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

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

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.

Somatic incompatibility

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

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

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

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

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)

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

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

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

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

Clamp connections

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

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

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)

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

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

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

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

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!)

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 H. annosum

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

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 combination 1 2 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

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

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

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

Saturation (rarefaction) curves No Of New alleles 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

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 Eliminate markers that are cosegregating (probable duplication, from same locus)

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

Example SPECIES X: 50%blue, 50% yellow

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

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

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 0.963 – 1.000 (0.993 ± 0.00864; mean ± SD) – about 96 – 100% similar. The range of SJ values for the whole P. pseudosyringae dataset was 0.912-1.000 (0.980 ± 0.0166; mean ± SD) – about 91 – 100% similar.

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 0.958 – 1.000 (0.991 ± 0.0104; mean ± SD) and between European isolates alone was 0.912 – 1.000 (0.978 ± 0.0203; mean ± SD)

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!

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.

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)---

RAPDS> not used often now

RAPD DATA W/O COSEGREGATING MARKERS

PCA

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

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 400-1600 bp you obtain amplification

Distances between study sites White mangroves: Corioloposis caperata

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

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

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

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)