UNIVERSIDAD AUTÓNOMA DE MADRIDFACULTAD DE CIENCIASDEPARTAMENTO DE BIOLOGÍA MOLECULARMULTIPARTITE VIRUSES: ORGANIZATION, EMERGENCE AND EVOLUTIONTESIS DOCTORALAdriana Lucía Sanz GarcíaMadrid, 2019MULTIPARTITE VIRUSESOrganization, emergence andevolutionTESIS DOCTORALMemoria presentada porAdriana Lucı´a Sanz Garcı´aLicenciada en Bioquı´micapor la Universidad Auto´noma de MadridSupervisada porDra. Susanna Manrubia CuevasCentro Nacional de Biotecnologı´a (CSIC)Memoria presentada para optar al grado deDoctor en Biociencias MolecularesFacultad de CienciasDepartamento de Biologı´a MolecularUniversidad Auto´noma de MadridMadrid, 2019Tesis doctoralMultipartite viruses: Organization, emergence and evolution, 2019, Madrid, Espana.Memoria presentada por Adriana Lucı´a-Sanz, licenciada en Bioqum´ica y con un ma´ster en Biofı´sica en la Universidad Auto´nomade Madridpara optar al grado de doctor en Biociencias Moleculares del departamento de Biologı´a Molecular en la facultad de Ciencias dela Universidad Auto´noma de MadridSupervisora de tesis: Dr. Susanna Manrubia Cuevas.Investigadora Cientı´fica en el Centro Nacional de Biotecnologı´a (CSIC), C/ Darwin 3, 28049 Madrid, Espana.to the readerCONTENTSAcknowledgments xiResumen xiiiAbstract xvIntroduction xviiI.1 What is a virus? xviiI.2 What is a multipartite virus? xixI.3 The multipartite lifecycle xxI.4 Overview of this thesis xxvPART I OBJECTIVESPART II METHODOLOGY0.5 Database management for constructing the multipartite and segmenteddatasets 30.6 Analytical solutions and stability analysis of the model of viralcompetition 40.6.1 Stability analysis of the model of viral competition 50.7 Results of the model of viral competition assisted by a satellite 60.7.1 Stability analysis of the model of viral competition assistedby a satellite 7viiviii CONTENTS0.8 Numerical simulations and algorithm implementation 90.9 Network analysis 100.10 Weighted distances of not-ultrametric trees 10PART III RESULTS AND DISCUSSION1 Prevalence and presence of multipartite virus in the virosphere 11.1 The virosphere in numbers 11.2 Prevalence and organization of segmented and multipartite viruses 52 Understanding the emergence of multipartite viruses 112.1 Qualitative observations of multipartitism 112.2 Proposed advantages of multipartite viruses 152.3 Quantitative approaches to disclosing the advantages of multipartitism 152.4 Emergence of multipartite viral forms through genome segmentation 162.4.1 Model of genome segmentation 172.4.2 Effect of the space in genome fragmentation 203 Associations in the viral world 233.1 An intuitive classification of satellites 253.2 Ecological and epidemiological effects of virus associations 263.3 Modelling the ecological effects of a satellite in a viral competition 293.3.1 Model of viral competition 293.3.2 Model of viral competition assisted by a satellite 313.3.3 Results of the model of viral competition assisted by a satellite 324 Evolutionary transitions 374.1 On the possible origins of multipartitism 374.2 Evolutionary pathways to and from multipartitism 384.2.1 Transitions from non-segmented to multipartite genomes 394.2.2 Relationship between non-segmented, segmented, andmultipartite viral genomes 404.2.3 Segment duplication 424.3 Evolution of RNA viruses 434.3.1 Evolution of genome configurations of RNA viruses 464.3.2 Evolutionary distances of segmented and multipartite RNAgenomes 474.3.3 Distance of appearance of segmented and multipartite RNAviruses 484.3.4 Diverging times for RNA species 484.4 Network of gene sharing of RNA viruses 494.5 RNA plant network 52CONTENTS ix5 Discussion & Perspectives 57PART IV CONCLUSIONES–CONCLUSIONSReferences 63A Scripts 81A.1 C scripts 81A.1.1 Model of genome segmentation 81A.1.2 Model of viral competition assisted by a satellite 87A.2 Matlab functions 91A.2.1 Calculate evolutionary distances in a phylogenetic tree 92ACKNOWLEDGMENTSThis thesis was financed by the Severo Ochoa Centers of Excellence Program (SVP-2014-068581), granted on November 16th of 2014 by the Spanish Ministerio de Economı´a,Industria y Competitividad (Secretarı´a de Estado Investigacio´n Desarrollo e Innovacio´n).This funds also supported an internship of 3 months in the group of Prof. Eugene V. Kooninat the National Center for Biotechnology Investigation of the National Institutes of Healthin Bethesda, Maryland (US).Expenses on travelling to congresses and publishing on scientific journals were sup-ported by the Spanish Ministerio de Economı´a, Industria y Competitividad and FEDERfunds of the EU through grants ViralESS (FIS2014-57686-P and FIS2017-84256-P).This thesis has been carried out with the core facilities of the National Centre of Biotech-nology in Madrid (Spain), and the National Center for Biotechnology Investigation of theNational Institutes of Health in Bethesda, Maryland (US).This thesis could not have been accomplished without the contribution of the followingpeople: Dr. Susanna Manrubia, Dr. Jacobo Aguirre, Dr. Jaime Iranzo, and the groups ofDr. Eugene V. Koonin and Dr. Mart Krupovic.I also want to thank the scientific discussions with Drs: Jose´ Cuesta, Pablo Catala´n, JuanAntonio (Ton˜o) Garcı´a and Fernando Puente-Sa´nchez and especially with Ce´sar Lo´pez-Pastrana.xiRESUMENxiiixiv RESUMENLos virus se encuentran entre las entidades replicativas ma´s simples de la Tierra. Sonpara´sitos intracelulares obligados que tı´picamente forman grandes poblaciones de ra´pidaevolucio´n. Ma´s alla´ de sus altas tasas de mutacio´n, los virus despliegan una serie de es-trategias de adaptacio´n nunca vistas en los organismos celulares. Dentro de la ce´lula, lacomplementacio´n entre genomas es una estrategia comu´n que a menudo permite a las vari-antes con genomas incompletos de menor fitness prosperar en la poblacio´n. Los virusmultipartitos son el caso extremo de la complementacio´n entre los genomas virales, yaque el genoma de estos virus esta´ fragmentado y cada fragmento geno´mico es encapsidadoen partı´culas virales independientes. Dado que todos los fragmentos geno´micos tienenque coincidir en el hue´sped para complementarse, los virus multipartitos esta´n obligadosa luchar contra la pe´rdida de informacio´n gene´tica. La necesidad de coinfeccio´n o com-plementacio´n exige una alta densidad viral que es aparentemente difı´cil de lograr en lanaturaleza. Si bien la multiparticio´n geno´mica tiene una desventaja obvia como estrategiaviral, no se ha llegado a un consenso sobre sus ventajas reales ¿Por que´ existen los virusmultipartitos?Con el fin de contribuir al entendimiento de esta estrategia viral, cuantificamos enprimer lugar la prevalencia en la Virosfera de los virus multipartitos mediante el ana´lisis debases de datos pu´blicas. Entre sus caracterı´sticas sobresalientes, encontramos que una can-tidad significativa de todas las especies virales anotadas son multipartitas, y la mayorı´a deellas infectan plantas. Aunque generalmente se asume que las poblaciones de virus multi-partitos son viables so´lo cuando la trasmisio´n ocurre a una alta densidad viral, la evidenciaindica que son comunes los cuellos de botella, especialmente en el caso de las poblacionesde virus de plantas. Por tanto, investigamos el efecto de la fragmentacio´n del genomacomo una presio´n evolutiva que favorece el e´xito de poblaciones multipartitas (a trave´sde la complementacio´n) sobre poblaciones monopartitas. Consideramos teo´ricamente lasituacio´n en la que se generan genomas incompletos debido a errores en la replicacio´n deun virus monopartito parental, y determinamos co´mo su persistencia se ve limitada por ladensidad viral. La propagacio´n de la infeccio´n en plantas esta´ fuertemente condicionadapor la naturaleza estructurada de los tejidos vegetales, que alivian la necesidad de comple-mentacio´n al favorecer una transmisio´n local de la infeccio´n. En consecuencia, estudiamosel caso de la fragmentacio´n geno´mica en el espacio. Como resultado, en competencia es-pacial, las formas virales multipartitas pueden desplazar a las parentales monopartitas encondiciones ambientales menos restrictivas.Desde un punto de vista ecolo´gico de la Virosfera, las infecciones son el resultado deun conjunto de virus y entidades subvirales que interactu´an entre sı´. Los sate´lites son enti-dades subvirales que dependen de un virus ayudador para su replicacio´n y mantenimiento.A cambio, los sate´lites proporcionan una nueva variedad de fenotipos de infeccio´n. Lascoinfecciones con un sate´lite son ubicuas en las infecciones de virus de plantas, y menoscomunes en otros hue´spedes. Los resultados ecolo´gicos de estas asociaciones pueden com-pensar el coste de la coinfeccio´n, y podrı´an representar un primer paso plausible hacia lamultiparticio´n geno´mica. Presentamos un sistema dina´mico que modela la competicio´nentre dos virus, uno de ellos asistido por un sate´lite, y encontramos soluciones dondeprevalece el ta´ndem virus-sate´lite a pesar de la necesidad de coinfectar.Los fragmentos geno´micos que constituyen los genomas multipartitos son, en principio,indistinguibles de los de otros virus; sin embargo, como virus vegetales, su origen podrı´aestar relacionado con la expansio´n de los virus de ARN durante el florecimiento de lasce´lulas eucariotas. La evolucio´n de los virus eucariotas se basa en gran medida en un prin-cipio modular de construccio´n, impulsado principalmente por una extensa transferenciahorizontal de genes. Posicionamos el origen de la multiparticio´n geno´mica en la filogeniaRESUMEN xvde los virus de ARN, encontrando caracterı´sticas sobresalientes en su evolucio´n. La multi-particio´n geno´mica ha surgido repetidamente a lo largo de la evolucio´n y la diversificacio´nde los virus de ARN, posiblemente impulsada por un principio constructivo que se carac-teriza por un mayor intercambio de genes dentro de este grupo, en comparacio´n con el delresto de virus de ARN.En resumen, proponemos posibles orı´genes y mecanismos evolutivos para las diferentesfamilias de virus multipartitos e hipotetizamos que la ventaja de la multiparticio´n geno´micase basa en su plasticidad y, al mismo tiempo, en su inevitable dependencia del contextoecolo´gico. So´lo explorando la interaccio´n entre evolucio´n y ecologı´a podemos dilucidarque´ es lo posible y que´ puede ser real en las estrategias de adaptacio´n viral.ABSTRACTxviixviii ABSTRACTViruses are among the simplest replicative entities on Earth. They are obligate intracel-lular parasites that typically form large and fast-evolving populations. Beyond their highmutation rates, viruses deploy a number of adaptive strategies unseen in cellular organisms.Inside the cell, complementation among genomes is a common strategy that often permitsvariants of low fitness with incomplete genomes to thrive in the population. Complemen-tation between viral genomes seems to be taken to its ultimate consequences in the caseof multipartite viruses: the genome of these viruses is fragmented and encapsidated intoindependent viral particles. Since all the genomic segments have to meet in the host forcomplementation, multipartite viruses are bound to fight the loss of genomic information.The need for coinfection or complementation demands a high viral density that is difficultto achieve in nature. While this is an obvious disadvantage of this strategy, no consensuson its actual advantages has been reached. What is more, the main open question aboutmultipartite viruses is why they exist at all.In order to contribute to the understanding of this viral strategy, we quantify the preva-lence of multipartite viruses by analysing publicly available databases. Amongst theiroutstanding characteristics, we found that a non-negligible amount of all annotated vi-ral species are multipartite, and most of them infect plants. Although it is generally as-sumed that multipartitism is viable only when propagation occurs at high viral density,evidence indicates that severe population bottlenecks are common, especially for plantviruses. Therefore, we investigate the effect of genome segmentation as an evolutionarypressure that favours multipartite over monopartite populations through complementation.We consider a situation where incomplete genomes are generated through errors in thereplication of a monopartite wild type virus and determine how their maintenance is con-strained by viral density. Propagation of infection in plants is strongly conditioned by thestructured nature of plant tissues, which alleviate the requirement of complementation byfavouring local clustering. As a result, we observe that in spatial competition multipar-tite viral forms can displace monopartite counterparts under less restrictive environmentalconditions.Taking an ecological viewpoint of the Virosphere, infections result from an ensembleof interacting viruses and subviral entities. Satellites are subviral entities that rely on ahelper virus for replication and maintenance. In return, satellites open up a new range ofinfection phenotypes. Virus-satellite coinfections are ubiquitous in plant infections and lesscommon in other hosts. The ecological outcomes of these associations may compensate forthe cost of coinfection and we conjecture that they represent a plausible first step towardsmultipartitism. We present a dynamical system for the competition between two viruses,one of them assisted by a satellite, and find solutions where the tandem virus-satelliteprevails despite the requirement of coinfection.The genomic segments composing multipartite genomes are, in principle, indistinguish-able from those of other viruses; however, as a plant virus, their origin might be linked withthe expansion of RNA viruses during the flourishing of eukaryotic cells. The evolution ofeukaryotic viruses is highly relying on a modular constructive principle, mostly driven byextensive horizontal gene transfer. We placed the origin of multipartitism in the global phy-logeny of RNA viruses and found outstanding features in their evolution. Multipartitismhas repeatedly emerged along the evolution and diversification of RNA viruses, possiblydriven by a constructive principle that enhances gene sharing within this group as comparedto the rest of viruses.In summary, we propose plausible mechanistic and evolutionary origins of differentfamilies of multipartite viruses and hypothesize that the power of multipartitism relies onits plasticity and, at the same time, unavoidably in an ecological context. It is only byABSTRACT xixexploring the interplay between evolution and ecology that we can elucidate the possibleand the actual in viral adaptive strategies.INTRODUCTIONI.1 What is a virus?The first question to address in a thesis related to viruses has to be ”What is a virus?”(Roossinck, 2016; Knipe and Howley, 2007). A quick look to the The Oxford EnglishDictionary defines a virus as:“an infective agent that typically consists of a nucleic acid molecule in aprotein coat, is too small to be seen by light microscopy, and is able tomultiply only within the living cells of a host.”The definition is a slightly vague taking into account that the characteristics proposed fora virus are shared by other unrelated organisms. First, it is said that viruses are infectiousagents, as many bacteria, fungi and protozoa. The term “infectious agent” is commonlyassociated to disease-causing organism. Viruses are related with disease, but the reality isthat many viruses do not cause any harm.The second feature is just half true. A prototypic virus has an icosahedral shell protect-ing the viral genome, but it uses to come in various shapes – filaments, rods, bottle-like,spider-like. Some viruses also have an external lipid membrane known as envelope, whichsurrounds the entire capsid; others, like narnaviruses or endornaviruses, simply don’t havea capsid at all.Third, it is said that they are too small to be seen by light microscopy – whose resolutionis about a half of the wavelength of visible light∼ 0.2µm. The average size of most virusesis around 0.01 µm well beyond the resolution limit of a light microscope. Therefore,viruses are small, much smaller than a bacteria or a blood cell, around 100 and 1000-foldsmaller Figure I.1. To make a graphical example we can compare a cell infected by a viruswith a human being attacked by a cockroach. Nonetheless, there are giant viruses, withxxixxii INTRODUCTIONFigure I.1: Allometric relationship between physical size and genome size for severalviruses and cellular organisms.The graphic shows a comparison of diameter size and the number of encoded proteins forseveral viruses and cellular organisms. The companion picture shows relative sizes of someof the organisms included in the graphic.sizes reaching almost 1.5µm that can actually be seen by light microscopy. Mimivirus andpandoravirus are giant viruses that redefine the concept of how small a virus can be (Scolaet al., 2003; Philippe et al., 2013). These viruses are only 5 times smaller than archaea,their natural host.Last feature mentioned is perhaps the most significant because it is shared by all viruses,as no known virus is able to multiply without parasitizing a living cell. However, not onlyviruses are mandatory intracellular parasites. There are fungi, protozoa and bacteria thatcannot replicate without a host such as, chlamydia, rickettsia, or leishmania (Knipe andHowley, 2007).Yet, a deeper understanding on viruses has to highlight the differences of viruses fromcellular life forms. The non-cross line for viruses is that of having their own translationmachinery, therefore they depend on the cellular one to express their genetic information.There are viruses that encode only one protein —narnavirus— but others like the giantpandoravirus encode a complete metabolism with thousands of proteins involved FigureI.1. The genetic information of viruses is coded in six different types of nucleic acids –single or double stranded RNA or DNA, of positive or negative polarity– according to theBaltimore classes (Baltimore, 1971), in contrast with the sole genetic molecule for cellularlife forms, the dsDNA. Another remarkable difference of viruses is the diversity in theirlifestyles as opposed with the uniformity of the cellular cycle.Viruses are everywhere. Every living organism –from bacteria to humans– when stud-ied, are infected by viruses. We cannot image a cellular form free of viral infection. Virusesare such an ubiquitous parasite, that we cannot think on life as we know it without theiraction. We find viral genes in genomes of living organisms and vice versa, in a way thatwe cannot discern the evolution of live without the action of viruses, as they are majorvehicles of horizontal gene transfer in the evolution of life. Probably, viruses existed sinceliving cells first evolved, ultimately meaning we cannot understand cellular life, withoutthe impact of viral infections, their role in the ecosystems as real players of evolution.WHAT IS A MULTIPARTITE VIRUS? xxiiiConsequently, in this thesis we define a virus as:“a non-cellular microscopic parasite, that lacks the capacity to translatetheir genetic information into proteins, infects all types of life forms andconsists of a genetic material based on DNA or RNA usually surroundedby a protein and/or membrane coat.”The mechanisms by which viruses evolved together with cellular life (Koonin and Dolja,2013), how they adapt in a continuous arms race towards parasitism (Koonin, 2016), thestrategies they display to find a way to colonize novel niches to infect, are as of yet majorunknowns from the viewpoint of evolution (Koonin et al., 2006). However, many aspectsof the variety of genetic strategies, viral lifestyles, genome complexity and phylogeny andglobal ecology of viruses have been extensively studied and set a basis to infer the bigquestions of virus evolution. While a major challenge for evolutionists is to address thesequestions, an ambitious goal of this thesis is to explore a particular virus strategy which isone of the most puzzling ones found in the Virosphere.I.2 What is a multipartite virus?This thesis focuses on a particular class of viruses known as multipartite viruses first de-scribed in the 1960 decade (Lister, 1966; van Kammen, 1967). Several names have beenascribed to multipartite viruses in the literature, such as coviruses, multicomponent viruses,multiparticle or multicompartment viruses. The particularity of these viruses is intrinsi-cally related to their genome configuration and their transmission style. We are consid-ering three main types of viral genome configurations: non-segmented or monopartite,segmented and multipartite viruses. Non-segmented and segmented viruses transport allthe genomic information needed to complete the viral cycle inside a unique viral particlewhereas multipartite viruses distribute their chromosomes into two or more virus particlesas shown in Figure I.2. Coinfection is therefore a requirement for survivability of multipar-tite viruses, a requirement that is absent in the rest of genome configurations. This is a viralstrategy bounded to fight the loss of genomic information, as viral particles containing thedifferent genomic segments are independently transmitted. There is a need for co-infectionor complementation that demands a high viral density —multiplicity of infection (MOI)—not attainable, or at least very difficult to achieve in most of the cases (Reanney, 1982;Gutie´rrez et al., 2010).There are many more open questions than certainties in our understanding of multipar-tite viruses (Nee, 2000; Sicard et al., 2016; Holmes, 2016). Among all, the main puzzleis why multipartite viruses do exist at all (Wu et al., 2017). Despite our current lack ofknowledge on the mechanisms that may endow multipartite viruses with an adaptive ad-vantage that compensates for their apparently weird and costly lifestyle, there is no alterna-tive but beginning by assuming that multipartitism is a stable evolutionary strategy, sensuMaynard-Smith (Maynard Smith, 1972). The following section will introduce the basicsof a multipartite virus lifestyle, its particularities and commonalities with non-segmentedand segmented viruses in order to introduce the terminology that will be discussed in partIII of Results and Discussion.xxiv INTRODUCTIONR CPcellNON-SEGMENTED SEGMENTED MULTIPARTITEcell cellreplicationencapsidationreplicationencapsidationreplicationencapsidationtransmission transmission transmissioninfection infection co-infectionR CPR CPFigure I.2: Infection cycles attending to genome configuration.Non-segmented (or monopartite) viruses transmit all the genomic information in a singlegenetic molecule contained in a single capsid coat. When they infect a cell, the main func-tions of replication, R, and encapsidation, CP, are codified in the genetic molecule that hasjust entered the cell. Segmented viruses transmit all the genomic information in two toseveral genetic molecules contained in a single capsid coat. When they infect a cell, allthe necessary functions to complete the infection cycle are codified in the various geneticmolecules that have just entered the cell. Multipartite viruses transmit the genomic infor-mation in two to several genetic molecules contained in independent capsid coats. Two toseveral viral particles must co-infect the same cell to ensure that all genomic functions arepresent to complete the infection cycle. Complementation is a constraint only reserved toa multipartite genome configuration.I.3 The multipartite lifecycleMultipartite viruses have shown to be really successful plant viruses. Despite this prefer-ence might be a bias in the host-range due to biased sampling, we will assume that plantsoffer adequate conditions for the maintenance of multiparticism.Possibly as a consequence, the nucleic acids linked to multipartitism are the same thanthose of other non-segmented and segmented plant viruses, with only one exception: plantretroviruses, which are all non-segmented. Multipartite genomes are molecules of dou-ble and single stranded RNA of positive and negative polarity (dsRNA, +RNA, -RNA),THE MULTIPARTITE LIFECYCLE xxvand in single stranded DNA (ssDNA). The nucleic acid is an important determinant of theinfection cycle as it imposes restrictions in the sequence of steps to complete the infec-tion (Ahlquist, 2006) and the proteins needed to interact with the different cellular com-ponents of the host. Whereas the nucleic acid strongly affects the infection cycle, thereis no evidence that the genome configuration plays any role on it, and in principle viralinfection cycles of multipartite, segmented and non-segmented viruses of a particular classof nucleic acid are undistinguishable.While RNA viruses limit their activity to the cytoplasm, with several exceptions (Crosand Palese, 2003; Krichevsky et al.), DNA viruses have to enter the nucleus for replicationand transcription, a step that requires the assistance of viral proteins that allow the exportand import through the nucleus (Whittaker and Helenius, 1998).The +RNA viruses havethe simplest infection strategy for replication of their genome and expression of their genes,since the same molecule serves as genetic material and translation template. Right after thegenome is released from the capsid, it is translated into viral proteins as an initial step inthe infection cycle. In contrast, dsRNA and -RNA viruses need a replicase to transcribe thegenome into viral messenger RNAs (mRNA), as first step before translation. Therefore,the replicase protein has to be transmitted together with the genome inside the virion.Although viruses with +RNA genomes are able to start the infection cycle without theassistance of any protein, some of them produce sub-genomic messenger RNAs (sgRNA)later in the infection cycle, that results in a second round of translation. The sgRNAs use tobe shorter mRNAs that derive from a replication intermediate molecule of dsRNA, the pre-cursor of genomic +RNA. This intermediate molecule of dsRNA has to be protected fromthe cytoplasm RNA silencing surveillance and preserved from degradation inside vesiclesor capsids. Viral complexes or factories made up from cellular membranes are observedin infections of +RNA viruses (Laliberte´ and Sanfac¸on, 2010; Laliberte´ and Zheng, 2014).Similarly, dsRNA viruses never release their genome from the capsid in order to avoid thecontact with the cytoplasm (Ahlquist, 2006). Instead a dsRNA intermediate, -RNA virusesuse a different mechanism for replication and transcription since -RNA molecule is alwaysprotected by a nucleocapsid, forming a ribonucleoprotein complex (RNP) (Reguera et al.,2016). The polarity of the -RNA genomes imposes transcription as the obligatory first stepin the virus gene expression programme, similar to dsRNA viruses. Thus, viral mRNAs arefirst produced from the parental RNPs while RNA replication intermediates are generatedat a later stage when viral nucleocapsids have been produced (Ortı´n and Martı´n-Benito,2015).Multipartite viruses can also have ssDNA genomes. While the mechanism of translo-cation of the genome to the nucleus is unknown, they do not package any additional pro-teins inside the capsid. However, they must encode viral proteins to mediate the transportthrough the nucleus of additional viral factors to initiate the replication through the rollingcircle mechanism (Krichevsky et al.).A detailed explanation of the viral cycles according to the nucleic acids composing thegenomes aforementioned are in the schemes of Figure I.3.Another factor that modulates the infection cycle is the host. Plants are particular hostsbecause the majority of their tissues are connected by specific channels named plasmod-esmata through which cells share nutrients, transmit information, and in a context of in-fection, serve as treadmills to spread viral infections (Niehl and Heinlein, 2010; Dall’Araet al., 2016). Most plant viruses are able to control these channels by means of move-ment proteins that allow the movement of viral particles, proteins or genomes from onecell to its neighbours (Kaido et al., 2011; Kawakami et al., 2004). A scheme of the molec-ular mechanisms that plant virus display to spread the infection from cell tissues to thexxvi INTRODUCTION+RNAdsRNAPROTEINS+RNA-RNAPROTEINSdsRNAsgRNAPROTEINSvesiclereplicase replicase+RNA-RNAPROTEINS-RNA+RNAssDNAdsDNA+RNAPROTEINSreplicasenucleusFigure I.3: Infection cycles of multipartite viruses constrained by genetic molecule.Once the virus particle gets disassembled in the cytoplasm, the genome of +RNA is di-rectly translated into viral proteins by cellular ribosomes. Then, the virus controls cellularmembranes and produce replication complexes inside vesicles where anti genomic -RNAstrands are produced directly from the genomic +RNA and a dsRNA intermediate of repli-cation is isolated from the cytoplasm. A second round of translation takes place from sgR-NAs and structural proteins are produced massively to generate capsids where the genomic+RNA molecules are encapsidated. Viruses with dsRNA do not get disassembled afterthe entrance in the cytoplasm and the replicase inside the capsid transcribe dsRNA intoviral messenger +RNA and genomic +RNA that exit the capsid and go to the cytoplasm.After translation, new capsids are assembled and genomic +RNA is encapsidated togetherwith the replicase. Then, genomic dsRNA is produced inside the new capsids. Similar todsRNA viruses, viral messenger +RNA and anti genomic +RNA are produced from -RNAsby viral replicases that are co-packaged with the genome. The genetic molecule of -RNAviruses and anti genomic +RNAs are always wrapped by a nucleocapsid forming a ribo-proteic filament. During replication nucleocapsids are displaced by the replicase from thetemplate genetic strand of +RNA and new genomic molecules of -RNA are wrapped bynewly synthesized nucleocapsids. Then the genomic ribonucleocapsids are encapsidatedtogether with the replicase. After uncoating, the viral ssDNA genome penetrates into thenucleus and is converted into dsDNA with the participation of cellular factors. dsDNAtranscription produces viral mRNAs and translation of viral proteins. Replication occursby rolling circle mechanism producing ssDNA genomes. These newly synthesized ssDNAcan either be converted into dsDNA and serve as a template for transcription/replication orbe encapsidated and released though cell lysis.rest of the plant are depicted in Figure I.4. There is a strong evidence that during themovement, multiple copies of a genome variant, probably inside genome complexes orviral factories (Kawakami et al., 2004; Cotton et al., 2009), are collectively delivered tothe neighbour cells, a process that assures that all the genetic segments of a multipartitevirus pass collectively to the next cell without loss of genetic information (Miyashita andKishino, 2010). In addition, several studies shown that viral proteins and sgRNAs actuallyTHE MULTIPARTITE LIFECYCLE xxviidiffuse long distances through the plasmodesmata from the initial infected cell (Heinleinet al., 1998; Sicard et al., 2019). This process could, in principle, complement any functionin trans to progress the course of infection in a cell where that specific genomic segmentwas not present (Sicard et al., 2019) Figure I.4.C.To efficiently travel to other leaves and cause systemic infections, plant viruses have tocross the vascular tissues, where they still move from one cell to another through widerchannels that connect vascular cells. There are plant viruses that cannot control the plas-modesmata and are they restricted to vascular tissues and phloem of the plant with noaccess to the leaves (Rasheed et al., 2006; Stewart et al., 2012).Once a virus establishes an infection in a plant cell it usually impairs the entry of otherrelated viruses to the same cell, a process called superinfection exclusion. This mechanismis based on the capacity of the viruses to control the plasmodesmata and other plant cellchannels, by blocking them when the infection is established in the cell. It is thought thatcell-to-cell movement is strongly affected by purifying selection (Zwart et al., 2014), andout of the 6 · 107 genomes that are approximately generated during a replication cycle ina cell (Nixon), the actual number of genetic variants that are available for transport arebetween one and two (Miyashita and Kishino, 2010; Tromas et al., 2014), and dependsmainly on the timing of movement in relation to the viral replication cycle (Tilsner andOparka, 2012). Cell-to-cell movement and superinfection exclusion are principal determi-nants for the low genetic variability observed in plant viruses (Dunham et al., 2014), andresult in a phenomenon called spatial clustering: a pattern that appears in infected leavesand veins when different virus biomarked strains are spatially segregated (Gutierrez et al.,2012). Whereas superinfection exclusion imposes a severe bottleneck during virus prop-agation in the plant (Zwart and Elena, 2015), it seems to be permissive to the need forcomplementation of the genetic segments of multipartite viruses. Collective cell-to-cellmovement and viral protein sharing are possibly a way to overcome the constraint of com-plementation for multipartite viruses, and a key factor for the success of multipartitism asa strategy of plant infection compared to other hosts. All together, a structured propagationmode seems to favour multipartite viruses (Aguirre and Manrubia, 2008), and plants offerthe perfect environment for this to take place.An exceptional feature of multipartite viruses is the observation that particles containingthe different genetic segments are not equally abundant after plant infection. The differ-ences of accumulation levels of the genome segments have been reported in laboratory con-ditions for several unrelated multipartite families (Grigoras et al., 2009; Sa´nchez-Navarroet al., 2013; Sicard et al., 2013; Wu et al., 2017). Interestingly, regardless of the initialinfection ratio, the relative frequencies amongst the genomic segments reach a constantcomposition that could be host-specific (Sicard et al., 2013). Genetic imbalance may occurat some point during the interaction with the host and, although the mechanism behind isstill unknown, it is likely to be linked to plant transportation and to the complementation offunctions in trans previously explained. Several experiments pointed out differences in themechanisms for systemic transportation of each segment of a tripartite virus. The systemicmovement of two of the genomic segments involve genomic RNA–RNA interactions butis independent of the third segment which interacts with other viral and host factors fortransportation (Torrance et al., 2009). It may be obvious that independent mechanisms forlong-distance movement in the plant will be reflected into differences in the accumulationlevels of each segment, although further data is needed to confirm this insight. Anotherrecent report indicates that different gene segments are preferentially replicated in eachcell when they co-localize with the replicase protein, even though the genetic segment thatencodes the replicase is absent. This way of replication might correspond with a winner-xxviii INTRODUCTIONR CPMPABco-infectioncell-to-cellmovementcollective transporttrans-complementationinfectionCDsuperinfectionexclusionvRNPPD12345123455cell nucleuschloroplastcapsidmovement proteinreplicaseviral nucleoproteinFigure I.4: General view of virus cell-to-cell and long-distance movement in plant tis-sues.(1) Viruses enter the plant epidermis through physical inoculation by insect vectors or bylesions in the leaves forming a local spot. Once inside a cell, virions are disassembled forreplication, and expression of the main functions encoded in the viral genome: replication,R, encapsidation, CP, and cell movement, MP (A). (2) Movement proteins, mediate thetransport of virions (B) or viral proteins (C) from cell-to-cell. They can enlarge cellularchannels called plasmodesmata, PD, and allow the circulation of viral particles or alter-natively, sometimes associated to cellular factors, they can interact with the viral genometo form transport complexes (vRNP) (D). Viral proteins, transported from cell-to-cell, cancomplement functions in trans, facilitating the infection in cells where the infection hasnot been fully established (C). Superinfection exclusion prevents a plant cell to get in-fected by surrounding viruses, until the current infection cycle is not finished (B). Virusescontinue replicating inside every infected cell until they reach the vein tissue for long-distance movement (3) before being finally getting released into systemic tissues (4) wherethey move freely without the assistance of MPs. Transported in the phloem, viruses enterupstream tissues to start new infection sites (5).takes-all strategy that could be behind segment unbalance. Task allocation might be a wayto optimize segment production in multipartite viruses, but this hypothesis has not beenproved yet.In order to move from one plant to another most plant viruses, including multipartite, aretransmitted by an insect vector that feeds on the plant (Power, 2000; Whitfield et al., 2015;Dietzgen et al., 2016). Other plant pathogens have shown to serve as vehicles of plant virustransmission such as other arthropods (Nault, 1997), nematodes and fungi, but horizontalOVERVIEW OF THIS THESIS xxixtransmission through seeds, or cell division is the second most frequent transmission form,causing persistent infections in plants (Roossinck, 2010). Vector transmission requiressome degree of specificity and in general specific viral proteins mediate the interaction ofthe virus with its vector (Rahim et al., 2007; Deshoux et al., 2018) and vice versa, whichmay be the result of a long-term co-evolution of virus with the vector. In fact, there isa high specificity of most plant viruses by their vectors, since only a few related insectspecies are usually capable of transmitting the virus, whereas the host-range can includeseveral species of different genera or even plant families (Power and Flecker, 2010; Lefeu-vre et al., 2019). The degree of specificity of the virus by its vector results in differences ontransmission efficiency. Viruses that only interact with vector mouth parts are poorly trans-mitted and only around 0.5 to 2 virus particles are transferred to susceptible plants (Mouryet al., 2007; Betancourt et al., 2008). However, viruses which enter the foregut or evenreplicate in the vector are transmitted in greater amounts (Gallet et al., 2018b). The overallestimated number of genomes transmitted by one insect vector is very narrow and, in prin-ciple imposes a severe bottleneck for the virus population. Although the benefits of vectortransmission are numerous, because these insects usually travel long distances and feedon different plant species providing many colonization options, low transmission numbersimpose a major constraint for plant virus evolution (Power, 2000; Zwart and Elena, 2015;Lefeuvre et al., 2019). Many vector insects propagate in plagues, a strategy that may resultin a larger number of virus particles entering the host, but all together, it is remarkablethat these severe bottlenecks are compatible with the persistence of multipartite viruses.Collective infection strategies may also contribute to the transmissibility of multipartiteviruses, including the possibility that several viral particles aggregate forming a collectiveinfection unit, or a versatile ploidy of the virions that permits the occasional encapsidationof multiple copies of the genome in one capsid. However, these transmission strategieshave not been confirmed in multipartite viruses (Richards and Tamada, 1992), but in sev-eral segmented (Lago et al., 2016; Galasso, 1967; Rager et al., 2002) and non-segmentedviruses (Bald and Briggs, 1937; Beniac et al., 2012).Plant virus populations have to cope with severe bottlenecks, both in the spread of thevirus from plant-to-plant and from cell-to-cell within the host. Considering that such re-duced populations are subjected to a strong genetic drift, the adaptation not only to theirhosts, but also to the vector required for their transmission is particularly constrained. Al-though certain plant particularities could favour the multiplication of multipartite viruses,it is still hard to imagine how multipartite virus populations adapt to the various imposedevolutionary pressures and how they maintain genome integrity.I.4 Overview of this thesisThis thesis is motivated by the intriguing evolution of multipartite viruses. We present anintegrated overview of multipartite viruses and their apparent costly lifestyles. We analysein depth the hypotheses about their emergence and persistence, as well as the evolutionarymechanisms that give rise to the transition to multipartitism. We explore the origin ofmultipartitism in the context of RNA virus evolution and the construction mechanisms ofthis genetic configuration.The Chapter 1 presents an exhaustive search and integration of data from different avail-able sources that serves to elaborate a detailed reference guide of multipartite and seg-mented viruses. The guide contains data on prevalence, families and genera, host range,number of genomic segments of all multipartite and segmented species found to date. Thexxx INTRODUCTIONoutstanding features of multipartite compared to segmented viruses are also discussed inthis chapter.The Chapter 2 presents an extensive review of the empirical observations of multipar-tite viruses, the proposed advantages of multipartitism to overcome the cost of multipleinfection, together with theoretical investigations of multipartite infection. Additionally,this chapter describes a theoretical model for the evolution of multipartitism motivated bythe most relevant empirical observations.The Chapter 3 discusses the ecological consequences of virus-satellite associations thatmay compensate for the cost of coinfection of both entities. That may represent a keyrole towards the emergence of a multipartite species. An original model is introduced anddiscussed in order to understand the ecological effects of those associations in a frameworkof viral competition. The implications on the evolution of multipartitism are also analysed.The Chapter 4 discusses the mechanisms governing the transition to multipartitism andpresents an hypothetical scenario for the transition towards multipartitism. The origin ofmultipartitism is contextualized for the different Baltimore classes were multipartitism hasbeen found. We focus in depth on the origin of multipartitism and genome segmentationin the evolution of RNA viruses. We present key evolutionary features of multipartite andsegmented viruses in the context of the global RNA phylogeny. This chapter is part ofthe work with the groups of Eugene V. Koonin and Mart Krupovic, and is contributed bythe collaboration of Yuri I Wolf, Darius Kazlauskas, Jaime Iranzo, Jens H Kuhn, MartKrupovic, Valerian V Dolja, Eugene V Koonin.The Chapter 5 is the last chapter dedicated to a general discussion of the thesis, and itemphasizes on the perspectives of this work.PART IOBJECTIVESThe global objective of this thesis is to advance in the understanding of genome mul-tipartition in virus evolution. The general objective of the thesis is divided into severalspecific objectives.1. Build a reference guide of multipartite viruses, to put them into context in relation tothe rest of the Virosphere in terms of outstanding features, prevalence and representa-tive families.2. Understand the principal mechanisms involved in the emergence of multipartite viruses.3. Study of the origin of multipartite viruses in the context of the evolution of RNAviruses.PART IIMETHODOLOGY0.5 Database management for constructing the multipartite and segmenteddatasetsWe initially look for multipartite and segmented families candidates retrieving informationfrom ViralZone, a database available at the website http://viralzone.expasy.org. The Viral-Zone project is handled by the virus program of the SwissProt group in the Swiss Instituteof Bioinformatics. ViralZone is an SIB Swiss Institute of Bioinformatics web-resourcefor all viral genus and families, providing general molecular and epidemiological informa-tion, along with virion and genome images. Each genus or family page gives an overviewof the basic biological information, virion and genome structure and composition, brieflifecycle and an easy access to UniProtKB/Swiss-Prot viral protein entries. Since data isonly available online, information was retrieved pseudo-manually. In particular, we wereinterested in the taxonomy browsed by the genome statistics where we retrieved familycandidates with segmented genomes. We set a preliminary dataset of segmented and mul-tipartite families and genera. We retrieved individual information about virion structure,number of genome segments and mode of transmission for each family. This dataset wassubsequently curated by looking at the literature to corroborate the preliminary candidates4and by manually adding new families and unassigned genera to obtain a final dataset ofmultipartite and segmented families and genera.The next step was to calculate the number of genera and species within each viral fam-ily. We used the latest version of the International Committee on Taxonomy of Virusesdatabase (ICTV) that was downloaded from the website https://talk.ictvonline.org/ in a ta-ble format. The ICTV provides a universal virus taxonomy and a classification schemefor most of the viral species described to date that is supported by verifiable data fromnucleotide sequences and expert consensus. We used the ICTV database to quantify thenumber of species and genera within each family and genera using simple python or Croutines that allow file input/output and string comparison.We assigned a host for each of the species in the dataset using a virus host databaseavailable at the website https://www.genome.jp/virushostdb/. The Virus-Host DB is anoriginal database produced by the Laboratory of Chemical Life Science. The data can bedownloaded from an FTP server in table format, where data is represented in the formof pairs taxonomy identifiers for viruses and their hosts. The taxonomy identifiers arethose of the of National Center for Biotechnology Information. We obtained the taxonomyidentifiers for each of the virus species in the dataset, retrieving the information directlyfrom the National Center for Biotechnology Information. We used the e–utilities an queryand database system at the National Center for Biotechnology Information (NCBI). Thee–utilities use a fixed URL syntax that translates a standard set of input parameters intothe values necessary for various NCBI software components to search for and retrieve therequested data.0.6 Analytical solutions and stability analysis of the model of viral compe-titionWe obtain the stationary solutions –fixed points– (H∗, X∗, Y ∗) of the system by equatingto zero the ordinary differential equations describing the model (H˙ = 0, X˙ = 0 and Y˙ = 0and obtaining the solutions H∗, X∗ and Y ∗. Conditions for existence and non-negativityof all variables have to be fulfilled for those solutions to be meaningful (being either astable or an unstable fixed point). Four stable non-negative equilibria solutions are found:(H∗, X∗, Y ∗) =(gd, 0, 0)(0.1)(H∗, X∗, Y ∗) =(d+ dxpx,gd+ dx− dpx, 0)(0.2)(H∗, X∗, Y ∗) =(d+ dypy, 0,gd+ dy− dpy)(0.3)(H∗, X∗, Y ∗) =(d+ dxpx, X∗,gd+ dy− dpy− pxpyX∗)(0.4)In order to simplify the mathematical formulation we are going to define the reproductiveratio, or reproductive success, Ri which measures the ability of either virus to invade thehost population.Ri =pid+ diwith i ∈ {x, y} (0.5)ANALYTICAL SOLUTIONS AND STABILITY ANALYSIS OF THE MODEL OF VIRAL COMPETITION 5The ratio d/g can be seen as a measure of the replacement or turnover time of healthyhosts.In the solution of eq. (0.1) none of the competing virus survives and the host populationH∗ = g/d is constant at equilibrium. The solutions in eqs. (0.2) and (0.3) correspond tothe survival of only one of the viruses infecting the population of hosts, thus entailing theextinction of the other one. As a result of the symmetry of the system, eqs (3.2) and (3.3)are analogous. Finally, the degenerate solution eq. (0.4) does not determine unequivocallythe variable X∗, indicating coexistence of the two competing viruses.0.6.1 Stability analysis of the model of viral competitionThe conditions for stability of the solutions are given by the sign of the eigenvalues ofthe Jacobian matrix associated to the system. When all eigenvalues are negative, the fixedpoint is stable. When one of the eigenvalues is zero and the rest are negative, the fixedpoints degenerate into a continuous set of quasi-stable solutions. The Jacobian matrix, J ,is the matrix of all first-order partial derivatives of a function or set of functions, and in oursystem takes the form:J =∂K1∂H∂K1∂X∂K1∂Y∂K2∂H∂K2∂X∂K2∂Y∂K3∂H∂K3∂X∂K3∂Y=−d− pxX∗ − pyY ∗ −pxH∗ −pyH∗pxX∗ pxH∗ − (d+ dx) 0pyY∗ pyH∗ − (d+ dy) 0(0.6)Where the functions K1,2,3 are:K1 = g − dH − pxXH − pyY HK2 = pxXH − (d+ dx)XK3 = pyY H − (d+ dy)YWe evaluate the J matrix of the system eq.( 0.6) for every fixed point in eqs.(0.4-7) asJ(H∗, X∗, Y ∗) and find, analytically when possible, the roots or eigenvalues, λi, of thecharacteristic polynomial p(λ) = det(J(H∗, X∗, Y ∗) − λI) by making p(λ) = 0. Solu-tions are stable if the λi are either negative or equal to zero. The conditions of existenceand stability are given for each solution and are discussed in depth in Chapter 3:1. Conditions for stability, existence and non-negativity of the solution in eq. (0.1)(H∗, X∗, Y ∗) =(gd , 0, 0)are:Rx < d/g and Ry < d/gThe conditions above indicate that if the growth of healthy hosts is sufficiently low, itprevents viral invasion of the population. Without the possibility to replicate in newsusceptible hosts, both viruses get extinct.2. Conditions for stability, existence and non-negativity of the solution in eq. (0.2)(H∗, X∗, Y ∗) =(d+dxpx, gd+dx − dpx , 0)are:Rx > d/g and Rx > Ry6A virus with a replicative success above that of its competitor and also higher than theturnover time of a healthy host will invade the population.3. Conditions for stability, existence and non-negativity of the solution in eq. (0.3)(H∗, X∗, Y ∗) =(d+dypy, 0, gd+dy − dpy)are:Ry > d/g and Ry > Rx4. Conditions for stability, existence and non-negativity of the solution in eq. (0.4)(H∗, X∗, Y ∗) =(d+dxpx, X∗, gd+dy − dpy −pxpyX∗)are:Rx = Ry >dg andgd+dx− dpx > X∗ > 0The conditions for coexistence of both viral types are very stringent in parameterspace, since only when the replicative successes of both viruses are equal and largerthan the turnover time of healthy hosts can coexistence be an stable outcome of thesystem.0.7 Results of the model of viral competition assisted by a satelliteWe obtain the fixed points (H∗, X∗, Y ∗, S∗) of the system by making the ordinary differ-ential equations equal to zero (H˙ = 0, X˙ = 0, Y˙ = 0 and S˙ = 0) and obtaining the valuesof H∗, X∗, Y ∗ and S∗ that verify them. The same way we did in the previous model, weonly consider solutions that exist and are non-negative for all variables.For S∗ = 0 four stable solutions are found analogous to solutions in eqs. (0.1–0.4) ofthe previous model:(H∗, X∗, Y ∗, S∗) =(gd, 0, 0, 0)(0.7)(H∗, X∗, Y ∗, S∗) =(d+ dxpx,gd+ dx− dpx, 0, 0)(0.8)(H∗, X∗, Y ∗, S∗) =(d+ dypy, 0,gd+ dy− dpy, 0)(0.9)(H∗, X∗, Y ∗, S∗) =(d+ dxpx, X∗,gd+ dy− dpy− pxpyX∗, 0)(0.10)For S∗ 6= 0 three additional solutions are found:Extinction of X population:H∗ =2gpsK ±√K2 + 4gps(− dps − pypsy + (d+dy)(psy+pY )d+ds )X∗ = 0,Y ∗ =d+ dsps− psypsH∗,S∗ = − (d+ dy − pyH∗)(d+ ds − psyH∗)ps(d+ ds − (psy + pY )H∗) . (0.11)RESULTS OF THE MODEL OF VIRAL COMPETITION ASSISTED BY A SATELLITE 7where K = ps(d+ g) + py(d+ ds)− (d+ dy)(psy + pY ).Co-existence of Y and S populations in eq.(0.11) indicates that when S*¿0 free-satellitepopulations Y*¿0 can still infect without the assistance of the satellite, and in addition,when Y*=0 automatically implies that S*=0 by eq. (3.8).Co-existence of all types (H∗, X∗, Y ∗, S∗) 6= 0 has two different solutions, a stablefixed point eq.(0.12) and a degenerate solution of type (H∗, X(S∗), Y ∗, S∗) for anyvalue of S∗ > 0 eq.(0.13):H∗ =d+ dxpx,X∗ =gd+ dx− dpx− pypY (d+ dx)pxps+(psy + pY )(d+ dy − pyH∗)(d+ ds − psyH∗)pxps(d+ ds − (psy + pY )H∗) ,Y ∗ =d+ dsps− psypsH∗,S∗ = − (d+ dy − pyH∗)(d+ ds − psy H∗)ps(d+ ds − (psy + pY ) H∗) . (0.12)H∗ =d+ dxpx,X∗ =gd+ dx− dpx− pY (d+ dy)pxps+psy + pYpxS∗,Y ∗ =pYpsd+ dxpx,S∗ > 0. (0.13)We are going to extend our definition of reproductive success in eq. (3.4) to includethe reproductive ratio of the tandem satellite-virus in order to simplify the mathematicalformulation. We had defined the productive success Ri as the ability of either virus (andvirus-satellite) to invade the host population.Ri =pid+ diwith i ∈ {x, y, sy, c} where pc = psy + pY (0.14)The meaning of Rsy differs from that of Rc. Let us assume that Rsy is the reproductivesuccess of the association between the virus and the satellite when they are jointly co-transmitted (coinfection), while Rc is the replicative success of the tandem virus-satellite,despite a potential loss of the satellite during transmission. This model assuresRc is alwayslarger than Rsy because intuitively the possibility of an independent transmission is largerthan a coinfection of the two entities.0.7.1 Stability analysis of the model of viral competition assisted by a satel-liteThe conditions of existence and non-negativity are those ensuring that every solution(H∗, X∗, Y ∗, S∗) are positive or zero. The conditions of stability (translating into cer-tain relationships to be fulfilled by the system parameters) guarantee that all eigenvalues8of the Jacobian matrix (described in the previous section) eq.(0.6) evaluated at a certainfixed point of the system, are negative. When one of the eigenvalues is zero and the restare negative, the fixed points degenerate into a continuous set of quasi-stable solutions.The Jacobian matrix for the model of viral competition assisted by a satellite is:−d− pxX∗ − pyY ∗ − pcS∗ −pxH∗ −pyH∗ −pcH∗pxX∗ pxH∗ 0 0pyY∗ + pY S∗ 0 pyH∗ −Dy − psS∗ pYH∗ − psY ∗psyS∗ 0 psS∗ psY ∗ −Ds + psyH∗where pc = psy + pY , Dy = d+ dy and Ds = d+ ds.We evaluate the Jacobian matrix for the solutions in eqs. (0.7–0.13) and solve the equationdet(J(H∗, X∗, Y ∗, S∗)−λI) = 0. The conditions of stability that satisfy that all λi <= 0are the following:Solutions for S∗ = 01. Conditions for stability, existence and non-negativity of the solution eq. (0.7)(H∗, X∗, Y ∗, S∗) =(gd , 0, 0, 0)are:Rx dg, Rx > Ry and Rx > RsyAn additional condition —compared to the model without the satellite— of Rx >Rsy is needed for invasion of X populations. One virus must overcome not only itscompetitor virus, but also its possible associations.3. Conditions for stability, existence and non-negativity of the solution eq. (0.9)(H∗, X∗, Y ∗, S∗) =(d+dypy, 0, gd+dy − dpy , 0)are:Ry >dg, Ry > Rx and Ry(1− γ)> Rsy (0.15)where γ =psd+ ds( gd+ dy− dpy)When the satellite successfully parasitizes its associate virus, S populations co-existwith Y populations (free of the satellite). However, parasitism is not unavoidable andfor condition in eq. (0.15) the virus gets rid of the satellite. This equation is translatedinto a threshold which forces the satellite to reach reproductive ratios —only whenNUMERICAL SIMULATIONS AND ALGORITHM IMPLEMENTATION 9it coinfects with the helper virus— much higher than the reproductive success ofthe virus alone; otherwise, the satellite cannot be maintained in the population. Inaddition, this implies that associations with satellites that milden the symptoms ofinfection or decrease the viral —and satellite— accumulation levels, are necessarilytransient because they lead to extinction.4. Conditions for stability, existence and non-negativity of the solution eq. (0.10)(H∗, X∗, Y ∗, S∗) =(d+dxpx, X∗, gd+dy − dpy −pxpyX∗, 0)are:Rx = Ry >dg, Rx = Ry > Rsy,gd+ dx− dpx> X∗ > 0d+ dsps− psypsd+ dxpx> Y ∗ > 0 andgd+ dx− dpx> X∗ >gd+ dx− dpx− py(d+ ds)− psy(d+ dy)pxpsSolutions for S∗ > 05. Conditions of stability, existence and non-negativity of the solution eq. (0.11) whenX∗ = 0 are:Ry ≶ H∗ ≶ Rc, H∗ > Rsy and H∗ > RxWhen H∗ = d+dxpx these conditions are equivalent to the conditions of stability andexistence of the solution in eq. (0.8), resulting in a bistable regime where the twosolutions are stable equilibrium states. Depending on the initial conditions, the systemapproaches one or another solution.6. Conditions of existence and non-negativity of the solution eq. (0.12) when all thevariables are different from zero (the conditions for stability cannot be analyticallyderived) are:Ry ≶ Rx ≶ Rc, and Rx > RsyNumerically, we found that the solution of eq. (0.12) is stable when Ry > Rx > Rc.This region corresponds to the brown and grey areas of co-existence in Figure 3.9.7. Conditions of existence, non-negativity and stability of the degenerate solution eq.(0.13)when all the variables are different from zero are:Rx = Ry = Rc >dg0.8 Numerical simulations and algorithm implementationThe algorithms of the models explained in the Chapters 2 and 3 were implemented in C-language. The compilation of the C-scripts was done using the compiler gcc version 4.8.4.The scripts models can be found in the Appendix A.1.The phylogenetic analysis were done using the Bioinformatics Toolbox of MATLABversion 7.11.0.584 (R2010b). The Bioinformatics Toolbox allows to read and write Newick-formatted tree files and translate them into MATLAB workspace phylogenetic tree objects.10This tool has as well a variety of built-in methods for the tree object such as: get prop-erty values, node names, calculate the patristic distances between pairs of leaf nodes andrepresentation. Additionally to the build-in functions several other MATLAB scripts andfunctions were developed and appear in the Appendix A.2 section.All the calculations were made in a Intel Xeon(R) CPU E5504 2.00GHz × 4 processor.0.9 Network analysisThe modules of the bipartite network of gene sharing, consisting on 622 plant virus genomes,are calculated using the tool Infomap. The Infomap is an algorithm that detects communi-ties in large networks, using the map equation framework (Rosvall and Bergstrom, 2008).The Infomap identify modules in the network by finding an efficiently coarse-grained de-scription of how information flows on the network. For example, a group of nodes amongwhich information flows quickly can be aggregated and described as a single highly con-nected module, and the modules are connected among each other by links which capturethe channels of information flow between them. It is based on a random walk which is theproxy for the information flow. The novelty of this algorithm is the efficiency to describea random walker on the network that makes it very fast in terms of computing time.We download and install Infomap as it is explained in the website https://www.mapeq-uation.org/code.html. We write the 622 virus species and the 102 gene families of the bi-partite network in pajek format and we run infomap using the options: undirected network,bipartite network, and a 100 number of trials. It determines 10 statistically significantmodules that are shown in Figure 4.7 and explained in Chapter 4.0.10 Weighted distances of not-ultrametric treesFor a specific branch x of length dx of a not-ultrametric tree we can define two subtrees iand j that sprout from it up to the leaves. The distance of consecutive branches are di anddj respectively. The weighted distance of the subtrees are wi and wj . Thus the weighteddistance to the leaves wx is:wx =dx2+diwi + djwjwi + wj(0.16)For the specific cases that x is the branch on which two leaves converge, the weighteddistance is:wx =dx2+d2i + d2jdi + dj(0.17)where di and dj are the lengths of the leaves.A more detailed explanation of these calculations can be found together with additionaldeveloped MATLAB functions in the Appendix A.2.PART IIIRESULTS AND DISCUSSIONCHAPTER 1PREVALENCE AND PRESENCE OFMULTIPARTITE VIRUS IN THEVIROSPHEREChapter overview: An exhaustive search and integration of data from different available sourcespermits the elaboration of a detailed reference guide of multipartite viruses. It contains dataon prevalence, families and genera, host range, number of genomic segments of all multipartitespecies found to date. The outstanding features of multipartite viruses are also discussed.1.1 The virosphere in numbersMost of our current knowledge on the prevalence of multipartite viruses is recorded onpublicly available databases from the International Committee on Taxonomy of Viruses(ICTV) (Lefkowitz et al., 2015) and ViralZone (Hulo et al., 2011). Figure 1.1 summa-rizes the distribution of multipartite and segmented viral species within the Virosphere,and highlights the heterogeneous distribution of genome types and hosts. About 14% of allannotated viral species have a multipartite genome, while 8% are segmented species (6%and 7% of the genera, respectively, according to data from 2018). There are 19 multipar-tite and 16 segmented families out of 143 viral families described. However, from 2015 to2018, the ICTV has recorded 1284 new viral species, including 89 multipartite, 112 seg-mented species, and several unassigned genera had established new families, illustrating inthis way a fast expanding of our knowledge about the Virosphere (Holmes, 2016; Li et al.,2015; Shi et al., 2016).Multipartite viruses: Organization, emergence and evolution.By Adriana Lucı´a Sanz12 PREVALENCE AND PRESENCE OF MULTIPARTITE VIRUS IN THE VIROSPHEREPotyviridae 27/195Bymovirus Ipomovirus Macluravirus Rymovirus TritimovirusTombusviridae 10/72DianthovirusUmbravirusAlphatetraviridae 3/10OmegatetravirusClosteroviridae 13/44CrinivirusGeminiviridae 322/362Begomovirus Nanoviridae 12/12NanovirusBabuvirusUnassignedBidnaviridae 1/1 BidensovirusVirgaviridae 19/56Hordeivirus Pecluvirus Pomovirus TobravirusFurovirusBromoviridae 33/33AlfamovirusAnulavirusBromovirusCucumovirus Ilarvirus OleavirusLuteoviridae 1/33EnamovirusPicobirnaviridae 2/2PicobirnavirusSecoviridae 68/77CheravirusSadwavirusTorradovirusComovirusFabavirusNepovirusSiphoviridae (66)Myoviridae (51)Papillomaviridae (49)Herpesviridae (16)Podoviridae (23)Polyomaviridae (5)Poxviridae (15)Baculoviridae (4)Polydnaviridae (2)Adenoviridae (5)Phycodnaviridae (6)Alloherpesviridae (4)Iridoviridae (5)Fuselloviridae (2)Lipothrixviridae (4)Pleolipoviridae (3)Sphaerolipoviridae (3)Tectiviridae (2)(5,1] (21)dsDNAssDNACaulimoviridae (8)Hepadnaviridae (2)Geminiviridae (7)Anelloviridae (12)Parvoviridae (13)Circoviridae (2)Inoviridae (2)Microviridae (6)Nanoviridae (3)(5,1] (4)dsDNA-RTPotyviridae (9)Betaflexiviridae (12)Secoviridae (9)Tombusviridae (17)Flaviviridae (4)Virgaviridae (7)Picornaviridae (31)Alphaflexiviridae (8)Closteroviridae (5)Tymoviridae (4)Coronaviridae (7)Luteoviridae (4)Bromoviridae (6)Togaviridae (2)Astroviridae (2)Sobemovirus (1)Dicistroviridae (3)Iflaviridae (1)Arteriviridae (2)Alphatetraviridae (2)Nodaviridae (2)Narnaviridae (2)Caliciviridae (5)Mesoniviridae (2)Hepeviridae (2)(5,1] (24)ssRNA(+)Bunyaviridae (5)Rhabdoviridae (14)Paramyxoviridae (7)Arenaviridae (2)Orthomyxoviridae (4)Ophioviridae (1)Tenuivirus (1)Filoviridae (3)Bornaviridae (1)Emaravirus (1)Pneumoviridae (2)(5,1] (9)ssRNA(-)Retroviridae (7)Metaviridae (3)Pseudoviridae (4)Reoviridae (15)Partitiviridae (6)Totiviridae (5)Endornaviridae (1)Chrysoviridae (1)Birnaviridae (4)(5,1] (6)dsRNA-RTdsRNACystoviridae1/1CystovirusMegabirnaviridae1/1MegabirnavirusUnassigned 7/40OurmiavirusIdaeovirusCilevirusJingmenvirusNodaviridae 9/9 Alphanodavirus BetanodavirusBenyviridae 4/4BenyvirusOrthomyxoviridae 8/8Influenzavirus A, B, CIsavirusQuaranjavirusThogotovirusRhabdoviridae 3/75VaricosavirusDichorhavirusUnassigned 7/19TenuivirusBunyaviridae 100/100HantavirusNairovirusOrthobunyavirusPhlebovirusTospovirusOphioviridae 7/7OphiovirusArenaviridae 34/34MammarenavirusReptarenavirusReoviridae 89/89CardoreovirusMimoreovirusOrbivirusRotavirusAquareovirusColitivirusOrthoreovirusSeadornavirusCypovirusDinovernavirusIdnoreovirusOryzavirusPhythoreovirusFijivirusMycoreovirusBirnaviridae 6/6AquabirnavirusAvibirnavirusBiosnavirusEntomobirnavirusPartitiviridae 60/60 AlphapartitivirusBetapartitivirusGammapartitivirusDeltapartitivirusCrypovirusUnassignedChrysoviridae 9/9ChrysovirusUnassigned 6/19Emaravirus10443745037333222191510100753412533934896028[6,12][5, 9][5, 8]20511689 86 76 69 55 53 5033121139589362685650432112897772605654[5, 9]4dsDNAdsDNA-RTssDNAdsRNA-RTssRNA(+)ssRNA(-)Polydnaviridae 53/53IchnovirusBracovirusdsRNAHostPlantInsectAnimalFungusBacteriaSegmentedMultipartite3819542 14THE VIROSPHERE IN NUMBERS 3Figure 1.1: Abundances of species in the Virosphere.Piechart showing the abundances of all currently annotated species in the ICTV (2015)for each viral family (Lefkowitz et al., 2015). Figures around the pie indicate the numberof species in a given family. Colours correspond to a viral family as in the color legend,where the number of genera corresponding to each family is given in parenthesis. Fami-lies with 4 or less species are merged together. Pop charts contain multipartite (solid line)and segmented (dashed line) viral families (names in bold face), and show the number ofmultipartite or segmented species/total number of species. Background colors of the popcharts indicate the preferred host.List of families with 4 or less viral species, with the number of genera first given betweenbrackets if different from one: *ssDNA: Bacilladnavirus 1, Spiraviridae 1, Genomoviridae1, Bidnaviridae 1; *+RNA: Leviviridae (2) 4, Hypoviridae 4, Benyviridae 4, Ourmiavirus3, Bacillarnavirus 3, Albetovirus 3, Sinaivirus 2, Jingmenvirus 2, Permutotetraviridae 2,Sarthroviridae 1, Carmotetraviridae 1, Barnaviridae 1, Alvernaviridae 1, Gammaflexiviri-dae 1, Marnaviridae 1, Roniviridae 1, Virtovirus 1, Polemovirus 1, Papanivirus 1, Labyr-navirus 1, Idaeovirus 1, Higrevirus 1, Cilevirus 1, Aumaivirus 1; *-RNA: Nyamiviridae (2)4, Deltavirus 1, Wastrivirus 1, Crustavirus 1, Chengtivirus 1, Arlivirus 1, Anphevirus 1,Sunviridae 1, Mymonaviridae 1; *dsRNA: Amalgaviridae 4, Picobirnaviridae 2, Botybir-navirus 1, Quadriviridae 1, Megabirnaviridae 1, Cystoviridae 1.The viromes of eukaryotes and prokaryotes have dissimilar abundances with respect toBaltimore’s classification of genome types (Baltimore, 1971). Whereas prokaryotic viruses(infecting bacteria and archaea) have a great preference for dsDNA genomes, the virusesof eukaryotes display a heterogeneous distribution among the six genome types (Kooninet al., 2015). Most viral species have a dsDNA genome, this encompassing almost allof the bacteriophages, viruses infecting archaea, and many viruses infecting eukaryotes—including animals— followed by single stranded RNA virus of positive polarity, whichcorrespond in their vast majority to plant viruses Figure 1.2. In fact, only two distantfamilies of RNA viruses infect bacteria: Leviviridae and Cystoviridae —the latter witha segmented genome and related to the animal virus family Picorbirnaviridae which isthought to be multipartite (Koonin et al., 2015; Krishnamurthy and Wang, 2018). Plantsare infected by all types of genomes, with the exception of dsDNA, and they are the mostcommon host followed by vertebrates —including humans. It is important to keep in mindthat this picture of the Virosphere reflects in all likelihood a strong sampling bias towardsthose viruses with an impact in economic activities and human health Figure 1.3.4 PREVALENCE AND PRESENCE OF MULTIPARTITE VIRUS IN THE VIROSPHERE020040060080010001200140016001800Number of SpeciesMultipartite speciesSegmented speciesAllRTdsRNA-RNAssDNA+RNAdsDNAFigure 1.2: Distribution of species according to the Baltimore classification.Histogram of the number of multipartite or segmented species depending on thegenome type with respect to the Baltimore classification (data obtained from the ICTV(2018) (Lefkowitz et al., 2015)). Black bars correspond to all species, blue and yellow barscorrespond to the abundance of multipartite and segmented species, respectively.RT stands for reverse transcribing RNA viruses.Multipartite speciesSegmented speciesAll020040060080010001200140016001800Number of SpeciesPlantsVertebratesBacteriaInvertebratesUnknownFungiArchaeaProtozoaAlgaeFigure 1.3: Distribution of viral species according to host.Histogram of the number of species depending on the host they infect (data obtained fromthe publicly available database “Virus-host DB” (Mihara et al., 2016). Black bars cor-respond to all species, blue and yellow bars represent the abundance of multipartite andsegmented species, respectively.PREVALENCE AND ORGANIZATION OF SEGMENTED AND MULTIPARTITE VIRUSES 51.2 Prevalence and organization of segmented and multipartite virusesMultipartite viruses infect mostly plants (90% of species and genera) followed, in muchlower frequency, by invertebrates and fungi Figure 1.3. They are present in 14 out of24 plant viral families, which represent 50% of all phytoviruses described. As plantpathogens, multipartite virus species display all possible genome types in phytoviruses,with the exception of retroviruses, and with the most abundant type being ssDNA genomesFigure 1.2. At the species level, this number is inflated due to the great success of bego-moviruses, a genus which contains over 300 species; however, most families with multi-partite species have positive polarity, ssRNA genomes Table 1.1. Figure 1.4 represents theabundances of plant virus species per genome type and viral family, where begomovirusesare again the most abundant multipartite virus, while other families could be underrepre-sented at the species level due to insufficient or skewed sampling.Most multipartite viruses (90% of species and 60% of genera) are transmitted by aninvertebrate vector, like aphids, whiteflies, planthoppers, mites or nematodes, in a non-circulative, circulative or propagative manner (Power, 2000). A small amount of generaare transmitted by plant pathogens, also acting as vectors, such as fungi or protozoans (5-6%). The rest, about 35% of the genera, are vertically transmitted plant viruses. Note thatvertical transmitted viruses are unaffected from the alleged cost of maintaining high MOI inwhich horizontally transmitted multipartite viral forms incur. Amongst 19 proposed multi-partite families, 6 infect animals. Bidnaviridae (Hayakawa et al., 2000; Hu et al., 2016) andAlphatetraviridae (Tomasicchio et al., 2007) are the only families that exclusively infectanimals (insects). Tenuiviruses, incorporated in 2018 to the family Phenuiviridae infectplants but they also replicate in their insect vectors (Falk and Tsai, 1998; Ramrez andHaenni, 1994). Nodaviridae is a particular multipartite family with the broadest host rangewhich infects fishes and insects, apart from fungi and plants (Olveira et al., 2009; Sellinget al., 1990). The dsRNA families Partitiviridae and Picobirnaviridae contain possiblebipartite species infecting molluscs and mammals, respectively (Kim et al., 2008; Nibertet al., 2014; McDonald et al., 2016). Finally, Jingmenvirus is an unclassified genus isolatedin ticks and mosquitoes (Ladner et al., 2016). The major representative infecting fungi isthe family Chrysoviridae (Ghabrial et al., 2008) together with several genera of the familyPartitiviridae (Nibert et al., 2014).Table 1.1 lists all described families of multipartite viruses and their capsid structure,their abundances in terms of genera and species, the number of genomic segments theyhave, the hosts they infect, and their transmission mode. Looking at the numbers, there isan overwhelming majority of bipartite genera, with a rapidly dropping number of genera asthe number of segments increases Figure 1.5. There is still a significant number of generawith 3 or 4 segments, but genera with over 4 segments are rare, with the exception of thequite diverse Nanoviridae family, holding up to 8 segments. It is relevant that most generainfecting hosts different from plants are bi- or tripartite, with some exceptions containingvery few species. For example, Phenuiviridae has 4 to 6 segments and beyond plants,infect planthoppers (Falk and Tsai, 1998); jingmenviruses, which infect only insects, have4 segments —two of them of flaviviridae origin and two other segments of unknown origin(Qin et al., 2014; Ladner et al., 2016).Attending to genome composition, multipartite RNA plant viruses present two consti-tutive segments containing essential genes for infection, with extra segments with not wellknown functions usually needed to accomplish infection, such as in the families Benyviri-dae and Aspiviridae. The tripartite family Bromoviridae and genera Hordeivirus and Po-movirus from the family Virgaviridae are an exception to the former rule, since they bear6 PREVALENCE AND PRESENCE OF MULTIPARTITE VIRUS IN THE VIROSPHEREGeminiviridaePotyviridaeBetaflexiviridaeSecoviridaeTombusviridaeCaulimoviridaeVirgaviridaeAlphaflexiviridaeClosteroviridaeTymoviridaeLuteoviridaeBromoviridaePartitiviridaePseudoviridaeRhabdoviridaeSobemovirusReoviridaeEndornaviridaeNanoviridaeBunyaviridaeTenuivirusOphioviridaeEmaravirusBenyviridaeAmalgaviridaeMetaviridaeAlbetovirusOurmiavirusMarnaviridaeCilevirusIdaeovirusAumaivirusPolemovirusVirtovirusPapanivirusHigrevirusMultipartite speciesSegmented speciesAll species100020406080Number of Species300Figure 1.4: Histogram of the number of plant virus species per viral family.Histogram of the number of virus species per plant viral families and unassigned plantgenera (data obtained from the ICTV (2015) (Lefkowitz et al., 2015)). Black bars corre-spond to all species, blue and yellow bars correspond to the abundance of multipartite andsegmented species, respectively. Only three segmented families infect plants, Fimoviridae(Emaravirus), Peribunyniaviridae (Tospovirus) and Reoviridae (4 genera).constitutive genes distributed among all the three segments. Multipartite DNA plant virusesappear with two types of genome configurations. A representative of the first type is thegenus Begomovirus (in the Geminiviridae family) which has a principal segment contain-ing the main genes for infection. This segment is accompanied either by an auxiliarysegment or by a satellite that modify host range and symptoms (Mansoor et al., 2003). Thesecond type is represented by the Nanoviridae family. This is an extreme case of multi-partitism, with each of the 6 to 8 separated segments coding for a gene —though not allsegments are essential for infection in vitro (Timchenko et al., 2006).Viruses with segmented genomes are highly represented among viruses with negativepolarity, ssRNA genomes Figure 1.2. In particular, 13 of 15 families bearing -RNA aresegmented. The complete order Bunyavirales, the families Arenaviridae and Ortomyx-oviridae and the recently isolated families Quiniviridae, Chuviridae and Yueviridae areexamples of this genome type (Maes et al., 2018; Li et al., 2015; Shi et al., 2016). In addi-tion, 4 segmented families have dsRNA genomes, and the class of dsDNA genomes has aunique representative family, Polydnaviridae.Segmented viruses infect plants, although not as efficiently as multipartite viruses do.Three segmented families are phytoviruses: Fimoviridae (Emaravirus), Peribunyniaviri-dae (Tospovirus) and Reoviridae Figure 1.4. The host range is much wider in segmentedviruses, as compared to multipartite viruses, and includes bacteria (Cystoviridae, as men-tioned above), fungi, plants, and animals, with vertebrates and invertebrates being the mostfrequent hosts. Reoviridae holds the broadest host range of the segmented families. Withinthis family, each genera is specialized in a different host, a feature speaking for specializa-tion that is absent in multipartite viruses.Whereas the number of multipartite genera decreases rapidly with the number of seg-ments, many segmented genera reach over 3 segments, suggesting different pressures areacting for each genome architecture Figure 1.5 —the most obvious being the cost of infor-mation loss upon virus propagation, which is not affecting segmented genomes. The major-PREVALENCE AND ORGANIZATION OF SEGMENTED AND MULTIPARTITE VIRUSES 7ity are membrane-enveloped viruses, barring Birna-, Megabirna- and Reoviridae familiesTable 1.2. With the exception of Tenuivirus —a multipartite genus in the family of seg-mented virus Phenuiviridae— it is remarkable that viral families with segmented genomesonly contain genera with this architecture, while most families with multipartite generaalso contain monopartite genera. This observation suggests that a possible evolutionarypathway towards multipartite genomes is their emergence from monopartite genomes, andeven also segmented genomes, as happens for tenuiviruses.MultipartiteSegmented05101520253035Number of Genera2 3 4 5 6 7 < 8Number of SegmentsFigure 1.5: Distribution of the number of genomic segments.The histogram shows the distribution of genera by the number of genomic segments theyhold. The number of segments decreases rapidly for multipartite genera, a tendency thatit is not followed by segmented genera (data obtained from the ICTV (2015) (Lefkowitzet al., 2015) and ViralZone (Hulo et al., 2011).8 PREVALENCE AND PRESENCE OF MULTIPARTITE VIRUS IN THE VIROSPHERETable 1.1: Multipartite familiesCapsid Family (genera) Species (Total) Segments Host Transmission-RNAAspiviridae (1) 7 (7) 3-4 Plants Insects, FungiPhenuiviridae (1) 7 (32) 4-6 Plants,InsectsaInsects(circulativea)Rhabdoviridae (2) 3 (135) 2 Plants Insects, Fungi+RNAAlphatetraviridae (1) 3 (10) 3 Insects Oral routeLuteoviridae (1) 2 (36) 1b Plants InsectsNodaviridae (2) 11 (11) 2 Vertebrates,Insects,Plants, FungiContactSecoviridae (7) 79 (86) 2 Plants NematodesTombusviridae (2) 12 (74) 2b Plants Contact, InsectsUnassigned (5c) 12 2, 2, 3, 3, 4 Plants, Insects Pollen, InsectsBromoviridae (6) 36 (36) 3 Plants InsectsBenyviridae (1) 4 (4) 2,4-5d Plants ProtozoaVirgaviridae (5) 20 (59) 2 or 3 Plants Seeds, Fungi,NematodeseClosteroviridae (1) 14 (50) 2 Plants InsectsPotyviridae (5) 30 (205) 2 Plants Fungi, InsectsdsRNAChrysoviridae (1) 9 (9) 2 Fungi, Plants Cell divisionPartitiviridae (5+15f ) 45 (60) 2 Plants, Fungi,MolluscsCell divisionPicobirnaviridae (1)g 2 (2) 2 Mammals Oral routessDNABidnaviridae (1) 1 2 Insects Oral routeNanoviridae (2+1h) 12 (12) 6 to 8 + satel-litesPlants InsectsGeminiviridae (1) 388 (441) 2, 1+satellite Plants Insectsa Tenuivirus in the family Phenuiviridae infects plants, but is transmitted by a insect vector where it also replicates (Ramrez and Haenni,1994).b The species Pea enantion mosaic virus is a bipartite virus whose segments belong to two unrelated genera, Luteovirus and Umbravirus(Tombusviridae), each expressing their own RdRp. The same happens with Ground rosette virus (Syller, 2003)c Unassigned genera: Ourmiavirus (3 species, 3 segments), Idaeovirus (one specie, 2 segments), Cilevirus (3 species, 2 segments) andJingmenvirus (2 species, 4 segments) (Ladner et al., 2016).d Benyviridae has only one genus, Benyvirus, which consist of 2 bipartite species and 2 species with 2 constitutive genes and a variablenumber of segments up to 5 of function unknown (Dall’Ara et al., 2016).e Virgaviridae has 5 genera Hordeivirus and Pomovirus with 3 segments,and Furovirus, Pecluvirus, and Tobravirus with 2 segments.Only Tobravirus is transmitted by a nematode vector, the rest of genera are transmitted by a fungus or are seed-borne.f Partitiviridae is a recently restructured family with 5 genera and 15 unassigned species which contains genera infecting only plants,only fungi, both simultaneously, and also one genera infecting protozoa (Nibert et al., 2014) and another infecting molluscs (Kim et al.,2008).g Picobirnaviridae has a bipartite genome thought to be independently encapsidated (McDonald et al., 2016)h Nanoviridae is a family with 2 genera (hexa- and octapartite) and 1 unassigned species. Many or the wild infections are accompaniedby Rep satellites (Grigoras et al., 2008).PREVALENCE AND ORGANIZATION OF SEGMENTED AND MULTIPARTITE VIRUSES 9Table 1.2: Segmented familiesCapsid Family (genera) Species Segments Host Transmission-RNA? Qinviridae (1) 8 2 Nematodes,Arthropods?? Chuviridae (1) 29 2 Arthropods,Nematodes,Vertebrates?? Yueviridae (1) 2 2 Arthropods ?Arenaviridae (3) 41 2 Mammals,ReptilesZoonosis, FomiteFimoviridae (1) 9 4 Plants, Che-licerataInsects, Cell divi-sionHantaviridae (4) 41 3 Mammals Zoonosis, OralrouteNairoviridae (3) 16 3 Chelicerata,MammalsZoonosis, InsectsPeribunyaviridae (4) 63 3 Mammals,Aves, PlantsZoonosis, InsectsPhasmaviridae (5) 9 3 Insects ?Phenuiviridae (11) 25 3 Insects, Mam-malsZoonosis, InsectsOrthomyxoviridae (7) 9 6 or 8 Chelicerata,Mammals,FishesZoonosis, InsectsdsRNABirnaviridae (4) 6 2 Molluscs,Fishes, Aves,InsectsContactMegabirnaviridae (1) 1 2 Fungi Cell divisionQuadriviridae (1) 1 4 Fungi Cell divisionCystoviridae (1) 7 3 BacteriaReoviridae (15) 91 8-12 Mammals,Aves, Fishes,Reptiles,Insects,Chelicerata,Plants, FungiInsects, Oralroute, EggsdsDNAPolydnaviridae (2) 53 10-11 Insects EggsCHAPTER 2UNDERSTANDING THE EMERGENCE OFMULTIPARTITE VIRUSESChapter overview: Empirical observations of multipartite viruses, the proposed advantages ofmultipartitism to overcome the cost of multiple infection, together with theoretical investigationsof multipartite infection are reviewed in this chapter. Additionally, we focus on certain relevantempirical observations that motivate a theoretical model for the evolution of multipartitism.2.1 Qualitative observations of multipartitismAs of today, quantitative data on multipartite viruses and their adaptive advantages is mea-gre, and to a large extent is restricted to the molecular properties of those viruses (Sicardet al., 2016; Dall’Ara et al., 2016; Zhang and Qu, 2015). Main empirical findings aresummarized in this section and in Table 2.1. We do not intend to be exhaustive, rather fo-cusing on the observations that improve the understanding of the evolutionary and adaptivemechanisms behind multipartitism.The first (indirect) evidence for the existence of multipartite viruses arose through ex-periments showing a relationship between viral dose and number of local lesions steeperthan predicted by the independent-action hypothesis model. This model, proposed in the1940 decade (Price and Spencer, 1943), assumes that different viral particles do not inter-act during the infection process. A nice summary of this initial discovery and the additionalresearch it triggered can be found in (Sa´nchez-Navarro et al., 2013). Only three families(seven genera) of multipartite viruses, all having +RNA genomes, were known in the earlyMultipartite viruses: Organization, emergence and evolution.By Adriana Lucı´a Sanz1112 UNDERSTANDING THE EMERGENCE OF MULTIPARTITE VIRUSES1980s. The first multipartite virus with DNA genome described was a begomovirus (Haberet al., 1981), and few years later a bipartite ophiovirus with a +RNA was the first examplewith a filamentous nucleocapsid (Derrick et al., 1988).A major leap in the quantitative characterization of multipartitism arrived with a cellculture experiment where the model system was an unsegmented animal virus in the Picor-naviridae family, foot-and-mouth disease virus (Garcı´a-Arriaza et al., 2004). After a longnumber of serial passages at high viral densities or multiplicities of infection (MOI), twodefective and complementary viral genomes spontaneously emerged. Competition experi-ments between the evolved bipartite form and the wild, parental type, demonstrated the su-periority of the former under high MOI conditions, while the wild parental type re-emergedthrough recombination as soon as the population was subjected to bottlenecks. Eventually,it was shown that defective complementary particles were more stable between infectionevents, this advantage sufficing to displace the parental wild type (Ojosnegros et al., 2011).The unequal abundances of fragments qualitatively present in early experiments (Frenchand Ahlquist, 1988; Hajimorad et al., 1991) have been unequivocally detected and quan-tified in other multipartite viruses (Sicard et al., 2013; Hu et al., 2016; Wu et al., 2017).Nanoviridae is a multipartite viral family with species having up to eight independent seg-ments. Though they manage to maintain all those segments in vivo, it has been shownthat some of the segments are actually dispensable in vitro (Timchenko et al., 2006). Thisis a puzzling observation considering the cost imposed by any additional segment on theminimum MOI for the maintenance of genetic information. The variation in orders ofmagnitude of the relative frequencies of the segments after an infection cycle and its de-pendence with the host have led to suggesting that multipartite viruses might benefit fromgene-copy number regulation (Sicard et al., 2013). The situation of segment unbalance ismuch more demanding regarding survivability than cases where all segments are equallyabundant, since the MOI must be high enough so as to guarantee that the less abundantsegment is not stochastically lost (Gallet et al., 2018a).Empirical knowledge on how multipartite viruses propagate between and within hosts isvery limited. The number of viral particles transmitted from plant to plant through an insectvector–a strategy used by most plant viruses (Dietzgen et al., 2016)– ranges from 0.5 to 3.2particles (Betancourt et al., 2008; Moury et al., 2007), although some authors suggestedthis number could be higher (Sicard et al., 2016). The question of how the requirement ofhigh MOI is circumvented remains as a main open problem, strongly suggesting that mech-anisms promoting non-independent propagation (Sanjuan, 2017; Taylor et al., 2014) mightbe at play. High MOI could be facilitated through the formation of complexes containingseveral heterogeneous particles, or alleviated by the fact that some vectors, like aphids,propagate in plagues, a dynamics that may result in a large number of viral particles in-fecting one host. Interestingly, it has been shown that a correlation between the populationsize of vectors and the maintenance of non-essential genomic segments exists (Betancourtet al., 2016). Once a host organism is infected, cell-to-cell propagation depends to a highextent on the molecular interactions between virus and host, and is often mediated by spe-cific proteins (Sicard et al., 2016; Niehl and Heinlein, 2010). It has been put forward thatgenome parts might independently move within the tissue of an infected plant, such thatthe simultaneous presence of all segments in a precise cell before the infection cycle startswould not be mandatory (Sicard et al., 2016).QUALITATIVE OBSERVATIONS OF MULTIPARTITISM 13Table 2.1: Empirical highlights and mathematical models of multipartitism in chronollog-ical orderReference Nomenclature Advantage of seg-mentsType of model Novelty and/or em-pirical basisPrice andSpencer,1943The relationship between the dose and the number of local lesions for different plantviruses is steeper than predicted by the independent-action hypothesis model Alfalfa mo-saic virus, Tobacco necrosis virus, Tobacco ringspot virusHaber etal, 1981First description of a multipartite virus with ssDNA genome of Bean golden mosaic virusPressing& Reaney,1984Multi-compartmentvirusIncrease in copyingfidelity under highmutation rateQuasispecies model,included thermody-namics of the (noisy)replication processFirst quantitativemodel. Most mul-tipartite virusesdescribed at the timehad an RNA genomeNee, 1987 Covirus Higher copying fi-delity and replica-tion rateStable states of thecompetition betweenmono-and multipar-tite forms. IndividualselectionWeighted the op-posite effects ofMOI and mutations.Followed argumentsin (Pressing andReanney, 1984)Derrick etal., 1988First description of a ssRNA multipartite virus of negative polarity and of the virion as afilamentous nucleocapsid of Citrus psorosis virusFrenchandAhlquist1988The different accumulation levels of genomic segments for the tripartite Brome mosaicvirus was shownIltis et al.,1989Multi-componentvirusN/A Probabilistic, in-cluded viral interfer-enceDynamics ofinfection-dilutionin cell culture series.Motivated by (Priceand Spencer, 1943)Nee &MaynardSmith,1990Covirus, mul-ticomponentvirusHigher copying fi-delityDeterministic, gametheoryOverall and inte-grative review ofmolecular para-sites. Discussionof pros and cons ofmultipartitismChao,1991Multi-componentvirusReduced mu-tational load,enhanced sex asre-assortmentStable states of thecompetition betweenmono-and multipar-tite formsComplementationand reassortment asa form of sex. Fol-lows (Nee, 1987) andincludes frequency-dependent replicationSzathma´ry,1992Covirus, multi-compartmentvirusLocal clustering Structured dememodel, game theoryLocal replication, ef-fects of compartmen-talization in the es-tablishment of defec-tive viral forms andcovirusTable continues on the next page14 UNDERSTANDING THE EMERGENCE OF MULTIPARTITE VIRUSESNee, 2000 Covirus Higher colonizationprobabilityEcological and epi-demiological modelVirus-covirus co-existence is unlikely,in agreement withobservationsGarcı´a-Arriaza etal, 2004An evolutionary transition from a monopartite to a fitter bipartite viral form is possible andspontaneously arises in laboratory conditions under high MOI; Foot-and-mouth diseasevirusTimchenkoet al, 2006Several genome segments are dispensable to develop infection in laboratory conditions,though they are maintained in vivo; Faba bean necrotic yellow virusMoury etal, 2007Narrow bottlenecks exist during vector transmission of a plant virus. The average numberof particles transmitted by an aphid is 0.5-3.2; Potato virus YMiyashitaet al, 2010Narrow bottlenecks exist in cell-to-cell movement during tissue infection, promoting su-perinfection exclusion, though it does not affect genome complementation; Soil-bornewheat mosaic virusOjosnegroset al, 2011The advantage of the bipartite form in (Garcı´a-Arriaza et al., 2004) is identified and quan-tified. Shorter genomes independently encapsidated are more stable. No advantages inreplication rate or in the effect of mutations are detected; Foot-and-mouth disease virusIranzo &Manrubia,2012Multipartite Higher stability,lower degradationCombinatorialstochastic model,game theoryGrounded in re-sults by (Garcı´a-Arriaza et al., 2004;Ojosnegros et al.,2011), implementsfrequency-dependentreplication and popu-lation bottlenecksSicard etal, 2013The significant imbalance in the abundances of different fragments in a multipartite virus ishypothesized to stem from a gene-copy number regulation; Faba bean necrotic stunt virusSa´nchez-Navarro etal, 2013Genome segments appear in different frequencies and lead to differences in the invasionprobabilities of each particle. Infection kinetics are delayed for a tripartite virus comparedto the monopartite situation (updating results in (Price and Spencer, 1943) and subsequentresearch); Alfalfa mosaic virus, Nicotiana tabacumValdano etal, 2019MultipartitevirusHigher transmissi-bilityEpidemic spatialmodelComplex network ofhost contacts. Gen-eralizes (Iranzo andManrubia, 2012) tothe ecological con-textN/A: Not applicableIn fact, it has been shown that individual segments replicate in different cells in awinner-takes-all-like strategy, with independence of the presence of products of that seg-ments, suggesting that mechanisms of replication and expression are not necessary cou-pled (Sicard et al., 2019). Also, different segments might form aggregates that move as aunit among cells –a characteristic that has been described jointly with superinfection ex-clusion (Miyashita and Kishino, 2010; Niehl and Heinlein, 2010), while cell-to-cell prop-agation in the plant tissue is in general strongly affected by severe bottlenecks (Wu et al.,2017; Gopinath and Kao, 2007; Zwart et al., 2014).Finally, the a priori narrow range of hosts of multipartite viruses has broadened withthe description of jingmenviruses, able to infect mosquitoes and ticks; tentative species ofthis genus have been isolated in non-human primates (Ladner et al., 2016). It cannot bediscarded that classifying multipartite viruses as plant viruses be a simplification resultingfrom a skewed and incomplete sampling (Holmes, 2016).PROPOSED ADVANTAGES OF MULTIPARTITE VIRUSES 152.2 Proposed advantages of multipartite virusesThere are several possible advantages of multipartitism that were conjectured early in theliterature. It was suggested that increased variability through segment shuffling could con-fer an adaptive advantage through the generation of hybrid viruses where clonal interfer-ence would be avoided (Fulton, 1980), a principle with possible applications to geneticengineering (van Vloten-Doting, 1983). Also, it was put forward that a multipartite viruscould benefit from the spatial or temporal regulation in the synthesis of different proteinsand genome parts (Harrison et al., 1976). The early identification of multipartitism withssRNA genomes of positive polarity associated that particular genomic organization togenome type, and led to propose different advantages relying on RNA plasticity (Reanney,1982). Other authors agreed that potential benefits of genome segmentation could be an in-creased genetic flexibility or a larger control of gene expression, and also suggested moreefficient packaging and a possible increased resistance to inactivation by environmentalagents as additional advantages (Lane, 1979). However, exceptions to the observationsmotivating most of those hypotheses soon caused their dismissal.Two advantages entertained that caused significant controversy were the potentiallylower mutational load of (shorter) segmented forms (Chao, 1991) and an increase in thereplication rate of segments with respect to the monopartite parental type (Nee, 1987) incases where this evolutionary pathway was assumed as the hypothetical origin of multi-partite viral forms. Interestingly, the only experiment to our knowledge where the muta-tional load and the replication rate were simultaneously measured (Ojosnegros et al., 2011)could not identify any significant difference between the parental and the derived, bipartitegenome.Perhaps the variable frequency of the segments could represent a genuine advantage ofmultipartitism by granting additional regulation of gene expression through differences inthe copy number of the genes (Sicard et al., 2013, 2019; Hu et al., 2016; Wu et al., 2017),although this possibility has not been fully tested yet.2.3 Quantitative approaches to disclosing the advantages of multipartitismThe infection process of multipartite viruses has been mathematically addressed by sev-eral authors. It took some time to formalize the relationship between the dose D and thenumber of local-lesions curve and the multipartite character of the infecting virus (Fulton,1962). Beyond dilution, the number of local lesions depends on the number of segmentsof the virus and on possible interferences among the infecting particles (Iltis et al., 1989).Killingtime curves measure the time required to completely kill a monolayer of culturecells as a function of the initial MOI (dose), and have different shapes depending on thevirus being mono or multipartite. A formalism similar to that in (Iltis et al., 1989) has beenused, together with propagation dynamics of infection by mono and bipartite viral formsto demonstrate that, in the former case, the killing time T is proportional to log(1/D),while in the latter it decreases as 1/D2 (Manrubia and La´zaro, 2006). That is, monopartiteviruses kill the cellular monolayer qualitatively faster than any multipartite form spreadingin similar conditions.The qualitative hypotheses in the former section can be cast in the form of mathematicalmodels that clarify their plausibility and limitations. All models introduced below assume ahigh MOI, needed to guarantee successful infection of the multipartite virus, and a model-dependent advantage to balance the high MOI cost. Table 2.1 shows the main properties16 UNDERSTANDING THE EMERGENCE OF MULTIPARTITE VIRUSESof several of the mathematical models discussed in this section, highlighting the overallviewpoint and/or the employed techniques.A broad class of models deals with the advantages enjoyed by multipartite forms com-pared to monopartite ones. The first mathematical model of this kind, largely inspiredby Manfred Eigens theory of molecular quasispecies (Eigen, 1971) and its application toviruses (Domingo et al., 1978), was proposed by Pressing and Reanney (Pressing and Re-anney, 1984). They argued that genome segmentation compensates for the high error levelsaffecting RNA replication, since smaller genetic subunits present a lesser target size to thevarious error-promoting agents. Group selection was implicit in this model, an assumptionnot needed if selection is to act on each individual segment. In such a scenario, the multi-partite virus could displace the monopartite parental type at high mutation rates if the MOIwas sufficiently high (Nee, 1987). Other models, understanding multipartite reproductionas a form of sex, proposed an alternative explanation based on the premise that co-infectiongroups are units of selection —but arguing that this is not group selection in the traditionalsense—and that the high mutation rate is actually the selective pressure responsible for theemergence of multipartite forms (Chao, 1991). A detailed analysis of the meaning of viralsex and of effective levels of selection can be found in (Szathma´ry, 1992). Viewing theproblem from an ecological perspective, the stable coexistence between multipartite andmonopartite cognate forms seemed highly unlikely (Nee, 2000).A stochastic model based on the experimental results in (Garcı´a-Arriaza et al., 2004;Ojosnegros et al., 2011) identified two evolutionarily stable states (Iranzo and Manrubia,2012). For sufficiently high MOI the multipartite form displaced the parental wild type,while coexistence occurred for low MOI. The assumption that multipartite variants arisefrom a non-segmented parental form limits to 3 or 4 the number of segments achievable,since the MOI required to displace the parental wild type turns unrealistically high. Theprecise cost of MOI to maintain a multipartite genome has been specifically addressed alsoin a model based on demes (Szathma´ry, 1992). Compartmentalization is an indirect formof group selection (Wilson, 1975) conceptually linked to game theory that was implicitin (Szathma´ry, 1992; Iranzo and Manrubia, 2012). The powerful context and tools of gametheory, also discussed in (Nee and Maynard-Smith, 1990; Nee, 2016) might be a fruitfulavenue for research that deserves further attention.Most mathematical approaches have used mean-field models and, in fact, the influenceof space in the competition between mono and multipartite viruses remains widely unex-plored. Exceptions are two structured models (Iranzo and Manrubia, 2012; Szathma´ry,1992), where mutualistic interactions occur among segments within a coinfection group,and an epidemic model developed in the context of complex network theory and basedin (Iranzo and Manrubia, 2012) where it has been shown that the architecture of contactsbetween hosts is a key factor for the survival of multipartitism (Valdano et al., 2019).Regular contacts favour the fixation of multipartite viral forms, indirectly explaining theobserved correlation between the intensification of agriculture and the radiation of mostcurrently known plant viruses (Fargette et al., 2008; Gibbs et al., 2008; Pagan and Holmes,2010) and, in all likelihood, also an expansion of multipartite viruses.2.4 Emergence of multipartite viral forms through genome segmentationThere are many different evolutionary pathways to multipartitism that can be entertained.Their likelihood is not identical, as will be discussed in depth in Chapter 4. Here, we wouldlike to discuss a simple model for the evolution of bipartite viral forms through generationEMERGENCE OF MULTIPARTITE VIRAL FORMS THROUGH GENOME SEGMENTATION 17of defective, complementary variants, generated by the monopartite virus under unfaithfulreplication. The motivation of the model comes from a series of empirical observationsthat suggest that cooperation between defective mutants is a plausible evolutionary path-way towards multipartitism (Garcı´a-Arriaza et al., 2004; White, 1996; Kim et al., 1997).However, there is no direct or indirect evidence of the plausibility of this pathway in nat-ural scenarios since, as of yet, it is unclear where the different segments in a multipartiteviral come form.Any plausible scenario for the emergence of a multipartite genome organization has toensure a high viral density if independent propagation of each segment is assumed. Oth-erwise, the complementation of the independent genome segments might repeatedly fail,leading to the extinction of the virus. The questions we tackle here are, what is the quantita-tive effect of the MOI? and, how does the mutation rate of the wild type affect its survival?The first question is directly related to the main constraint ascribed to multipartite genomefor the maintenance of the genomic information; the second is inspired in quasispeciestheory, where a high mutation rate might entail a cost sufficiently high so as to cause theextinction of the wild type form.Here we investigate the effect of genome segmentation as an unavoidable mechanismfor the emergence of multipartite virus in the wild. Viral infections often produce defectiveparticles which contain incomplete genomes derived from errors in the replication processof the original virus (Bangham and Kirkwood, 1993). The deletion mutants lack someof the essential functions to complete the infection cycle by themselves, but they can bemaintained in the population together with the parental virus —the wild type— throughcomplementation. The relative abundance of these mutants in the population of viruses isconstrained by the MOI (Manrubia et al., 2010), and importantly, depends on how faithfulis the replication of the wild type (Manrubia et al., 2010).In this scenario, we will analyse the joint effect of the mutation rate at which defec-tive genomes are produced and the MOI in the evolutionary outcomes of the system. Thismodel is based on the one introduced in (Iranzo and Manrubia, 2012), where the competi-tion between monopartite and multipartite cognate viral forms, both present in the systemfrom the onset and replicating without errors, was studied.2.4.1 Model of genome segmentationWe consider an initial population of wild type virus (wt), that infects a population of sus-ceptible cells at a given MOI, m. During the replication inside an infected cell, the wtgenome produces deletion mutants with probability µ. For simplicity, we assume that onlytwo mutant forms arise, either an N-terminal or a C-terminal fragment, and that they aremutually complementary. They act therefore as a bipartite species and can spread withvariable ease depending on the multiplicity of infection, m. Populations of wt and the bi-partite species in principle do not differ in terms of infective ability, thus they compete inequal conditions to infect new cells.Within an infected cell c, replication of each segment is divided into two steps. First,each segment has to satisfy a complementarity condition, i.e., the cell has to be simultane-ously coinfected either with the complementary segment or with the wt virus to guaranteethe presence of all genomic functions needed to successfully complete the infection cycle.The offspring of each of the segments of the bipartite virus is proportional to the mini-mal amount of available complementation in the same cell, eq. (2.1). Other conditionsfor the number of offspring generated are possible, but no qualitative differences are ex-pected (Iranzo and Manrubia, 2012). The second step is the linear generation of novel18 UNDERSTANDING THE EMERGENCE OF MULTIPARTITE VIRUSES...cell ccell 1 cell NWild Typeinfection, m complementationreplicationtransmission+wild typeN-terminalmutantC-terminalmutantμN-terminalmutantC-terminalmutantFigure 2.1: Schematic representation of the model of genome segmentation.Susceptible cells are infected at a multiplicity of infection m. During replication, wildtype genomes produce deletion mutants N and C-terminal with probability µ. Within acell, a given deletion mutant can complement its counterpart or be complemented by theparental wild type to succeed at replicating. The number of offspring of each of the deletionmutants is proportional to the minimal amount of available complementation counterpartsin the same cell.deletion fragments by wt at a probability µ in eq. (2.2). A schematics of the dynamics ofthe model is shown in Figure 2.1. The following equations describe the discrete dynamicsof the replication process,nci = min(nci , ncj 6=i + ncwt) , (2.1)where the subindex i = N,C. This condition describes the number of offspring for eachof the deletion mutants in each cell c.~n(t = 0) =nwt(t = 0)00 ; ~n(t+ 1) =(1− µ) 0 0µ 1 0µ 0 1~n(t) (2.2)where ~n(t) = (nwt, nN , nC) is a vector whose components stand for the number of viralparticles of each type at time t and ~n(t = 0) is the initial condition.We use a Poisson distribution to estimate the distribution of viruses among cells. ThePoisson distribution yields the probability that a certain cell, c, gets infected by exactly kviral particles when the average number of virus particles per cell in the population is m.In this case, as three different types of viruses are infecting the cells, the probability that acell, c, gets infected by a certain configuration of viruses ~n, if the relative fraction of eachvirus are ~x is given by the product of three independent Poisson distributions.This is an assumption that holds if we consider the populations are large enough suchthat the actual average abundances for each virus are close to mxk, eq.(2.3).Pr(~n|~x) =∏k=wt,N,Ce−mxk(mxk)nknk!(2.3)We start with a population of wt viruses nwt(t = 0) that infects a population of suscep-tible cells at an MOI,m. Once the infection cycle is completed within each cell (this entailsEMERGENCE OF MULTIPARTITE VIRAL FORMS THROUGH GENOME SEGMENTATION 19bipartitewt m µ>µc10.80.60.40.200 20 40 60 80 100GenerationFrequency10.80.60.40.200 50 100 150 200Generationµ<µcm2 20 100Figure 2.2: Dynamics of virus segmentation.The graphics show the relative frequencies of the bipartite (blue) and wt (black) populationsas a function of time (measured in discrete generations, or infectious cycles) for differentvalues of m when µ is below (left panel) or above (right panel) the critical mutation rateµc. The color shade indicates the population dynamics for m = [2, 100].complementation-permitting replication and mutation), defective genomes have been gen-erated for any µ > 0. Then, the offspring population is added up and mixed to infect a newensemble of susceptible cells. This process does not take into account local interactionsamong viruses or cells, and therefore puts the model within the class of mean-field mod-els. The ratio between the frequencies of bipartite and wt viruses varies attending to theMOI and to complementation constraints. After several iterations, the population reachesan equilibrium characterized by a configuration ~n(t→∞) = (n∗wt, n∗N , n∗C). The relativefrequencies of each type are xi = ni/ntot where ntot is the total amount of viral particlesin the population. We measure the equilibrium composition ~x(t → ∞) = (x∗wt, x∗N , x∗C)of the viral population at different values of µ and m. For a fixed µ, the ratio x∗N/x∗wt in-creases as m increases, since higher multiplicities of infection facilitate complementation,as illustrated in Figure 2.2. In the same way, the relative frequency of bipartite virus in thepopulation is low for low µ, and it gradually grows as µ increases until a certain criticalvalue µc that causes the extinction of the wt, see Figure 2.3.A.The critical mutation probability µc indicates a transition from a situation of co-existenceto the extinction of the original wt population. Its precise value depends on m, yielding acurve in the parameter space of µ and m as is depicted inFigure 2.3.B. In this model, itturns out that the functional relationship between µc and m,µc =√2pim(2.4)is equivalent to that obtained in (Iranzo and Manrubia, 2012) for the relationship be-tween σ (which characterized the relative stability of the wt with respect to the defective,complementary forms, and m. In essence, it appears that any disadvantage for a wt virusover the bipartite form in the framework of this model promotes the fixation of the latter asm increases.The predicted value of MOI at which the bipartite virus displaces the parental wt virus isrelatively large even for high segmentation probabilities (high µ). Cases where the parentalvirus underwent segmentation into more than two fragments were explored in (Iranzo andManrubia, 2012), and it was predicted that increasingly higher values of m were needed20 UNDERSTANDING THE EMERGENCE OF MULTIPARTITE VIRUSES1 10 1000.80.60.40.201mμCo-existenceExtinctionMean FieldAnalitical: µc= √2/πm1 DimFit: µc= -0.15 m (-0.32)A Bm = 2Frequency0 0.2 0.4 0.6 0.8 100.20.40.60.81µwtbipartiteCo-existenceExtinctionFigure 2.3: Evolutionary regions as a function of m and µ.Panel A. For a fixed MOI, m = 2, the relative frequencies at equilibrium of the bipartite(blue) and the wt (black) virus change with µ. The vertical dashed line indicates the criticalµc of the transition. This critical transition is depicted as a function of m in Panel B. Twoevolutionary regions are possible: co-existence of the parental wt population and the de-fective segments (blue) and extinction of the wt population (grey). Black dots indicate theµc of the mean-field model obtained in simulations and the curve corresponds to Eq. (2.4).Red dots indicate the µc obtained in spatial simulations of the model, and the red dashedcurve corresponds to a fit to a function of the form µc = Cm−γ , with fitting parameters inthe figure legend and R2 = 0.98.for the wt population to be displaced by the multipartite virus. For example, a tetrapartiteform would be fixed only form > 100−200, well above known typical values of MOI. Asthe MOI detected in natural infections is very low, and it ranges from just 1 to 6 in artificialplant infections (Gutierrez et al., 2015; Gonzalez-Jara et al., 2009; Zwart et al., 2014), thelikelihood of reaching, in natural conditions, values of MOI as large as those predictedby the model seems implausible. Therefore, even if genome segmentation from a parentalmonopartite type is a plausible evolutionary pathway leading to multipartite viruses, it doesnot appear as a very likely possibility that multipartite viruses with more than two segmentscan emerge within the scenario assumed by this model.2.4.2 Effect of the space in genome fragmentationThe explicit introduction of space is essential when modelling the viral infection in realsystems, since it may cause important qualitative changes (Cuesta et al., 2011; Boerlijstand van Ballegooijen, 2010). Susceptible hosts are often spatially structured, such as cellswithin the tissue or the arrangement of plants in crops fields, and so are the dynamics ofinfection propagation. For example, the offspring of a virus that has infected a cell withina structured tissue will be distributed preferentially among neighbouring cells, limitingthe spread of the infection to the local environment. The limitations imposed by a spatialstructure result in many interesting phenomena mostly driven by local clustering of viral(or pathogenic, in general) types. Local clustering is responsible for the emerging diver-sity in structured ecological and biological systems (Aguirre and Manrubia, 2008; Tilmanand Kareiva, 1997) and can relieve the requirement of complementation in the genomesegmentation model.As an illustration of the effects of space, we consider now an array of susceptible cells ina large (one-dimensional) triangular lattice. This kind of spatial arrangement is appropriateEMERGENCE OF MULTIPARTITE VIRAL FORMS THROUGH GENOME SEGMENTATION 21SpaceBipartite Bipartite200 400 600 800500100015002000250030003500400045005000Space200 400 600 800500100015002000250030003500400045005000Space200 400 600 800500100015002000250030003500400045005000SpaceGenerationsWT200 400 600 800500100015002000250030003500400045005000 00.511.522.533.54mWTμ < μc μ > μc Figure 2.4: Dynamics over time and space.The images show virus propagation of bipartite and wt populations over the generationtime for a fixed MOI,m = 4 when µ is below (left panel) or above (right panel) the criticalµc. A triangular lattice has been used. The color stands form values, as shown in the colorbar.to model the propagation front of an infection (Cuesta et al., 2011). A susceptible cell c,can be infected only by viruses coming from the two closer neighbours in the upper row.The offspring of a particular cell in a row will infect, in a secondary infection, the next row:in this way, subsequent generations of viruses are tracked in space wherever the infectionstarts.We have implemented this protocol of infection in the framework of the segmentationmodel, and looked at the relative abundances at equilibrium of each virus type as a functionof µ and m. Representative examples are shown in Figure 2.4. For a fixed MOI, thepopulation of wt and the bipartite species co-exist below a critical µc, generating localpatches of wt-free virus. When the values of mutation rate µ increase beyond µc, weobserve that the wt population becomes extinct after a transient whose length depends onm and on the distance to the threshold.Similarly to the mean-field model, two evolutionary regions can be observed in theparameter space. However, the region of co-existence is much smaller compared to themean-field model, and appears in a darker shade of blue in Figure 2.3.B. The change is notonly qualitative, since the functional relationship between µ and m does not follow thatobtained for the mean-field model; with explicit space, it can be approximated by an func-tion of form, µ ∝ m(−γ). The bipartite viral forms can displace the parental monopartitewt in spatial competition under significantly less restrictive environmental conditions, ascompared to the mean-field model. The effect of local clustering that emerges in an spa-tial model closely resembles the propagation of viral infections in plants, where it is oftenstrongly conditioned by the two-dimensional nature of plant tissues and organization ofcrops fields. Eventually, this architecture might restrict the type of host where multipartiteviruses emerge and thrive.CHAPTER 3ASSOCIATIONS IN THE VIRAL WORLDChapter overview: Satellites are likely remnants of genomes that make their way by coinfectingwith fully fledged viruses. In return, satellites open up a range of new infection phenotypes. Theecological consequences of virus-satellite associations may compensate for the cost of coinfectionand represent a stepping stone towards the emergence of a multipartite species. An original modelis introduced and analysed in order to understand some ecological effects of those associationsin a framework of viral competition. The implications on the evolution of multipartitism are alsodiscussed.There are many instances in the Virosphere of associations of viruses with kin or withsubviral agents (Elena et al., 2014) that often modify the aetiology of infections (Rooss-inck, 2005). These associations use to be transient and imply temporary interactions be-tween the two associates which normally operate with independence of the association. Aprominent example are the so-called virus satellites that is, subviral agents that require theassistance of a specific helper virus for its replication or encapsidation (Kassanis, 1962).These associations are ubiquitous in plants, and less frequent in other hosts. Several no-table exceptions infecting animals are the Hepatitis δ virus (HDV) (Makino et al., 1987),the genus Dependoparvovirus, in the Parvoviridae family (Cotmore et al., 2014; Krupovicet al., 2016) and two satellite virus infecting bees (Ribie`re et al., 2010), and planthop-pers (Nakashima et al., 2006). Associated with the family Totiviridae are two isolatedcases of dsRNA satellites that infect unicellular eukaryotes (Khoshnan and Alderete, 1995;Schmitt and Breining, 2002).Multipartite viruses: Organization, emergence and evolution.By Adriana Lucı´a Sanz2324 ASSOCIATIONS IN THE VIRAL WORLDSputnikvirus, Mavirus, Organic lake satelliteReplicaseCapsidOtherLife dependantorganismsViroid circular ssRNAVirus +/- ssRNA, dsRNA, dsDNA, +/- ssDNA, RTVirus dependantSatellite virusssRNATombusviridae, Virgaviridae, NodaviridaeSatellite Nucleic AcidSatellite CBPV, Albetovirus, Aumiavirus,Papanivirus, Virtovirus, MacronovirusNilaparvata lugens commensal X virusMimivirdae, PhycodnaviridaedsDNA(Viriophage)ssDNAdsRNAGeminiviridae BetasatelliteAlphasatelliteGeminiviridaeNanoviridaeGeminiviridae DeltasatelliteAdenoviridae Herpesviridae DependoparvovirusTotiviridae M virus satellite, T1 virus satellitessRNASecoviridae, AlphaflexividaeTombusviridae, BromoviridaeSecoviridae, LuteoviridaeSolemoviridaeHepadnaviridae DeltavirusLarge linearSmall linearSmall circularSmall circular(Virusoid)DIPNonessDNAFigure 3.1: Table of organisms depending on cellular life.This table shows all types of organisms/replicators/entities that depend on cellular life fortheir maintenance. Satellites are classified attending to the nucleic acid. Colours show theprotein they encode, if any (blue: capsid, green: replicase, yellow: other, and grey: none).In each row, helper virus families are highlighted in bold font.DIP: defective interfering particle.A sophisticated kind of satellite-like organism are virophages. Their helper virus aretypically giant viruses of the Mimiviridae family. Virophages normally inhibit the replica-tion of their helper virus (Scola et al., 2008).Another interesting class of hyper-parasites are viroid-like satellites —virusoids— con-sisting of a non-coding, circular ssRNA dependent on plant viruses for replication and en-capsidation. Virusoids and hepatitis delta virus (HDV) are likely related to viroids (Floreset al., 2011), non-coding circular RNAs which have been exclusively described infectingplants (Symons, 1991). A detailed list of all known entities depending on cellular life canbe found in Figure 3.1. Interestingly, some virus-satellite associations are closely linked tothe presence of multipartite genomes (Murant, 1990). Geminiviridae is the family of plantviruses with the largest number of examples: actually, this family contains many bipartitespecies but also a large number of species formed by non-segmented viruses that modifytheir virulence and host-range through the action of a satellite (ul Rehman and Fauquet,2009).Mixed infections are usual for plant viruses in nature, where there is a potential for mu-tual interaction (Roossinck, 2005). Surprisingly, unrelated viruses within these mixturesdo not seem to compete, but rather to cooperate. This observation seems in contradic-tion with superinfection exclusion (Gutierrez et al., 2015; Bergua et al., 2014; Gutierrezet al., 2012), where two related viruses or strains compete for (early) infection of a cellto guarantee success. The masters of synergistic interactions are potyviruses: in coinfec-AN INTUITIVE CLASSIFICATION OF SATELLITES 25tions with a species of a different family, they usually enhance the virulence of the partnervirus (Scheets, 1998; Pruss et al., 1997).An increase of virulence and the emergence of new phenotypes may grant access tonovel niches and cause persistent infections. The latter usually entail a prolonged interac-tion between the two associates. In this scenario, genetic recombination and gene loss arepossible outcomes of the interaction, which may in this way end up in the rise of a novelspecies. A modular evolution, and eventually a multipartite viral species, could emerge asa consequence of independent evolutionary histories for individual genes within a genomethat once belonged to two associates (Roossinck, 2005).3.1 An intuitive classification of satellitesThere is no straightforward classification of satellites, although distinguishable groupscome to light whether looking at the genetic material, helper virus family, host, or encodedproteins. A currently accepted classification considers two types of satellites, as listed inFigure 3.1. Satellite viruses are those satellites that encode a coat protein and compriseplant satellites with jelly roll capsid proteins, dependoparvovirus and viriophages (Krupovicand Cvirkaite-Krupovic, 2012; Krupovic et al., 2016). Satellite nucleic acids are anothergroup of satellites that may also encode a protein, but different from a capsid. For ex-ample, plant α/β–satellites and Secoviridae satellites encode a replicase or a replicationhelper protein, M virus satellite expresses a toxin and HDV encodes an antigen. In addi-tion, this group further includes non-coding —circular— ssDNAs (Stanley et al., 1997),and RNAs with a compact folded structure that consists of a high content of base pairing—above the average (Cuesta and Manrubia, 2016)— and ribozyme activity (Hadid et al.,2017; Roossinck et al., 1992).Another sensible way to classify satellites could consider their evolutionary origin asshown in Figure 3.2. It could be argued that satellites are deletion mutants formed in thesame way as a defective interfering particle (DIPs), but keeping parts of the genome thatstill express some of the original functions or proteins. The remaining proteins of satel-lites are often related to an ancestral viral protein. Those satellites come from a previousvirus that had reduced its genome so as to become irreversibly dependent on the parentalvirus. These defective interfering genomes could be identified in the presence of coin-fecting viruses if they endow them with additional functions. Dependoparvoviruses, plantsatellites encoding jelly roll capsid proteins, α/β/γ-satellites and viriophages could haveemerged this way; they are listed in Figure 3.2, upper panel (Cotmore et al., 2014; Saun-ders and Stanley, 1999; Krupovic et al., 2016; Krupovic and Cvirkaite-Krupovic, 2011;Fischer, 2011). Specialization of the satellite for its helper virus becomes a major issuealong this process. Specialization is found to occur as one viral species associates with oneto several satellite species, but not vice versa (Wang et al., 2017a) —with the exception ofthe Dependoparvoviridae family (Wang et al., 2017b).An alternative hypothesis puts forward the possibility that satellites are pieces of genome“stolen” from the host that cause a fast phenotypic change of the infecting virus. This isthe most plausible origin of satellite RNAs in the examples that follow, and summarized inFigure 3.2, bottom panel. Serial passages of a cucumovirus spontaneously generate novelsatellite RNAs that derive from the host genome (Hajimorad et al., 2009; Zahid et al.,2015) and interfere with the progression of the infection (Hu et al., 2009). Tombusvirus’small RNA satellites have no similarity with their helper virus with the exception of ashort initial sequence that serves as a replicase recognition site (White and Nagy, 2004).26 ASSOCIATIONS IN THE VIRAL WORLDUnknownTombusviridaeVirgaviridaeAlbetovirusAumiavirusPapanivirusVirtovirusMacronovirusSecoviridaeLuteoviridaeSolemoviridaeAntigenMimiviridae VirophageAdenoviridaeHerpesviridaeGeminiviridaeNanoviridaeJR CapsidReplicaseOthersHepadnaviridaeNodaviridaeTriatovirus DependoparvovirusAlphasatellitevirusBetasatellitevirusDeltasatellitevirusSCBPVNLCXVVirus originHost originBromoviridaeTombusviridae DeltavirusSecoviridaeAlphaflexiviridae Helper virus SatelliteSatellite virusPlant Vertebrate Invertebrate AmoebaViroid ?Satellite nucleic acidssRNAdsDNAssDNAssRNAFigure 3.2: Classification of satellites attending to their origin.Two groups of satellites arise attending to the evolutionary origin. Colours of the boxesdetermine the host. Multipartite families are in red. The satellites derived from a virusoften encode one to several proteins and comprise RNA satellites encoding a jelly rollcapsid protein, dependoparvovirus, virophages and α/β/δ-satellites. The other group ofsatellites is derived from the host —plants— and comprises the rest of RNA satellites,including viroid-like satellites.Replicase helper proteins encoded in RNA satellites found in Secoviridae and Alphaflex-iviridae infections show high resemblances between each other, but not with any otherknown virus (Lamprecht et al., 2013; Hadid et al., 2017). Small —circular— RNAs satel-lites or virusoids fold into rod-like structures that resemble those of viroids. Besides a lackof sequence similarity (Pantaleo and Burgya´n, 2008; Roossinck et al., 1992; Hadid et al.,2017) these structures have shown to be energetically favoured and spontaneously appearfor small, circular RNAs (Catala´n et al., 2019) without significant sequence constraints.Another resemblance between viroids and virusoids is that both infect plants —with theexception of HDV— and a principle of parsimony might connect their origin to this host.3.2 Ecological and epidemiological effects of virus associationsViral associations usually modify the aetiology of infections because they often imply achange in the phenotype. For animal satellites such as SCBPV and NLCXV and for vi-rophages, the association with their respective helper virus always results in a straightfor-ward attenuation of viral symptoms (Nakashima et al., 2006; King et al., 2012; Yau et al.,2011). The exception to this rule is HDV, which induces an acute hepatitis B (Farci et al.,1988). Many more examples of satellites are known in plants, where a broader spectrumECOLOGICAL AND EPIDEMIOLOGICAL EFFECTS OF VIRUS ASSOCIATIONS 27TombusviridaeAlfanecrovirus (2)Betanecrovirus (1)Auresvirus(1)Dianthovirus (1)Panicovirus (1)Panicovirus (1)Carmovirus (1)Tombusvirus (2)Umbravirus (2)Satellite VirusSmall linear satellite RNAAlbetovirus (3)Aurmiavirus (1)Satellite CBPV Papanivirus (1)Multipartite virus Dianthovirus (3)Umbravirus (9)Panicovirus (1)AssociatedEnamovirus (2)Luteovirus (1) LuteoviridaeJelly rollcapsidLuteoviridaeTombusviridaeSatellite VirusSmall linearsatellite RNAPermanentInterdependence Defective Interfering RNAA BTransientExacerbation or attenuation Transient orpermanentExacerbation or attenuationMultipartitismTransientAttenuation (artificial)Figure 3.3: Associations during Tombusviridae infections.Panel A shows all possible associates for the Tombusviridae family. The number ofspecies is shown in parenthesis. Panel B shows the phenotypic effect of associations inA. Tombusviriuses of genus Umbravirus lack the capsid protein and are permanently asso-ciated with ans specie of the genus Enamovirus of Luteoviridae family, forming a bipartitespecies. Some umbraviruses need, beyond a luteovirus helper, the assistance of a satelliteRNA to properly get transmitted by an aphid vector. However, satellite RNAs usually acttransiently in tombusvirus infections, exacerbating or attenuating the symptoms of infec-tion. Defective interfering particles have been found only in cultured conditions and not innature, but they have shown to attenuate the symptoms of infection.of phenotype modifications have been described. Very transient interactions may only in-volve an increase or decrease in viral accumulation levels. These are common effects ininfections where DIPs or satellites have spontaneously emerged. Long-term interactions,in contrast, may display a variety of changes in transmission, host range, or cell tropism,ultimately leading to an interdependence —symbiosis— of the two interacting elements.In this section we are going to address the possible phenotypic changes emerging fromviral associations using the family Tombusviridae as an example.Tombusviridae is a family of plant virus that presents a large heterogeneity of associa-tions, including multipartite species, and a variety of satellites. In addition, tombusvirusesspontaneously generate DIPs under passaging that systematically milden the symptoms ofthe infection by interfering with the replication process. It has been suggested that DIPsmay produce complex responses in persistent infections, apart from extinction (Manrubiaet al., 2010), such as cyclical variations of viral titer and even chaotic dynamics (Kirk-wood and Bangham, 1994; Bangham and Kirkwood, 1993; Moreno et al., 2017). However,tombusvirus’ DIPs —and DIPs in general— are not found in the wild (Celix et al., 1997).A list of the known associations of tombusviruses is shown in Figure 3.3.A and an schemeof the phenotypic result of these associations in Figure 3.3.B.The associations with satellites are unbalanced because their evolution is restrainedfrom the very beginning by the evolution of the virus they parasitize. Satellite coinfec-tions are not usually essential for the virus life cycle, and they use to be transient, with theparticularity that sometimes exacerbate the symptoms of infections and others attenuatethem. It has been proposed that the reason of these opposed effects is related with how theRNA satellite interacts with the immune system of the plant. If the satellite has sequencesimilarity with the host —host origin— it may interfere with the RNA silencing mechanism28 ASSOCIATIONS IN THE VIRAL WORLDof the plant by dismantling its immune system, ending up in an exacerbation of the infec-tion symptoms. On the other hand, if the RNA satellite does not have sequence similaritywith the host, it may trigger an acute activation of RNA silencing mechanisms, hinderingthe progression of the infection and leading to an attenuation of its symptoms (Hu et al.,2009). Additional examples of these phenomena are found in other plant families likesatellite Tobacco mosaic virus, which worsens the symptoms of the helper virus (Dodds,1998), or the tripartite virus CMV, whose association with a fourth non-essential satellitecomponent modifies its virulence depending on the satellite component (Betancourt et al.,2011) or on the infected host (Betancourt et al., 2013). The role of satellites as virulencemodifiers of their helper virus has an ecological importance in regulating both virus andhost populations. Several authors explored the theoretical consequences in the ecologyof the mimivirus-virophage interaction. The dynamics of virus and host population werealtered considering a sequential infection —hyperparasitism— (Wodarz, 2013) or coin-fection (Taylor et al., 2014). In both cases, they focused on phenotypes that emergedassuming that the virophage negatively affected virus replication, which is not the generalcase for satellites.Associations with satellites have an impact beyond virus virulence, especially in thosecases that imply a long-term interaction between virus and satellite. Coevolution may led toan interdependency of the two associates. For example, natural isolates of the tombusvirusGroundnut rosette virus (GRV), are always accompanied with an RNA satellite which is themain cause of the symptoms in groundnut and is essential for aphid transmission (Murant,1990): without the RNA satellite, GRV is still able to infect, but it cannot be transmitted byits vector. Symbiosis has been documented for at least two tombusviruses: Pea enation mo-saic virus (PEMV) and GRV. These viruses are unusual species of the genus Umbraviruswhich do not encode a capsid protein (Syller, 2003; Dall’Ara et al., 2016; Roossinck,2005). They are encapsidated in trans by hijacking the capsid of a coinfecting virus thatbelongs to the family Luteoviridae. As pay off, they allow the entry into blocked tissues fortheir luteovirus partners, endowing them with a systemic movement in the plant host. Thesymbiosis observed between umbraviruses and enamoviruses has evolved towards the spe-ciation of PEMV into a bipartite virus whose fragments belong to two independent geneticbackgrounds. Another illustration of this phenomenon is found in the family Geminiviri-dae. Virus-satellite associations are extremely abundant in geminivirus infections. Theseassociations became permanent in the genus Begomovirus: a bipartite species constitutedby an ancestral geminivirus and a satellite of nanovirus origin (ul Rehman and Fauquet,2009; Mansoor et al., 2003). Theoretical works on virus associations showed a fast transi-tion from a mutualistic two-species to a single species dynamics, supporting the fact thatmutualistic and symbiotic interactions are usually the prelude of speciation (Nee, 2000).In order to overcome the cost of their replication, satellites should be beneficial to thehelper virus, either by providing a rapid phenotypic change or an adaptive ecological re-sponse to certain environments. In a context of viral competition, an association with asatellite can quickly modify the outcome of the competition without the need of findingadaptive solutions in the form of genomic changes. Furthermore, ecological consequencesof satellite associations are expected as a result of the changes in virus and host dynam-ics caused by the association. Altogether, we will explore theoretically in the followingsection the effects of a non-mandatory satellite-virus association in a context of viral com-petition. Our goal is to evaluate the likelihood of transient associations being a first steptowards multipartitism, as it seems to have happened for PEMV and begomoviruses.MODELLING THE ECOLOGICAL EFFECTS OF A SATELLITE IN A VIRAL COMPETITION 293.3 Modelling the ecological effects of a satellite in a viral competitionIn this section we explore the ecological consequences of the introduction of a satellitethat assists one of the two competing viruses.To this end, we devise an epidemiologicalmodel to examine the role of different parameters, in particular those related to how the in-teraction between virus and satellite modifies emergent phenotypes. We will first considera model of virus competition that serves as a null model. Later, we will investigate howthe introduction of a satellite that associates with one of the competing viruses modifiesthe dynamical outcome. Similarities and differences between both models are discussed inthis section.3.3.1 Model of viral competitionConsider a host (plant) which can be infected by two types of viruses. Healthy hostsappear at a constant rate g and decay at a rate d. The amount of susceptible hosts at time tis H(t) and that of hosts infected by either virus are X(t) and Y (t). The model does notexplicitly consider free viral populations, just susceptible or infected hosts in the differentstates. The model works in the mean-field approximation, and therefore assumes that hostsinteract homogeneously through averaged values. Each virus is characterized by the ratepx,y at which it infects a susceptible host (in contact with an infected host of its class) anda parameter dx,y which quantifies the increase in mortality of the host due to the infectedstate. A scheme of the model including the parameters is depicted in Figure 3.4.The equations that describe the dynamics for this system of two competing viruses are:H˙ = g − dH − pxXH − pyY H (3.1)X˙ = pxXH − (d+ dx)X (3.2)Y˙ = pyY H − (d+ dy)Y (3.3)As a consequence of the symmetry of eqs (3.2) and (3.3), there will always be a virus that,in the mean-field scenario, will displace the other —with the exception of a set of pointsof zero-measure. For consistency, all parameters are strictly positive.HXYpxpygdOOd+dxd+dyOFigure 3.4: Scheme of two virus competition.Healthy hosts (plants), H , are seed at a constant rate g and die with basal rate d. Plants getinfected by either virus with rate pi, being i ∈ {x, y} for virus x and y. Infected plants Xand Y see their basal mortality increased in an amount di when infected.30 ASSOCIATIONS IN THE VIRAL WORLDamountdx0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.4501234 H*X*Y*BAd xg0.1 0.2 0.3 0.4 0.500.10.20.30.40.5Figure 3.5: Solutions for the model of viral competition in parameter space.Panel A shows the solutions in the parameter space of the model of two virus competitionusing the parameters g and dx. The blue region correspond to the solution where bothcompeting viruses are extinct which satisfies the condition d/g > Ri. Black dashed linecorrespond to the condition d/g = Ri. The green and yellow regions correspond to so-lutions that satisfy Rx > Ry and Ry > Rx respectively, where either one of the virusesinvades the population while the other becomes extinct. Red dashed line correspond tocoexistence solution. Panel B shows the bifurcation diagrams of a cross section of panelA for g = 0.5. Dashed lines correspond to unstable solutions while solid lines correspondto stable solutions. Green and yellow regions in panel A correspond to dx < 0.25 anddx > 0.25. Transcritical bifurcations are distinguishable. The circle and vertical linesshow the degenerate solution of coexistence of the two viruses for dx = 0.25.Results of the model of viral competitionDepending on model parameters, there are four different, positive and stable solutions: i)none of the virus is able to invade the population of hosts and both get extinct; ii) and iii)either virus x or y invades the population with the subsequent extinction of their counter-part; iv) the two virus coexist in the population. The solutions, their existence and theirstabilities were obtained as explained in the Methodology section and are summarized inTable 3.1. The solutions of the model are shown as coloured areas in the parameter spacerepresented in Figure 3.5.A, and the bifurcation maps of the solutions in Figure 3.5.B. Thebifurcation maps show how the solutions of the system change their stability as a parame-ter (in this particular case, dx) is varied. The analysis of this system presents transcriticalbifurcations. In this type of bifurcations the solutions (fixed points) do not disappear afterthe bifurcation, instead they switch their stability. Comparable figures can be obtained withan alternative set of parameters.In order to simplify the analysis and discussion, we define the replicative lifetime, orreproductive success, Ri, which measures the ability of either virus to invade the hostpopulation.Ri =pid+ diwith i ∈ {x, y} (3.4)The ratio d/g is a measure of the replacement or turnover time of healthy hosts. Largerd/g means longer times are needed for the appearance of susceptible healthy hosts. Ac-cording to this measure, and the replicative ratios defined in eq.( 3.4) we can differentiatethree regions in the space of parameters for the solutions found:MODELLING THE ECOLOGICAL EFFECTS OF A SATELLITE IN A VIRAL COMPETITION 31Table 3.1: Conditions for stability non-negativity and existanceEq. Fixed point (H∗, X∗, Y ∗) Conditions for existence andnon negativityConditions of stability(0.1)(gd , 0, 0)None Rx < d/g and Ry d/g Rx > Ry(0.3)(d+dypy, 0, gd+dy − dpy)Ry > d/g Ry > Rx(0.4)(d+dxpx, X∗, gd+dy− dpy−pxpyX∗)Rx = Ry >dg andgd+dx− dpx > X∗ > 0None1. Extinction of viral populations. If the replacement time of healthy hosts is largerthan the replicative success of both viruses, the viral population goes to extinctiond/g > Ri, i ∈ {x, y}. This region corresponds to the blue area in Figure 3.5.A.In other words, this solution corresponds to a situation in which the rate at whichnew susceptible hosts appear is slower than the typical time needed to invade thepopulation.2. Invasion of one of the virus and extinction of the other. Two symmetric solutions ofcompetition appear when the reproductive ratios of the viruses are different. If eithervirus x or y has a replicative success higher than the one of its competitor, and alsohigher than the replacement time of healthy hosts (Ri > Rj and Ri > d/g, wherei 6= j, i, j ∈ {x, y}) the virus invades the population of hosts and its competitior getsextinct. These situations correspond to the green and yellow regions in Figure 3.5.A.3. Coexistence of both viruses. Between the two symmetric solutions above, there is asolution of coexistence when the replicative successes of both viruses are equal andlarger that the replacement time of healthy hosts, Ry = Rx > d/g. This solution cor-responds to the red dashed line between green and yellow areas in Figure 3.5.A. Theset of conditions for coexistence in this model has zero measure (it reduces to a linein the two-dimensional plane). Therefore, coexistence is not expected in any naturalsystem, where slight differences between the viruses or randomness are unavoidable.3.3.2 Model of viral competition assisted by a satelliteIn the former scenario of competition between two virus, we are now introducing a possibleassociation with a satellite. The satellite can only replicate in presence of its helper virus,which we choose to be y without loss of generality. Plants simultaneously infected by virusy and the satellite belong to a new class S, and are characterized by an increased mortalityds. The satellite infects plants in the Y state at a rate ps, but since it cannot replicate on itsown, only plants in class Y are affected by the satellite. Simultaneous coinfection of thevirus and the satellite is also possible and occurs at a rate psy under contacts between Hand S plants; the former go to state S without an intermediate state Y . A coinfected plantS can infect plants in the H state only with virus y at a rate pY , thus contributing to plantsin the Y state. A scheme of the interactions of this model can be seen Figure 3.6.32 ASSOCIATIONS IN THE VIRAL WORLDpyd+dypsSHXYOOOpỹpsyOd+dspxd+dxdgFigure 3.6: Scheme of two virus competition with a satellite.Healthy hosts (plants), H , are seeded at a constant rate g and decay at a rate d. Plantsget infected by either virus with a rate pi specific of each virus, with i ∈ {x, y}. Infectedplants X and Y increase their mortality rates in amounts dx and dy , respectively. Plants oftype Y can get infected at a rate ps by a satellite, thus becoming coinfected plants S withan increased mortality rate ds. Simultaneous coinfection of H plants by the helper virusand the satellite occurs at a rate psy . A coinfected plant S can also infect H plants onlywith virus y at a rate pY .The new set of equations reads:H˙ = g − dH − pxXH − pyY H − psySH − pY SH (3.5)X˙ = pxXH − (d+ dx)X (3.6)Y˙ = pyY H − (d+ dy)Y − psY S + pY SH (3.7)S˙ = psY S − (d+ ds)S + psySH (3.8)Note that if we set S(t = 0) = 0 as an initial condition, this system is equivalent to theprevious model. Contrary to that case, here it is not straightforward to predict which of thecompeting viruses or associates is going to invade the population, and depending on thenew parameters different outcomes are possible. The satellite can act as a metaparasite ofthe helper virus and impair its propagation, in which case it might represent an advantagefor the competing virus. The satellite can rescue its associate from extinction (effectivelybehaving as a cooperator) where the benefit is mutual. One cannot exclude situationswhere the satellite permits the coexistence of the previously competing viruses, given thatthe symmetry of the equations is now broken.3.3.3 Results of the model of viral competition assisted by a satelliteThe model has four solutions equivalent to those of the model without the satellite whenthe initial S population is set to 0, and three additional solutions when the satellite is con-sidered. Two out of the three new solutions imply coexistence of all the species of thesystem, and the third one considers an equilibrium between Y populations, with S popula-tions coinfected by the tandem virus-satellite, when the competitor virus x is absent. Thesolutions, their existence and their stabilities were obtained as explained in the Methodol-ogy section and are summarized in Table 3.2. In presence of the satellite, new ecologicalphenomena beyond competition arise for different relations of the parameters. In partic-ular, the characteristics of the interaction between the virus and the satellite have a directMODELLING THE ECOLOGICAL EFFECTS OF A SATELLITE IN A VIRAL COMPETITION 33Table 3.2: Summary of the principal solutions of the model and the conditions for stabilitynon-negativity and existanceEq. Fixed point (H∗, X∗, Y ∗, S∗) Conditions for existence, non neg-ativity and stability(0.7)(gd , 0, 0, 0)Rx < d/g and Ry < d/g andRsy < d/g(0.8)(d+dxpx, gd+dx − dpx , 0, 0)Rx > d/g and Rx > Ry and Rx >Rsy(0.9)(d+dypy, 0, gd+dy − dpy , 0)Ry > d/g and Ry > Rx andRy(1− γ) > Rsy(0.11)(H∗, 0, Y ∗(H∗), S∗(H∗))Ry > H∗ > Rc and H∗ > Rsy andH∗ > Rx(0.12)(d+dxpx, X∗(H∗), Y ∗(H∗), S∗(H∗))Ry > Rx > Rc and Rx > Rsyimpact on the outcome of the competition without an explicit modification of the helpervirus infection parameters.In order to simplify the notation, we extend our definition of reproductive success ineq. (3.4) to include the reproductive ratio of the tandem virus-satellite. We recall that theproductive success Ri stands for the ability of either virus (and virus-satellite) to invadethe host population.Ri =pid+ diwith i ∈ {x, y, sy, c} where pc = psy + pY (3.9)The significance behind Rsy differs from that of Rc: Rsy is the reproductive success ofthe association between the virus and the satellite when they are co-transmitted in an insep-arable way (simultaneous coinfection), while Rc is the replicative success of the tandemvirus-satellite, despite a potential loss of the satellite during transmission. By definition,it is assured that Rc is always larger than Rsy , since the possibility of an independenttransmission is larger than a coinfection of the two entities.According to the parameters that define the interaction between the satellite and thehelper virus, we observe three different effects on the viral competition that also satisfyconditions of existence for the solutions of the model. Therefore, the parameter spaceof the competition is altered under the constraints of the parameters of the satellite, asdiscussed below.1. Commensalism is neutral for the viral competition. Coinfection with the satellitemight neither benefit nor hinder the infective abilities of the helper virus. This typeof parasitic interaction is called commensalism. Formally, the combination of thevirus and the satellite infects with a reproductive success equal to that of the virusalone, that is Rc = Ry . Satisfying this condition results in no effect on the viralcompetition, showing that satellites acting as commensal parasites do not alter theperformance of the helper virus during competition for infection. When we look atthe parameter space in Figure 3.7 we observe identical boundaries for the regions thatcorrespond to the solutions previously described in the model without the satellite inFigure 3.5. Parasitism corresponds to the orange area, given by the stable solutionwhere Y ∗ 6= 0 and S∗ 6= 0. This solution indicates that although satellites are persis-tent in helper virus populations, the virus still can infect in the absence of the satellite.34 ASSOCIATIONS IN THE VIRAL WORLD0.1 0.2 0.3 0.4 0.500.10.20.30.40.5d xgFigure 3.7: Solutions in the parameter space for a commensal satellite.The graphic shows the solutions of the model of viral competition assisted by a satelliteas a function of the parameters g and dx and for the particular case Rc = Ry . Similarly,to the Figure 3.5 the blue region corresponds to the infection-free situation, where bothcompeting viruses are extinct. The green and yellow regions correspond to solutions whereone of the viruses invades the population while the other becomes extinct. A novel regionin orange corresponds to the coexistence of Y and S populations, with X∗ = 0. The reddashed line corresponds to the degenerate solution of coexistence of X and Y populations.Black dashed lines correspond to the condition g/d = Ri for both viruses x and y. Theblue dashed line signals the boundary g = R−1y [(d+ ds − psy/Ry)py/ps + d].In fact, the model shows that S populations depend on Y populations, and it is easyto demonstrate that Y ∗ = 0 implies S∗ = 0. In addition, under certain parameters(Ry(1 − γ) > Rsy), where γ = (g/(d + dy) − d/py)ps/(d + ds), the helper viruspopulation can get rid of the satellite. This condition is shown as a blue dashed linein Figure 3.7. Whether the coinfective success of the tandem virus-satellite, Rsy,increases, it becomes harder for the helper virus to get rid of the satellite.2. Mutualistic interactions can prevent the extinction of the helper virus. Undera situation of disadvantage of the y virus in the absence of the satellite (i.e. forRy < Rx, which mean extinction of the helper virus), the cooperation of the satellitecan be essential to prevent its extinction. If the satellite confers a sufficiently strongincrease in fitness through its association with y, the helper virus can invade the hostpopulation, leading to Y ∗ 6= 0 and S∗ 6= 0 simultaneously. This happens when theinfective abilities of the tandem virus-satellite are such that Rc > Rx > Ry . The out-come of this condition appears as a bistable regime in the parameter space of Figure3.8, in a region that was previously only occupied by the competitor virus (pink andgrey areas in Figure 3.8 A and B, respectively). A bistable regime indicates that, de-pending on the initial conditions, it is possible to reach one or another solution, sinceboth are stable. In this particular case, the two possible solutions are an extinctionof the helper virus —with the concomitant extinction of the associated satellite—, oran extinction of the competitor virus x. The satellite still acts as a parasite, as can beseen in the orange area in Figure 3.8 A, though in this case it confers an additionaladvantage to the helper virus by acting as a mutualistic parasite and rescue it from ex-MODELLING THE ECOLOGICAL EFFECTS OF A SATELLITE IN A VIRAL COMPETITION 350.2 0.3 0.4 0.500.20.30.40.5ABistabilityBdx0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4amount01234 H*X*Y*S*Bistability0.10.1gd xFigure 3.8: Solutions in parameter space for a mutualistic satellite.Panel A. The graphic shows the solutions of the model of viral competition assisted by asatellite as a function of parameters g and dx for the particular case Rc < Ry . A regionof bistability is observed in pink. The regions shared with Figure 3.7 are explained in thecorresponding caption. Panel B shows the bifurcation diagrams of a cross section of panelA for g = 0.5. Dashed lines correspond to unstable solutions, while solid lines correspondto stable solutions. A region of bistability is shown in grey for 0.09 < dx < 0.25 (itcorresponds to the solutions of eq. (0.8) and eq. (0.3)), meaning that, depending on theinitial conditions, either populations S∗ and Y ∗ coexist and displace X∗ or vice versa.Transcritical bifurcations are indicated with black circles, whereas red circles correspondto the collision with the degenerated solution eq. (0.10).tinction. Satellites which endow the helper virus with an immediate increase in fitnessmay be a low-cost solution to guarantee rapid adaptation to new environments.3. Parasitism is a burden in viral competition. The interaction with the satellite couldbe expected to be a burden for the helper virus, as it has the cost of an extra replicationof the satellite. Beyond that burden, parasitism can result in a clear disadvantage in aviral competition when the association with the satellite entails a loss in fitness, evenif the initial situation is advantageous, i.e. Ry > Rx. If the replicative ratio of thecombination of virus-satellite is the lowest in rank, Rc < Rx < Ry , then an invasionof competitor populations in a region of parameters previously forbidden (brown andgrey regions in Figure 3.9 A and B, respectively) takes place. The association witha satellite that lowers the fitness of the helper virus comes as a pure disadvantage forthe virus being parasitized, but endows the whole system with a region of coexistenceof all species that in the previous situations did not exist. That reflects a phenomenonwhereby satellites which decrease the fitness of the helper virus might allow possiblecoexistence, and therefore interactions, with other circulating virus in the host.A satellite parasite can interact in three different ways with the helper virus accordingto the solutions of this model and the different relationships identified among the modelparameters. First, when the interaction does not alter the fitness of the helper virus asit is co-transmitted with the satellite, the model predicts that satellites act as commensalparasites that, under certain conditions, the helper virus can get rid of. The model alsopredicts that this situation does not alter the performance of the helper virus under compe-tition. Secondly, if a virus is fated to extinction under a situation of disadvantage in a viral36 ASSOCIATIONS IN THE VIRAL WORLDAdx0.1 0.15 0.2 0.25 0.3 0.35 0.4amount01234Co-existenceH*X*Y*S*B0.2 0.3 0.4 0.500.20.30.40.50.10.1gd xCo-existenceFigure 3.9: Solutions in parameter space for the case of a parasitic satellite.Panel A. The graphic shows the solutions of the model of viral competition assisted by asatellite as a function of parameters g and dx, for the particular case Rc > Ry . A regionof coexistence is observed in brown. Common regions with Figure 3.7 are explained inthe corresponding caption. Panel B shows the bifurcation diagrams of a cross-section ofpanel A for g = 0.5. Dashed lines correspond to unstable solutions, while solid linescorrespond to stable solutions. A region of co-existence of all the populations is shownin grey for 0.25 > dx > 0.37, corresponding to the solution in eq. (0.12). Red circlescorrespond to transcritical bifurcations, while black circles correspond to the collision withthe degenerated solution eq. (0.10).competition, an association with a satellite that increases its fitness may rescue the virusfrom extinction. The association must occur when the viral density is still high enough,otherwise extinction of the virus could also happen. This type of interactions would berelevant when viruses are in their way to colonize new niches or in situations where a virusis forced to alter infections in two to several different hosts. These situations often implythat viruses are not well adapted to the new environment. Associations with satellites cantemporarily increase the fitness of the virus infection, potentially allowing a fast adaptationto the new host. Third, when a virus is very virulent, an association with a satellite maymilden the infective symptoms and promote coexistence of less virulent competing virusescirculating on the same host. These types of interactions are very common in virus-satelliteassociations, as we reviewed in the initial sections of this chapter. Coexistence in naturalecosystems open the door to interactions that may foster the genetic exchange and a varietyof evolutionary outcomes.The model predicts that associations of viruses and satellites, either promoting an in-crease or decrease in fitness, result in ecological phenomena that may compensate forthe cost of coinfection. Coexistence and fast adaptation in competing environments arefavourable outcomes of virus-satellite coinfection that may be behind the emergence ofmultipartite viruses.CHAPTER 4EVOLUTIONARY TRANSITIONSChapter overview: How multipartite species emerge is, as of yet, an unresolved question thatmay, however, have several possible answers. While the mechanisms governing the transitionto multipartitism must be general and independent on the genome molecule, the possible originof current multipartite species must be concomitant with the evolution of RNA viruses infectingeukaryotes.4.1 On the possible origins of multipartitismThe viromes of prokaryotes and eukaryotes are qualitatively different, especially regardingthe prevalence of different genome types. While prokaryotes are infected predominantlyby dsDNA viruses, the diversity of RNA viruses seems to have flourished in eukaryoticcells. Together with information on the origin of some important hallmark viral proteins,a picture of a coarse-grained evolutionary hierarchy for viral genomes begins to emerge.Multipartite virus, which infect eukaryotes exclusively, are suspected to have different evo-lutionary origins, mainly attending to their genome type. Whereas eukaryotic RNA virusesseem to have a possible old origin in an +RNA ancestor, linked to eukaryogenesis (Kooninet al., 2015), eukaryotic ssDNA viruses could have appeared later in viral evolution, re-sulting from multiple events of recombination between bacterial or phytoplasma plasmids,pre-existing RNA viruses, and even viral satellites (Koonin and Dolja, 2014; Simmondset al., 2017; Kazlauskas et al., 2017). Among ssDNA genomes, and in the Virosphere asMultipartite viruses: Organization, emergence and evolution.By Adriana Lucı´a Sanz3738 EVOLUTIONARY TRANSITIONSa whole, Begomovirus is the most prevalent multipartite genus, representing a main con-tribution to the overall abundance of multipartite species. These successful viruses use aprotein called Rep to initiate replication through the rolling circle replication mechanism,which is widely used for plasmid replication in bacteria. It has been hypothesized thata recombination event between an RNA satellite and a phytoplasma plasmid could havebeen at the origin of Geminivirus-like viruses (Koonin and Dolja, 2014; Koonin et al.,2015). Nanoviridae Reps, unrelated to Begomovirus Reps, are more similar to those foundin alpha-satellites or Circoviruses (Simmonds et al., 2017; Kazlauskas et al., 2017). There-fore, ssDNA viruses are unlikely to stem from a single ancestral virus.Except for the genus Ourmiavirus, whose RNA-dependent RNA polymerase (RdRp) isrelated to the Narnaviridae family of RdRps (Rastgou et al., 2009), the rest of the multi-partite RNA families can be rooted in one of the three major superfamilies of RNA virusesinfecting eukaryotes (Picornavirus-, Alphavirus- and Flavivirus-like) (Koonin et al., 2015).These facts, together with the diversity of genomes of multipartite viruses (Figure 1.4B)suggest that multipartitism could have emerged independently a number of times in evolu-tion.Although the genomic “pieces” of multipartite viruses are, as far as we know, indistin-guishable from those of non-segmented or segmented viruses —those pieces being as oldas cellular life itself, and even older for RNA viruses—, many plant viruses seem to be re-cent discoveries of natural selection (Desbiez et al., 2011). Extant populations of differentviral species are only up to centuries old, likely as a result of an evolutionary burst pro-moted through an intensification of agricultural practices (Gibbs et al., 2010). Specifically,the radiation of Potyviridae, Luteoviridae and Sobemoviruses can be traced back to themid-Holocene and to the beginning of agriculture (Fargette et al., 2008; Gibbs et al., 2008;Pagan and Holmes, 2010). Also, there are no evidences of long-term co-evolution betweenvirus and host (Desbiez et al., 2011). A recent origin of plant viruses, however, does notpreclude a broad host range. A single plant virus often infects hosts across plant ordersor even classes, suggesting that host switches are frequent despite a lack of obvious co-evolution (Gibbs et al., 2008). Horizontal gene transfer (HGT) and co-option of molecularfunctions appear in this context as a more-than-plausible mechanism for the adaptation tonew hosts.4.2 Evolutionary pathways to and from multipartitismThe different genome configurations observed in extant viruses might be solutions foundafter major viral families formed. In general, viruses experience frequent deletion and re-combination events during replication, and HGT is common. Actually, homologies thatindicate an evolutionary relationship between viruses with different genetic configurations(mono-, bi- or tripartite, in early cases) and belonging to separated taxonomical groupshave been known for long (Goldbach, 1986). Gene sharing is in all likelihood directlyinvolved in the plasticity observed in viruses at different taxonomic levels. The eventualsuccess of a viral genome structure and configuration results from a highly contingentprocess. Nevertheless, viral host range does not seem to be conditioned by genome con-figuration: with the exception of dsDNA and retrotranscribing viruses, plants are infectedby all types of genomes. There are examples of generalists such as the tripartite Cucumbermosaic virus (which infects over 1000 different plant hosts, both mono- and dicotyledons)or the tripartite Tomato spotted wilt tospovirus that infects 360 species from 50 families.However, uniform habitats and vegetative propagation may limit fitness optimization inEVOLUTIONARY PATHWAYS TO AND FROM MULTIPARTITISM 39generalists. Strains of Bean yellow mosaic virus have a limited range to local culturesof domesticated plants and Citrus tristeza virus infections are restricted to a few generain the Rutaceae (Dawson and Hilf, 1992; Moreno et al., 2008; Wylie and Jones, 2009).Anyhow, most plant viruses are generalists, and less than 10% of plant viruses infect onesingle host species (Power, 2008). As many as three to four different classes of viruses areoften detected in an infected plant (Roossinck, 2005; Elena et al., 2014), giving plenty ofopportunities to explore the joint action of different viral genomes. This behaviour couldexplain in part why multipartitism might have appeared repeatedly in the evolution of plantviruses. Still, possible evolutionary pathways and the specific advantage of multipartitismremain as open questions. In this section we present some ideas in this respect, follow-ing the hypothetical pathways depicted in Figure 4.1. Evidence to support one or anotherevolutionary pathway is at present uneven.4.2.1 Transitions from non-segmented to multipartite genomesDefective particles are routinely generated upon replication of viral genomes (Banghamand Kirkwood, 1993). Mutants with changes that preclude their viability in isolation anddeletion mutants that lack genes essential to complete the viral cycle can however survive ifcomplemented by viable genomes. Indeed, under conditions of high multiplicity of infec-tion (MOI), defective genomes thrive thanks to the activity in trans of products from viablegenomes. In an infection cycle, segmentation might happen through additional mecha-nisms. Many RNA viruses regulate gene expression through subgenomic RNAs (sgRNA).Encapsidation of sgRNA with loose or non-existent sequence signals is possible (Manda-har, 2006; Sa´nchez-Navarro et al., 2013). However, as defective genomes, sgRNA particlesare frequently lost in presence of the wild type.There are some in vitro examples of a transition from an originally non-segmentedvirus to a bipartite one (O’Neill et al., 1982), and some cases where genetic engineer-ing techniques produced similar outcomes (Geigenmu¨ller-Gnirke et al., 1991; Kim et al.,1997). These facts suggested that an evolutionary transition from a non-segmented virusto a bipartite form should be possible, given an appropriate environment. An experimentaldemonstration of this possibility was realized with Foot-and-mouth disease virus (FMDV),an animal virus that was subjected to over 200 cell culture passages at a high MOI (Garcı´a-Arriaza et al., 2004). The bipartite, in vitro generated form that spontaneously appearedthrough evolution of the virus displaced the wild type in competition under the experi-mental conditions. Subsequent experiments demonstrated that the superiority of the bi-partite form was due to an increased stability of the viral capsid, which translated intoan increased particle lifespan (Ojosnegros et al., 2011). Finally, when the conditions ofpropagation were changed to low MOI, the two defective genomes recombined to producea non-segmented form (Ojosnegros et al., 2011). This experiment represents a proof-of-concept that a transition to bipartitism may occur as a result of a change in the ecologicalcontext (from low to high MOI in that case).There is some recent evidence that genome segmentation might be a rare though pos-sible route to multipartitism in virus. This has been suggested for genus Jingmenvirus forwhich evolutionary relationships with Flavivirus genus (non-segmented) have been estab-lished at least for 2 out of the 4 segments of the virus —the two other segments are ofunknown origin. Flaviviruses infect various arthropods and vertebrates, and are arthropod-borne. Several Jingmenvirus species have been described: Jingmen tick virus in ticks,mosquitoes (Qin et al., 2014) and red colobus monkey (Ladner et al., 2016), and Guaicoculex virus in mosquitoes (Ladner et al., 2016). In addition to these multipartite species,40 EVOLUTIONARY TRANSITIONSseveral other segmented flavi-like viruses have been reported infecting ticks and round-worms (Maruyama et al., 2014; Callister et al., 2008). They have been tentatively groupedinto a genus, although the impossibility to culture them has made it difficult to assess theiractual phylogenetic relationship. Within the species of this group, only Guaico culex virushas proved to be multipartite, while the rest are possibly segmented viruses. A transitionto multipartitism plus a new association to other functional genes could have acted syner-gistically in the origin of Jingmenvirus.4.2.2 Relationship between non-segmented, segmented, and multipartiteviral genomesThe pathways towards and from multipartitism are largely unknown, but parsimoniousmechanisms compatible with observations are much more diverse than those formalizedup to now.A first step towards multipartitism might be the generation of a segmented genome.During replication, defective genomes are unavoidably generated (S. and Baltimore, 1970),and they can persist in the population if they encapsidate in the same coat as a segmentedvirus Figure 4.1.1a, or complement each other in a favouring environment as a multipartitevirus Figure 4.1.1b. Although in a parsimonious scenario one might expect segmentedspecies to represent an intermediate state between non-segmented and multipartite forms,cooperation can only succeed if genome parts coincide in the same host Figure 4.1.2.However, mechanisms causing genome segmentation are counteracted by recombination.Defective genomes that initially compete for replication may subsequently specialize andturn into successful cooperators, such that recombinant forms cannot displace them anylonger.The transition towards multipartitism from segmented species might occur in differ-ent ways Figure 4.1.1a-2 depending on the capsid shape. Icosahedral segmented virusescould have parsimoniously evolved towards independent encapsidation of the genome seg-ments, a process that might underlie the origin of the families Partitiviridae and Chryso-viridae. Species within these families are vertically transmitted, a dispersion mechanismthat is devoid of MOI restrictions. Their genome segments lack encapsidation signalsand a mandatory co-encapsidation is not required. On the other hand, enveloped viruseswhose genomes are assembled into helical nucleocapsids, such as the orders Bunya- andMononegavirales could simply release the fragments from the enveloped membrane tobecome filamentous or rod-like multipartite viruses. The multipartite viruses within thefamilies Phenuiviridae and Rhabdoviridae could have originated through this pathway. Inparticular, the tetrapartite tenuivirus is the only multipartite representative genus of thesegmented family Phenuiviridae. Interestingly, the nucleocapsids of this family have ho-mologies with filamentous capsids of bipartite genera in the families Closteroviridae andPotyviridae (Krupovic and Koonin, 2017; Kormelink et al., 2011). This transition appearsas a very plausible way for a recent origin of multipartitism and it could have been facili-tated through the horizontal acquisition of different or smaller capsids. A new capsid and,in general, the incorporation of novel segments, might grant access to a new collection ofpossible hosts, as it might happen for tenuiviruses —which actually are the only repre-sentative tetrapartite genus infecting plants of their segmented family bearing 3 segments.Similarly, the bipartite genera Dichoravirus and Varicosavirus are plant infecting virusesof the non-segmented family Rhabdoviridae that may be derived from insect viruses thatfeed from plants (Whitfield et al., 2018). The transmission from invertebrates to plants,EVOLUTIONARY PATHWAYS TO AND FROM MULTIPARTITISM 41and therefore the adaptation to a new ecological context, might have been concomitantwith multipartition (Kormelink et al., 2011).Parasitism to MutualismCooperation to CompetitionDefection to CooperationMONOPARTITE MULTIPARTITEGenome Segmentation / Recombination(In)dependent EncapsidationPartitiviridaePicobirnaviridaeMembrane Acquisition/LossRhabdoviridaeAspiviridaeAssociationsGene DuplicationBegomovirusNanoviridae Membrane Acquisition/LossMononegaviralesVirgaviridaeSecoviridaeLuteoviridae+Umbravirus1b1a1a22SEGMENTEDSEGMENTED34Figure 4.1: Evolutionary pathways towards and from multipartitism.The chart depicts hypothetical transitions —from 1 to 4— from a non-segmented ances-tor to a multipartite virus. Boxes indicate viral families or genera that could be behindthese transitions. Mechanisms that could allow these transitions are annotated in arrows.(1) Segments generated from a parental non-segmented virus can in principle (1a) encap-sidate in the same capsid (in the cases where the capsid is icosahedral or is envelopedby a membrane) or (1b) establish a novel multipartite species in the case of filamentousviruses. Transition (1a) could follow a pathway that first implies the acquisition of a mem-brane as in the case of Mononegavirales and Bunyavirales. Genome segmentation is re-verted by recombination of the fragments. (2) Segmented enveloped viruses can releasethe fragments generating a filamentous multipartite species or, alternatively, segmentedicosahedral viruses evolve towards an independent encapsidation. De novo segments canbe originated by a gene duplication event (3) or by a virus association —especially withsatellites— that becomes permanent (4).An intermediate segmented state of the former transition might be avoided by viruseswith filamentous capsids Figure 4.1.1b, representing an additional example of the transi-tion from non-segmented to multipartite viruses described for Jingmenvirus in the previoussection. Several genera in the families Secoviridae and Virgaviridae could be examples ofa transition from non-segmented to multipartite species Figure 4.1.1b since these fami-lies comprise both types of genome organization. The origin of filamentous families likeBenyviridae and Virgaviridae may include the acquisition of TMV-like capsids in order toinfect plants (Krupovic and Koonin, 2017). A mechanism of co-encapsidation of genomicsegments generated due to replication errors or of sgRNAs is no longer possible in the caseof filamentous and rod-like capsids, so an independent —i.e. multipartite— encapsidationbecomes unavoidable.42 EVOLUTIONARY TRANSITIONSAnother plausible mechanism towards multipartitism is the incorporation of novel ge-nomic segments, be it through gene duplication or de novo acquisition Figure 4.1.3-4,as was extensively explored in the previous chapter. In the first case, several observationssupport the hypothesis that in a context of high viral MOI, genomic pieces could dupli-cate and persist in the population allowing mutations to fix and evolve to novel functionsFigure 4.1.3 representing an advantage compared to the original virus. For example, sim-ilar genome configurations are found in either of the two begomovirus fragments DNAA and B (Reiko Kikuno, 1984), and conserved regulatory regions are found in nanovirusgenome fragments (Grigoras et al., 2010). De novo acquisition of fragments is the onlymechanism towards multipartitism that may respond to ecological constraints and does notexplicitly entail high MOI. The rationale behind this mechanism relies on the observationthat transient associations are common in the Virosphere, specifically during co-infections.These associations —especially with satellites Figure 4.1.4— often modify the aetiologyof infections, and therefore the ecological niche (Roossinck, 2005; Betancourt et al., 2013).The interaction between a satellite and a virus or between two different co-infecting virusescould evolve from an initial competitive or parasitic state to a cooperative or mutualisticsituation. This transition could be a way to generate a novel species, as it could have hap-pened in the origin of Pea enation mosaic virus, a hybrid of Enamovirus and Umbravirusgenera, or in some Begomovirus species (Mansoor et al., 2003).Reversibility through cooperation of the segments, and recombination of the genomesegments, is likely a main force to revert to a non-segmented state. In support of thisstatement comes the observation that non-segmented species are also found in familiescontaining bi- or tripartite viral genera, at odds to what is observed in segmented viralfamilies, which do not simultaneously contain non-segmented or multipartite species.4.2.3 Segment duplicationReplication errors and the high numbers of viable and defective genomes simultaneouslyfound within cells might enable mechanisms analogous to gene duplication and subfunc-tionalization (Conant and Wolfe, 2008) in viral genomes. Complementation in trans opensthe door to the incorporation of new mutations in defective genomes —without losingfitness— and, eventually, to the uncovering of new functions. Defective viral genomes cancoexist for long in persistent infections, for instance evolving to truly hyper-parasitic formsand eventually causing the extinction of the viral population (Grande-Pe´rez et al., 2005).The persistence of defective segments is strongly linked to the frequency of population bot-tlenecks (Manrubia et al., 2010), and their presence is rarely observed in vivo. However,it is not unthinkable that the once defective genome might change the overall properties ofthe initial wild type, allow adaptation to a new ecological niche, and eventually turn out tobe essential for the survival of the new, bipartite species.Although examples of recent gene duplications in RNA viruses are not abundant, thereis evidence of one such event in Benyviridae, a multipartite family (Simon-Loriere andHolmes, 2013). It cannot be discarded that many duplications are masked due to the rapidevolution of RNA viruses, and that remote paralogs can only be identified through thecombined use of non-conventional techniques and manual curation (Kuchibhatla et al.,2014).Gene duplications are far more frequent in viruses with DNA genomes (Shackelton andHolmes, 2004). In Begomoviruses (Mansoor et al., 2003; Reiko Kikuno, 1984), homolo-gies between genes in the same segment have been identified, speaking for duplicationevents. A mechanism of the kind might have acted to cause the large number of segmentsEVOLUTION OF RNA VIRUSES 43of Nanoviruses: some of the parts of this viral family are dispensable in vitro (Timchenkoet al., 2006). Also, there is a significant degree of homology detected between someNanovirus segments corresponding to regulatory sequences (Grigoras et al., 2010). An-other evidence for the rapid evolution of DNA multipartitism is recombination (ul Rehmanand Fauquet, 2009): the incorporation of key regulatory sequences that control the addi-tion of foreign genes in Begomoviruses might be instrumental to permit the independentreplication of the segments in a short time (Roberts and Stanley, 1994) and, consequently,the expansion to novel ecological niches (Lefeuvre and Moriones, 2015).4.3 Evolution of RNA virusesWhereas monopartite genomes do not shown a strong preference for any nucleic acidmolecule in their genomes (DNA or RNA) most multipartite and segmented families areRNA viruses, as we indicated in Chapter 1. Consequently, although the mechanism bywhich segmented genomes originated seems to be universal and independent of the nu-cleic acid, the origin of segmented and multipartite viruses must be concomitant with theevolution of RNA viruses.Understanding the evolution of RNA viruses is not possible without a comprehensivephylogeny, which is an intricate endeavour primarily due to a lack of conservation of fullgenes and to extensive sequence divergence. The only universal gene in this group isthe virus RNA-dependent RNA polymerase (RdRp), which has suffered from strong diver-gence. There are, however, several conserved motifs that are required for polymerase activ-ity. Viral RdRps are a large class of polymerases containing three catalytic domains calledThumb, Fingers and Palm —this latter exhibiting the polymerase activity— (Ferrero et al.,2018). Palm domains are a wide class of proteins also present in reverse transcriptases(RT) of retroelements and retroviruses and DNA polymreases of viral (dsDNA viruses)and cellular (DNA polymerase II) origin (Ferrero et al., 2018; Ng et al., 2008). ViralRdRps of +RNA viruses are closely related to RT of group II introns, present in prokary-otic retrotransposons, and both are thought to belong to a monophyletic group (Gladyshevand Arkhipova, 2011; Stamos et al., 2017). As we showed in Chapter 1, RNA viruses in-fect mostly eukaryotes, especially invertebrates and plants, with only two exceptions thatinfect prokaryotes: Cystoviridae and Leviviridae. However, although the origin of eu-karyotic RNA viruses from prokaryotic RNA viruses remains a remote possibility (Kooninet al., 2008; Koonin, 2015), levivirus RdRps seem to be distantly related to the those of therest of RNA viruses, and also distant from those of cystoviruses, which are more similar toeukaryotic dsRNA viruses (El Omari et al., 2013; Auguste et al., 2015).A previous analysis of the +RNA virosphere proposed three main supergroups: picorna-like, alphavirus-like and flavivirus-like, whose ancestor was possibly an ancient picor-navirus (Koonin and Dolja, 1993). In this context, the origin of -RNA viruses was anchoredto an flavi-like ancestor, whereas dsRNA viruses seemed to had multiple origins in thepicorna- and alphavirus-like supergroups (Koonin and Dolja, 1993; Koonin, 2015). How-ever, the conception of RNA virus phylogeny has been recently redefined in the light oftwo important advances. First, new developments in virus metagenomics, combined withan extensive sampling of invertebrate taxa, have massively expanded the knowledge of thediversity of RNA viruses, especially of those groups infecting invertebrates. The expansionin RNA species eventually gave rise to at least 5 novel RNA families and permitted to fill anumber of gaps in the history of RNA virus evolution (Shi et al., 2016). The correspondingphylogenetic reconstruction of the viral genomes assembled from the meta-transcriptomics44 EVOLUTIONARY TRANSITIONSSegmentedMultipartiteFigure 4.2: Phylogeny of RNA virus RNA-dependent RNA polymerases and reversetranscriptases (modified from (Wolf et al., 2018)).Reconstruction of the RdRp phylogeny using a combination of methods detailed in (Wolfet al., 2018). Each branch represents a collapsed set of RdRp sequences. The 5 mainbranches or supergroups are obtained using supergroup alignment of several representa-tives within each cluster. The clusters are the result of an iterative clustering and aligningprocedure where the global RdRp tree was split into separate clusters. Two independentbootstrap support values (aBayes and BOOSTER) indicated by the numerator/denomina-tor are shown for each internal branch. LTR, long-terminal repeat. Branch colors indicatethe Baltimore class, and the circles indicate segmented (yellow) and/or multipartite (blue)genome configurations that have evolved in that branch.contemplated the unification of +RNA viruses of the three major supergroups, while -RNAviruses remained as a separate group (Li et al., 2015; Shi et al., 2016). These findingspave the way to a re-examination of RNA taxonomy with advanced techniques in deepphylogeny reconstruction (Wolf et al., 2018). In Wolf et al., a specific computational pro-cedure was developed to coalesce 4617 RNA virus RdRps into a single phylogenetic treewith 5 major branches. The results of this work position several previously unallocatedfamilies into the RNA virus phylogeny, divide +RNA viruses in a novel way and revealthe monophyly of -RNA viruses and their apparent origin from dsRNA viruses. Also,dsRNA viruses seem to have evolved from distinct branches of +RNA viruses on at least 2independent occasions, a result consistent with previous works (Koonin et al., 1989, 2015).EVOLUTION OF RNA VIRUSES 45The global phylogeny of RNA viruses revealed in the paper of Wolf et al., is summarizedin the phylogenetic tree of Figure 4.2. A brief overview of the important highlights of thiswork is given below.Regarding genome molecule and configuration, Branch 1, 2 and 3 consist of +RNAviruses. Two groups of dsRNA viruses emerge from Branch 2 and 3 independently, andBranch 5 consist of all -RNA viruses. Segmented viruses are located in Branches 4 and5, while multipartite viruses seem to have evolved independently in every branch of thisphylogenetic tree. In addition, although segmented viruses have likely evolved severaltimes in the evolution of RNA viruses —at least twice, as dsRNA and -RNA—, they aredensely located into groups with multiple families. In contrast, multipartite viruses aregrouped into small clusters that mostly contain only one or a few genera.Concerning the viral taxonomy, Branch 1 consists of the families phylogeneticallycloser to prokaryotic introns and RT, which are prokaryotic leviviruses and their eukaryoticrelatives, namely, “mitoviruses”, “narnaviruses”, and “ourmiaviruses” —the latter beingmultipartite. The quotation marks indicate that this analysis contradicts the current ICTVtaxonomy, which classifies mitoviruses and narnaviruses as members of the Narnaviridaefamily, and Ourmiavirus as a free-floating genus. Ourmiaviruses are phytoviruses with atripartite genome of chimeric nature that consist of 3 genes separated into 3 genetic seg-ments. The RdRp is similar to the one found in narnaviruses, the movement protein isrelated to tombusviruses and the single jelly-roll capsid protein belongs to the group ofpicornaviruses (Rastgou et al., 2009).Branch 2 consists of a large group of +RNA viruses infecting eukaryotes named “pi-cornavirus supergroup”. Apart from the order Picornavirales which includes the familySecoviridae with several bipartite genera, the order Nidovirales was moved adjacent tothis group. Several families within this branch are less reliable with regard to the relativepositions in the tree, such as the families Caliciviridae, Potyviridae, Astroviridae and Sole-moviridae which is a natural hybrid of polerovirus and sobemovirus (So¯mera et al., 2015).Apparently, only two genera of the family Luteoviridae are nested within solemoviruses:Enamovirus —a bipartite genus— and Polerovirus. A lineage of dsRNA viruses consti-tuted by partitiviruses and picobirnaviruses is located in this branch separated, from therest of dsRNA viruses; both families are thought to be multipartite (Nibert et al., 2014;McDonald et al., 2016). Potyviruses in this branch are located next to astroviruses andshow in separate linages monopartite genus (Potyvirus) and bipartite genera (Macluravirus,Bymovirus and Ipomovirus).Branch 3 consists of a distinct and heterogeneous subset of +RNA viruses that coa-lesces the “alphavirus supergroup” along with the “flavivirus supergroup”, nodaviruses,tombusviruses and some recently discovered virus groups: “statovirus”, “we`ivirus”, “ya`nvirus,and “zha`ovirus”. A great amount of plant viruses are located in this branch. The orderTymovirales appears nested along with the “alphavirus supergroup” that contains a largediversity of families with multipartite genera, including the plant-infecting families Vir-gaviridae, Bromoviridae, Benyviridae and Closteroviridae and the family Alphatetraviri-dae, which infects insects (Tomasicchio et al., 2007). Both nodaviruses and tombusvirusescontain multipartite genera, and together with the novel virus groups form a heterogeneousgroup in this branch. Within the “flavivirus supergroup”, jingmenviruses are the only mul-tipartite —and segmented— representatives (Ladner et al., 2016; Qin et al., 2014).Branch 4 is anchored to Branch 3 with limited support. It consists of dsRNA virusesforming a clade separated from Branch 2 that includes totiviruses, and well known familiesof segmented viruses: cystoviruses, reoviruses; and a multipartite dsRNA family: Chryso-viridae (Ghabrial et al., 2008).46 EVOLUTIONARY TRANSITIONSBranch 5 consists of a strongly supported lineage of all -RNA viruses considered, whichis anchored very consistently to Branch 4. It splits into 2 well defined clades correspond-ing to two orders: Mononegavirales and the segmented Bunyavirales both containing sidedgroups and multipartite genera. The multipartite family Aspiviridae, the recently discov-ered segmented groups “chuvirus”, “qinivirus” and “yuevirus” and the family Rhabdoviri-dae consisting of several monopartite and 2 multipartite genera: Dichoravirus and Vari-cosavirus also belong to the Mononegavirales clade. Segmented Orthomyxovirus togetherwith the order Bunyavirales are grouped into a single clade that contains the majority ofsegmented families and one tetrapartite genus, Tenuivirus, which is the only multipartiterepresentative of the family Phenuiviridae.The global RNA phylogeny proposes a scenario where +RNA are the primary ancestorsof dsRNA and -RNA viruses, which makes sense in terms of molecular logic of replicationand expression strategies. It is conceivable that dsRNA evolved from +RNA viruses sinceit is an intermediate of +RNA replication. However, the most surprising outcome of theanalysis in Wolf et al., is probably the derivation of -RNA viruses from dsRNA viruses,being the RdRp the only gene shared among these virus groups. This work provides a novelintegrative picture of RNA virus evolution. Nonetheless, a better characterization of newlyidentified virus groups, and a structural support to the evolutionary relationships found inthis work are new research directions that derive from this phylogenetic analysis (Ahola,2019).4.3.1 Evolution of genome configurations of RNA virusesThe evolution of the three types of RNA genomes is a main result of the work in Wolfet al. However, additional interesting information can be retrieved from the tree structureregarding the evolution of genome configuration of RNA viruses, and in particular theemergence of segmented and multipartite genomes. For example, the evolutionary rates ofmultipartite and segmented viruses can be extracted from the branch lengths of the tree, aswell as other measurements that will be explained.The pathways towards multipartitism presented previously and summarized in Fig-ure 4.1 are consistent with the findings of this analysis. It is most probable that the ances-tor of multipartite viruses is a +RNA monopartite virus. Multipartite +RNA viruses canemerge directly by genome segmentation from monopartite +RNA viruses —especiallyin the case of filamentous viruses— such in the families clustered in Branches 1, 2, 3.A striking observation is that there are no segmented families described to date bearing+RNA genomes, with the exception of the controversial unclassified genus Jingmenvirusthat preliminarily could contain segmented and multipartite species (Qin et al., 2014; Lad-ner et al., 2016). Therefore, the phylogenetic tree suggests that it is most probable thatsegmented viruses have flourished from animal dsRNA and -RNA viruses in at least twoindependent occasions (Branch 3 and 4). In addition, segmented -RNA and dsRNA virusescould be also a possible first step towards multipartitism. This appears particularly clearwhen looking at multipartite families with dsRNA genomes like Chrysoviridae or with -RNA genomes like Tenuivirus, both deeply nested within well known segmented groups.The mechanism governing the transition to an individual packaging of the genomic seg-ments might depend on the capsid properties, but it could also be associated to a changein the host. Tenuiviruses and chrysoviruses infect plants, whereas most related viruses areinfecting other hosts. In the case of tenuiviruses, the infection of plants entails the loss ofthe membrane, due the impossibility to form buds of infection in this host. Chrysovirusescould jump to plants from a fungus symbiont, and this could induce the loss of regulationEVOLUTION OF RNA VIRUSES 47 Distance of appearance from mrcaMultipartite Segmented****mrcaAppearance of the blue traitDlDr Distance of appearance from mrca=DrDr Dl+Figure 4.3: Appearance of multipartite and segmented genomes.Phylogenetic distances of appearance of multipartite (blue) and segmented (yellow)genomes calculated from the global RdRp tree in (Wolf et al., 2018). The distances werecalculated first identifying the specific points where multipartite and segmented traits ap-peared in the tree. Then, the distance to the most recent common ancestor (MRCA), Dr,and a weighted distance to the leaves, Dl, of the tree were calculated. The normalized dis-tance of appearance of segmented and multipartite genomes was obtained and the distribu-tions are shown in the violin plots. Statistical significance between both distributions wasobtained using several two-sample tests (t-student, Wicolxon and KolmogorovSmirnov).∗ ∗ ∗∗ p-value<0.0001.of packaging signals in the transition. Although the mechanism underlying multipartiteevolution does not seem to be unique and it could depend on the specific genome configu-ration of the ancestor virus —whether segmented or monopartite— multipartitism appearsoften enough in RNA virus evolution so as to dissociate this strategy from the kind ofgenome molecule, which certainly does not appear as a hindrance to its origin.4.3.2 Evolutionary distances of segmented and multipartite RNA genomesA phylogenetic tree depicts hypothetical evolutionary relationships amongst a group ofspecies normally based upon similarities in their physical characteristics or sequence sim-ilarity. The structure of branching reflects how species or groups evolved from a seriesof common ancestors. The species considered in the analysis are located in the leaves ofthe tree which represent the present moment. The leaves coalesce in a series of commonancestors, forming the internal nodes or branches of the tree. The branches may ultimatelyend in a most recent common ancestor (MRCA) in the case of rooted trees. The distancefrom the MRCA to a specific leaf indicates the evolutionary time or evolutionary rate ofthat species. The combination of methods used to build the tree of Wolf et al., results ina rooted, not-ultrametric tree, from where it is assumed that the evolutionary time that haspassed for each species is different, therefore the distance from the MRCA to each leafvaries. However, despite these differences in de distances of the leaves, there is no statisti-cally significant difference on the means of distribution of distances for species attending48 EVOLUTIONARY TRANSITIONSto their genomic configurations. This suggests that, on average, the evolutionary rates ofmultipartite, segmented and monopartite RNA species are comparable.4.3.3 Distance of appearance of segmented and multipartite RNA virusesMultipatite and segmented species are clustered into more or less dense groups in the tree,as previously stated. Those clusters are composed by several adjacent leaves which sharethe same genetic configuration. We assume that the ancestor of two adjacent species whichshare a particular trait —or configuration— might also had that trait. Therefore, the specificnode at the origin of the whole cluster is most likely the point where the trait emerged. Inorder to calculate when, in the evolution of RNA viruses, multipartitism and segmentationemerged, we select a cluster and travel down the tree until we find the ancestor where thatspecific genetic configuration appeared. The distance from this point to the root of the tree—the MRCA— is defined as Dr. The distance from the point of appearance of the trait toa specific leaf where a species within the cluster is located is defined asDl. The differencesof evolutionary time for each species in the global RNA tree, results in differences of Dlin the cluster. In order to compensate such differences we calculate a weighted distance ofthe cluster, as we explain in the Methodology section and is depicted in Figure 4.3.We calculate the weighted distances for 346 multipartite and 368 segmented virus speciesincluded in this work. The distribution of weighted distances of appearance of segmentedand multipartite genomic configurations from the MRCA are shown in Figure 4.3. Differ-ences in the mean of the distributions are statistically significant, speaking for the likeli-hood that multipartitism appears, on the average, before segmentation in the global RNAphylogeny. That could also relate that multipartitism emerges more easily, evolutionarilyspeaking, than segmentation. Actually, multipartite species are spread all over the globalRNA tree while segmented viruses only appear in upper Branches 4 and 5 further awayfrom the MRCA. There are reasons to expect a shorter evolutionary time for the appearanceof multipartite species as compared to segmented species. Assuming the quantitative dif-ferences observed are not a result of a biased sampling, one may argue that plants —mainhosts of multipartite viruses— appeared before animals —principal hosts of segmentedviruses— in the evolutionary sequence. However, whereas virus-host co-evolution mayoccur at the level of species (Madinda et al., 2016), there is insufficient empirical evidencecorrelating the evolution of eukaryotic hosts and viruses at the level of family (Geoghe-gan et al., 2017), with the exception of tobamoviruses in the family Virgaviridae (Stobbeet al., 2012; Gibbs et al., 2015). Actually, there is evidence that several plant virus groupsoriginated from arthropod viruses (Koonin et al., 2015).4.3.4 Diverging times for RNA speciesWe now address the question of the evolutionary time required for two sequences to beconsidered as separated species. To this end, we define the branching distance as the evo-lutionary distance of two sequences that have diverged. A scheme in Figure 4.4 showshow we perform the calculation. The question of interest is whether the branching dis-tance is affected by genome configuration, as it could be expected that multipartitism andsegmentation be subjected to different evolutionary pressures.As we did before, we identify the clusters where segmented, and/or multipartite speciesare grouped in the global RNA tree and we travel down the tree until we find the specificpoint were those configurations emerged. We extract the lengths of every branch from thatpoint to the leaves conforming the cluster, Db, and divide them by the total length of thatNETWORK OF GENE SHARING OF RNA VIRUSES 49Log(Branching Distance)*****mrcaDbDBranching distance =DbD*****Figure 4.4: Branching distances attending to the genome configuration.Branching distances for monopartite (brown), multipartite (blue) and segmented (yellow)genomes were calculated from the global RdRp tree in (Wolf et al., 2018). The distanceswere calculated first identifying the specific points where monopartite, multipartite andsegmented traits appeared in the tree. Then, the length that corresponds to the evolutionarydistance of that particular trait needed to speciate is calculated,Db. The branching distanceis the ratio between Db, and the total length from the leaves (the trait) to the most recentcommon ancestor (MRCA), D. ∗∗ p-value<0.01, ∗ ∗ ∗∗ p-value<0.0001.specific branch. Since the majority of species in the tree are monopartite, the branchesconsidered for this genome configuration are those that start at the MRCA and end in aleaf where a monopartite species is located.Figure 4.4 shows the distributions of branching distances for the different genomicconfigurations. Statistically significant differences in the mean values have been foundfor those distributions. Segmented species have on average shorter branching distancesthan monopartite and multipartite species. This can be interpreted as segmented speciesrequiring less point mutations for a sequence to diverge (in function?). This result could berelated to segment exchange —reassortment— which is a common mechanism to generatevariation in segmented virus (McDonald et al., 2016). Indeed, reassortment introducesshuffling of genome fragments that usually is accompanied by a phenotypic change thatcauses additional sequence variability, as compared to purely vertical inheritance (low).Overall, this result seems at odds with the hypothesis that segment shuffling might conferan advantage to multipartite species, since they seem to have, on average, larger branchingdistances.4.4 Network of gene sharing of RNA virusesIf there is something that characterizes the evolution of viruses and specially RNA virusesis the high degrees of sequence divergence and an extensive HGT (Dolja and Koonin,2018). Viruses with RNA genomes use low fidelity replicases (Drake and Holland, 1999)that cause heavy mutational loads behind the fast divergence of RNA sequences. In the lastyears the great advance in genomic analysis grant the access to comparative measures of50 EVOLUTIONARY TRANSITIONS10 1 10 2 10 3 10 4k10 -210 -110 010 110 210 310 4p(k)GenesGenomesp(k)=10 3.0509 k -1.7098R2=0.97883Figure 4.5: Degree distribution of RNA network.The degree distributions of genes (black dots) and genomes (red dots). In the case of genes,the best fit to a power law distribution is shown.the rates of gene gain and loss, and demonstrated that they are comparable to those of pointmutation (Dolja and Koonin, 2018; Simmonds et al., 2017). HGT has been put forwardas an evolutionary mechanism whose power is comparable to the fundamental, Darwinianmutation selection balance (Keeling and Palmer, 2008). Interestingly, viruses are thoughtto be major vehicles of action of HGT (Gilbert and Cordaux, 2017; Sano et al., 2004; Liuet al., 2010; Koonin, 2016).High rates of sequence change and extensive gene exchange limit the applicability oftraditional phylogenetic approaches to the study of virus evolution on a large time scale.In addition, in the classic phylogenetic approach, the reconstruction of the evolutionaryhistory of a virus only takes into account the phylogeny of one gene within the genome.Single-gene phylogeny yields incongruous taxonomical trees, because genes are shared bydisparate subsets of viruses: cladrograms for genes within a genome do not usually overlap.Network genomics, instead, takes into account all the evolutionary homologies within agenome, reconstructing the evolutionary history of a complete genome. The introduction ofnetwork analysis as a method that complements phylogenetic approaches revealed robusthierarchical modularity in the genomes of dsDNA viruses, bringing to light non-obviousconnections among disparate groups of dsDNA viruses (Iranzo et al., 2016a,b). In such anetwork, each viral genome is connected to their genes by edges, and those genes are inturn linked to other genomes, forming a bipartite network structure (Iranzo et al., 2017).The network analysis performed for the subset of RNA viruses in Wolf et al., was similarto the one in (Iranzo et al., 2016a,b). It consists in a combination of methods that beginby identifying sequence similarity among genes in the set of genomes, in order to build alist of gene families —families of homologs. This first step was performed by the groupof Dr. Mart Krupovic and the specific details of the analysis are explained in (Iranzo et al.,2016b; Wolf et al., 2018). Once the families of gene homologs have been identified, thenext step is to carry out an analysis of the resulting network.In network theory, the degree of a node is the number of connections it has to othernodes. The degree distribution is therefore the probability distribution of node degreesover the whole network. A bipartite network has two types of nodes, in this case genesNETWORK OF GENE SHARING OF RNA VIRUSES 51●●●●●●●●●●●●●●●●●●●● ●●●●●● ●●0.000.250.500.751.00number shared genes / genome sizegenome configurationseg_segseg_multseg_monmult_segmult_multmult_monmon_segmon_multmon_monFigure 4.6: Gene sharing distributions.Violin plots of gene sharing distributions attending to genome configuration. mon:monopartite, seg: segmented, mult: multipartite. Red dots represent the mean of the distri-butions. Boxplots represent lower and upper quartiles divided by a line with represent themedian. Vertical lines are the minimum and maximum values. Black dots are outliers. Ineach case, the number of shared genes between a pair of genomes divided by the numberof genes in the genome is shown.and genomes. The number of genomes in which a gene family is found is represented bythe degree distribution of the genes in the network. This function follows a power-lawdistribution p(k) v k−γ shown in Figure 4.5. This shape means that few gene familiesare present in a large amount of genomes, that the average degree is not representativeof “typical” nodes (there are no such nodes, actually), and that the frequency of geneswith high degrees is not negligible compared to a random distribution. The degree of agenome, in contrast, follows a uniform distribution up to k ≈ 10 − 20, which means thatthe fraction of genomes that are connected to one or more genes is independent of thedegree. The frequency drops fast for more than approximately 10 genes, perhaps reflectinga limitation in genome length of RNA viruses (Campillo-Balderas et al., 2015).When we look at the number of genes shared by a RNA virus species in the network wesee differences attending to the genome configuration. We calculate the total number ofgenes shared by a genome and then we divide by the number of genes in the genome. Theresulting distributions are shown in Figure 4.6. We observe that multipartite viruses sharemore genes among other multipartite species than with the rest of the genomic configura-tions. This result is compatible with the idea that multipartite genomes might harbour aprinciple of construction more plastic than the other genomic configurations. In contrast,52 EVOLUTIONARY TRANSITIONSsegmented viruses are likely to share less genes. Despite reassortment is a common fea-ture for this genomic configuration, it seems that interspecies gene sharing is limited. Inaddition, low gene sharing also suggests a larger divergence of segmented families.The degree distribution of genes and genomes in the bipartite network, and the differ-ences in gene sharing as a function of genome configuration are empirical results that callfor a mechanistic explanation. The interpretation of the preliminary results described in thissection would benefit from comparisons with analogous results obtained for DNA viruses,to date only partly available. We suspect that the quantitative shape of p(k) might revealconstructive processes and perhaps capture intrinsic limitations of evolutionary mecha-nisms in RNA versus DNA genomes, while the qualitative shape of the degree distribu-tion (and perhaps other generic features of gene-sharing bipartite networks) should revealgeneric processes in virus evolution. As for gene sharing, we believe that the significantdifferences between the three configurations considered might reflect how suitable eachof those are for the ecological niche where they are mostly found. The application ofcomplex network theory to genome evolution is at its beginnings and, consequently, opensmore questions than yields answers. Further investigation on this and related topics seemsa promising avenue to broaden our understanding of virus evolution using a different, com-plementary viewpoint to phylogenetic studies.4.5 RNA plant networkMultipartite viruses infect predominantly plants. Phytoviruses usually consist of 3 maindomains: a replicase, a capsid, and a movement protein. They may have auxiliary proteinsfor replication (helicases), translation (capping proteins), protein processing (proteases),etc. In order to explore the common features of phytoviruses, we extract from the globalRNA network the subset of 622 genomes corresponding to that group. As previouslydone with all RNA viruses, we build the bipartite RNA plant network, which is obviouslyenriched in multipartite species. The resulting network is connected, i.e. does not haveisolated groups. Many families are linked to few gene domains that appear as hubs in thenetwork, and few families are connected to taxon-specific hallmark genes. The power-lawdistribution of gene sharing is conserved in this network and is quantitatively comparable(has similar fitting parameters) to the global RNA network shown in Figure 4.5.To gain evolutionary insight we performed a modularity analysis using the tool in-fomap (Rosvall and Bergstrom, 2008). The details of the analysis can be found in theMethodology section. The infomap analysis for bipartite networks yields 10 consistentand statistically significant modules that we will analyse in depth in the following para-graphs. There are modules which combine many virus groups connected to very frequentgene domains, while other modules consist of one to few families connected by taxon-specific hallmark genes. The bipartite RNA plant network with the indicated modules isshown in Figure 4.7.Module 1 is the most populated module in the network and consists of an assembly of+RNA viruses of several families corresponding to Branches 2 and 3 of the global RNAphylogeny, with the exception of the families Tombusviridae, Secoviridae, Luteoviridaeand Sobemovirus, in Module 2. Module 1 is held together by the superfamily 1 heli-case domain (S1H), which is the only gene shared by all the members of the module, andby several secondary domains, which are shared by different subgroups in the module.The capping protein methyltransferase guanydyltransferase is a common domain for manyviruses; it allows viral mRNAs to be translated in the cytoplasm of eukaryotic cells (KooninRNA PLANT NETWORK 53and Moss, 2010). It is shared by viruses in different modules of the network and it is alsoshared by members of Module 1, except by Potyviruses —which bear the viral proteingenome-linked (VPg). Filamentous viruses like Potyviridae, Alpha/Betaflexiviridae andClosteroviridae share the potyvirus-like capsid protein, and the papain-like cystein pro-tease domain for polyprotein processing (Mann and Sanfaon, 2019; Rodamilans et al.,2018). However, contrary to the rest of potyviruses, the bipartite genus Bymovirus islinked to a TMV-like and TMV-readthrough capsid domains, a result that coincides witha recent publication (Kirsip and Abroi, 2019). Usually, the TMV-like capsid forms rodsin benyviruses and virgaviruses (Krupovic and Koonin, 2017); two families that are alsopresent in this module. Two superfamilies of movement proteins are grouped in Module 1:the 30K (Melcher, 2000) and the triple gene block (TGB) (Morozov and Solovyev, 2003)superfamilies. Although, not surprisingly, the analysis shows that the multipartite generaHordeivirus, Pomovirus and Goravirus are linked to TGB domains, whereas Furovirus andTobravirus together with the rest of monopartite genera in the Virgaviridae family sharethe 30K gene (Adams et al., 2009).Module 2 gathers together secoviruses, ourmiaviruses, and the families Tombusviridaeand Luteoviridae which are separated in branches 2 and 3 of the global RNA phyloge-netic tree. They all share the single jelly roll capsid protein (SJR-CP). Module 2 is linkedto Module 1 through two genes: the SJR-CP, that is shared with tymoviruses; and thechymotripsin-like protease that is shared with potyviruses. The Secoviridae family is theonly member of the group that contains the superfamily 3 helicase domain (S3H) canoni-cal with a picornavirus-like configuration (Thompson et al., 2014). Ourmiaviruses reveal aquimeric composition of their genomes with a SJR-CP probably of a secovirus-like originand the MP 30K that shows resemblances to those of tombusviruses (Rastgou et al., 2009).Sobemoviruses are located in an independent module —Module 6— characterized by theirown independent VPg and MPs. They are connected to module 2 through the SJR-CP andto Module 1 through the chymotrypsin-like protease, shared with potyviruses.It is interesting how vOTU-like deubiquitinase domain (cystein protease) gathers to-gether many modules in the network connecting +RNA, -RNA and dsRNA plant viruses.This includes several species of the order Tymovirales (Module 1), endornaviruses (Mod-ule 3), the genus Tenuivirus (Module 4), cileviruses and bluneviruses (Module 8) and thefamily Aspiviridae (Module 9).Plant -RNA viruses of the order Bunyavirales (Module 4), the family Aspiviridae (Mod-ule 9) and the order Mononegavirales (rhabdoviruses in Module 7) are separated into dif-ferent modules despite the high similarity found in their RdRp gene (Shi et al., 2016; Wolfet al., 2018). Viruses of the order Bunyavirales share taxon specific hallmark genes: theenvelope protein (except for tenuiviruses, which are not enveloped), the phlebo-like nu-cleocapsid (Krupovic et al., 2016), the cap-snatching endonuclease —necessary for tran-scription (Reguera et al., 2010; Decroly et al., 2012)— and the fusion protein class II. Thislatter protein is integrated in the viral envelope and facilitates the fusion of membraneswhen the virus infects the host cell. Tenuiviruses are not enveloped and constitute a tetra-partite genus. They seem to use the fusion protein class II for a different function that isrelated to overcoming the insect midgut barriers (Lu et al., 2019) —in which might be aninteresting example of exaptation.Interestingly, every plant -RNA species is linked to Module 1 by the 30K MP (Mushe-gian and Elena, 2015). This result is consistent with the hypothesis that plant -RNA viruseswere acquired from animals through HGT (Dolja and Koonin, 2011), and arthropod vectorsare likely the HGT vehicles of the 30K MP that prompted the host shift from arthropodsto plants (Xiong et al., 2008; Ammar et al., 2009; Whitfield et al., 2018). Additionally,54 EVOLUTIONARY TRANSITIONStenuiviruses are the only member of the group bearing vOTU-like deubiquitinase domainwhich also connects to Module 1.Phytoreoviruses are held together into Module 5 in a very nested way including specificgenes of the family Reoviridae. They are linked to Module 1 through the S1H domain andthe capping protein methyltransferase guanydyltransferase, and to Module 2 by the S3H.Recently discovered totivirus species infecting plants (Chen et al., 2016) form an indepen-dent module despite the high similarities found with reovirus infecting fungi (Luque).Although plant dsRNA viruses are located in different modules: Modules 3 (Endor-naviridae), 5 (Reoviridae) and 10 (Totiviridae) share the p7-reovirus dsRNA binding do-main (Masliah et al., 2013; Zhong et al., 2005).As it was expected, the gene sharing network of plant viruses also follows a hierar-chical modular organization. The modularity analysis presented here yields modules thatare consistent with the taxonomical organization of the plant virus groups. In addition,some of the evolutionary relationships that are found in the reconstruction of RdRp phy-logeny (Wolf et al., 2018) also emerged in this analysis as modules in the bipartite plantnetwork. However, it is remarkable that several independent groups —attending to theRdRp phylogeny— coalesce in network modules mostly by sharing the capsid and/or he-licase domains. For example, the families belonging to the “alphavirus-supergroup” aregrouped in the same module as potyviruses, which appear as a separated group attendingto the RdRp. Similarly, sobemoviruses form an independent module despite the similari-ties with potyviruses, probably as a result of the divergence of the families in genes beyondRdRp.Overall, the analysis of the bipartite network separates families of plant viruses in a waysimilar to conventional phylogeny, though it integrates all-genome information and showsa principle of modularity. In particular, structural and non-strutural domains have been of-ten shuffled across the viral groups. For example, it is shown that the capsids and helicaseswere horizontally transferred from group to group several times. A special case is the MP,an essential gene for plant infection. Broad horizontal spread of MPs led to major shifts inthe lifestyle of viruses, especially of -RNA viruses of arthropods. Ecological interactionspermitted the infection of plants after the acquisition of MPs by arthropod -RNA virusesthat were relegated to become a vector in the transmission. Although further analysis mustbe carried out, it is likely that multipartitism favours domain shuffling. Multipartite virusesshare, in general, more genes than other genome configurations. A paradigm of modularityare ourmiaviruses, whose genomes are a chimera of unrelated modules. The network anal-ysis shows that, within a family, multipartite genera might change the capsid or movementprotein, but conserve the replicative domain. In addition, multipartite viruses can acquiredomains additional to those that are family-specific, as it happens with tenuiviruses, theonly member of its group with vOTU-like deubiquitinase domain.From an evolutionary perspective, the genome composition of viruses in the networkis highly conditioned by the family to which the virus belongs. Consequently, the resultsobtained with the analysis of the RNA bipartite network are congruent with classical tax-onomical groups. In this way, the adaptation to novel niches can be achieved with theadaptation of pre-existing family-specific domains to novel functions —exaptation. How-ever, the extensive HGT across family groups suggests that extant viral families are theresult of a bottom-up constructive process that may have started with an initially smallgroup of genes and gene combinations, to be followed by subsequent expansion.RNA PLANT NETWORK 55VirgaviridaeLuteoviridaeSobemovirusBunyaviralesBromoviridaeClosteroviridaeBenyviridaeRhabdoviridaeAspiviridaeTombusviridaeSecoviridaeEndornaviridaePotyviridaeReoviridae1 2 3 4 5 6 7 8 9 10 TotiviridaeTymoviralesOurmiavirusFigure 4.7: Bipartite RNA network of plant viruses.Coloured nodes are genomes which are connected to genes depicted as grey dots. Differ-ent node colours are modules in the network calculated with the infomap algorithm (Ros-vall and Bergstrom, 2008). The contour around the nodes describes multipartite (lightblue) and segmented (yellow) genomes. Module 1 (red):Tymovirales (Alphaflexiviridae,Betaflexiviridae, Tymoviridae), Benyviridae, Bromoviridae, Closteroviridae, Potyviridae,Tombusviridae (Umbravirus), Idaeovirus, Virgaviridae. Module 2 (green):Luteoviridae,Secoviridae, Tombusviridae, Ourmiavirus. Module 3 (purple): Endornaviridae. Mod-ule 4 (dark blue):Bunyavirales (Tospoviridae, Fimoviridae, Phenuiviridae (Tenuivirus)).Module 5 (yellow): Reoviridae. Module 6 (turquoise): Sobemovirus. Module 7 (pink):Rhabdoviridae, (Nucleorhabdovirus, Cytorhabdovirus). Module 8 (black): Cilevirus andBlunevirus. Module 9 (lavender): Aspiviridae (Ophiovirus). Module 10 (light green):Totiviridae.CHAPTER 5DISCUSSION & PERSPECTIVESThe ultimate expansion of our knowledge of viral diversity, fostered by the last advanceson virus transcriptomics (Shi et al., 2016), has just begun. Studies using those techniqueshave led to the discovery of new viral species at an unprecedented rate. Major gaps invirus evolution understanding will in all likelihood start to close in forthcoming years.The catalogue of multipartite species, mostly infecting plants, will benefit from the newsampling and sequencing techniques. It is foreseeable that their, in principle narrow, host-range will broaden. Actually, we already have evidence indicating that multipartitism is notlimited to plants: possible multipartite candidates are infecting fungi and animals (Ghabrialet al., 2008; Olveira et al., 2009; Ladner et al., 2016).Though general molecular advantages of multipartitism are, as of yet, a puzzle, theseviruses do depend on the metabolism and the structure of plant tissues (Miyashita andKishino, 2010; Sicard et al., 2019). However, it would be important to study the lifestylesof non-plant multipartite viruses at a level of detail similar to that of multipartite plantviruses to find out whether the complementation among genomic segments follows a mech-anism analogous to that known to take place in plant tissues. Spatial structure might be es-sential to guarantee the persistence of multipartite viruses by reducing the cost of coinfec-tion through local clustering. Recent theoretical work illustrates the importance of spatialpropagation for the maintenance of multipartite viruses, not only in regard to their within-host transmission: actually, most multipartite species described infect monocultures whereplants are spatially distributed. In particular, a structured host-to-host transmission, to-gether with genetic drift, as been put forward as a plausible explanation for a contingentsuccess of multipartite viruses over monopartite counterparts (Valdano et al., 2019). AnMultipartite viruses: Organization, emergence and evolution.By Adriana Lucı´a Sanz5758 DISCUSSION & PERSPECTIVESindependent approach considers a spatial distribution of hosts that can be progressivelyinfected. Intermediate infected hosts with one to few genomic segments that remain fora long enough time in the host might boost a more pervasive infection compared to themonopartite case (Zhang et al., 2019). The previous theoretical results notwithstanding,this intermediate, latent state, is not likely to exist in nature.Different ways in which the complementation requirement can be alleviated are possi-ble, but not yet investigated. Perhaps the success of multipartitism is partly relying on adynamical strategy where its advantages are the opportunistic colonization of new hostsby eliciting fast adaptive responses through complementation of formerly independent ge-nomic segments. Cooperation of such segments is a must for this strategy to be plausible.In fact, it is plants that offer a particularly suitable environment for loose cooperative asso-ciations between virus and virus-like agents of different origins. The association of geneticelements might have turned permanent when the ecological conditions drove the partnersto an interdependent relationship. For example, if infection becomes conditional on thejoint cooperation of a pair of genetic elements, a bipartite species appears as a natural so-lution. We hypothesize that this scenario offers a plausible origin for multipartite species.However, further mathematical models and additional empirical evidence will be essentialto reveal which ecological conditions might favour the emergence of multipartite forms incompetition with monopartite viral species.Cooperation of genetic elements could be followed by recombination as a fundamentalmechanism for the generation of novel viruses (Sachs and Bull, 2005). However, multipar-tite and segmented viruses apparently break that rule (Varsani et al., 2018). In particularcases, such as -RNA viruses, the coverage of the genetic material by a nucleocapsid proteinrepresents a physical limitation to recombination, since the genetic material is not exposed.Nonetheless, the lack of recombination of the rest of Baltimore classes might be compen-sated by a more plastic mechanism of reassortment or gene shuffling. In fact, multipartiteRNA viruses are more prone to share genes with other multipartite species than segmentedor monopartite viruses with akin genome configurations. Cooperation might show up as amodular constructive principle of multipartite viral species. In fact, cooperation acquiresa broader meaning if ensembles of viral species are portrayed as complex, gene-sharingnetworks, where it emerges as a distributed property of the ensemble. Contrary to highlystable associations of genes in chromosomes, gene sharing in multipartite viruses offersan exploding number of combinatorial possibilities that might translate into many possibledifferent viral species in short evolutionary time.Whether associations among genetic segments is a stable strategy or whether they mightevolve to monoparticle viral forms (segmented or monopartite) remains as a further openquestion (Nee and Maynard-Smith, 1990). Actually, there might be restrictions to recombi-nation and single particle encapsidation that either stabilize the multipartite state or at leastdelay the emergence of a monopartite cognate form —provided it would be a fitter solu-tion. Multipartite viruses have emerged many times in evolution, and at present it seemseasier to understand them as a fit, but only transiently stable solution in evolutionary time.Multipartite viruses might be the dynamic product of a huge and plastic pangenome that isconstantly proposing, permitting and sustaining new associations in a complex, changingand diverse global ecology.PART IVCONCLUSIONES–CONCLUSIONS60CONCLUSIONESEl trabajo desarrollado en esta tesis ha dado lugar a las siguientes conclusiones principales:1. Una bu´squeda exhaustiva en bases pu´blicas de datos geno´micos muestra que la preva-lencia de los virus multipartitos en la Virosfera es de alrededor del 14%. Esta abun-dancia apoya el hecho de que los virus multipartitos son una estrategia evolutiva es-table.2. Sobre el 90% de las especies multipartitas infectan plantas. Adema´s, sorprendente-mente, cerca de la mitad de los virus conocidos que infectan plantas son multipartitos.Las plantas ofrecen condiciones particularmente adecuadas para esta alta prevalenciade virus multipartitos.3. Las poblaciones virales que sufren de deleciones gene´ticas pueden experimentar unatransicio´n hacia la multiparticio´n geno´mica. Se ha demostrado que la complementacio´nentre genomas defectivos so´lo es posible a densidades virales suficientemente altas.La presio´n por complementarse se reduce si los virus se propagan es hospedadoresespacialmente estructurados, tal y como se demuestra en el caso de los tejidos vege-tales.4. La asociacio´n entre virus y sate´lites da lugar a fenotipos emergentes. Estas nuevaspropiedades etiolo´gicas y epidemiolo´gicas pueden representar una ventaja evolutivaque contrarreste el coste de la coinfeccio´n. La asociacio´n entre virus y sate´lites puedeser el primer paso hacia el surgimiento de especies multipartitas.5. Los virus multipartitos de ARN tienen un origen evolutivo polifile´tico, ya que hansurgido varias veces a lo largo de la filogenia de los virus de ARN.6. El intercambio de genes entre virus multipartitos de ARN es ma´s probable que den-tro de otras configuraciones geno´micas. El intercambio gene´tico puede actuar comoun principio constructivo modular para especies multipartitas y conferir una ventajaadaptativa ante cambios ambientales.61CONCLUSIONSThe work developed in this thesis leads to the following main conclusions:1. The exhaustive search of publicly available genomic databases shows that the preva-lence of multipartite viruses in the Virosphere is around 14%. Their abundancestrongly supports that multipartite viruses can be considered a stable evolutionarystrategy.2. Up to 90% of multipartite species infect plants. Remarkably, about one half of allknown plant viruses are multipartite. Plants must offer particularly suitable conditionsfor this high prevalence of multipartite viruses.3. Viral populations that suffer from genetic deletions can experience a transition to-wards multipartitism. However, it had been shown that complementation of defectivegenomes is only possible at sufficiently high viral densities. The critical viral densitycan be lowered down if viruses propagate in spatially structured hosts, as it occurswithin-host propagation of plant infections.4. The association between viruses and satellites lead to emerging phenotypes. Thesenew aetiological and epidemiological properties can represent an evolutionary advan-tage counteracting the cost of coinfection. The association between independent vir-suses and satellites can act as a stepping stone towards the rise of multipartite species.5. Multipartite RNA viruses have a polyphyletic origin, as they have emerged manytimes along RNA phylogeny.6. 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Evol., 31(1):121–134, 2014.APPENDIX ASCRIPTSA.1 C scriptsA.1.1 Model of genome segmentationAn implementation of stochastic simulations in C language are used to solve the model ofgenome segmentation presented in Chapter 2. The script is given below:Listing A.1: C script of genome segmentation model/∗s e g m e n t s p a c e 8 . c − 0 1 / 0 6 / 1 6 A SanzI s e q u i v a l e n t t o s e g m e n t s p a c e 1 . c b u t c h a n g i n g some t h i n g s i n o r d e r t o savet h e d a t a .In o r d e r t o d e c r e a s e t h e c o m p u t a t i o n t ime we on ly need p r e v i o u s s t e pL e t s t r a n s f o r m M t h e m a t r i x t h a t s t o r e s t h e v i r a l t y p e s , i n t ime and s p a c e :M( i , x , t )−>M( i , x ) ” i n d e p e n d e n t ” from t ime .Two m a t r i c e s . M and M−STEPS :1− I n f e c t . MOI2− Segment . MU3− Complement . min c o n d i t i o nMultipartite viruses: Organization, emergence and evolution.By Adriana Lucı´a Sanz8182 C SCRIPTS−COMPILATION : gcc −s t d =c99 −o r u n a b l e s e g m e n t s p a c e . c −lm−FILE I /O: Three f i l e s a r e p roduced ( one f o r each v i r a l t y p e ) . Change t h e p a t h s f o r yourp u r p o s e s i n t h e s e c t i o n ” s u b s t i t u t i o n v a r i a b l e s ” .−OTHER NOTESBUG RESOLVED . [ F i / sum ( F i )= nan ] i f you have empty c e l l s . In t h i s c a s e your F i =0 ;CAUTION . THe m a t r i x u s h o u l d save i s M a f t e r i n f e c t i o n . O t h e r w i s e you w i l l n e v e rc o n s e r v e t h e number o f v i r u s p e r c e l l .∗ /# i n c l u d e / / s t a n d a r d l i b r a r y# i n c l u d e < s t d l i b . h> / / RAND MAX t o have a d i s t r i b u t i o n from 0 t o 1# i n c l u d e / / NULL t o change seed# i n c l u d e $/ / t y p i c a l f u n c t i o n s/ / DIRECTLY SUBSTITUTION VARIABLES# d e f i n e TYPES 3# d e f i n e TIME 1000# d e f i n e CELLS 100# d e f i n e MU 0 . 1# d e f i n e MOI 2# d e f i n e PATH1 ” / home / newton / Documentos / I n t r o d C p r o g r a m m i n g / some−f i g u r e s / s p a c e w t i m 4 . d a t ”# d e f i n e PATH2 ” / home / newton / Documentos / I n t r o d C p r o g r a m m i n g / some−f i g u r e s / s p a c e a i m 4 . d a t ”# d e f i n e PATH3 ” / home / newton / Documentos / I n t r o d C p r o g r a m m i n g / some−f i g u r e s / s p a c e b i m 4 . d a t ”/ / HEAD: FUNCTION DECLARATIONvoid segment ( i n t [ ] [ CELLS] ) ; / / i n p u t a m a t r i x by r e f e r e n c e .void i n f e c t ( i n t [ ] [ CELLS ] , i n t [ ] [ CELLS ] ) ;void z e r o s ( i n t [ ] [ CELLS ] , i n t ) ;void complement ( i n t [ ] [ CELLS ] , i n t [ ] [ CELLS ] ) ;i n t sumc ( i n t [ ] [ CELLS ] , i n t ) ;i n t sum ( i n t [ ] [ CELLS ] , i n t ) ;i n t min ( i n t , i n t ) ;double r a n d u n i ( ) ;/ / START THE COMPUTATION HEREi n t main ( ) {/ / Seed i s s t a r t e d wi th c l o c k t ime .s r a n d ( t ime (NULL ) ) ;C SCRIPTS 83/ / V a r i a b l e s/ / F i l e s t o save d a t a . One f o r each v i r a l s p e c i e .FILE ∗wt ,∗A,∗B ;wt= fopen (PATH1 , ”w” ) ;A= fopen (PATH2 , ”w” ) ;B= fopen (PATH3 , ”w” ) ;/ / V a r i a b l e si n t WT 0=MOI ;i n t M[TYPES ] [ CELLS ] ;i n t M [TYPES ] [ CELLS ] ;/ / Two ways t o i n i t i a l i z e t h e m a t r i x wi th wt v i r u sf o r ( i n t x =0; x k f o r a l l c e l l s x86 C SCRIPTSk=M[ 0 ] [ x ] ;whi le ( k>0 ){k−−;r = r a n d u n i ( ) ;i f ( r<=MU ){M[ 0 ] [ x ]−=1; / / wtM[ 1 ] [ x ] + = 1 ; / / aM[ 2 ] [ x ] + = 1 ; / / b}}}}/∗ ” r a n d u n i ” R e t u r n s a un i fo rm random number from 0 t o 1∗ /double r a n d u n i ( ) { re turn r and ( ) / ( double )RAND MAX;}/∗ ” z e r o s ” f i l l s w i th z e r o s t h e column ” column ” of any m a t r i x ”M”Need t o s p e c i f y t h e t o t a l number o f columns ” t o t c o l u m n s ”∗ //∗ I n i t i a l i z e t h e m a t r i x M wi th z e r o s f o r a l l t h e s p e c i e s i n t h e i n p u t columns ∗ /void z e r o s ( i n t M[ ] [ CELLS ] , i n t c e l l ){f o r ( i n t l =0 ; l # i n c l u d e < s t d l i b . h># i n c l u d e 88 C SCRIPTS/ / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / // / DEFINITIONS DECLARATIONS/ / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /# d e f i n e TIME 250000# d e f i n e PATH1 ” p s y d x x y . d a t ”# d e f i n e PATH2 ” p s y d x y y . d a t ”# d e f i n e PATH3 ” p s y d x s y . d a t ”# d e f i n e PATH4 ” p s y d x h y . d a t ”# d e f i n e U 100# d e f i n e V 100double dh ( double ∗ , double , double , double , double ) ;double dx ( double ∗ , double , double , double ) ;double dy ( double ∗ , double , double , double , double ) ;double ds ( double ∗ , double , double , double , double ) ;void rk ( double ∗ , double ∗ , double , double , double , double ) ;void i n t e g r a t i o n r k ( double [ ] [ 4 ] , double ∗ , double ∗ , double ∗ , double ) ;double sum ( double ∗ ) ;void l i n s p a c e ( double ∗ , double , double , i n t ) ;/ / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / // / START/ / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /i n t main ( ) {/ / p a r a m e t e r sdouble i n i t i a l [ ] = { 1 , 0 . 2 , 0 . 2 , 1 } ; / / v e c t o r f o r i n i t i a l c o n d i t i o n sdouble f i n a l [ 4 ] ; / / v e c t o r f o r f i n a l c o n d i t i o n sdouble k [ 4 ] [ 4 ] ; / / v e c t o r t o c a l c u l a t e runge k u t t adouble p [U ] ;double q [V ] ;l i n s p a c e ( p , 0 . 0 , 1 . ,V ) ;l i n s p a c e ( q , 0 . 0 , 1 . ,U ) ;double param [ 1 0 ] ; / / y wins x winsparam [ 0 ] = 0 . 2 ; / / g=1 1param [ 1 ] = 0 . 0 5 ; / / d =0 .05 0 . 0 5param [ 2 ] = 0 . 2 3 7 5 ; / / p x =0 .2 0 . 3param [ 3 ] = 0 . 1 ; / / p y =0 .3 0 . 2param [ 4 ] = 0 . 2 5 ; / / d x =0 .1 0 . 0 1param [ 5 ] = 0 . 1 8 1 6 ; / / d y =0 .01 0 . 0 1param [ 6 ] = 1 ; / / p s =0 .8 0 . 2 5param [ 7 ] = 1 . 2 5 ; / / p s y =0 .25 0 . 2 5param [ 8 ] = 0 . 5 4 5 1 2 ; / / d s =0 .3 0 . 0 1param [ 9 ] = 0 . 0 2 2 7 ; / / p Y =0.3 0 . 2 5C SCRIPTS 89double t s t e p = 0 . 0 1 ; / / t ime s t e p/ / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / // / F i l e IO/ / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /FILE ∗ fp1 , ∗ fp2 , ∗ fp3 , ∗ fp4 ; / / c r e a t e a p o i n t e r ” fp ” t o a t y p e named FILE−−>c r e a t e s an empty f i l efp1 = fopen (PATH1 , ”w” ) ; / / i n d i c a t e where t o save / namet h e f i l e and t h e o p t i o n ”w”fp2 = fopen (PATH2 , ”w” ) ;fp3 = fopen (PATH3 , ”w” ) ; / / i n d i c a t e w r i t efp4 = fopen (PATH4 , ”w” ) ;/ / SET TIMEt i m e t s t a r t , s t o p ;t ime (& s t a r t ) ;f o r ( i n t i =0 ; i l e n g t h ( m u t a n t n o d e s )MATLAB FUNCTIONS 95i n d e x = l e n g t h ( m u t a n t n o d e s ) ;i n d e x m u t = l e n g t h ( l i s t O f m u t a n t s ) ;e l s ei n d e x m u t = m u t a n t n o d e s ( i n d e x ) ;endendi f rem ( index mut ,10 )==0f p r i n t f ( ’ a n c e s t o r=%d , l e a v e=%d\n ’ , a n c e s t o r m u t , max ( f i n d ( l i s t O f m u t a n t s ( mu tan tLeaves ) = = 1 ) ) ) ;endenda n c e s t o r s = un iqu e ( A n c e s t o r n o d e s ) ;$

Multipartite viruses: Organization, emergence and evolution

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