2. 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

3. 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

4. Seed diseases Up to 88% mortality in tropical Uganda More significant when seed production is episodic

6. Stress cone cropBS on DF

7. 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

8. 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

11. Systemic infections Viral? Phytoplasmas Peronospora and smuts can lead to over 50% mortality Endophytism: usually considered beneficial

12. 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

13. 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

18. 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

19. 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

24. Removing food base causes infection of roots of other trees Hyphae in plant tissue or soil (short- lived) Melanin-covered rhizomorphs will allow for fungus to move to new food Sources (Armillaria mellea)

28. Effects of fire exclusion

30. 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

32. 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

33. 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)

34. 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

35. 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

36. 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

37. 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

38. 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

39. 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

40. 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

41. Phylogenetic relationships within the Heterobasidion complex Fir-Spruce Pine Europe Pine N.Am.

42. Geneaology of “S” DNA insertion into P ISG confirms horizontal transfer. Time of “cross-over” uncertain 890 bp CI>0.9 NA S NA P EU S EU F

43. Complexity of forest diseases At the individual tree level: 3 dimensional At the landscape level” host diversity, microclimates, etc. At the temporal level

44. 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

45. Stem canker on coast live oak

46. Progression of cankers Older canker with dry seep Hypoxylon, a secondary sapwood decayer will appear

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

50. 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

52. Phylogenetic relationships within the Heterobasidion complex Fir-Spruce Pine Europe Pine N.Am.

53. 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.

54. 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)

55. 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

56. 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

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

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

59. 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

60. 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

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

63. 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

70. Evolution and Population genetics Positively selected genes:…… Negatively selected genes…… Neutral genes: normally population genetics demands loci used are neutral Loci under balancing selection…..

71. Evolution and Population genetics Positively selected genes:…… Negatively selected genes…… Neutral genes: normally population genetics demands loci used are neutral Loci under balancing selection…..

72. Evolutionary history Darwininan vertical evolutionray models Horizontal, reticulated models..

73. Phylogenetic relationships within the Heterobasidion complex Fir-Spruce Pine Europe Pine N.Am.

74. Geneaology of “S” DNA insertion into P ISG confirms horizontal transfer. Time of “cross-over” uncertain 890 bp CI>0.9 NA S NA P EU S EU F

75. 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)

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

77. 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!)

78. 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

79. Sequence information Multiple gene genealogies=definitive phylogeny Need to ensure gene histories are comparable” partition of homogeneity test Need to use unlinked loci

80. Thermalcycler DNA template Forward primer Reverse primer

81. Gel electrophoresis to visualize PCR product Ladder (to size DNA product)

82. 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

83. Presence absence of amplicon AAAGGGTTTCCCNNNNNNNNN CCCGGGTTTAAANNNNNNNNN AAAGGGTTTCCC (primer)

84. Presence absence of amplicon AAAGGGTTTCCCNNNNNNNNN CCCGGGTTTAAANNNNNNNNN AAAGGGTTTCCC (primer)

85. 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)

86. 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)

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

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

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

91. Ponderosa pineIncense cedar

92. Yosemite Lodge 1975 Root disease centers outlined

93. Yosemite Lodge 1997 Root disease centers outlined

97. 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

98. RAPD combination 1 2 1010101010 1010000000 1011101010 1010111010 1010001010 1011001010 1011110101

99. 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

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

101. Jaccard’s Only 1-1 and 1-0 count, 0-0 do not count 1010011 1001011 1001000

102. Jaccard’s Only 1-1 and 1-0 count, 0-0 do not count A: 1010011 AB= 0.60.4 (1-AB) B: 1001011 BC=0.50.5 C: 1001000 AC=0.20.8

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

104. 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

105. 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

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

107. 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

108. Results: Jaccard similarity coefficients 0.3 0.900.920.94 0.960.98 1.00 0 0.1 0.2 0.4 0.5 0.6 0.7 Coefficient Frequency P. nemorosa P. pseudosyringae: U.S. and E.U. 0.3 Coefficient 0.900.920.940.960.981.00 0 0.1 0.2 0.4 0.5 0.6 0.7 Frequency

109. 0.90.910.920.930.940.950.960.970.980.99 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.

110. P. nemorosa P. ilicis P. pseudosyringae Results: P. nemorosa

111. Results: P. pseudosyringae P. nemorosa P. ilicis P. pseudosyringae = E.U. isolate

112. 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

116. 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)---

117. S-P ratio in stumps is highly dependent on distance from true fir and hemlock stands.. San Diego

118. Have we sampled enough? Resampling approaches Saturation curves

121. If we have codominant markers how many do I need Probability calculation based on allele frequency.

122. White mangroves: Corioloposis caperata

123. White mangroves: Corioloposis caperata Distances between study sites

124. Coriolopsis caperata on Laguncularia 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

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

129. 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

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