PATHOGEN VARIABILITY

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PATHOGEN VARIABILITY

• All pathogens exist as populations of
  individuals, not unlike the people in this room,
  this city, etc. They, like people, share many
  common characteristics but vary greatly in many
  others.
• Let's look at an example of a single pathogen -
  Puccinia graminis - stem rust of grasses.
• Four subspecies, depending on species of host
  plant These subspecies are host-specific. One
  will not infect another
• a. tritici - wheat
• b. hordei - barley
• c. secale - rye
• d. avenae - oat

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PATHOGEN VARIABILITY

• Below this level there are races (gt 200 for P.
  graminis tritici alone!). Races are defined by
  ability to develop on specific host genotypes
  (varieties, cultivars, hybrids, etc.).
• Races are identified by ability to cause disease
  on members of a set of 10 differential varieties
  that contain specific resistance genes.

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Races cont.

• From this information several things can happen
• 1. Prevalent races in any given area will be
  identified. This will allow identification of
  varieties with resistance to these races, and
  these can be recommended for planting.
• 2. USDA Cereal Rust lab in St. Paul, MN keeps
  tracks of changing race status in US, identifying
  new races, etc.
• Within a given race, there are many biotypes.
  These are distinguished by the severity of
  disease they cause on any given host genotype.
  They vary in many ways, such as spore longevity
  and survival, lesions/leaf, spores/lesion, etc.

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• "Breakdown" of Resistance
• This term is used when a previously resistant
  variety suddenly develops disease. It implies
  that the host has changed, that the resistance
  mechanisms no longer work, etc. New pathogen
  races have developed because of selection
  pressure that was put on population by the host
  resistance mechanism.
• Mechanisms of Change in Pathogen Populations
• 1. Mutations - occur during mitosis an accident
  in duplication of genome results in slight or
  severe changes.
• 2. Sexual recombination - fusion of two In cells
  gt 2n gt two In cells with mixed genetic info.

n n 2n n n
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Mechanisms of Change in Pathogen Populations cont.

• 3. Heterokaryosis - 2 or more nuclei in same
  somatic cell. We've seen this commonly in smut
  fungi, rust fungi, etc. Traits are governed by
  two nuclei, which makes for much more
  variability.
• 4. Transformation (in bacteria) - cells rupture
  and release genetic material. Adjacent cells
  "absorb" this material and incorporate it.
  Acceptor cell is modified by new material.
• 5. Conjugation (in bacteria) - cells contact each
  other directly and exchange genetic material.

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Mechanisms of Change in Pathogen Populations cont.

• Sectoring - one portion of fungal culture (from a
  single propagule) is phenotypically different.
  Can result from mutation, heterokaryosis, etc.

Phenotype A Phenotype B
Phenotype C
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Monitoring Disease in Time - Disease Progress
Curves

• Disease progress curves show the progress of a
  disease across time. These are important for
  evaluating total impact and loss by a disease, as
  well as determining when to initiate control
  measures.

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Two Basic Types of DPCs

• Monocyclic diseases - these are characterized by
  a large release of inoculum early in the season.
• Ex Charcoal rot of most dicots, caused by
  Macrophomina phaseolina
• Ex Cabbage club root, caused by Plasmodiophora
  brassicae
• Many soilborne root pathogens tend to be
  monocyclic.

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Two Basic Types of DPCs

• 1. Monocyclic diseases
• 2. Polycyclic diseases - these are characterized
  by a small amount of primary inoculum but several
  secondary cycles to increase inoculum.
• Ex Potato late blight, caused by Phytophthora
  infestans
• Ex Apple scab, caused by Venturia inaequalis
• Most diseases are polycyclic, including nearly
  all we discussed for Ascomycetes.

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Disease Progress Curves

• The curve appearance also depends on both
  pathogen and host factors. For example
• 1. Initial inoculum dose can determine rate of
  disease increase, to a point. Extremely high
  doses do not necessarily result in very high
  disease levels.
• Remember - disease is limited by number of
  available infection courts.

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Disease Progress Curves

• The curve appearance also depends
• Initial inoculum dose
• Host resistance can modify rate of disease
  increase, through any of the mechanisms we
  discussed earlier

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Monitoring Disease in Space - Disease Gradient
Curves

• These measure the amount of disease as you go
  away from a source. These do not work for
  diseases such as wheat leaf or stem rust, where
  inoculum tends to settle in a field like a cloud
  and foci are uniformly distributed everywhere.
  They work best with discrete sources of inoculum.

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Area Under Disease Progress Curve AUDPC

• Sometimes it is hard to distinguish Resistant
  from Susceptible varieties if the curves aren't
  as 'perfect' as the previous diagrams.
• Therefore we determine, the AUDPC

AUDPC S ( yi yi1 / 2) (ti1-ti)
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Disease Progress Curve
Lag phase
1/2 of crop diseased
0 25 50 75 100
Disease severity ()
Exponential growth
Time (days)
Note Slide is the property of B.M. Pryor, U.
Arizona
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Thresholds for action

• Area under the disease progress curve (AUDPC) is
  an important determinant for management action
• Damage threshold the point at which an increase
  in disease will result in an economic loss
• Economic threshold the point at which the cost
  of management equals the increase in yield due to
  management

Lag phase
0 25 50 75 100
1/2 of crop diseased
Disease severity ()
Exponential growth
Time (days)
harvest
Note Slide is the property of B.M. Pryor, U.
Arizona
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Comparison of disease progress curves
Slope r1
Condition A
Slope r2
disease
Condition B
time
Slope equals disease units/time (usually
expressed in days)
Note Slide is the property of B.M. Pryor, U.
Arizona
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Disease Forecasting

• These are efforts to predict the amount of
  disease that you will have sometime in the
  future.
• There are several important factors necessary for
  accurate disease forecasting
• I. Pathogen factors - we've seen these before
• 1. Virulence
• 2. Quantity of primary inoculum
• a. This is the amount available at the beginning
  of the season.
• b. More important for mono- than for polycyclic
  diseases.
• c. It's very hard to determine the amount of
  secondary inoculum.

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Disease Forecasting

• These are efforts to predict the amount of
  disease that you will have sometime in the
  future.
• 3. Length of reproductive cycle
• 4. Location of reproductive structures
• 5. Mode of spread

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Early Work in Disease Forecasting

• One of the first diseases targeted was late
  blight of potato.
• In 1926, the first predictors were published
• 1. gt 4 hr dew at night .
• 2. Min temperature gt 10 C
• 3. gt 80 cloud cover the next day
• 4. gt 1 mm rainfall in next 24 h
• .