Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory Eric Bapteste, Philippe Huneman

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Eric Bapteste, Philippe Huneman. Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory. BMC Biology, BioMed Central, 2018, 16 (1), ￿10.1186/s12915-018- 0531-6￿. ￿hal-01968453￿

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1 OPINION Open Access

2 Towards a Dynamic Interaction Network of

3 Life to unify and expand the evolutionary

4 theory

1,2* 3 Q1 6785 Eric Bapteste and Philippe Huneman

’ 9 Abstract Mendel s idea of particular inheritance, giving rise to 39 population and quantitative genetics, a theoretical frame 40 10 The classic Darwinian theory and the Synthetic ’ that corroborated Darwin s hypothesis of the paramount 41 11 evolutionary theory and their linear models, while power of selection for driving adaptive evolution [2]. 42 12 invaluable to study the origins and evolution of species, This framework progressively aggregated multiple disci- 43 13 are not primarily designed to model the evolution of plines: behavioural ecology, microbiology, paleobiology, 44 14 organisations, typically that of ecosystems, nor that of etc. Overall, this classic framework considers that the 45 15 processes. How could evolutionary theory better principal agency of evolution is natural selection of 46 16 explain the evolution of biological complexity and favourable variations, and that those variations are con- 47 17 diversity? Inclusive network-based analyses of dynamic stituted by random mutations and recombination in a 48 18 systems could retrace interactions between (related or Mendelian population. The processes of microevolution, 49 19 unrelated) components. This theoretical shift from a modelled by population and quantitative genetics, are 50 20 Tree of Life to a Dynamic Interaction Network of Life, likely to be extrapolated to macroevolution [3]. To this 51 21 which is supported by diverse molecular, cellular, extent, models that focus on one or two loci are able to 52 22 microbiological, organismal, ecological and evolutionary capture much of the evolutionary dynamics of an organ- 53 23 studies, would further unify evolutionary biology. ism, even though in reality many interdependencies be- 54 24 Keywords: Evolutionary biology, Interactions, tween thousands of loci (epistasis, dominance, etc.) 55 Theoretical biology, Tree of Life, Web of Life occur as the basis of the production and functioning of a 56 25 phenotypic trait. Among forces acting on populations 57 and modelled by population geneticists, natural selection 58 26 Deciphering diversity through evolution is the one that shapes traits as adaptations and the de- 59 27 The living world is nested and multilevel, involves mul- sign of organisms; adaptive radiation then explains much 60 28 tiple agents and changes at different timescales. Evolu- of the diversity; and common descent from adapted 61 29 tionary biology tries to characterize the dynamics organisms explains most of the commonalities across 62 30 responsible for such complexity to decipher the pro- living forms (labelled homologies), and allows for classi- 63 31 cesses accounting for the past and extant diversity ob- fying living beings into phylogenetic trees. Evolution is 64 32 served in molecules (namely, genes, RNA, proteins), gradual because the effects of mutations are generally 65 33 cellular machineries, unicellular and multi-cellular or- small, large ones being most likely to be deleterious as 66 ’ 34 ganisms, species, communities and ecosystems. In the theorized by Fisher s geometric model [4]. 67 35 1930s and 1940s, a unified framework to handle this task Many theoretical divergences surround this core view: 68 36 was built under the name of Modern Synthesis [1]. It not everyone agrees that evolution is change in allele fre- 69 ’ 37 encompassed Darwin s idea of evolution by natural selec- quencies, or that population genetics captures the whole 70 38 tion as an explanation for diversity and adaptation and of the evolutionary process, or that the genotypic view- 71 — ‘ ’ point tracking the dynamics of genes as replicators [5] 72 or the strategy ‘choices’ of organisms as fitness maximiz- * Correspondence: [email protected] 73 1 — Q7 Sorbonne Universités, UPMC Université Paris 06, Institut de Biologie ing agents [6] should be favoured to understand evolu- 74 Paris-Seine (IBPS), F-75005 Paris, France tion. Nevertheless, it has been a powerful enough 2 75 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 76

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77 speciation, adaptation, phylogenies, evolution of sex, we argue that evolutionary biology could become a sci- 131 78 cooperation altruism, mutualism, etc., and incorporate ap- ence of evolving networks, which would allow biologists 132 79 parent challenges such as neutral evolution [7], acknow- to explain organisational complexity, while providing a 133 80 ledgement of constraints on variation [8], or the recent novel way to reframe and to unify evolutionary biology. 134 81 theoretical turn from genetics to genomics following the 82 achievement of the Human Genome Program [9]. Caus- Biology is regulated by networks 135 83 ation is here overall conceived of as a linear causal relation Networks at the molecular level 136 84 of a twofold nature: from the genotype to the phenotype Although numerous studies have focused on the functions 137 85 (assuming of course environmental parameters), and from of individual genes, proteins and other molecules, it is in- 138 86 the environment to the shaping of organisms via natural creasingly clear that each of these functions belongs to 139 87 selection. For instance, in the classic case of evolution of complex networks of interactions. Starting at the molecu- 140 88 peppered moths in urban forests at the time of the indus- lar scale, the importance of a diversity of molecular agents, 141 89 trial revolution, trees became darkened with soot, and such as (DNA-based) genes and their regulatory se- 142 90 then natural selection favored darker morphs as ‘fitter’ quences, RNAs and proteins, is well recognized. Import- 143 91 ones, due to their being less easily detected by predator antly, in terms of their origins and modes of evolution, 144 92 birds, resulting in a relative increase in frequency of the these agents are diverse. Genes are replicated across gen- 145 93 darker morphs in the population [10]. erations, via the recruitment of bases along a DNA tem- 146 94 Yet in the last 15 years biologists and philosophers of plate, thereby forming continuous lineages, affected by 147 95 biology have regularly questioned the genuinely unifying Darwinian evolution. By contrast, proteins are recon- 148 96 character of this Synthesis, as well as its explanatory ac- structed by recruitment of amino acids at the ribosomal 149 97 curacy [11]. Those criticisms questioned notably the set machinery. There is no physical continuity between gener- 150 98 of objects privileged by the Modern Synthesis, arguably ations of proteins, and thus no possibility for these agents 151 99 too gene-centered [12], and its key explanatory to directly accumulate beneficial mutations [25]. 152 100 processes, since niche construction [13], lateral gene Moreover, all these molecular entities are compositionally 153 101 transfer [14, 15], phenotypic plasticity [16, 17], and mass complex, in the sense that they are made of inherited or 154 102 extinction [18] could, for example, be added [11]. reassembled parts. E pluribus unum: genes and proteins 155 103 Usually these critiques emphasize aspects rooted in a are (often) conglomerates of exons, (eventually) introns 156 104 particular biological discipline: lateral gene transfer from [26–28], and domains [29–31]. Similar claims can be 157 105 microbiology, plasticity from developmental biology, made about composite molecular systems, such as 158 106 mass extinction from paleobiology, ecosystem engineer- CRISPR and Casposons [32, 33], etc. This modular organ- 159 107 ing from functional ecology, etc. There were also isation has numerous consequences: among them, genes 160 108 recurring claims for novel transdisciplinary fields: evo- can be nested within genes [34]; proteins congregate in 161 109 eco-devo [19], investigating the evolutionary dynamics larger complexes [35]. Importantly, this modularity is not 162 110 of host and microbe associations (forming combinations the mere result of a divergence from a single ancestral 163 111 often referred to as holobionts), evolutionary cell biology form, but also involves combinatorial processes and mo- 164 112 [20], or microbial endocrinology [21], among others. lecular tinkering of available genetic material [36–38]. The 165 113 This latter discipline aims at understanding the evolved coupling and decoupling of molecular components can 166 114 interactions between microbial signals and host develop- operate randomly, as in cases of presuppression proposed 167 115 ment. Indeed, it is compelling for evolutionary biologists to neutrally lead to large molecular complexes [39–41]. 168 116 to decipher how such multi-species interactions became Presuppression, also known as constructive neutralism, is 169 117 established (namely, whether they involved specific mi- a process that generates complexity by mechanically in- 170 118 crobial species and molecules, and whether they evolved creasing dependencies between interacting molecules, in 171 119 independently in different host lineages). the absence of positive selection. When a deleterious mu- 172 120 Evolutionary biology is thus currently undergoing vari- tation affects one molecular partner, existing properties of 173 121 ous theoretical debates concerning the proper frame to another molecule with which the mutated molecule 174 122 formulate it [11, 22–24]. Here, we introduce an original already interacted can compensate for its partner defect. 175 123 solution which moves this debate forward, acknowledg- Presuppression operates like a ratchet, since the likelihood 176 124 ing that nothing on Earth evolves and makes sense in to restore the original independency between molecules 177 125 isolation, thereby challenging the key assumption of the (by reverting the deleterious mutation) is lower than the 178 126 Modern Synthesis framework that targeting the individ- likelihood to move away from this original state (by accu- 179 127 ual gene or organism (even when in principle knowing mulating other mutations). Molecular associations can 180 128 that it is part of a set of complex interactions) allows us also evolve under constraints [42], eventually reinforcing 181 129 to capture evolution in all its dimensions. Since the liv- the relationships between molecular partners, as suggested 182 130 ing world evolves as a dynamic network of interactions, for some operons [43] and fused genes [44, 45]. 183 Bapteste and Huneman BMC Biology _#####################_ Page 3 of 16

184 Consistently, interconnectedness is a striking feature At the molecular level, it is reasonable to assume that 238 185 of the molecular world [46, 47]. Genes belong to regula- processes resulting from interactions of a diversity of 239 186 tory networks with feedback loops [48]. Proteins belong intertwined agents offer a crucial explanans of biological 240 187 to protein–protein interaction networks. This systemic complexity. Rather than ‘one agent, one action’, it would 241 188 view contrasts with former atomistic views assigning one be more accurate to consider ‘a relationship between 242 189 function to one gene. First, it is not always correct that a agents, one action’ as the modus operandi of life. 243 190 gene produces only a protein, in the case of alternative Multiple drivers, of different nature, contribute to the 244 191 splicing. Second, it is also unlikely that a protein evolution of these interactions: among others, gene co- 245 192 performs one function, because no protein acts alone. expression/co-regulation [57], sometimes mediated by 246 193 Rather, biological traits result from co-production pro- transposons [58–61]; the evolutionary origin of the 247 194 cesses. This is nicely illustrated by the actual process of genes [62]; and also physical and chemical laws, as well 248 195 translation, during which both proteins and DNA neces- as the presence of targeting machineries that constrain 249 196 sarily interact, allowing for the collective reproduction of and regulate diffusion processes in the cell. These types 250 197 these two types of molecular agents. How these different of relationships described at the molecular level are also 251 198 components became so tightly integrated is a central recovered at other levels of biological organisations. 252 199 issue for explaining evolution. Understanding how the 200 molecular world functions and evolves therefore re- Networks at the cellular level 253 201 quires analysing molecular organisation and the evolu- Similar conclusions have been reached at the cellular 254 202 tion of the architecture of interaction networks, level, also crucial for understanding life history. All pro- 255 203 especially since this structure can partly explain karyotes and protists are unicellular organisations, and 256 204 molecular reactions [46, 47, 49, 50]. Thus, systems the cell is a fundamental building block of multicellular 257 205 biologists search for common motifs in molecular inter- organisms. Cells must constantly evaluate the states of 258 206 action networks from different organisms, such as feed- their inner and outer environments, i.e. to adjust their 259 207 forward loops, assuming that some of these recurring gene expression and react accordingly [46]. This involves 260 208 patterns, because they affect different gene or protein sets, regulatory, transduction, developmental, and protein 261 209 may reflect general rules and constraints affecting the con- interaction networks, etc. Cells are built upon inner net- 262 210 struction and evolution of biological organisations [46]. works of interacting components, and involved in or af- 263 211 Focusing evolutionary explanations on the structure of fected by a diversity of exchanges, influences and modes 264 212 the interactions between genes rather than on the pri- of communications (namely, genetic, energetic, chemical 265 213 mary sequence of the genes is fundamentally different and electrical modes). Microbiology has gone a long way 266 214 from sequencing genes and inferring history from their toward unraveling these processes since its heyday of 267 215 sequences alone; one could think here of the case of pure culture studies, a fruitful reductionist approach 268 216 explaining gene activation/repression. Comparative now complemented by environmental studies. These lat- 269 217 works on molecular interaction networks show that in- ter further unraveled that cells compete and cooperate 270 218 teractions affect the evolution of the molecules compos- with, and even compensate for each other, within mono- 271 219 ing networks, which means that beyond compositional or multispecific microbiomes [63, 64]. Both types of 272 220 complexity, organisational complexity must be modeled microbiomes have a fundamental commonality: they 273 221 to understand biological evolution [46, 51–54]. Before produce collective properties and co-constructed pheno- 274 222 the analysis of complex networks, compensatory sets of types (Fig. 1) evolving at the interface between cells. 275 F1 223 elements, such as groups of sub-functional paralogous Such properties cannot be understood without consider- 276 224 genes [55], or groups of genes with pressupressed muta- ing networks of influences: the oscillatory growth of bio- 277 225 tions [39, 40], already stressed the evolutionary inter- films of Bacillus subtilis cannot be deduced from the 278 226 dependence of molecules. However, compensatory analyses of the complete genomes of these clones, but 279 227 interactions between agents, each of them being by requires modeling metabolic co-dependence within a 280 228 themselves poorly adapted, ran counter to the intuition monogenic community affected by a delayed feedback 281 229 that natural selection will eliminate dysfunctional indi- loop, involving chemical and electrical signals [65, 66]. 282 230 vidual entities. Their recognition invites one to consider Furthermore, many cellular agents show a relative lack 283 231 Earth as possibly populated by unions of individually of autonomy. In nature, some groups of prokaryotes dis- 284 232 dysfunctional agents rather than by the fittest survivors play complementary genomes with incomplete metabolic 285 233 within individual lineages, possibly since early life, ac- pathways, consistent with the black queen hypothesis, 286 234 cording to Woese’s theory on progenotes, namely com- which predicts that our planet is populated by groups of 287 235 munities of interacting protocells unable to sustain (inter)dependent microbes [67, 68]. More precisely, this 288 236 themselves alone, evolving via massive lateral genetic ex- hypothesis predicts the loss of a costly function, encoded 289 237 changes [56]. by a gene or a set of genes, in individuals, when this 290 Bapteste and Huneman BMC Biology _#####################_ Page 4 of 16

components from different lineages, no complete picture 323 of evolution can be provided without these jumps, which 324 are naturally modeled by networks. Indeed, genetic 325 information has been flowing both vertically and hori- 326 zontally between prokaryotes for over 3.5 billion years 327 [71–77], and possibly earlier, according to Woese, who 328 proposed that our universal ancestor was not an entity 329 but a process, that is, genetic and energetic exchanges 330 within protocellular communities [56]. Remarkably, this 331 latter case indicates that network modeling could help 332 to tackle a fundamental issue in evolutionary biology: 333 modeling the evolution of biological processes that 334 emerge from interactions between biological entities. 335 f1:1 Fig. 1. An example of co-construction, the case of holobionts. The left Since these interactions can be represented by a net- 336 f1:2 circle represents the set of traits associated with a host, the right circle work, the evolution of these interactions, describing the 337 f1:3 represents the set of traits associated with its microbial communities; evolution of biological processes, can then be repre- 338 : f1 4 the intersected area represents traits that are produced jointly as a sented by dynamic networks. Likewise, eukaryogenesis 339 f1:5 result of the interaction between hosts and microbes. When this area rested on the co-construction of a novel type of cell, as a 340 f1:6 becomes large or when co-constructed traits are remarkable, they f1:7 cannot be correctly explained under a simple model treating hosts and result of the endosymbiosis of a bacteria within an 341 f1:8 microbes in isolation. This scheme holds for different types of partners archaeon [78–80]. Later, the evolution of photosynthetic 342 f1:9 protists emerged from endosymbioses involving unicel- 343 lular eukaryotes and cyanobacteria, or various lineages 344 291 function becomes dispensable at the individual level, of protists, namely in secondary and tertiary endosymbi- 345 292 since it is achieved by other individuals that produce oses [81]. Such endosymbioses, and their outcomes as il- 346 293 (usually leaky) public goods in sufficient amount to sup- lustrated in our work [82, 83], are also naturally 347 294 port the equilibrium of the community. Thus, gene modeled using networks. 348 295 losses in some cells are compensated by leaks of sub- Moreover, the long-term impact of these introgressive 349 296 strates from other cells, formerly encoded by the lost processes on cellular evolution should not be underesti- 350 297 genes. Some microbes experience labor division [69]. mated. For instance, endosymbiosis does not merely intro- 351 298 Symbionts and endosymbionts depend on their hosts. duce new cellular lineages, it also favors the evolution of 352 299 The ‘kill the winner’ theory [70] further challenges the chimeric structures and chimeric processes within cells 353 300 notion that the microbial world is a world of fit cellular [83–91]. Such intertwining cannot be modeled using a sin- 354 301 individuals. This theory stresses a collective process via gle genealogical tree, which only recapitulates cellular di- 355 302 which viruses mechanically mostly attack cells that re- vergence from a last common ancestor. Even though cells 356 303 produce faster and thus regulate bacterial populations, always derive from other cells, a full cellular history cannot 357 304 these latter sustaining their diversity because these pop- be reduced to the history of some cellular components 358 305 ulations are comprised of individual prokaryotic cells that are assumed to track the history of cellular division 359 306 that make a suboptimal use of a diversity of resources. [92]. In particular, phylogenetic analyses of informational 360 307 Thus, cells belong to networks that affect their growth genes cannot be the only clue to understanding the origins 361 308 and survival, which might explain why most bacteria of cellular diversity, since these genes do not reflect how 362 309 cannot be grown in pure culture. They only truly thrive cells are organized, how they gather their energy, and how 363 310 within communities, whose global genetic instructions they interact with each other. Analyzing the co- 364 311 are spread over several genetically incomplete microbes. construction side of evolution requires enhanced models: 365 312 Accounting for these internal and external cellular net- understanding eukaryotic evolution requires mixed con- 366 313 works requires considering processes that are not central siderations of cellular architecture, population genetics 367 314 in the synthetic evolutionary theory. Typically, the no- and energetics, which go beyond classic phylogenetic 368 315 tion that cellular evolution makes jumps, because new models, which not so long ago were still prone to consid- 369 316 components and processes (such as metabolic pathways) ering three primary domains of life [93–95]. 370 317 are acquired from outside a given cellular lineage, con- Although invoking multiple agents rather than a single 371 318 trasts with more gradual accounts of biological change, ancestor in evolutionary explanations might appear to 372 319 like accounts based on point mutations affecting genes contradict the famous Ockham’s razor [96], it does so 373 320 already present in the lineage. Because saltations only superficially when it is likely that many cells are co- 374 321 (macromutations) are essential evolutionary outcomes of constructed, especially in the context of a web of life. 375 322 introgressive processes, via the combination of Enhanced models including intra- and extracellular 376 Bapteste and Huneman BMC Biology _#####################_ Page 5 of 16

377 interactions appear necessary to understand cellular recruit environmental microbes and transmit them (with a 430 378 complexity, including the predictable disappearance of non-null heritability [113]) to their progeny. Therefore, 431 379 traits (and processes), namely the convergent gene loss nuclear gene inheritance alone may provide too narrow a 432 380 of mitochondria and plastids [97] by a process called perspective to account for the evolution of all animal 433 381 dedarwinification [98, 99]. traits; as an example, aphid body color depends on animal 434 genetics and the presence of Rickettsiella [114]. Population 435 382 Networks beyond the cellular level genetics gets included in a broader community genetics, 436 383 Studies of multicellular organisms—we will focus on ani- which also considers transmission of microbes and their 437 384 mals—have led to similar general findings. Understand- genes [108, 114]. The use of gnotobiotic and transbiotic 438 385 ing animal traits and their evolution requires analyzing animals becomes a new experimental standard to analyze 439 386 the relationships between a multiplicity of agents be- multigenomic collectives without counterparts in modern 440 387 longing to different levels of biological organisation, synthesis theories. These collectives harbor morphological, 441 388 eventually nested, some of which co-constructs animals physiological, developmental, ecological, behavioral and 442 389 and guarantees their complete lifecycle [100]. Because evolutionary features [115–119] that are not purely con- 443 390 no sterile organism lives on Earth, animal development, structed by animal genes, but rather appear to be co- 444 391 health and survival depend on microbes. Granted, bac- constructed at the genetic and metabolic interface 445 392 teria can often legitimately be seen as part of the envir- between the microbial and macrobial worlds, while the 446 393 onmental demands in an evolutionary model focused on content of the respective animal genomes only provides 447 394 the host’s lineage; or sometimes bacteria and host could incomplete instructions. Understanding animal evolution 448 395 also be considered as part of a coevolution process, with requires understanding the interaction networks between 449 396 no need to posit the whole as a unit of selection [101]. components from which these taxa evolved, and the net- 450 397 However, asking ‘who is the beneficiary of the symbiosis works to which these taxa still belong. 451 398 as the result of evolution?’ may in some cases lead to the In ecology, an analogous turn towards network think- 452 399 recognition that bacteria and host evolved together and ing has been promoted since the 1990s with the general 453 400 were selected together [102]. More generally, while some acceptance of the notions of metapopulations [120] and 454 401 microbes contribute to animals’ lives possibly as a result then metacommunities [121]. These views suggest that 455 402 of host-derived selection, others contribute as a result of the dynamics of ecological biodiversity is not so much 456 403 selectively neutral processes (like microbial priming located within a community of species but rather in a 457 404 [103]) [101, 104]. These interactions produce communi- metacommunity, which can be thought of as a network 458 405 cation networks within the animal body: chemical infor- of communities exchanging species, while targeting one 459 406 mation circulates between the animal brain and the gut community blinds one to what genuinely accounts for 460 407 microbiome. These interactions also result in communi- biodiversity and ecosystem functioning [122]. 461 408 cation and interaction networks between individuals. In This quick overview provides evidence that networks 462 409 some animal lineages, the microbiome affects social be- are at the origin of the genes of unicellular and multicel- 463 410 haviors, for instance fermenting microbes inform about lular organisms and central for their functions. The liv- 464 411 the gender and reproductive status in hyena [105]. Com- ing world is a world of ‘and’ and ‘co-’. From division of 465 412 ponents of the microbiome also affect mating choice labor and compensations, to dependencies and co- 466 413 [106], reproductive isolation and possibly speciation. constructions, etc.: interactions (which only begin to be 467 414 Consequently, the microbiome now appears as an essen- deciphered) are everywhere in biology. Thus, explaining 468 415 tial component of animal studies [107]. Microbiome the actual features of biodiversity requires explaining 469 416 studies, the significance of which is overstated in some how multiple processes, interface phenomena (co-con- 470 417 respects, nevertheless have shown that the evolutionary struction of biological features, niche construction, 471 418 intertwining between many metazoa and commensal or metabolic cooperation, co-infection and co-evolution) 472 419 symbiotic bacteria could not be neglected anymore and and organisations (for instance, from molecular path- 473 420 black-boxed in favor of purely host gene-centered evolu- ways to organisms and ecosystems) arose from interact- 474 421 tionary models. And the associations between hosts and ing components, and how these processes, phenomena 475 422 microbes do not need to be units of selection to be part and organisations may have been sustained and trans- 476 423 of the recent insights that support the novel theoretical formed on Earth. 477 424 framework proposed here. Their interplay imposes 425 reconfigurations of practices, theories and disciplines Reframing evolutionary explanations from the 478 426 [108]. As a result of our improved insight into evolution, scaffolded evolution perspective 479 427 zoology and immunology [109] become theaters of new Introducing a classification of interacting components 480 428 ecological considerations [110], sometimes strangely While classic evolutionary models, prompted by 481 429 qualified as Lamarckian [111, 112], because animals can Darwin’s famous tree [123], mostly stress how related 482 Bapteste and Huneman BMC Biology _#####################_ Page 6 of 16

483 entities diverge in relative independence, it appears im- alter the evolution of the biotic components, for ex- 520 484 portant to show how a diversity of components, which ample, environmental change can drive genetic and or- 521 485 may not be related, interact and produce various evolu- ganismal evolution and selection. The history of life 522 486 tionary patterns. clearly depends on the interplay of both types of compo- 523 487 The notion of scaffolding [124], which describes how nents. Biotic components, however, deserve a specific 524 488 one entity continues an event initiated by another entity, focus. Some of them form lineages (for instance, genes 525 489 and relies on it up to the point that at some timescale it replicate), while others do not (for instance, proteins are 526 490 becomes dependent upon it for further evolution, ap- reconstructed). Finally, interacting replicated compo- 527 491 pears as a fundamental relationship to describe the evo- nents can be further classified into fraternal components 528 492 lution of life. We propose scaffolding should become when they share a close last common ancestor (e.g. in 529 493 more central in explanations of evolution because no kin selection cases), and egalitarian components, when 530 494 components from the biological world are actually able they belong to distinct lineages (as an example, think of 531 F2 495 to reproduce, or persist, alone (Fig. 2). Each entity influ- the evolution of chimeric genes by fusion and shuffling 532 496 ences or is influenced by something external to it, and is [29, 45, 126]) [63]. 533 497 consequently part of a process. Scaffolding thus defines 498 the causal backbone of collective evolution. It describes Introducing dynamic interaction networks 534 499 the historical continuity between temporal slices of Biodiversity usually evolves from interactions between 535 500 interaction networks, since any evolutionary stage relies the diverse types of components described above. For 536 501 on previously achieved networks and organisations. example, metalloproteases emerge from the interaction 537 502 Therefore, describing the evolution of interactions re- between reconstructed biotic components (proteins) and 538 503 quires explanations to address the following issues: what a metal ion. Regulatory networks involve biotic compo- 539 504 scaffolds what, what transforms the environment of what, nents that can be either replicated (i.e. genes and pro- 540 505 and are these influences reciprocal? Characterizing the moters) or reconstructed (i.e. proteins). Protein 541 506 types of components that, together, have evolutionary im- interaction networks intertwine reconstructed egalitarian 542 507 portance through their potential interaction is therefore a biotic components, which means proteins that are not 543 508 central step to expanding evolutionary theory. homologous. Evolutionary transitions such as eukaryo- 544 509 We propose that a first distinction can be made be- genesis result from the interweaving of biotic compo- 545 510 tween obligate and facultative components. Suppressing nents (cells) from multiple lineages. Holobionts evolve 546 511 the former impacts the course and eventually the from interactions between egalitarian biotic components 547 512 reproduction of the process to which they contribute (macrobial hosts and microbial communities) and pos- 548 F3 513 (Fig. 3), whereas facultative components do not hold sibly abiotic components, such as the mineral termite 549 514 such a crucial role, and may simply be involved by mounds, or the volatile chemicals produced by the mi- 550 515 chance. A second distinction is whether the components crobial communities of hyenas [105]. 551 516 are biotic (genes, proteins, organisms…) or abiotic (such Taking collectives of interacting components as central 552 517 as minerals, environmental, cultural artefacts). Abiotic objects of study in evolutionary biology invites us to ex- 553 518 components can be recruited from the environment or pand the methods of this field. It encourages developing 554 519 be shaped by biological processes [125]. They can also statistical approaches or inference methods beyond those 555

ab c d

f2:1 Fig. 2. Different types of scaffolding, at four levels of biological organisations. a Functional interactions at the molecular level. b Introgression and f2:2 vertical descent at the cellular level. c Co-construction at the multicellular level. d Niche-construction and physico-chemical interactions at the f2:3 eco-systemic level f2:4 Bapteste and Huneman BMC Biology _#####################_ Page 7 of 16

f3:1 Fig. 3. Classification of major types of components in evolving systems. A process/collective cannot be completed in the absence of obligate f3:2 components, whereas facultative components do not affect the outcome of the process/function of the collective. Biotic components are f3:3 biological, material products, whereas abiotic components are environmental, geological, chemical, physical or cultural artefacts. Replicated f3:4 components are produced by replication, which implies a physical continuity between ancestral and descendent components; they undergo a f3:5 paradigmatic Darwinian evolution. Reconstructed components are reproduced without direct physical continuity, and cannot directly accumulate f3:6 beneficial mutations. Fraternal components belong to the same lineage, whereas egalitarian components belong to different lineages f3:7

556 operating under the very common assumption that bio- recognized as a more inclusive object of study (Fig. 4). 572 F4 557 logical components are independent. Therefore, we Where phylogenies describe relationships, networks can 573 558 propose to represent interactions between components describe organisations. How such organisations evolve 574 559 in the form of networks in which components are nodes could for example be described by identifying evolution- 575 560 and their interactions (of various sorts) are edges. These ary stages, that is, sets of components and of their inter- 576 561 networks are conceptually simple objects. They can be actions simultaneously present in the network (Fig. 4). 577 562 described as adjacency lists of interactions, in the form Investigating the evolution of an ecosystem corresponds 578 563 ‘component A interacts with component B, at time t to studying the succession of evolutionary stages in such 579 564 (when such a temporal precision is known)’. Such dy- networks and detecting possible regularities—in the 580 565 namic interaction networks could become more central sense that some evolutionary stages would fully or partly 581 566 representations and analytical frameworks, and serve as reiterate over time—or hinting at rules or constraints 582 567 a common explanans for various disciplines in an ex- (like architectural contingencies [127, 128] or principles 583 568 panded evolutionary theory. Importantly, because these of organisations [46]) on the recruitment, reproduction 584 569 networks embed both abiotic and biotic, related and un- and heritability of their components. 585 570 related components (like viruses, cells and rocks), they Thus, we suggest that evolutionary biology could be 586 571 should not be conflated with phylogenetic networks, but reframed as a science of evolving networks, because 587

f4:1 Fig. 4. An evolving interaction network. Nodes are components (circles are full when the component is biotic). Thick black edges represent f4:2 interactions between these components. The network topology evolves as nodes or their connection change. Dashed edges represent the f4:3 phylogenetic ancestry of lineage-forming components f4:4 Bapteste and Huneman BMC Biology _#####################_ Page 8 of 16

588 such a shift would allow inclusive, multilevel studies of a reveal conservation and divergence in gene regulation 610 589 larger body of biological and abiotic data, via approaches [138]. GCNs are already used for micro-evolution stud- 611 590 from network sciences. ies, as in the case of fine-grained comparisons of expres- 612 sion variations between orthologous genes across closely 613 591 Concrete strategies to enhance network-based related species, and for the analysis of minor evolution- 614 592 evolutionary analyses ary and ecological transitions, such as changes of ploidy 615 593 Enhancing network-based evolutionary analyses, beyond [139, 140], adaptation to salty environments [141]or 616 594 the now classic research program of phylogenetic net- drugs [142], or the effects of plant domestication 617 595 works, could consolidate comparative analyses in the [143, 144]. Likewise, GRNs are starting to be used in 618 596 nascent field of evolutionary systems biology [129, 130], micro-evolution and phenotypic plasticity studies 619 597 as illustrated by examples based on molecular networks. [145]. Understanding the dynamics of GRNs appears 620 598 Network construction/gathering constitutes the first step critical to inferring the evolution of organismal traits, 621 599 of such analyses. This involves first defining nodes of the in particular during metazoan [146–148], plant [149]and 622 600 network, namely components suspected to be involved fungal [150] evolution. We suggest that PPI, GCN and 623 601 in a given system, and edges, namely qualitative (or GRN studies could become mainstream and also be con- 624 602 quantitative, when weighted) interactions between these ducted at (much) larger evolutionary and temporal scales, 625 603 entities. Many biological interaction networks (gene co- to analyze additional, major, transitions. 626 604 expression networks (GCNs), gene regulatory networks Based on these established networks, two major types 627 605 (GRNs), metabolic networks, protein–protein interaction of evolutionary analyses (network-decomposition and 628 Q8 606 networks (PPIs), etc. [46]) are already known for some graph-matching; Fig. 5b) can be easily further developed 629 F5 607 species, or can be inferred [131–136]. For example, by evolutionary biologists. More precisely, the above- 630 608 GCNs offer an increasingly popular resource to study mentioned kinds of biological networks could be system- 631 609 the evolution of biological pathways [137], as well as to atically turned into what we call evolutionary colored 632

f5:1 Fig. 5. Workflow of the evolutionary analysis of interaction networks. From left to right: triangles represent components of interaction networks, f5:2 edges between triangles represent interactions between these components. Interaction networks are first constructed/inferred, then their nodes f5:3 and edges are colored to produce evolutionary colored networks (ECNs) that represent both the topological and the evolutionary properties of f5:4 the networks. ECNs can be investigated individually by graph decomposition and centrality analyses, or several ECNs can be compared by graph f5:5 alignment. The two types of comparisons can return conserved subgraphs that allow understanding of the dynamics of interaction networks, f5:6 meaning when different sets of interactions (hence processes) evolved, and whether these interactions were evolutionarily stable. Ancient and f5:7 Contemporary refer to the relative age of the sub-graphs, identifying new clade-specific relationships (here called refinement); introgression f5:8 indicates that a component, and the relationship it entertains with the rest of the network, was inferred to result from a lateral acquisition f5:9 Bapteste and Huneman BMC Biology _#####################_ Page 9 of 16

633 biological networks (ECNs). In ECNs, each node of a This focus would complement a classic tree-based 687 634 given biological network is colored to reflect one or sev- view. For instance, under the reasonable working hy- 688 635 eral evolutionary properties. For example, in molecular pothesis that pairs of connected nodes of a given age re- 689 636 networks, nodes correspond to molecular sequences flect an interaction between nodes that may have arisen 690 637 (genes, RNA, proteins) that belong to homologous at that time [154, 171], ECNs can easily be easily decom- 691 638 families that phylogenetic distribution across host spe- posed into sub-networks, featuring processes of different 692 639 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 693 640 family at the node can thus become one evolutionary interacting genes). This strategy allows identification of 694 641 color (Fig. 5a). Likewise, several processes affecting the conserved network patterns, possibly under strong se- 695 642 evolution of a molecular family (selection, duplication, lective pressure [159]. Constructing and exploiting ECNs 696 643 transfer, and divergence in primary sequence) can be in- from bacteria, archaea, and eukaryotes thus has the po- 697 644 ferred by classic phylogenetic analyses or, as we pro- tential to define conserved ancestral sets of relationships 698 645 posed, by analyses of sequence similarity networks [157]. between components, allowing evolutionary biologists to 699 646 Such studies provide additional evolutionary colors (like infer aspects of the early biological networks of the last 700 647 quantitative measures: intensity of selection, rates of du- common ancestor of eukaryotes, archaea and bacteria 701 648 plication, transfer, and percentage of divergence), which and even of the last universal common ancestor of cells. 702 649 can be associated with nodes in ECNs [139, 149, 154, Assuming that some of these topological units corres- 703 650 158–161]. Thus, ECNs contain both topological informa- pond to functional units [172], especially for broadly 704 651 tion, characteristic of the biological network under investi- conserved subgraphs [138, 149, 152, 166, 173–182], 705 652 gation, as well as evolutionary information: what node would allow network decompositions to propose sets of 706 653 belongs to a family prone to duplication, divergence, or important processes associated with the emergence of 707 654 lateral transfer, as well as when this family arose. Combin- major lineages. 708 655 ing these two types of information in a single graph allows Moreover, graph-matching of ECNs allows several 709 656 us to test specific hypotheses regarding evolution. complementary analyses. First, for interaction networks, 710 657 Using ECNs, it is first fruitful to test whether (or such as GRNs, whose sets of components and edges 711 658 which of) these evolutionary colors correlates with topo- evolve rapidly [183–185], it becomes relevant to analyze 712 659 logical properties of the ECNs [162–164]. The null hy- where in the network such changes occur in addition to 713 660 pothesis that nodes’ centrality, e.g. nodes’ positions in (simply) tracking conserved sets of components and 714 661 the network, is neither correlated with the age nor with edges. Whereas the latter can test to what extent conser- 715 662 the duplicability, transferability or divergence of the mo- vation of the interaction networks across higher taxa 716 663 lecular entities represented by these nodes can be tested. supports generalizations made from a limited number of 717 664 Rejection of this hypothesis would hint at processes that model species [186], the former allows us to test a gen- 718 665 affect the topology of biological networks or are affected eral hypothesis: are there repeated types of network 719 666 by the network topology. For example, considering de- changes? For example, does network modification 720 667 gree in networks, proteins with more neighbors are less primarily affect nodes with particular centralities, as 721 668 easily transferred [163], highly expressed genes, more exemplified by terminal processes [187], or modules? 722 669 connected in GCNs, evolve slower than weakly Systematizing these analyses would provide new insights 723 670 expressed genes [165], and genes with lower degrees into whether the organisation principles of biological 724 671 have higher duplicability in yeast, worm and flies [166]. networks changed when major lineages evolved or 725 672 Considering position in networks, node centrality corre- remained conserved. In terms of the ECN, can the same 726 673 lates with evolutionary conservation [136], gene eccen- model of graph evolution explain the topology of ECNs 727 674 tricity correlates with level of gene expression and from different lineages? The null hypothesis would be 728 675 dispensability [167], and proteins interacting with the that these major transitions left no common traces in 729 676 external environment have higher average duplicability biological networks. An alternative hypothesis would be 730 677 than proteins localized within intracellular compart- that the biological networks convergently became more 731 678 ments [168]. Additionally, network structure gives a clue complex (more connected and larger) during these tran- 732 679 to evolution since old proteins have more interactions sitions to novel life forms. Indeed, analyses conducted 733 680 than new ones [169, 170]. Generalizing these disparate on a few taxa have reported quantifiable and qualifiable 734 681 studies could help to understand the dynamics of bio- modifications in biological networks (in response to en- 735 682 logical networks, in other words how the architecture, vironmental challenges [188], during ecological transi- 736 683 the nodes and edges of present day networks, evolved tions [189] or as niche specific adaptations [190]). More 737 684 and whether their changes involved random or biased systematic graph-matching [191–193] and motif ana- 738 685 sets of nodes and edges or follow general models of net- lyses, comparing the topology of ECNs from multiple 739 686 work growth with detectable drivers. species, could likewise be used to test the hypothesis 740 Bapteste and Huneman BMC Biology _#####################_ Page 10 of 16

741 that major lineages are enriched in particular motifs Further justifications for a shift toward network 795 742 (either modules of colored nodes and edges, or specific thinking 796 743 topological features, such as feed-forward loops [46]or Enlargement of evolutionary biology 797 744 bow-ties [194]). It would also allow identification of Focusing evolutionary explanations and theories on collec- 798 745 functionally equivalent components across species, tives of interacting components, which may be under se- 799 746 namely different genes with similar neighbors in differ- lection, facilitate selection, or condition arrangements 800 747 ent species [176]. through neutral processes [39, 40, 202], and representing 801 748 While inferences on conserved sets of nodes and edges these scaffolding relationships using networks with biotic 802 749 in ECNs are likely to be robust (since the patterns are and abiotic components and a diversity of edges represent- 803 750 observed in multiple species), missing data (missing ing a diversity of interaction types would be an enlarge- 804 751 nodes and edges) constitute a recognized challenge, es- ment. Enlargements, as expressing the need to consider 805 752 pecially for the interpretation of what will appear in structures that are more general than what already exists, 806 753 ECN studies as the most versatile (least conserved) parts have already occurred within evolutionary theory, when 807 754 of the biological networks. The issue of missing data, simplifications from population genetics were relaxed with 808 755 however, is not specific to network-based evolutionary respect to the original formalization in the Modern 809 756 analyses, and should be tackled, as with other compara- Synthesis [203], to account for within-genome interaction 810 757 tive approaches, by the development and testing of im- [9], gene–environment covariance [204], parental effects 811 758 putation methods [195–197]. Moreover, issues of [205], and extended fitness though generations [206]. It 812 759 missing data can also be addressed by the production of also occurred when reticulations representing introgres- 813 760 high coverage -omics datasets in simple systems, allow- sions were added to the evolutionary tree. 814 761 ing for (nearly) exhaustive representations of the entities Interestingly, replacing standard linear models in evolu- 815 762 and their interactions (i.e. PPIs, GCNs and GRNs within tionary theory with network approaches would transcend 816 763 a cell, or metabolic networks within a species poor eco- several traditional axes structuring the debates in evolu- 817 764 system). This kind of data would allow testing for the tionary biology. For instance, scaffolded evolution, the idea 818 765 existence of selected emergent ecosystemic properties that evolution relies on what came before, is orthogonal to 819 766 (like carbon fixation), as stated by the ITSNTS hypoth- the distinction between vertical and horizontal descent, 820 767 esis [198]. For instance, deep coverage time series of since both tree-like and introgressive evolution are par- 821 768 metagenomic/metatranscriptomic data coupled with en- ticular cases of scaffolding. Scaffolded evolution is also or- 822 769 vironmental measures from a simple microbial ecosys- thogonal to the distinction between gradual and 823 770 tem, such as carbon fixation, could produce enough data saltational evolution. Likewise, scaffolded evolution is or- 824 771 to allow the evolutionary coloring of nodes of metabolic thogonal to the debates between the actual role of adapta- 825 772 networks. Comparing ECNs representing, at each time tions vs neutral processes. Selection is a key mode of 826 773 point, the origin and abundance of the lineages hosting evolution of collectives but not the only one. The pro- 827 774 the enzymes involved in carbon fixation could test cesses involved in the forming and evolution of collectives 828 775 whether some combinations of lineages are repeated are not even restricted to the key processes of the Modern 829 776 over time, and whether the components (e.g. genes and Synthesis (drift, selection, mutation and migration) but 830 777 lineages) vary, whereas carbon fixation is maintained in embrace interactions such as facilitation—namely antag- 831 778 the ecosystem, which would suggest that this process onistic interactions between two species that allow a third 832 779 evolves irrespective of the nature of the interacting species to prosper by restraining one of its predators or 833 780 components. parasites [207], presuppression [39, 40], etc. Consequently, 834 781 Finally, entities from different levels of biological or- some evolutionary concepts may become more important 835 782 ganisation (domains, genes, genomes, lineages, etc.) than they currently are to explain evolution. For example, 836 783 could also be studied together in a single network frame- contingency, which means the dependence of an evolu- 837 784 work, by integrating them into multipartite networks tionary chain of events upon an event that itself is contin- 838 785 [199]. Recently, our studies and others (see [200] and gent, in the sense that it can’t be understood as a selective 839 786 references therein) have demonstrated that various pat- response to environmental changes [18, 208, 209], is often 840 787 terns in multipartite graphs can be used to detect and associated with extraordinary events, like mass decima- 841 788 test combinatorial (introgressive) and gradual evolution tion. Contingency could come to be seen as a less extraor- 842 789 (by vertical descent) affecting genes and genomes. dinary mode of evolution in the history of life, since the 843 790 Decomposing multipartite networks into twins and ordinary course of evolution might include many cases of 844 791 articulation points could for example then be used to contingent events, that is, associations of entities in a tran- 845 792 represent and analyze the evolution of complex compos- sient collective, including any scaffolds—associations that 846 793 ite molecular systems, such as CRISPR, or the dynamics are not necessarily selective responses or the outcomes of 847 794 of invasions of hairpins in genomes [201]. processes modeled in population genetics. 848 Bapteste and Huneman BMC Biology _#####################_ Page 11 of 16

849 Likewise, adopting a broader ontology could affect edges and components). For example, the fermentation 902 850 how evolutionary theorists think about evolution. Popu- hypothesis for mammalian chemical communication 903 851 lation thinking and tree-thinking came after essentialist could be analyzed in a multipartite network framework, 904 852 conceptions of the living words, when populations and which would involve nodes corresponding to individual 905 853 lineages were recognized as central objects of evolution- mammals, nodes corresponding to microbes, and nodes 906 854 ary studies [210]. A shift towards collectives and scaf- corresponding to odorous metabolites. Nodes corre- 907 855 folded evolution might encorage a similar development: sponding to mammals could either be colored to reflect 908 856 the emergence of an openly pluralistic processual think- an individual’s properties (its lineage, social position, 909 857 ing, consistent with Carl Woese’s proposal to reformu- gender, sexual availability), or these nodes could be con- 910 858 late our view of evolution in terms of complex dynamic nected by edges that reflect these shared properties, 911 859 systems [211]. which defines a first host subnetwork. This host subnet- 912 work can itself be further connected to a second subnet- 913 860 Further unifying the evolutionary theory work, namely the microbial subnetwork in which nodes 914 861 Using a network-based approach to analyse dynamic sys- representing microbes, colored by phylogenetic origins, 915 862 tems also permits explanations that rely purely on statis- could be connected to reflect microbial interactions 916 863 tical properties [212] or on topological or graph (gene transfer, competition, metabolic cooperation, etc.). 917 864 theoretical properties [213, 214] besides standard expla- Connections between the host and microbial subnet- 918 865 nations devoted to unravelling mechanisms responsible works could simply be made by drawing edges between 919 866 for a phenomenon. Moreover, because of the inclusive- nodes representing individuals hosting microbes, and 920 867 ness of the network model, disciplines already recog- nodes representing these microbes. Moreover, nodes 921 868 nized for their contribution to evolutionary theory representing mammals and nodes representing microbes 922 869 (microbiology, ecology, cell biology, genetics, etc.) could could be connected to nodes representing odorous me- 923 870 become even more part of an interdisciplinary research tabolites to show what odours are associated with what 924 871 program on evolution, effectively addressing current is- combinations of hosts and microbes. Elaborating this 925 872 sues, consistent with the repeated calls for transdisci- network in a piecemeal fashion would involve cooper- 926 873 plinary collaborations [19–21, 215]. Disciplines that ation between chemists, microbiologists, zoologists and 927 874 were not central in the Modern Synthesis—chemistry, evolutionary biologists. 928 875 physics, geology, oceanography, cybernetics or linguis- Of note, the use of integrated networks could prag- 929 876 tics—could aggregate with evolutionary biology. Since a matically address a deep concern for evolutionary stud- 930 877 diversity of components gets connected by a diversity of ies, by connecting phenomena that occur at different 931 878 edges in networks featuring collectives, as a result of a timescales: development and evolution [219] or ecology 932 879 diversity of drivers, several explanatory strategies could and evolution [220]. Considering transient collectives 933 880 be combined to analyze evolution. This extension to (thus processes) as stable entities at a given time-scale, 934 881 seemingly foreign fields makes sense when the compo- when these collectives change much more slowly than 935 882 nents/processes studied by these other disciplines are the process in which they take part, amounts to a focus 936 883 evolutionarily or functionally related to biotic compo- on interactions occurring at a given time scale by treat- 937 884 nents and processes (either as putative ancestors of bio- ing the slower dynamics as stable edges/nodes. Then, 938 885 logical components and processes, like the use of a various parts of the networks embody distinct time- 939 886 proton gradient in cells, which possibly derived from scales, which may provide a new form of timescale inte- 940 887 geological processes affecting early life [216], or as de- gration, working out the merging of timescales from the 941 888 scendants of biological systems, e.g. technically synthe- viewpoint of the model, and with resources intrinsic to 942 889 sized life forms, which have a potential to alter the the model itself. The reason for this is that a node in an 943 890 future course of standard biological evolution). interaction network Ni, describing processes relevant at 944 891 Remarkably, this mode of unification of diverse scien- a time scale i, can itself be seen as the outcome of an- 945 892 tific disciplines would be original: the integration would other (embedded) interaction network Nj, unfolding at a 946 893 not be a unification in the sense of logical positivism time scale j. This nestedness typically occurs when the 947 894 [217]—namely reducing a theory to a theory with more node in Ni represents a collective process, involving 948 895 basic laws, or a theory with a larger extension. It would components that evolve sufficiently slowly with respect 949 896 be a piecemeal [218] unification. Some aspects would be to the system considered at the time scale i to figure as 950 897 unified through a specific kind of graph modeling an entity, a node in Ni. In the case of a PPI network Ni, 951 898 (because some interactions, namely mechanical, chem- each node conventionally represents a protein, but the 952 899 ical, ecological ones, and a range of time scales are privi- evolution of each protein could be further analysed as 953 900 leged in a set of theories), while other theories might be the result of mutation, duplication, fusion and shuffling 954 901 unified by other graph properties (like different types of events affecting the gene family coding the proteins over 955 Bapteste and Huneman BMC Biology _#####################_ Page 12 of 16

956 time; for instance, each protein could thus be repre- descendants and those of other life forms will be pro- 1008 957 sented as the outcome of interaction between domains cesses too. Some one hundred and fifty years after On 1009 958 in a domain–domain interaction network Nj. Consider- the Origin of Species, which started a great evolutionary 1010 959 ing these two time-scales, it becomes apparent that gene inquiry, evolutionists should prepare to face a larger 1011 960 families enriched in exon shuffling events, a process dir- challenge: expanding evolutionary theory to study the 1012 961 ectly analysable in Nj, have a higher degree in PPI net- evolution of processes. With the development of -omics 1013 962 works represented at the time-scale Ni [221]. and network sciences, the concepts, data and tools for 1014 this research program are increasingly available. 1015 963 Predictions: discovery of co-constructed phenotypes Acknowledgements 1016 964 What possible findings may result from this perspective This manuscript is dedicated to the memory of Jean Gayon, a great historian 1017 965 shift? One can only speculate, but the nature of the po- and philosopher of biology, and a great friend. We thank Ford Doolittle, P. 1018 966 tential discoveries is exciting. At the molecular level, the Lopez, G. Bernard, A. Watson, FJ Lapointe for critical reading and discussion. 1019 967 structure and composition of regulatory networks and Funding 1020 968 protein interaction networks could be substantially en- EB is funded by the European Research Council under the European 1021 969 hanced to scaffolding elements. Currently, these net- Community’s Seventh Framework Program FP7 (grant agreement number 1022 615274). PH is funded by the ANR (ANR 13 BSH3 0007 ‘Explabio’). 1023 970 works represent interactions within a single individual/ 971 species. Yet, viruses are everywhere, viral genes and pro- Authors’ contributions 1024 972 teins clearly influence the networks of their hosts, and EB and PH wrote, read and approved the final manuscript. 1025 973 likely constitute an actual part of their evolution. Thus, Competing interests 1026 974 virogenetics, a novel transdiscipline, may prosper in an The authors declare that they have no competing interests. 1027 975 expanded evolutionary theory to show how and to what 976 extent viruses co-construct their hosts, including Publisher’sNote 1028 977 perhaps reproductive-viruses, allowing their hosts to Springer Nature remains neutral with regard to jurisdictional claims in 1029 978 complete their lifecycles. At the cellular level, new published maps and institutional affiliations. 1030 979 modes of communication [222, 223] could be discov- Author details 1031 980 ered, as possible viral and microbial languages and com- 1Sorbonne Universités, UPMC Université Paris 06, Institut de Biologie 1032 Q7 981 munication networks in biofilms would exemplify. At Paris-Seine (IBPS), F-75005 Paris, France. 2CNRS, UMR7138, Institut de Biologie 1033 3 ’ 1034 982 the level of multicellular organisms and holobionts, ‘sym- Paris-Seine, F-75005 Paris, France. Institut d Histoire et de Philosophie des Sciences (CNRS / Paris I Sorbonne), F-75006 Paris, France. 1035 983 biotic codes’, guiding the preferential association between 984 hosts and symbionts, could be identified. At the level of 1036 985 phyla, hidden evolutionary transitions may be unraveled. References 1037 986 While secondary (and tertiary) acquisitions of plastids 1. Huxley J. Evolution: the modern synthesis. 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