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Submitted on 28 Nov 2018

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Jump then Climb: can rearrangements predict theoccurrence of mutational bursts?

Guillaume Beslon, Vincent Liard, Santiago Elena

To cite this version:Guillaume Beslon, Vincent Liard, Santiago Elena. Jump then Climb: can rearrangements predictthe occurrence of mutational bursts?. Evolution 2018 - Congress on Evolutionary Biology, Aug 2018,Montpellier, France. pp.1. �hal-01938800�

Ques%on:predictabilityofevolu%onatthemolecularlevel

JumpthenClimb:canrearrangementspredicttheoccurrenceofmuta%onalbursts?

GuillaumeBeslon1,VincentLiard1,San6agoF.Elena2,31:INRIA-Beagleteam(INSA-Lyon),Lyon,France

2:IBMCP(CSIC-UPV),Valencia,Spain;3:SantaFeIns6tute,SantaFeNM,USA

•  Duetothestochas6cnatureofmuta6ons,evolu6onisgenerallysupposedtobeunpredictableatthemolecularlevel.•  Butmuta6onsarefiltered-outbyselec6onwhichmayintroducecorrela6onsinthemuta6onalpaSerns.•  Therearemanydifferentkindsofmuta6onalevents(switches,InDels,rearrangements,HGT…).•  Someoftheseeventsmaypoten6atetheoccurrenceofothers,resul6nginanon-randomfixa6on.

! Howtostudythisprocess?

•  Modelingandsimula6oncanbeusedtostudyhowarandomspontaneousmuta6onalprocesscanturnintoanon-randomprocesswhenlookingatfixedmuta6ons.

•  Weneedamodelinwhichmuta6onalpaSernscanaccountforthevarietyofmoleculareventsthatcanalterrealgenomes.•  Themodelshouldincludeacomplexgenotype-to-phenotypemap.•  Bothproper6esareatthecoreoftheAevolmodel(www.aevol.fr).

!HereweusedAevoltotesttheinterac%onsbetweenthedifferentkindofmuta%ons…

Method:InSilicoexperimentalevolu%onwiththeAevolmodel

Results:Randomspontaneouseventsdon’tfixindependently

Discussion:Canrearrangementsbeusedaspredictorofmolecularevolu%on?

TheAevolmodel:Aevol is an In Silico ExperimentalEvo lu6on p la[orm that mode l smicroorganisms evolu6on with explicitselec6on and replica6on processes (A).Aevol uses a realis6c genome structure(B.1) and a sound genotype-to-phenotypemap (B). All func6onal levelsare modeled as mathema6cal func6ons(B.2-3).Fitnessiscomputedbycomparingthe phenotype with a predefined target(in red on B.3). Muta6on operatorsincludechromosomalrearrangements(C.1),switchesandIndels(C.2).

Experimentalframework:•  Weevolved30viralwild-typesbysimula6ng200,000genera6onsofevolu6on under a high muta6onpressure(10-4mut.bp-1.gen-1).

•  EachWThasbeencloned30xandthe 900 clones were furtherevolvedfor30,000genera6ons.

•  Weanalyzedthesequenceoffixedmuta6onsintermsof(1)effectonfitness, genome size, robustnessand evolvability (2) wai6ng 6mebetweentwomuta6onalevents.

Genome size

FavorableDeleteriousNeutral

Switch/InDelRearrangement

Evolu%onarydynamics:•  215 of the 900 clones significantlyimprovedtheirfitness.

•  Fitness gain ocen occurs duringshort muta6onal bursts with rapidfixa6onofmuta6onalevents.

•  Theseburstsarecharacterizedbyastrongincreaseofevolvability.

•  More than 50%of the bursts startwithasegmentalduplica6on.

•  Compared to spontaneous rates,muta6ons are rare, except duringthebursts.

Wai%ng%mebetweenmuta%ons:Delays from the previous fixa6onevent (top) and to the next one(boSom)arees6matedperkindofmuta6on for the 215 clones thatsignificantly gain fitness (Hodges–Lehmann es6mator). Segmentalduplica6ons show a strong skew:they are fixed acer a “muta6onaldesert” and are likely to beimmediately followed by anothermuta6onfixa6onevent.InDelsarealso skewed although the skew islesspronounced.

Inourexperimentevolu%onproceedsby“jump-and-climb”steps:1.  Thevirusesclimbtheirlocalfitnesspeak.Thisprocessmainly

reliesonsubs6tu6ons.2.  Atthetopofthefitnesspeak,no

morefavorablesubs6tu6onsareavailable.S6llmanyrearrangementsremaintobetested.

3.  Arearrangementisfixed;virusesjumptoanewpeakwherenewfavorablesubs6tu6onsareavailable.Theclimbingprocessstartsagain.

Thissequen6alprocessenablespar6alpredic6onat themolecular level:fixa6onofarearrangementopensthepathtonewadapta6ons…

Thejump-and-climbprocessisrootedinthecombinatoricsofmuta%onaleventsIncompactedgenomes,likeviralones,thecombinatoricsofpointmuta6onsisquicklyexhausted.Yet,thecombinatoricsofrearrangementsismuchlargerandcannotbeexploredinareasonable6me.Whenfixed,theyopennewpathsinthefitnesslandscapethatenablefixa6onofpreviouslyimpossiblepointmuta6ons…Similarprocesseshavebeenobservedinviruses(e.g.Chikungunya)andbacteria(Blountetal.,2012).Ourresultsopenthreeimportantques6ons:(1)isthisprocessrestrictedtoshort,compact,genomesorcanitbegeneralized,e.g.tocancerevolu6on?(2)Arethereother“jumping”muta6onalevents(3)cantheseeventsbeusedtopredictdiseaseemergenceorevolu6onofdrugresistance?

CloneWT2C1

1

23

(A)Popula6ononagridandgenera6onalevolu6onaryloop

Localselec6onandreplica6on

scale : 471 bp(B.1)Genome

(B.2)Proteome (B.3)Phenotype

(B)Genomedecodingandfitnessevalua6on

Func6onalspace

Func6onalspace

Ac6va6onlevel

Ac6va6onlevel

scale : 471 bp

scal

e : 4

71 b

p

scale : 471 bp

scale : 471 bp

scal

e : 4

71 b

p

scale : 471 bp

scale : 471 bpscale : 471 bpscale : 471 bpscale : 471 bpscale : 471 bp

(C)Genomereplica6onwithrandomrearrangementsandmuta6ons

scale : 471 bpscale : 471 bpscale : 471 bpscale : 471 bpscale : 471 bpscale : 471 bp

(C.1)Chromosomalrearrangements

Targetfunc6on

(C.2)Switches,Indels

ExampleofadigitalvirusWTacer200,000genera6onsofevolu6onontheAevolpla[orm

Es6matedwai6ng6me

frompreviousmuta6onfixa6onevent

Es6matedwai6ng6me

tonextmuta6onfixa6onevent

Smallins.

(916

)

429531

620

211

620

1932

1238

398 426

1193

376

770*** **

**

Switche

s(117

2)

Smalldel.

(911

)

Duplica6

ons

(168

)

Largede

l.(29)

Translo

c.

(8)

Inversions

(64)

661

2020

Popula6on Bestfinalclone

mRNAs Genes

Phenotypeofthebestindividual(coloredarea),phenotypesofthewholepopula6on(bluelines),targetphenotype(redcurve)

NeighborsinthefitnesslandscapeofWT2C1

Pointmuta%ons 521Smallinser%ons 65646Smalldele%ons 3126Duplica%ons 141149320Largedele%ons 270920Transloca%ons 140607480Inversions 270920