1 of 27

Development of microsatellites from whole-transcriptome sequences in Phytophthora capsici for population studies��

Camilo Parada

Lina Quesada Ocampo

@chparadaNCSU

December 9, 2015

2 of 27

Phytophthora capsici

Important disease of worldwide occurrence in cucurbits, solanaceous, and other vegetable crops
Cause of Phytophthora crown, root and fruit rot
Disease is favored by warm and wet conditions
Sexual reproduction results in high genetic diversity and new isolates that can overcome control strategies

3 of 27

Infested water has resulted in rapid spread

Heavy rains, flooding and culls

Unknown population structure in NC

Preliminary phenotypic data show differences in virulence and fungicide resistance

11 Oct 15 12 Oct 15

Flooding in NC after heavy raining

4 of 27

Population Genetics

High genetic diversity at a global scale

Genetic stratification by geography and host

(no wind dispersal = isolated populations)

Understanding local population is important for effective disease management

(Quesada-Ocampo et al. 2011)

A recent study using hundreds of isolates from locations world- wide and diverse hosts has confirmed the high genetic diversity of P . capsici at a global scale and detected the presence of population structure by geography (Fig. 5D) and by host (98).

This was in agreement with previous investigations documenting differences in pathogenicity of isolates from cucurbitaceous and solanaceous hosts (102), and that P . capsici is not wind dispersed, creating geo- graphically isolated subpopulations (46). Finding genetic stratifica- tion in P . capsici populations by geography and host highlight the importance of including isolates from different subpopulations that represent the genetic variation in P . capsici for development of diagnostic tools, fungicides, and host resistance. Management can be tailored to control local populations of P . capsici; nevertheless, this approach requires a detailed knowledge of regional pathogen population structure and distribution.

of including isolates from all detected clusters that represent the genetic variation in P . capsici for development of diagnostic tools, fungicides, and host resistance

5 of 27

Project Objectives

Collect isolates from NC and SC in 2014 - 2016

Phenotypic characterization (hosts, regions, mating type, mefenoxam sensitivity, virulence on fruit, field and greenhouse)

Marker development – SSRs from transcriptome

Diversity assessment of Microsatellite markers

Characterize population structure in NC to improve management strategies

6 of 27

P. capsici collection: North Carolina

7 of 27

P. capsici collection: Host

8 of 27

Isolate Virulence

Mating type

Mefenoxam sensitivity

9 of 27

Microsatellites or Simple Sequence Repeats

SSRs are short DNA sequences consisting of tandemly repeated units, generally 1 – 6 base pairs in length
SSRs can be found in either non-coding or coding regions
SSRs from coding regions are more likely to be conserved across species, high level of transferability to closely related species

10 of 27

Transcriptome data

SSR identification

(MISA)

Primer design

and

selection

Validation

&

Determination of polymorphism

P. capsici LT1534 v11.0

Microsatellite discovery pipeline

Obtain P. capsici isolates
Grow, filtrate, and lyophilize mycelia
Extract high quality and quantity DNA
PCR and validation by agarose gel
Fragment analysis to confirm polymorphism

11 of 27

Distribution of SSRs

P. capsici exhibit the lowest SSR density and abundance

<

12 of 27

Frequency distribution by motif length and number of repeats

13 of 27

Similarly to previous studies, frequency of trinucleotide SSRs was larger than rest of SSRs.

Trinucleotide SSRs may affect the protein structure or play a role in gene regulation and transcription

14 of 27

SSR validation

L

NC19835

NJ328

RCZ-11

WLB-8

LT1534

12889

SP98

W

L

NC19835

NJ328

RCZ-11

WLB-8

LT1534

12889

SP98

W

L

NC19835

NJ328

RCZ-11

WLB-8

LT1534

12889

SP98

W

L

NC19835

NJ328

RCZ-11

WLB-8

LT1534

12889

SP98

W

L

Polymorphic (17)

Monomorphic (31)

15 of 27

Summary Statistics and Diversity Measures

SSR	Motif	Alleles	He	Ho	PIC
SSR36	(AAG)6	126, 129	0.490	0.571	0.3698
SSR1	(CAG)4	112, 145, 178, 211, 217, 262	0.776	1.000	0.7406
SSR39	(AAG)7	248, 251, 254, 257, 287	0.714	1.000	0.6707
SSR27	(GA)6	350, 352, 354, 358	0.622	0.571	0.5474
SSR37	(AGC)6	262, 272, 274, 280	0.663	1.000	0.6003
SSR18	(CAG)5	121, 124, 127, 130, 133, 151, 157, 169, 178	0.857	0.857	0.8415
SSR31	(AGC)4	178, 181	0.245	0.286	0.2149
SSR19	(AAG)6	215, 218, 221, 224, 227	0.714	0.857	0.6707
SSR65	(ACTTCA)4	236, 248, 260, 272	0.612	0.571	0.5407
SSR55	(CCAG)6	152, 284, 292, 296, 300	0.724	1.000	0.6853
SSR75	(TCCTC)3	210, 215	0.490	0.857	0.3698
SSR20	(CACGAC)5	112, 124	0.408	0.571	0.3249

pectate lyase

Heterozygosity is of major interest to students of genetic variation in natural populations. It is often one of the first "parameters" that one presents in a data set. It can tell us a great deal about the structure and even history of a population. Just for example, very low heterozygosities for allozyme loci in cheetahs and black-footed ferrets indicate severe effects of small population sizes (population bottlenecks or metapopulation dynamics that severely reduced the level of genetic variation relative to that expected or found in comparable mammals). High heterozygosity means lots of genetic variability. Low heterozygosity means little genetic variability. Often, we will compare the observed level of heterozygosity to what we expect under Hardy-Weinberg equilibrium (HWE). If the observed heterozygosity is lower than expected, we seek to attribute the discrepancy to forces such as inbreeding. If heterozygosity is higher than expected, we might suspect an isolate-breaking effect (the mixing of two previously isolated populations).

16 of 27

PIC values ranged from 0.21 to 0.84 with a mean of 0.54

PIC values range from 0.19 to 0.78 with a mean of 0.51 in P. infestans

PIC values ranged from 0.375 to 0.553 with a mean of 0.58 in P. irregulare

17 of 27

Allele frequency for 7 P. capsici isolates

SSR37 SSR18 SSR19 SSR65

SSR55 SSR1 SSR39 SSR27

18 of 27

Other research

Evaluating commercial pepper cultivars for resistance to P. capsici

Assays of fungicide sensitivity in vitro (Ranman, Ridomil, Presidio..)

Assays to characterize virulence in pepper fruits

32 pepper lines vs mix of isolates

48 pepper lines vs 3 isolates

19 of 27

R

T

S

Martha- R showed the highest levels of resistance to the isolates of P. capsici, while Plato, Red Knight and Bastille showed the highest susceptibility.

Results from field trial

20 of 27

S

T

R

Results from the GH trial

21 of 27

Fidel (Land race)

Control NC21810 MI12889 NCCP3

Resistant cultivars

Martha - R

22 of 27

Future work

Use the genotyping by sequencing protocol to obtain SNPs

Determinate population structure of collected isolates from NC

23 of 27

Acknowledgments

Research collaborators:

Dr. David Ritchie
Dr. Shaker Kousik
Dr. Mary Hausbeck
Dr. Anthony Keinath

Isolates

Dr. Jean Ristaino
Dr. Kurt Lamour

Funding:

The Quesada Lab

24 of 27

Results

Control NC21810 12889 NCCP3

H1653

25 of 27

Results

Fidel (Land race)

Control NC21810 12889 NCCP3

26 of 27

Results

Control NC21810 12889 NCCP3

Marta - R

27 of 27

Results

Control NC21810 12889 NCCP3

Red Knight