S A. 2016;113:3579–84. 111. Barr JJ, Auro R, Furlan M, Whiteson KL, Erb ML, Pogliano J, et al. Bacteriophage adhering to mucus provide a non-host-derived immunity. 84. Bailleul B, Berne N, Murik O, Petroutsos D, Prihoda J, Tanaka A, et al. Proc Natl Acad Sci U S A. 2013;110:10771–6. Energetic coupling between plastids and mitochondria drives CO2 assimilation in diatoms. Nature. 2015;524:366–9. 112. Rosenberg E, Sharon G, Zilber-Rosenberg I. The hologenome theory of 85. Bogumil D, Alvarez-Ponce D, Landan G, McInerney JO, Dagan T. Integration of evolution contains Lamarckian aspects within a Darwinian framework. two ancestral chaperone systems into one: the evolution of eukaryotic Environmental Microbiol. 2009;11:2959–62. molecular chaperones in light of eukaryogenesis. Mol Biol Evol. 2014;31:410–8. 113. Ley RE. The gene-microbe link. Nature. 2015;518:S7. 86. Dorrell RG, Howe CJ. Functional remodeling of RNA processing in 114. Tsuchida T, Koga R, Horikawa M, Tsunoda T, Maoka T, Matsumoto S, et al. replacement chloroplasts by pathways retained from their predecessors. Symbiotic bacterium modifies aphid body color. Science. 2010;330:1102–4. Proc Natl Acad Sci U S A. 2012;109:18879–84. 115. Bravo JA, Forsythe P, Chew MV, Escaravage E, Savignac HM, Dinan TG, et al. 87. Gavelis GS, Hayakawa S, White RA 3rd, Gojobori T, Suttle CA, Keeling PJ, Ingestion of Lactobacillus strain regulates emotional behavior and central et al. Eye-like ocelloids are built from different endosymbiotically acquired GABA receptor expression in a mouse via the vagus nerve. Proc Natl Acad components. Nature. 2015;523:204–7. Sci U S A. 2011;108:16050–5. 88. Husnik F, Nikoh N, Koga R, Ross L, Duncan RP, Fujie M, et al. Horizontal 116. Dupressoir A, Lavialle C, Heidmann T. From ancestral infectious retroviruses gene transfer from diverse bacteria to an insect genome enables a tripartite to bona fide cellular genes: role of the captured syncytins in placentation. nested mealybug symbiosis. Cell. 2013;153:1567–78. Placenta. 2012;33:663–71. 89. Martin W, Koonin EV. Introns and the origin of nucleus-cytosol 117. Emera D, Casola C, Lynch VJ, Wildman DE, Agnew D, Wagner GP. compartmentalization. Nature. 2006;440:41–5. Convergent evolution of endometrial prolactin expression in primates, mice, 90. Nowack EC, Price DC, Bhattacharya D, Singer A, Melkonian M, Grossman AR. and elephants through the independent recruitment of transposable Gene transfers from diverse bacteria compensate for reductive genome elements. Mol Biol Evol. 2012;29:239–47. evolution in the chromatophore of Paulinella chromatophora. Proc Natl 118. Ezenwa VO, Gerardo NM, Inouye DW, Medina M, Xavier JB. Microbiology. Acad Sci U S A. 2016;113:12214–9. Animal behavior and the microbiome. Science. 2012;338:198–9. 91. Stairs CW, Leger MM, Roger AJ. Diversity and origins of anaerobic 119. Scarborough CL, Ferrari J, Godfray HC. Aphid protected from pathogen by metabolism in mitochondria and related organelles. Philos Trans R Soc endosymbiont. Science. 2005;310:1781. Lond Ser B Biol Sci. 2015;370:20140326. 120. Hanski I. Metapopulation ecology. Oxford: Oxford University Press; 1999. 92. Bapteste E, O'Malley MA, Beiko RG, Ereshefsky M, Gogarten JP, Franklin-Hall 121. Leibold MA, Holyoak M, Mouquet N, Amarasekare P, Chase JM, Hoopes MF, L, et al. Prokaryotic evolution and the tree of life are two different things. et al. The metacommunity concept: a framework for multi-scale community Biol Direct. 2009;4:34. ecology. Ecol Lett. 2004;7:601–13. 93. Koonin EV. Energetics and population genetics at the root of eukaryotic 122. Ricklefs RE. Disintegration of the ecological community. Am Nat. 2008;172: cellular and genomic complexity. Proc Natl Acad Sci U S A. 2015;112:15777–8. 741–50. 94. Martin W, Muller M. The hydrogen hypothesis for the first eukaryote. Nature. 123. Darwin CA. On the origin of species by means of natural selection. London: 1998;392:37–41. John Murray; 1859. 95. Williams TA, Embley TM. Changing ideas about eukaryotic origins. Philos 124. Caporael L, Griesemer J, Wimsatt W. Scaffolding in evolution, culture, and Trans R Soc Lond Ser B Biol Sci. 2015;370:20140318. cognition. Cambridge: MIT Press; 2013. 96. Simplicity BA. The Stanford Encyclopedia of Philosophy: Metaphysics 125. Laland K, Matthews B, Feldman MW. An introduction to niche construction Research Lab, Stanford University; 2016. https://plato.stanford.edu/archives/ theory. Evol Ecol. 2016;30:191–202. win2016/entries/simplicity/ 126. Rogers RL, Hartl DL. Chimeric genes as a source of rapid evolution in 97. Allen JF. Why chloroplasts and mitochondria retain their own genomes and Drosophila melanogaster. Mol Biol Evol. 2012;29:517–29. genetic systems: Colocation for redox regulation of gene expression. Proc 127. Gould SJ, Lewontin RC. The spandrels of San Marco and the Panglossian Natl Acad Sci U S A. 2015;112:10231–8. paradigm: a critique of the adaptationist programme. Proc R Soc Lond B 98. Godfrey-Smith P. Reproduction, symbiosis, and the eukaryotic cell. Proc Natl Biol Sci. 1979;205:581–98. Acad Sci U S A. 2015;112:10120–5. 128. Sole RV, Valverde S. Are network motifs the spandrels of cellular complexity? 99. Karnkowska A, Vacek V, Zubacova Z, Treitli SC, Petrzelkova R, Eme L, et al. A Trends Ecol Evol. 2006;21:419–22. eukaryote without a mitochondrial organelle. Curr Biol. 2016;26:1274–84. 129. O'Malley MA, Soyer OS, Siegal ML. A philosophical perspective on 100. Gilbert SF, Sapp J, Tauber AI. A symbiotic view of life: we have never been evolutionary systems biology. Biol Theory. 2015;10:6–17. individuals. Q Rev Biol. 2012;87:325–41. 130. Soyer OS, O'Malley MA. Evolutionary systems biology: what it is and why it 101. Moran NA, Sloan DB. The hologenome concept: helpful or hollow? PLoS matters. BioEssays. 2013;35:696–705. Biol. 2015;13:e1002311. 131. Karimpour-Fard A, Hunter L, Gill RT. Investigation of factors affecting 102. Bouchard F. Understanding colonial traits using symbiosis research and prediction of protein-protein interaction networks by phylogenetic profiling. ecosystem ecology. Biol Theory. 2009;4:240–6. BMC Genomics. 2007;8:393. 103. Selosse MA, Bessis A, Pozo MJ. Microbial priming of plant and animal 132. Koch C, Konieczka J, Delorey T, Lyons A, Socha A, Davis K, et al. Inference immunity: symbionts as developmental signals. Trends Microbiol. 2014;22: and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large 607–13. Phylogenies. Cell Syst. 2017;4:543–58. e8 Bapteste and Huneman BMC Biology (2018) 16:56 Page 15 of 16 133. Shahdoust M, Pezeshk H, Mahjub H, Sadeghi M. F-MAP: a Bayesian 157. Corel E, Lopez P, Méheust R, Bapteste E. Network-thinking: graphs to approach to infer the gene regulatory network using external hints. PLoS analyze microbial complexity and evolution. Trends Microbiol. 2016;24: One. 2017;12:e0184795. 224–37. 134. Simonsen M, Maetschke SR, Ragan MA. Automatic selection of reference 158. Friedlander T, Prizak R, Barton NH, Tkačik G. Evolution of new regulatory taxa for protein-protein interaction prediction with phylogenetic profiling. functions on biophysically realistic fitness landscapes. Nat Commun. Bioinformatics. 2012;28:851–7. 2017;8:216. 135. Spanier KI, Jansen M, Decaestecker E, Hulselmans G, Becker D, Colbourne JK, 159. Gouy A, Daub JT, Excoffier L. Detecting gene subnetworks under selection et al. Conserved transcription factors steer growth-related genomic in biological pathways. Nucleic Acids Res. 2017;45:e149. programs in Daphnia. Genome Biol Evol. 2017;9:1821–42. 160. MacKintosh C, DEK F. Recent advances in understanding the roles of whole 136. Wang P, Yu X, Lü J. Identification and evolution of structurally dominant genome duplications in evolution. F1000Res. 2017;6:1623. nodes in protein-protein interaction networks. IEEE Trans Biomed Circuits 161. Nguyen Ba AN, Strome B, Osman S, Legere E-A, Zarin T, Moses AM. Parallel Syst. 2014;8:87–97. reorganization of protein function in the spindle checkpoint pathway 137. Ruprecht C, Vaid N, Proost S, Persson S, Mutwil M. Beyond genomics: through evolutionary paths in the fitness landscape that appear neutral in studying evolution with gene coexpression networks. Trends Plant Sci. 2017; laboratory experiments. PLoS Genet. 2017;13:e1006735. 22:298–307. 162. Alvarez-Ponce D, Feyertag F, Chakraborty S. Position matters: network 138. Netotea S, Sundell D, Street NR, Hvidsten TR. ComPlEx: conservation and centrality considerably impacts rates of protein evolution in the human divergence of co-expression networks in A. thaliana, Populus and O. sativa. protein-protein interaction network. Genome Biol Evol. 2017;9:1742–56. BMC Genomics. 2014;15:106. 163. Cohen O, Gophna U, Pupko T. The complexity hypothesis revisited: connectivity rather than function constitutes a barrier to horizontal gene 139. Conant GC, Wolfe KH. Functional partitioning of yeast co-expression transfer. Mol Biol Evol. 2011;28:1481–9. networks after genome duplication. PLoS Biol. 2006;4:e109. 140. You Q, Xu W, Zhang K, Zhang L, Yi X, Yao D, et al. ccNET: Database of co- 164. Raymond J, Segrè D. The effect of oxygen on biochemical networks and expression networks with functional modules for diploid and polyploid the evolution of complex life. Science. 2006;311:1764–7. Gossypium. Nucleic Acids Res. 2017;45:D1090–D9. 165. Wolf YI, Carmel L, Koonin EV. Unifying measures of gene function and 141. Tang J, Lin J, Li H, Li X, Yang Q, Cheng Z-M, et al. Characterization of CIPK evolution. Proc Biol Sci. 2006;273:1507–15. family in Asian Pear (Pyrus bretschneideri Rehd) and co-expression analysis 166. Hase T, Niimura Y, Tanaka H. Difference in gene duplicability may explain related to salt and osmotic stress responses. Front Plant Sci. 2016;7:1361. the difference in overall structure of protein-protein interaction networks 142. Siwo GH, Tan A, Button-Simons KA, Samarakoon U, Checkley LA, Pinapati RS, among eukaryotes. BMC Evol Biol. 2010;10:358. et al. Predicting functional and regulatory divergence of a drug resistance 167. Xu K, Bezakova I, Bunimovich L, Yi SV. Path lengths in protein-protein transporter gene in the human malaria parasite. BMC Genomics. 2015;16:115. interaction networks and biological complexity. Proteomics. 2011;11:1857–67. 143. Hu G, Hovav R, Grover CE, Faigenboim-Doron A, Kadmon N, Page JT, et al. 168. Prachumwat A, Li W-H. Protein function, connectivity, and duplicability in Evolutionary conservation and divergence of gene coexpression networks yeast. Mol Biol Evol. 2006;23:30–9. in Gossypium (cotton) seeds. Genome Biol Evol. 2016;8:3765–83. 169. Peterson GJ, Pressé S, Peterson KS, Dill KA. Simulated evolution of protein- 144. Lu X, Li Q-T, Xiong Q, Li W, Bi Y-D, Lai Y-C, et al. The transcriptomic protein interaction networks with realistic topology. PLoS One. 2012;7: signature of developing soybean seeds reveals the genetic basis of seed e39052. trait adaptation during domestication. Plant J. 2016;86:530–44. 170. Pawlowski PH, Kaczanowski S, Zielenkiewicz P. A kinetic model of the 145. Zandveld J, van den Heuvel J, Mulder M, Brakefield PM, Kirkwood TBL, Shanley evolution of a protein interaction network. BMC Genomics. 2013;14:172. DP, et al. Pervasive gene expression responses to a fluctuating diet in Drosophila 171. Ruprecht C, Proost S, Hernandez-Coronado M, Ortiz-Ramirez C, Lang D, melanogaster: The importance of measuring multiple traits to decouple Rensing SA, et al. Phylogenomic analysis of gene co-expression networks potential mediators of life span and reproduction. Evolution. 2017;71:2572–83. reveals the evolution of functional modules. Plant J. 2017;90:447–65. 146. Halfon MS. Perspectives on gene regulatory network evolution. Trends 172. Zhao Y, Mooney SD. Functional organization and its implication in Genet. 2017;33:436–47. evolution of the human protein-protein interaction network. BMC 147. Simakov O, Kawashima T. Independent evolution of genomic characters Genomics. 2012;13:150. during major metazoan transitions. Dev Biol. 2017;427:179–92. 173. Akinola RO, Mazandu GK, Mulder NJ. A quantitative approach to analyzing 148. Wang P, Zhao D, Rockowitz S, Zheng D. Divergence and rewiring of genome reductive evolution using protein-protein interaction networks: a regulatory networks for neural development between human and other case study of Mycobacterium leprae. Front Genet. 2016;7:39. species. Neurogenesis (Austin). 2016;3:e1231495. 174. Briones-Moreno A, Hernández-García J, Vargas-Chávez C, Romero-Campero 149. Masalia RR, Bewick AJ, Burke JM. Connectivity in gene coexpression FJ, Romero JM, Valverde F, et al. Evolutionary analysis of DELLA-associated networks negatively correlates with rates of molecular evolution in transcriptional networks. Front Plant Sci. 2017;8:626. flowering plants. PLoS One. 2017;12:e0182289. 175. Hahn MW, Kern AD. Comparative genomics of centrality and essentiality in 150. Muñoz A, Santos Muñoz D, Zimin A, Yorke JA. Evolution of transcriptional three eukaryotic protein-interaction networks. Mol Biol Evol. 2005;22:803–6. networks in yeast: alternative teams of transcriptional factors for different 176. Hansen BO, Vaid N, Musialak-Lange M, Janowski M, Mutwil M. Elucidating species. BMC Genomics. 2016;17:826. gene function and function evolution through comparison of co-expression 151. Kacharia FR, Millar JA, Raghavan R. Emergence of New sRNAs in Enteric networks of plants. Front Plant Sci. 2014;5:394. Bacteria is Associated with Low Expression and Rapid Evolution. J Mol Evol. 177. Kelliher CM, Leman AR, Sierra CS, Haase SB. Investigating conservation of 2017;84:204–13. the cell-cycle-regulated transcriptional program in the fungal pathogen, 152. Kim HS, Mittenthal JE, Caetano-Anollés G. MANET: tracing evolution of Cryptococcus neoformans. PLoS Genet. 2016;12:e1006453. protein architecture in metabolic networks. BMC Bioinformatics. 2006;7:351. 178. Martinez-Pastor M, Tonner PD, Darnell CL, Schmid AK. Transcriptional 153. Leyn SA, Suvorova IA, Kazakov AE, Ravcheev DA, Stepanova VV, Novichkov regulation in Archaea: from individual genes to global regulatory networks. PS, et al. Comparative genomics and evolution of transcriptional regulons Annu Rev Genet. 2017;51:143–70. inProteobacteria. Microb Genom. 2016;2:e000061. 179. Phan HTT, Sternberg MJE. PINALOG: a novel approach to align protein 154. Liang C, Luo J, Song D. Network simulation reveals significant contribution interaction networks–implications for complex detection and function of network motifs to the age-dependency of yeast protein-protein prediction. Bioinformatics. 2012;28:1239–45. interaction networks. Mol BioSyst. 2014;10:2277–88. 180. Romero-Campero FJ, Perez-Hurtado I, Lucas-Reina E, Romero JM, Valverde F. ChlamyNET: a Chlamydomonas gene co-expression network reveals global 155. Mustafin ZS, Lashin SA, Matushkin YG, Gunbin KV, Afonnikov DA. properties of the transcriptome and the early setup of key co-expression Orthoscape: a cytoscape application for grouping and visualization KEGG patterns in the green lineage. BMC Genomics. 2016;17:227. based gene networks by taxonomy and homology principles. BMC Bioinformatics. 2017;18:1427. 181. Tamames J, Moya A, Valencia A. Modular organization in the reductive 156. Thompson JR, Erkenbrack EM, Hinman VF, McCauley BS, Petsios E, Bottjer evolution of protein-protein interaction networks. Genome Biol. 2007;8:R94. DJ. Paleogenomics of echinoids reveals an ancient origin for the double- 182. Wang D, He F, Maslov S, Gerstein M. DREISS: using state-space models to negative specification of micromeres in sea urchins. Proc Natl Acad Sci U S infer the dynamics of gene expression driven by external and internal A. 2017;114:5870–7. regulatory networks. PLoS Comput Biol. 2016;12:e1005146. Bapteste and Huneman BMC Biology (2018) 16:56 Page 16 of 16 183. Aguilera F, McDougall C, Degnan BM. Co-option and de novo gene 210. O'Hara RJ. Population thinking and tree thinking in systematics. Zool Scr. evolution underlie molluscan shell diversity. Mol Biol Evol. 2017;34:779–92. 1997;26:323–9. 184. Auman T, Chipman AD. The evolution of gene regulatory networks that 211. Woese CR. A new biology for a new century. Microbiol Mol Biol Rev. 2004; define arthropod body plans. Integr Comp Biol. 2017;57:523–32. 68:173–86. 185. Mateos JL, Tilmes V, Madrigal P, Severing E, Richter R, Rijkenberg CWM, et al. 212. Lange M. Really statistical explanations and genetic drift. Philosophy Sci. Divergence of regulatory networks governed by the orthologous 2013;80:169–88. transcription factors FLC and PEP1 in Brassicaceae species. Proc Natl Acad 213. Huneman P. Diversifying the picture of explanations in biological sciences: Sci U S A. 2017;114:E11037–E46. Ways of combining topology with mechanisms. Synthese. 2018;195:115–46. 214. Jones N. Bowtie structures, pathway diagrams, and topological explanation. 186. Renvoisé E, Kavanagh KD, Lazzari V, Häkkinen TJ, Rice R, Pantalacci S, et al. Erkenntnis. 2014;89:1355–555. Mechanical constraint from growing jaw facilitates mammalian dental 215. Lane N, Martin WF, Raven JA, Allen JF. Energy, genes and evolution: diversity. Proc Natl Acad Sci U S A. 2017;114:9403–8. introduction to an evolutionary synthesis. Philos Trans R Soc Lond Ser B Biol 187. Cary GA, Cheatle Jarvela AM, Francolini RD, Hinman VF. Genome-wide use Sci. 2013;368:20120253. of high- and low-affinity Tbrain transcription factor binding sites during 216. Sousa FL, Thiergart T, Landan G, Nelson-Sathi S, Pereira IA, Allen JF, et al. echinoderm development. Proc Natl Acad Sci U S A. 2017;114:5854–61. Early bioenergetic evolution. Philos Trans R Soc Lond Ser B Biol Sci. 2013; 188. Noble R, Noble D. Was the watchmaker blind? Or was she one-eyed? 368:20130088. Biology (Basel). 2017;6:47. 217. Nagel E. The structure of science. Harcourt, Brace & World: New-York; 1961. 189. Capela D, Marchetti M, Clérissi C, Perrier A, Guetta D, Gris C, et al. 218. Booth A, Mariscal C, Doolittle WF. The Modern Synthesis in the light of Recruitment of a lineage-specific virulence regulatory pathway promotes microbial genomics. Annu Rev Microbiol. 2016;70:279–97. intracellular infection by a plant pathogen experimentally evolved into a 219. Gilbert SF, Opitz JM, Raff RA. Resynthesizing evolutionary and legume symbiont. Mol Biol Evol. 2017;34:2503–21. developmental biology. Dev Biol. 1996;173:357–72. 190. Orsini L, Brown JB, Shams Solari O, Li D, He S, Podicheti R, et al. Early 220. Kokko H, Chaturvedi A, Croll D, Fischer MC, Guillaume F, Karrenberg S, et al. transcriptional response pathways in Daphnia magna are coordinated in Can evolution supply what ecology demands? Trends Ecol Evol. 2017;32: networks of crustacean-specific genes. Mol Ecol. 2018;27:886–97. 187–97. 191. Dohrmann J, Puchin J, Singh R. Global multiple protein-protein interaction 221. Cancherini DV, França GS, de Souza SJ. The role of exon shuffling in shaping network alignment by combining pairwise network alignments. BMC protein-protein interaction networks. BMC Genomics. 2010;11(Suppl 5):S11. Bioinformatics. 2015;16(Suppl 13):S11. 222. Amin SA, Hmelo LR, van Tol HM, Durham BP, Carlson LT, Heal KR, et al. 192. Emmert-Streib F, Dehmer M, Shi Y. Fifty years of graph matching, network Interaction and signalling between a cosmopolitan phytoplankton and alignment and network comparison. Inf Sci. 2016;346:180–97. associated bacteria. Nature. 2015;522:98–101. 193. Kelley BP, Yuan B, Lewitter F, Sharan R, Stockwell BR, Ideker T. PathBLAST: a 223. Erez Z, Steinberger-Levy I, Shamir M, Doron S, Stokar-Avihail A, Peleg Y, tool for alignment of protein interaction networks. Nucleic Acids Res. 2004; et al. Communication between viruses guides lysis-lysogeny decisions. 32:W83–8. Nature. 2017;541:488–93. 194. Ni B, Ghosh B, Paldy FS, Colin R, Heimerl T, Sourjik V. Evolutionary remodeling 224. Dorrell RG, Gile G, McCallum G, Meheust R, Bapteste EP, Klinger CM, et al. of bacterial motility checkpoint control. Cell Rep. 2017;18:866–77. Chimeric origins of ochrophytes and haptophytes revealed through an 195. Huisman M. Imputation of missing network data: some simple procedures. ancient plastid proteome. elife. 2017;6:e23717. In: Alhajj R, Rokne J, editors. Encyclopedia of Social Network Analysis and Mining. New York: Springer New York; 2014. p. 707–15. 196. Ogundijo OE, Elmas A, Wang X. Reverse engineering gene regulatory networks from measurement with missing values. EURASIP J Bioinform Syst Biol. 2016;2017:2. 197. Shao M, Zhou S, Guan J. Revisiting topological properties and models of protein-protein interaction networks from the perspective of dataset evolution. IET Syst Biol. 2015;9:113–9. 198. Doolittle WF, Inkpen SA. Processes and patterns of interaction as units of selection: An introduction to ITSNTS thinking. Proc Natl Acad Sci U S A. 2018;115:4006–14. 199. Wong DCJ, Matus JT. Constructing integrated networks for identifying new secondary metabolic pathway regulators in grapevine: recent applications and future opportunities. Front Plant Sci. 2017;8:505. 200. Corel E, Méheust R, Watson AK, McInerney JO, Lopez P, Bapteste E. Bipartite network analysis of gene sharings in the microbial world. Mol Biol Evol. 2017;35:899–913. 201. Seligmann H, Raoult D. Unifying view of stem–loop hairpin RNA as origin of current and ancient parasitic and non-parasitic RNAs, including in giant viruses. Curr Opin Microbiol. 2016;31:1–8. 202. Stoltzfus A. Constructive neutral evolution: exploring evolutionary theory's curious disconnect. Biol Direct. 2012;7:35. 203. Gillespie JH. Population genetics: A concise guide. Baltimore: The Johns Hopkins University Press; 2004. 204. Barker G, Desjardins E, Pearce T. Entangled Life: Organism and Environment in the Biological and Social Sciences. Dordrecht: Springer; 2013. 205. Bonduriansky R. Rethinking heredity, again. Trends Ecol Evol. 2012;27:330–6. 206. Lehmann L. The adaptive dynamics of niche constructing traits in spatially subdivided populations: evolving posthumous extended phenotypes. Evolution. 2008;62:549–66. 207. Bruno JF, Stachowicz JJ, Bertness MD. Inclusion of facilitation into ecological theory. Trends Ecol Evol. 2003;18:119–25. 208. Beatty J. Replaying life’s tape. J Philosophy. 2006;103:336–62. 209. Turner D. Historical contingency and the explanation of evolutionary trends. In: Malaterre C, Braillard PA, editors. Biological explanation: An enquiry into the diversity of explanatory patterns in the life sciences. Dordrecht: Springer; 2015. p. 73–90.

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