Summary of previous lesson Dominant vs. codominant genetic markers Concept of “genotype” Alternatively fixed allele vs.difference in frequencies PLANT.

Summary of previous lesson

• Dominant vs. codominant genetic markers

• Concept of “genotype”

• Alternatively fixed allele vs.difference in frequencies

• PLANT HOST INTERACTION: timing, physical/chemical interaction, basic genetic compatibility leads to virulence, gene for gene hypothesis, pathogenicity

Categories of wild plant diseases

• Seed decay

• Seedling diseases

• Foliage diseases

• Systemic infections

• Parasitic plants

• Cankers, wilts , and diebacks

• Root and butt rots

• Floral diseases

Seed diseases

• Up to 88% mortality in tropical Uganda

• More significant when seed production is episodic

Stress cone crop BS on DF

Seedling diseases

• Specific diseases, but also diseases of adult trees can affect seedlings

• Pythium, Phytophthora, Rhizoctonia, Fusarium are the three most important ones

• Pre- vs. post-emergence• Impact: up to 65% mortality in black cherry.

These diseases build up in litter• Shady and moist environment is very conducive to

these diseases

Foliar diseases

• In general they reduce photosynthetic ability by reducing leaf area. At times this reduction is actually beneficial

• Problem is accentuated in the case of small plants and in the case other health issues are superimposed

• Often, e.g. with anthracnose,needle cast and rust diseases leaves are point of entry for twig and branch infection with permanent damage inflicted

Systemic infections

• Viral?

• Phytoplasmas

• Peronospora and smuts can lead to over 50% mortality

• Endophytism: usually considered beneficial

Grass endophytes

• Clavicipetaceae and grasses, e.g. tall fescue• Mutualism: antiherbivory, protection from

drought, increased productivity• Classic example of coevolutionary

development: Epichloe infects “flowers” of sexually reproducing fescue, Neotyphodium is vertically transmitted in species whose sexual reproductive ability has been aborted

Parasitic plants

• True (Phoradendron) and dwarf mistletoe (Arceuthobium)

• Effects: – Up to 65% reduction in growth (Douglas-fir)

– 3-4 fold mortality rate increase

– Reduced seed and cone production

Problem accentuated in multistoried uneven aged forests

Cankers, wilts, and die-backs

• Includes extremely aggressive, often easy to import tree diseases: pine pitch canker, Dutch elm disease, Chestnut blight, White pine blister rust

• Lethal in most cases, generally narrow host range with the exception of Sudden Oak Death

Root diseases

• Extremely common, probably represent the most economically damaging type of diseases

• Effects: tree mortality (direct and indirect), cull, effect on forest structure, effect on composition, stand density, growth rate

• Heterobasidion, Armillaria, Phellinus weirii, Phytophthora cinnamomi

Removing food base causes infection of roots of other trees

Hyphae in plant tissue or soil (short-lived)

Melanin-covered rhizomorphs willallow for fungus to move to new food Sources (Armillaria mellea)

Effects of fire exclusion

Floral diseases

• Pollinator vectored smut on silene offers an example of well known dynamic interaction in which pathogen drives genetic variability of hosts and is affected by environmental condition

• Puccinia monoica produces pseudoflowers that mimic real flowers. Effects: reduction in seed production, reduction in pollinators visits

Density-dependence

• Most diseases show positive density dependence

• Negative dependence likely to be linked to limited inoculum: e.g. vectors limited

• If pathogen is host-specific overall density may not be best parameter, but density of susceptible host/race

• In some cases opposite may be true especially if alternate hosts are taken into account

Counterweights to numerical effects

• Compensatory response of survival can exceed negative effect of pathogen

• “carry over” effects?– NEGATIVE: progeny of infected individuals

less fit;– POSITIVE; progeny more resistant (shown

with herbivory)

Disease and competition

• Competition normally is conducive to increased rates of disease: limited resources weaken hosts, contagion is easier

• Pathogens can actually cryptically drive competition, by disproportionally affecting one species and favoring another

Janzen-Connol

• Regeneration near parents more at risk of becoming infected by disease because of proximity to mother (Botryosphaeria, Phytophthora spp.). Maintains spatial heterogeneity in tropical forests

• Effects are difficult to measure if there is little host diversity, not enough host-specificity on the pathogen side, and if periodic disturbances play an important role in the life of the ecosystem

Diseases and succession

• Soil feedbacks; normally it’s negative. Plants growing in their own soil repeatedly have higher mortality rate. This is the main reason for agricultural rotations and in natural systems ensures a trajectory towards maintaining diversity

• Phellinus weirii takes out Douglas fir and hemlock leaving room for alder

The red queen hypothesis

• Coevolutionary arm race• Dependent on:

– Generation time has a direct effect on rates of evolutionary change

– Genetic variability available

– Rates of outcrossing (Hardy-weinberg equilibrium)

– Metapopulation structure

Diseases as strong forces in plant evolution

• Selection pressure

• Co-evolutionary processes– Conceptual: processes potentially leading to a

balance between different ecosystem components

– How to measure it: parallel evolution of host and pathogen

• Rapid generation time of pathogens. Reticulated evolution very likely. Pathogens will be selected for INCREASED virulence

• In the short/medium term with long lived trees a pathogen is likely to increase its virulence

• In long term, selection pressure should result in widespread resistance among the host

More details on:

• How to differentiate linear from reticulate evolution: comparative studies on topology of phylogenetic trees will show potential for horizontal transfers. Phylogenetic analysis neeeded to confirm horizontal transmission

Phylogenetic relationships Phylogenetic relationships within the within the HeterobasidionHeterobasidion complexcomplex

Het INSULARE

True Fir EUROPE

Spruce EUROPE

True Fir NAMERICA

Pine EUROPE

Pine NAMERICA

0.05 substitutions/site

NJ

Fir-SpruceFir-Spruce

Pine EuropePine Europe

Pine N.Am.Pine N.Am.

Geneaology of “S” DNA insertion into P Geneaology of “S” DNA insertion into P ISG confirms horizontal transfer.ISG confirms horizontal transfer.

Time of “cross-over” uncertainTime of “cross-over” uncertain

11.10 SISG CA

2.42 SISG CA

BBd SISG WA

F2 SISG MEX

BBg SISG WA

14a2y SISG CA

15a5y M6 SISG CA

6.11 SISG CA

9.4 SISG CA

AWR400 SPISG CA

9b4y SISG CA

15a1x M6 PISG CA

1M PISG MEX

9b2x PISG CA

A152R FISG EU

A62R SISG EU

A90R SISG EU

A93R SISG EU

J113 FISG EU

J14 SISG EU

J27 SISG EU

J29 SISG EU

0.0005 substitutions/site

NJ

890 bpCI>0.9

NA S

NA P

EU S

EU F

Complexity of forest diseases

• At the individual tree level: 3 dimensional

• At the landscape level” host diversity, microclimates, etc.

• At the temporal level

Complexity of forest diseases

• Primary vs. secondary

• Introduced vs. native

• Air-dispersed vs. splash-dispersed, vs. animal vectored

• Root disease vs. stem. vs. wilt, foliar

• Systemic or localized

Stem cankerStem cankeron coast live oakon coast live oak

Progression of cankersProgression of cankers

Older canker with dry seepOlder canker with dry seep

HypoxylonHypoxylon, a secondary , a secondary sapwood decayer will appearsapwood decayer will appear

Root disease center in true fir caused by Root disease center in true fir caused by H. annosumH. annosum

HOST-SPECIFICITY

• Biological species• Reproductively isolated• Measurable differential: size of structures• Gene-for-gene defense model• Sympatric speciation: Heterobasidion,

Armillaria, Sphaeropsis, Phellinus, Fusarium forma speciales

Phylogenetic relationships Phylogenetic relationships within the within the HeterobasidionHeterobasidion complexcomplex

Het INSULARE

True Fir EUROPE

Spruce EUROPE

True Fir NAMERICA

Pine EUROPE

Pine NAMERICA

0.05 substitutions/site

NJ

Fir-SpruceFir-Spruce

Pine EuropePine Europe

Pine N.Am.Pine N.Am.

Recognition of self vs. non self

• Intersterility genes: maintain species gene pool. Homogenic system

• Mating genes: recognition of “other” to allow for recombination. Heterogenic system

• Somatic compatibility: protection of the individual.

INTERSTERILITY

• If a species has arisen, it must have some adaptive advantages that should not be watered down by mixing with other species

• Will allow mating to happen only if individuals recognized as belonging to the same species

• Plus alleles at one of 5 loci (S P V1 V2 V3)

MATING

• Two haploids need to fuse to form n+n

• Sex needs to increase diversity: need different alleles for mating to occur

• Selection for equal representation of many different mating alleles

SEX

• Ability to recombine and adapt

• Definition of population and metapopulation

• Different evolutionary model

• Why sex? Clonal reproductive approach can be very effective among pathogens

Long branches in between Long branches in between groups suggests no sex is groups suggests no sex is occurring in between occurring in between groupsgroups

Het INSULARE

True Fir EUROPE

Spruce EUROPE

True Fir NAMERICA

Pine EUROPE

Pine NAMERICA

0.05 substitutions/site

NJ

Fir-SpruceFir-Spruce

Pine EuropePine Europe

Pine N.Am.Pine N.Am.

Small branches within a clade indicate Small branches within a clade indicate sexual reproduction is ongoing within that sexual reproduction is ongoing within that

group of individualsgroup of individuals

11.10 SISG CA

2.42 SISG CA

BBd SISG WA

F2 SISG MEX

BBg SISG WA

14a2y SISG CA

15a5y M6 SISG CA

6.11 SISG CA

9.4 SISG CA

AWR400 SPISG CA

9b4y SISG CA

15a1x M6 PISG CA

1M PISG MEX

9b2x PISG CA

A152R FISG EU

A62R SISG EU

A90R SISG EU

A93R SISG EU

J113 FISG EU

J14 SISG EU

J27 SISG EU

J29 SISG EU

0.0005 substitutions/site

NJ

890 bpCI>0.9

NA S

NA P

EU S

EU F

SOMATIC COMPATIBILITY

• Fungi are territorial for two reasons– Selfish– Do not want to become infected

• If haploids it is a benefit to mate with other, but then the n+n wants to keep all other genotypes out

• Only if all alleles are the same there will be fusion of hyphae

• If most alleles are the same, but not all, fusion only temporary

The biology of the organism drives an epidemic

• Autoinfection vs. alloinfection• Primary spread=by spores• Secondary spread=vegetative, clonal spread, same

genotype . Completely different scales (from small to gigantic)

Coriolus

Heterobasidion

Armillaria

Phellinus

OUR ABILITY TO:

• Differentiate among different individuals (genotypes)

• Determine gene flow among different areas

• Determine allelic distribution in an area

WILL ALLOW US TO DETERMINE:

• How often primary infection occurs or is disease mostly chronic

• How far can the pathogen move on its own

• Is the organism reproducing sexually? is the source of infection local or does it need input from the outside

Evolution and Population genetics

• Positively selected genes:……• Negatively selected genes……• Neutral genes: normally population genetics

demands loci used are neutral• Loci under balancing selection…..

Evolution and Population genetics

• Positively selected genes:……• Negatively selected genes……• Neutral genes: normally population genetics

demands loci used are neutral• Loci under balancing selection…..

Evolutionary history

• Darwininan vertical evolutionray models

• Horizontal, reticulated models..

Phylogenetic relationships Phylogenetic relationships within the within the HeterobasidionHeterobasidion complexcomplex

Het INSULARE

True Fir EUROPE

Spruce EUROPE

True Fir NAMERICA

Pine EUROPE

Pine NAMERICA

0.05 substitutions/site

NJ

Fir-SpruceFir-Spruce

Pine EuropePine Europe

Pine N.Am.Pine N.Am.

Geneaology of “S” DNA insertion into P Geneaology of “S” DNA insertion into P ISG confirms horizontal transfer.ISG confirms horizontal transfer.

Time of “cross-over” uncertainTime of “cross-over” uncertain

11.10 SISG CA

2.42 SISG CA

BBd SISG WA

F2 SISG MEX

BBg SISG WA

14a2y SISG CA

15a5y M6 SISG CA

6.11 SISG CA

9.4 SISG CA

AWR400 SPISG CA

9b4y SISG CA

15a1x M6 PISG CA

1M PISG MEX

9b2x PISG CA

A152R FISG EU

A62R SISG EU

A90R SISG EU

A93R SISG EU

J113 FISG EU

J14 SISG EU

J27 SISG EU

J29 SISG EU

0.0005 substitutions/site

NJ

890 bpCI>0.9

NA S

NA P

EU S

EU F

Because of complications such as:

• Reticulation

• Gene homogeneization…(Gene duplication)

• Need to make inferences based on multiple genes

• Multilocus analysis also makes it possible to differentiate between sex and lack of sex (Ia=index of association)

Basic definitions again

• Locus• Allele• Dominant vs. codominant marker

– RAPDS– AFLPs

How to get multiple loci?

• Random genomic markers:– RAPDS– Total genome RFLPS (mostly dominant)– AFLPS

• Microsatellites• SNPs• Multiple specific loci

– SSCP– RFLP– Sequence information

Watch out for linked alleles (basically you are looking at the same thing!)

Sequence information

• Codominant

• Molecules have different rates of mutation, different molecules may be more appropriate for different questions

• 3rd base mutation

• Intron vs. exon

• Secondary tertiary structure limits

• Homoplasy

Sequence information

• Multiple gene genealogies=definitive phylogeny

• Need to ensure gene histories are comparable” partition of homogeneity test

• Need to use unlinked loci

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

Thermalcycler

DNA template

Forward primer Reverse primer

Gel electrophoresis to visualize PCR product

Ladder (to sizeDNA product)

From DNA to genetic information (alleles are distinct DNA sequences)

• Presence or absence of a specific PCR amplicon (size based/ specificity of primers)

• Differerentiate through:– Sequencing– Restriction endonuclease– Single strand conformation polymorphism

Presence absence of amplicon

• AAAGGGTTTCCCNNNNNNNNN

• CCCGGGTTTAAANNNNNNNNN

AAAGGGTTTCCC (primer)

Presence absence of amplicon

• AAAGGGTTTCCCNNNNNNNNN

• CCCGGGTTTAAANNNNNNNNN

AAAGGGTTTCCC (primer)

RAPDS use short primers but not too short

• Need to scan the genome

• Need to be “readable”

• 10mers do the job (unfortunately annealing temperature is pretty low and a lot of priming errors cause variability in data)

RAPDS use short primers but not too short

• Need to scan the genome

• Need to be “readable”

• 10mers do the job (unfortunately annealing temperature is pretty low and a lot of priming errors cause variability in data)

RAPDS can also be obtained with Arbitrary Primed PCR

• Use longer primers

• Use less stringent annealing conditions

• Less variability in results

Result: series of bands that are present or absent (1/0)

Root disease center in true fir caused by Root disease center in true fir caused by H. annosumH. annosum

Ponderosa pine Incense cedar

Yosemite Lodge 1975 Root disease centers outlined

Yosemite Lodge 1997 Root disease centers outlined

Are my haplotypes sensitive enough?

• To validate power of tool used, one needs to be able to differentiate among closely related individual

• Generate progeny

• Make sure each meiospore has different haplotype

• Calculate P

RAPD combination1 2

• 1010101010

• 1010101010

• 1010101010

• 1010101010• 1010000000

• 1011101010

• 1010111010

• 1010001010

• 1011001010• 1011110101

Conclusions

• Only one RAPD combo is sensitive enough to differentiate 4 half-sibs (in white)

• Mendelian inheritance?

• By analysis of all haplotypes it is apparent that two markers are always cosegregating, one of the two should be removed

Dealing with dominant anonymous multilocus markers

• Need to use large numbers (linkage)

• Repeatability

• Graph distribution of distances

• Calculate distance using Jaccard’s similarity index

Jaccard’s

• Only 1-1 and 1-0 count, 0-0 do not count

1010011

1001011

1001000

Jaccard’s

• Only 1-1 and 1-0 count, 0-0 do not count

A: 1010011 AB= 0.6 0.4 (1-AB)

B: 1001011 BC=0.5 0.5

C: 1001000 AC=0.2 0.8

Now that we have distances….

• Plot their distribution (clonal vs. sexual)

Now that we have distances….

• Plot their distribution (clonal vs. sexual)

• Analysis: – Similarity (cluster analysis); a variety of

algorithms. Most common are NJ and UPGMA

Now that we have distances….

• Plot their distribution (clonal vs. sexual)

• Analysis: – Similarity (cluster analysis); a variety of

algorithms. Most common are NJ and UPGMA– AMOVA; requires a priori grouping

AMOVA groupings

• Individual

• Population

• Region

AMOVA: partitions molecular variance amongst a priori defined groupings

Now that we have distances….

• Plot their distribution (clonal vs. sexual)

• Analysis: – Similarity (cluster analysis); a variety of

algorithms. Most common are NJ and UPGMA– AMOVA; requires a priori grouping– Discriminant, canonical analysis

Results: Jaccard similarity coefficients

0.3

0.90 0.92 0.94 0.96 0.98 1.00

00.10.2

0.40.50.60.7

Coefficient

Fre

quen

cy

P. nemorosa

P. pseudosyringae: U.S. and E.U.

0.3

Coefficient0.90 0.92 0.94 0.96 0.98 1.00

00.10.2

0.40.50.60.7

Fre

quen

cy

Fre

quen

cy

0.9 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99

Pp U.S.

Pp E.U.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Jaccard coefficient of similarity

0.7

P. pseudosyringae genetic similarity patterns are different in U.S. and E.U.

0.1

4175A

p72

p39

p91

1050

p7

2502

p51

2055.2

2146.1

5104

4083.1

2512

2510

2501

2500

2204

2201

2162.1

2155.3

2140.2

2140.1

2134.1

2059.2

2052.2

HCT4

MWT5

p114

p113

p61

p59

p52

p44

p38

p37

p13

p16

2059.4

p115

2156.1

HCT7

p106

P. nemorosa

P. ilicisP. pseudosyringae

Results: Results: P. nemorosaP. nemorosa

Results: Results: P. pseudosyringaeP. pseudosyringae

0.1

4175A2055.2p44

FC2DFC2E

GEROR4 FC1B

FCHHDFCHHCFC1A

p80FAGGIO 2FAGGIO 1FCHHBFCHHAFC2FFC2CFC1FFC1DFC1Cp83p40

BU9715 p50

p94p92

p88p90

p56Bp45

p41p72p84p85p86p87p93p96p39p118p97p81p76p73p70p69p62p55p54

HELA2HELA 1

P. nemorosaP. ilicis

P. pseudosyringae

= E.U. isolate

Now that we have distances….

• Plot their distribution (clonal vs. sexual)

• Analysis: – Similarity (cluster analysis); a variety of algorithms. Most

common are NJ and UPGMA

– AMOVA; requires a priori grouping

– Discriminant, canonical analysis

– Frequency: does allele frequency match expected (hardy weinberg), F or Wright’s statistsis

The “scale” of disease

• Dispersal gradients dependent on propagule size, resilience, ability to dessicate, NOTE: not linear

• Important interaction with environment, habitat, and niche availability. Examples: Heterobasidion in Western Alps, Matsutake mushrooms that offer example of habitat tracking

• Scale of dispersal (implicitely correlated to metapopulation structure)---

S-P ratio in stumpsS-P ratio in stumps is highly dependent is highly dependent on distance from true fir and hemlock standson distance from true fir and hemlock stands.

.

San Diego

Have we sampled enough?

• Resampling approaches

• Saturation curves

If we have codominant markers how many do I need

• Probability calculation based on allele frequency.

White mangroves:Corioloposis caperata

White mangroves:Corioloposis caperata

Coco Solo Mananti Ponsok DavidCoco Solo 0Mananti 237 0Ponsok 273 60 0David 307 89 113 0

Distances between study sites

Coriolopsis caperataCoriolopsis caperata on on Laguncularia racemosaLaguncularia racemosa

Forest fragmentation can lead to loss of gene flow among previously contiguous populations. The negative repercussions of such genetic isolation should most severely affect highly specialized organisms such as some plant-parasitic fungi.

AFLP study on single spores

Site # of isolates # of loci % fixed alleles

Coco Solo 11 113 2.6

David 14 104 3.7

Bocas 18 92 15.04

Distances =PhiST between pairs ofpopulations. Above diagonal is the ProbabilityRandom distance > Observed distance (1000iterations).

Coco Solo Bocas David

Coco Solo 0.000 0.000 0.000

Bocas 0.2083 0.000 0.000

David 0.1109 0.2533 0.000

From Garbelotto and Chapela, From Garbelotto and Chapela, Evolution and biogeography of matsutakesEvolution and biogeography of matsutakes

Biodiversity within speciesBiodiversity within speciesas significant as betweenas significant as betweenspeciesspecies

Using DNA sequences

• Obtain sequence

• Align sequences, number of parsimony informative sites

• Gap handling

• Picking sequences (order)

• Analyze sequences (similarity/parsimony/exhaustive/bayesian

• Analyze output; CI, HI Bootstrap/decay indices

Using DNA sequences

• Testing alternative trees: kashino hasegawa • Molecular clock• Outgroup• Spatial correlation (Mantel)

• Networks and coalescence approaches

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.