Beslon, Guillaume

Elena, Santiago,

Liard, Vincent,

Liard, Vincent, F

Elena, Santiago, F

Jump then Climb: can rearrangements predict the occurrence of mutational bursts?

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HAL Id: hal-01938800https://hal.archives-ouvertes.fr/hal-01938800Submitted on 28 Nov 2018HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Jump then Climb: can rearrangements predict theoccurrence of mutational bursts?Guillaume Beslon, Vincent Liard, Santiago ElenaTo 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:	predictability	of	evolu%on	at	the	molecular	level	Jump	then	Climb:	can	rearrangements	predict	the	occurrence	of	muta%onal	bursts?		Guillaume	Beslon1,	Vincent	Liard1,	San6ago	F.	Elena2,3	1:	INRIA-Beagle	team	(INSA-Lyon),	Lyon,	France	2:	IBMCP	(CSIC-UPV),	Valencia,	Spain;	3:	Santa	Fe	Ins6tute,	Santa	Fe	NM,	USA	•  Due	to	the	stochas6c	nature	of	muta6ons,	evolu6on	is	generally	supposed	to	be	unpredictable	at	the	molecular	level.	•  But	muta6ons	are	filtered-out	by	selec6on	which	may	introduce	correla6ons	in	the	muta6onal	paSerns.	•  There	are	many	different	kinds	of	muta6onal	events	(switches,	InDels,	rearrangements,	HGT…).	•  Some	of	these	events	may	poten6ate	the	occurrence	of	others,	resul6ng	in	a	non-random	fixa6on.	! How	to	study	this	process?	•  Modeling	and	simula6on	can	be	used	to	study	how	a	random	spontaneous	muta6onal	process	can	turn	into	a	non-random	process	when	looking	at	fixed	muta6ons.	•  We	need	a	model	in	which	muta6onal	paSerns	can	account	for	the	variety	of	molecular	events	that	can	alter	real	genomes.	•  The	model	should	include	a	complex	genotype-to-phenotype	map.	•  Both	proper6es	are	at	the	core	of	the	Aevol	model	(www.aevol.fr).	!	Here	we	used	Aevol	to	test	the	interac%ons	between	the	different	kind	of	muta%ons…	Method:	In	Silico	experimental	evolu%on	with	the	Aevol	model	Results:	Random	spontaneous	events	don’t	fix	independently	Discussion:	Can	rearrangements	be	used	as	predictor	of	molecular	evolu%on?		The	Aevol	model:	Aevol	 is	 an	 In	 Silico	 Experimental	Evo lu6on	 p la[orm	 that	 mode l s	microorganisms	 evolu6on	 with	 explicit	selec6on	 and	 replica6on	 processes	 (A).	Aevol	 uses	 a	 realis6c	 genome	 structure	(B.1)	 and	 a	 sound	 genotype-to-phenotype	map	 (B).	 All	 func6onal	 levels	are	 modeled	 as	 mathema6cal	 func6ons	(B.2-3).	Fitness	is	computed	by	comparing	the	 phenotype	 with	 a	 predefined	 target	(in	 red	 on	 B.3).	 Muta6on	 operators	include	chromosomal	rearrangements	(C.1),	switches	and	Indels	(C.2).		Experimental	framework:	•  We	evolved	30	viral	wild-types	by	simula6ng	200,000	genera6ons	of	evolu6on	 under	 a	 high	 muta6on	pressure	(10-4	mut.bp-1.gen-1).	•  Each	WT	has	been	cloned	30x	and	the	 900	 clones	 were	 further	evolved	for	30,000	genera6ons.	•  We	analyzed	the	sequence	of	fixed	muta6ons	in	terms	of	(1)	effect	on	fitness,	 genome	 size,	 robustness	and	 evolvability	 (2)	 wai6ng	 6me	between	two	muta6onal	events.	Genome size Favorable	Deleterious	Neutral	Switch/InDel	Rearrangement	Evolu%onary	dynamics:	•  215	 of	 the	 900	 clones	 significantly	improved	their	fitness.	•  Fitness	 gain	 ocen	 occurs	 during	short	 muta6onal	 bursts	 with	 rapid	fixa6on	of	muta6onal	events.	•  These	bursts	are	characterized	by	a	strong	increase	of	evolvability.	•  More	 than	 50%	of	 the	 bursts	 start	with	a	segmental	duplica6on.	•  Compared	 to	 spontaneous	 rates,	muta6ons	 are	 rare,	 except	 during	the	bursts.		Wai%ng	%me	between	muta%ons:	Delays	 from	 the	 previous	 fixa6on	event	 (top)	 and	 to	 the	 next	 one	(boSom)	are	es6mated	per	kind	of	muta6on	 for	 the	 215	 clones	 that	significantly	 gain	 fitness	 (Hodges–Lehmann	 es6mator).	 Segmental	duplica6ons	 show	 a	 strong	 skew:	they	 are	 fixed	 acer	 a	 “muta6onal	desert”	 and	 are	 likely	 to	 be	immediately	 followed	 by	 another	muta6on	fixa6on	event.	InDels	are	also	 skewed	 although	 the	 skew	 is	less	pronounced.	In	our	experiment	evolu%on	proceeds	by	“jump-and-climb”	steps:	1.  The	viruses	climb	their	local	fitness	peak.	This	process	mainly		relies	on	subs6tu6ons.	2.  At	the	top	of	the	fitness	peak,	no		more	favorable	subs6tu6ons	are		available.	S6ll	many		rearrangements	remain	to	be	tested.	3.  A	rearrangement	is	fixed;	viruses	jump	to	a	new	peak	where	new		favorable	subs6tu6ons	are	available.		The	climbing	process	starts	again.		This	sequen6al	process	enables	par6al	predic6on	at	 the	molecular	 level:	fixa6on	of	a	rearrangement	opens	the	path	to	new	adapta6ons…	The	jump-and-climb	process	is	rooted	in	the	combinatorics	of	muta%onal	events		In	compacted	genomes,	like	viral	ones,	the		combinatorics	of	point	muta6ons	is	quickly		exhausted.	Yet,	the	combinatorics	of		rearrangements	is	much	larger	and	cannot		be	explored	in	a	reasonable	6me.	When	fixed,	they	open	new	paths	in	the	fitness		landscape	that	enable	fixa6on	of	previously		impossible	point	muta6ons…	Similar	processes	have	been	observed	in	viruses	(e.g.	Chikungunya)	and	bacteria	(Blount	et	al.,	2012).	Our	results	open	three	important	ques6ons:	(1)	is	this	process	restricted	to	short,	compact,	genomes	or	can	it	be	generalized,	e.g.	to	cancer	evolu6on?	(2)	Are	there	other	“jumping”	muta6onal	events	(3)	can	these	events	be	used	to	predict	disease	emergence	or	evolu6on	of	drug	resistance?		Clone	WT2C1	1	2	3	(A)	Popula6on	on	a	grid	and	genera6onal	evolu6onary	loop	Local	selec6on	and			replica6on		scale : 471 bp(B.1)	Genome	(B.2)	Proteome	 (B.3)	Phenotype	(B)	Genome	decoding	and	fitness	evalua6on	Func6onal	space	Func6onal	space	Ac6va6on		level	Ac6va6on	level	scale : 471 bpscale : 471 bpscale : 471 bpscale : 471 bpscale : 471 bpscale : 471 bpscale : 471 bpscale : 471 bpscal 1 bpscale : 471 bp(C)	Genome	replica6on	with	random	rearrangements	and	muta6ons	scale : 471 bpscale : 471 bpscal 1 bpscale : 471 bpscale : 471 bp(C.1)	Chromosomal	rearrangements	Target	func6on	(C.2)	Switches,	Indels	Example	of	a	digital	virus	WT	acer		200,000	genera6ons	of	evolu6on	on		the	Aevol	pla[orm	Es6mated	wai6ng	6me	from	previous	muta6on	fixa6on	event		Es6mated	wai6ng	6me	to	next	muta6on	fixa6on	event		Small	ins.	(916)	429	531	620	211	620	1932	1238	398	 426	1193	376	770	***	 ***	*	Switches	(1172)	Small	del.	(911)	Duplica6ons	(168)	Large	del.	(29)	Transloc.	(8)	Inversions	(64)	661	2020	Popula6on	 Best	final	clone	mRNAs	 Genes	Phenotype	of	the	best	individual	(colored	area),	phenotypes	of	the	whole	popula6on	(blue	lines),	target	phenotype	(red	curve)	Neighbors	in	the	fitness	landscape	of	WT2C1		Point	muta%ons	 521		Small	inser%ons	 65	646		Small	dele%ons	 3	126		Duplica%ons	 141	149	320		Large	dele%ons	 270	920		Transloca%ons	 140	607	480		Inversions	 270	920	

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Jump then Climb: can rearrangements predict the occurrence of mutational bursts?

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