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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