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Adrien Rieux, Fabien Halkett, Virginie Ravigné, Marie-Françoise Zapater, Luc Pignolet, Luc de Lapeyre de Bellaire and Jean Carlier

LANDSCAPE GENETICS AND Gene flow in the banana pathogenic fungus

Mycosphaerella fijiensis

UMR BGPI : "Biology and genetics of plant / pathogen interactions" Campus International de Baillarguet - 34 398 MONTPELLIER – France

The 7th International Mycosphaerella and Stagonospora Symposium

(2)

Mutation Recombinaison Genetic drift Gene flow Selection Reproductive strategy Dispersal processes Population size Demographic fluctuations Plant resistances Fungicides Adaptation of pathogen populations

 Efficient and durable strategies of disease management should be defined in time and space taking into account epidemiology and evolutionary potential of pathogens

Objective: To infer gene flow and dispersal processes in

Mycosphaerella fijiensis

(3)

Direct methods  Disease gradient analysis  Average dispersal distance

Indirect methods  Population genetics

 Neutral molecular markers

 Genetic differentiation between populations analysis Source of inoculum 0 20 40 60 80 100 120 140 160 180 200 220 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33

distance from the inoculum source (m)

Number of lesions

Problems :

- Experiments costly and hard to realize (Ex : problems with source identification)

T T s ST Q Q Q F   = 1 Qt : Probability of identity

between 2 genes sampled in the whole habitats

Qs : Probability of identity between 2 genes sampled in the

same habitat

How to measure parameters related to gene flow ?

(4)

A high migration (rate or distance) will homogenize allelic frequencies between populations and will lead to a decrease of the FST parameter.

High Gene flow

FST weak Weak Gene flowFST moderate

No Gene flow FST high

 Objective : Use FST (easy to measure) in order to estimate parameters related to dispersal (rate, average distance…) (difficult to measure)

 Method : Use a model to explicitly link FST and dispersal  dispersal parameters inference

How to measure parameters related to gene flow ?

(5)

Wright’s Island model

 Population size : constant and identical  Mutation – genetic drift equilibrium  Equal contribution to the migrants pool

Migration rate / generation :

Nm FST 4 1 1 +  NOT REALISTIC

Isolation by distance Model (IBD)

  = average squared axial parent-offspring distance

D = effective density of adults ST ST F F  1 2 2 1  D Slope = Ln(Distance)

Exemple with two models

 Because of the limited migration in space, the probability of identity between genes is higher at short distance than at long distance.

Effective size Migration rate

(6)

The patho-system Mycosphaerella fijiensis – Banana plant: A good model to study gene flow in pathogenic fungus populations

• Aerial Ascomycete, Haploïd, Heterothallic • Black leaf streak of Banana

1963 : Fiji South east of Asia :

Center of Origin

(7)

• Aerial Ascomycete, Haploïd, Heterothallic • Black leaf streak of Banana

• 3 dispersal modes :

 Infected plant material transport

 Conidia dispersion (asexual reproduction)  Ascospores dispersion (sexual reproduction)

• Panmictic populations

• Relative demographic stability

Application of classical population genetics methods

The patho-system Mycosphaerella fijiensis – Banana plant: A good model to study gene flow in pathogenic fungus populations

(8)

Previous studies related to the populations genetic structure of Mycosphaerella fijiensis

Carlier, 2003

 High level of genetic diversity

 Relative importance of the dispersal modes of M.fijiensis as a function of geographical scale

• Different scales & disease dissemination modes :

Geographical scale

FST Level of

(9)

Cameroon Transect of 270 km 10 sites (26.8 km) 287 isolats / 15 microsatellites markers Costa Rica Transect of 297 km 15 sites (26.4 km) 347 isolats / 7 PCR-RFLP and 9 microsatellites markers

Previous studies related to the populations genetic structure of Mycosphaerella fijiensis

(10)

0.012 to 0.17 0.014 to 0.26 Differentiation between populations, Fst < 0.04 NS < 0.02, NS Linkage disequilibrium, rD ~ 1.00 ~ 1.00 Genotypic diversity, DG 0.41 to 0.59 0.31 to 0.46 Gene diversity, HE Costa Rica Cameroon

Basic genetic analysis

Previous studies related to the populations genetic structure of Mycosphaerella fijiensis

(11)

27 Km 1 2 3 4 5 6 7 8 9 10 270 Km 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 1-Lim 2-Agr 3-Yo 4-BaK 5-Fo 8-Ey 6-KoB 7-Man 9-AMb 10-AMa A B C

 Differentiation between populations:

Costa Rica: Fst = 0.012 to 0.17 (Costa Rica) Cameroon: Fst = 0.014 to 0.26 (Cameroon)

 Clustering analysis (STRUCTURE, v2.2 – Pritchard et al., 2000)

Costa Rica: no structure detected Cameroon: number of cluster K = 3

Previous studies related to the populations genetic structure of Mycosphaerella fijiensis

(12)

27 Km 1 2 3 4 5 6 7 8 9 10 270 Km 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 1-Lim 2-Agr 3-Yo 4-BaK 5-Fo 8-Ey 6-KoB 7-Man 9-AMb 10-AMa A B C

 Differentiation between populations:

Costa Rica: Fst = 0.012 to 0.17 (Costa Rica) Cameroon: Fst = 0.014 to 0.26 (Cameroon)

 Clustering analysis

Cameroon: number of cluster K = 3

Previous studies related to the populations genetic structure of Mycosphaerella fijiensis

• Within a production area (few 100 Km long)

A B

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y = 0.0331x - 0.0675 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 3 3.5 4 4.5 5 5.5 6 FST/(1 - FST) Log distance (km) 0 0.05 0.1 0.15 0.2 0.25 0 1 2 3 4 5 6 FST/(1 - FST) Log distance (km) Cameroon Costa Rica

Isolation by distance analysis with discrete populations

Whole transect : IBD not significative Sites 1 5 : IBD significative

Genetic

D

ifferentiation

Previous studies related to the populations genetic structure of Mycosphaerella fijiensis

• Within a production area (few 100 Km long)

Whole transect : IBD significative

P mantel = 0,0268

Direct method : d <<< 1 Km !

(14)

• IBD Analysis on a distance too long in regard with dispersal

capacity of the pathogen (geographical scale considered still too big)

- Effects of barriers

- Effects of demographic events

• Unrealistic genetic model (discrete populations)

Previous studies related to the populations genetic structure of Mycosphaerella fijiensis

• Within a production area (few 100 Km long)

(15)

To delimit pathogen

populations & to detect barriers

to gene flow

Not presented today…New analysis still under construction

Landscape

genetics

analysis

Population analysis in M. fijiensis at a local scale (50x50 Km)

10 Km

Estimation of dispersal

parameters

New

2D

&

3D

Sampling (Cameroon)

IBD in a

continuous

population

analysis

(16)

• Dispersal parameters

estimation along a

continuous population

• 90 sites of sampling : 1Km, 250 m, 50 m

• 2-6 isolates / sites genotyped with 20 microsatellites markers

• Continuous population IBD method (haploïds) (Rousset, unpublished)

Transect of 33km

Population analysis in M. fijiensis at a local scale (50x50 Km)

(17)

 No IBD signal detected along the transect 1 2 b   D   Geographical distance Genetic d ifferentiation F(1-F) = f (Ln(d)) y = 0,004x + 0,0097 -0,4 -0,3 -0,2 -0,1 0 0,1 0,2 0,3 0,4 0,5 -5 -4,5 -4 -3,5 -3 -2,5 -2 -1,5 -1 Ln (d) F/(1-F)

 = average squared axial parent-offspring distance

D = Density

Population analysis in M. fijiensis at a local scale (50x50 Km)

(18)

 High level of migration

 Lack of statistical power

 Method of IBD inappropriate to pathogenic fungus

populations specificity

In particular : High effective size

How to explain an absence of IBD signal ? :

Population analysis in M. fijiensis at a local scale (50x50 Km)

(19)

 Lack of statistical power ?

 Individual-based simulation model, spatially-explicit, based on the coalescence theory (Leblois et al. 2003)

 Simulation of our sampling (transect 33 km, X,Y,Z)

 Statistical power related to our sampling : slope of 10-3 statistically detectable

Population analysis in M. fijiensis at a local scale (50x50 Km)

IBD in a continuous population analysis

- Reproduction stage - Migration stage - Selection stage

(20)



High effective size

Slope = 2 2 1  N 2 4 6 8 10 200 400 600 800 1000 Detectable slope of 10-4 Detectable slope of 10-3 Effective size  1 200

Population analysis in M. fijiensis at a local scale (50x50 Km)

(21)



IBD model might not be adapted to described

dispersal processes and gene flow in

M. fijiensis

 Test new methods to infer parameters related to dispersal processes (migration-selection models, estimation of dispersal curve, new directs methods ?…)



Realize the spatialized genetic clustering analyse

with a landscape genetics method (No

a priori

definition of populations)

 Understand the genetic structure of M.fijiensis through an agricol landscape

 Detect and define some eventual barriers to gene flow

Références

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