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.