1. 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

3. OUR ABILITY TO: Differentiate among different individuals (genotypes)
Determine gene flow among different areas
Determine allelic distribution in an area

4. 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

5. IN ORDER TO UNDERSTAND PATTERNS OF INFECTION
If John gave directly Mary an infection, and Mary gave it to Tom, they should all have the same strain, or GENOTYPE (comparison=secondary spread among forest trees)
If the pathogen is airborne and sexually reproducing, Mary John and Tom will be infected by different genotypes. But if the source is the same, the genotypes will be sibs, thus related

6. 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.

7. Somatic incompatibility

8. 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

9. SOMATIC COMPATIBILITY
SC can be used to identify genotypes
SC is regulated by multiple loci
Individual that are compatible (recognize one another as self, are within the same SC group)
SC group is used as a proxy for genotype, but in reality, you may have some different genotypes that by chance fall in the same SC group
Happens often among sibs, but can happen by chance too among unrelated individuals

10. Recognition of self vs. non self
What are the chances two different individuals will have the same set of VC alleles?
Probability calculation (multiply frequency of each allele)
More powerful the larger the number of loci
…and the larger the number of alleles per locus

11. Recognition of self vs. non self: probability of identity (PID)
4 loci
3 biallelelic
1 penta-allelic
P= 0.5x0.5x0.5x0.2=0.025
In humans 99.9%, 1000, 1 in one million

12. 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)

13. INTERSTERILITY Basis for speciation
These alleles are selected for more strongly in sympatry
You can have different species in allopatry that have not been selected for different IS alleles

14. 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

15. MATING
If one individuals is source of inoculum, then the same 2 mating alleles will be found in local population
If inoculum is of broad provenance then multiple mating alleles should be found

16. MATING How do you test for mating?
Place two homokaryons in same plate and check for formation of dikaryon (microscopic clamp connections at septa)

17. Clamp connections

18. MATING ALLELES
All heterokaryons will have two mating allelels, for instance a, b
There is an advantage in having more mating alleles (easier mating, higher chances of finding a mate)
Mating allele that is rare, may be of migrant just arrived
If a parent is important source, genotypes should all be of one or two mating types

19. Two scenarios:
A, A, B, C, D, D, E, H, I, L
A, A, A,B, B, A, A

20. Two scenarios: A, A, B, C, D, D, E, H, I, L
Multiple source of infections (at least 4 genotypes)
A, A, A,B, B, A, A
Siblings as source of infection (1 genotype)

21. 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

22. Long branches in between groups suggests no sex is occurring in between groups
Fir-Spruce
Pine Europe
Pine N.Am.

23. Small branches within a clade indicate sexual reproduction is ongoing within that group of individuals
NA S
NA P
EU S
890 bp
CI>0.9
EU F

24. Index of association
Ia= if same alleles are associated too much as opposed to random, it means sex is not occurring
Association among alleles calculated and compared to simulated random distribution

25. If SEX is not happening
Number of genotypes less than that theorethically expected
E.G. Three biallelic loci should give 8 genotypes

26. Basic definitions again
Locus
Allele
Dominant vs. codominant marker
RAPDS
AFLPs

27. 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!)

28. 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)

29. 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)

30. RAPDS can also be obtained with Arbitrary Primed PCR
Use longer primers
Use less stringent annealing conditions
Less variability in results

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

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

34. WORK ON PINES HAD DEMONSTRATED INFECTIONS ARE MOSTLY ON STUMPS
Use meticulous field work and genetics information to reconstruct disease from infection to explosion
On firs/sequoia if the stump theory were also correct we would find a stump within the outline of each genotype

41. 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

42. RAPD combination

43. 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

44. If we have codominant markers how many do I need
IDENTITY tests = probability calculation based on allele frequency… Multiplication of frequencies of alleles
10 alleles at locus 1 P1=0.1
5 alleles at locus 2 P2=0,2
Total P= P1*P2=0.02

45. Do the data make sense, based on the known biology?
Fungus that disperses through basidiospores
If we find the same genotype in different locations…..
Markers may not be sensitive enough

46. Have we sampled enough? Resampling approaches Saturation curves
A total of 30 polymorphic alleles
Our sample is either 10 or 20
Calculate whether each new sample is characterized by new alleles

47. Saturation (rarefaction) curvesNo Of
New
alleles

48. Dealing with dominant anonymous multilocus markers
Need to use large numbers (linkage)
Repeatability
Graph distribution of distances
Calculate distance using Jaccard’s similarity index

49. Jaccard’s Only 1-1 and 1-0 count, 0-0 do not count 1010011 1001011

50. Jaccard’s Only 1-1 and 1-0 count, 0-0 do not count
A: AB= (1-AB)
B: BC=
C: AC=
Eliminate markers that are cosegregating (probable duplication, from same locus)

51. Now that we have distances….
Plot their distribution (clonal vs. sexual)

52. 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

53. 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

54. AMOVA groupings Individual Population Region
AMOVA: partitions molecular variance amongst a priori defined groupings

55. Example
SPECIES X: 50%blue, 50% yellow

56. AMOVA: example
Scenario 1
Scenario 2 v
POP 1
POP 2 v

57. Expectations for fungi
Sexually reproducing fungi characterized by high percentage of variance explained by individual populations
Amount of variance between populations and regions will depend on ability of organism to move, availability of host, and
NOTE: if genotypes are not sensitive enough so you are calling “the same” things that are different you may get unreliable results like 100 variance within pops, none among pops

58. Results: Jaccard similarity coefficients
P. nemorosa
0.3
0.90
0.92
0.94
0.96
0.98
1.00
0.1
0.2
0.4
0.5
0.6
0.7
Coefficient
Frequency
P. pseudosyringae: U.S. and E.U.
0.3
Coefficient
0.90
0.92
0.94
0.96
0.98
1.00
0.1
0.2
0.4
0.5
0.6
0.7
Frequency
These histograms show the distribution of pairwise comparisons of genetic distance between each isolate and every other isolate.
For P. nemorosa, our measure of degree of genetic similarity, Jaccard similarity coefficients (SJ), fell between – (0.993 ± ; mean ± SD) – about 96 – 100% similar.
The range of SJ values for the whole P. pseudosyringae dataset was (0.980 ± ; mean ± SD) – about 91 – 100% similar.

59. P. pseudosyringae genetic similarity patterns are different in U. S
P. pseudosyringae genetic similarity patterns are different in U.S. and E.U.
Frequency
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
When the Pp data for the two regions are separated, the range of SJ values for pairwise comparisons between U.S. isolates alone was – (0.991 ± ; mean ± SD) and between European isolates alone was – (0.978 ± ; mean ± SD)

60. Results: P. nemorosa P. ilicis P. pseudosyringae P. nemorosa
Trans: From the AFLP fragment data, we calculated the genetic similarity between each pair of isolates; using the Jaccard coefficient of similarity as our measure.
1) Here are the results that we obtained for P. nemorosa calculated using the Fitch algorithm depicted as a dendrogram.
2) Again, in interpreting this tree, keep in mind that horizontal length of the branches is proportional to genetic similarity.
3) Although we found 11 unique genotypes, you can see that they are all extremely similar: in fact, many of them had the identical AFLP fingerprints: show clonal area with zero branch length! By contrast, the closest relatives, here, are quite genetically dissimilar, as you can see by these long branches.
4) The isolate from the geographically isolated area in the sierras: here! Genetically similar to the rest!

61. Results: P. pseudosyringae
P. nemorosa
P. ilicis
P. pseudosyringae
= E.U. isolate
1) We found more different genotypes for Pp: a total of 17 different EU Pp genotypes, 8 from US, 9 from EU. However, none are shared!. Isolates from geographically distinct region in CA in with the rest.

62. 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)---

66. RAPDS> not used often now

67. RAPD DATA W/O COSEGREGATING MARKERS

69. PCA

70. AFLP Amplified Fragment Length Polymorphisms Dominant marker
Scans the entire genome like RAPDs
More reliable because it uses longer PCR primers less likely to mismatch
Priming sites are a construct of the sequence in the organism and a piece of synthesized DNA

71. How are AFLPs generated?
AGGTCGCTAAAATTTT (restriction site in red)
AGGTCG CTAAATTT
Synthetic DNA piece ligated
NNNNNNNNNNNNNNCTAAATTTTT
Created a new PCR priming site
Every time two PCR priming sitea are within bp you obtain amplification

74. Distances between study sites
White mangroves:
Corioloposis caperata

75. AFLP study on single spores
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
Coriolopsis caperata on
Laguncularia racemosa

78. 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

79. Using DNA sequences Testing alternative trees: kashino hasegawa
Molecular clock
Outgroup
Spatial correlation (Mantel)
Networks and coalescence approaches

82. From Garbelotto and Chapela, Evolution and biogeography of matsutakes
Biodiversity within species
as significant as between
species

83. Microsatellites or SSRs
AGTTTCATGCGTAGGT CG CG CG CG CG AAAATTTTAGGTAAATTT
Number of CG is variable
Design primers on FLANKING region, amplify DNA
Electrophoresis on gel, or capillary
Size the allele (different by one or more repeats; if number does not match there may be polimorphisms in flanking region)
Stepwise mutational process (2 to 3 to 4 to 3 to2 repeats)