Fungus data from Chapter 5 of the PhD thesis: Thermal thresholds in the amphibian disease chytridiomycosis.

Thermoregulation in frogs may be a normal part of day-to-day physiology or a behavioral fever response to infection. Regardless of the reason, thermoregulatory behaviors may cause frogs to experience daily spikes in temperature, which can help frogs avoid, tolerate, or clear infections by cold-tolerant parasites. The chytrid pathogen Batrachochytrium dendrobatidis (Bd) has had severe effects on populations of hundreds of rainforest-endemic amphibian species but we know little about the effects of rainforest-specific body temperature spikes on infection patterns. To address this challenge, we used body temperature regimes experienced in nature by tropical Australian frogs to guide a controlled experiment investigating the effects of body temperature fluctuations on infection patterns in a model host (Litoria spenceri), with emphasis on exposing frogs to realistic ‘heat pulses’ that only marginally exceed the thermal optimum of the fungus. We then exposed cultured Bd to an expanded array of heat pulse treatments and measured parameters of population growth to help resolve the role of host immunity in our in vivo results.

We generated eight temperature treatments with body temperature data from Litoria serrata. The temperature treatments represented median body temperatures of frog populations. Four temperature treatments represented high elevation conditions and four temperature treatments represented low elevation conditions. The four high elevation temperature treatments were: a daily rectangular wave with trough at 15°C and crest at 26°C for (1) 7 h, (2) 4 h, and (3) 1 h, as well as (4) a constant 15°C control treatment. The four low elevation temperature treatments were: a daily rectangular wave with trough at 18°C and crest at 29°C for (1) 7 h, (2) 4 h, and (3) 1 h, as well as (4) a constant 18°C control treatment.

To ensure infection, we inoculated frogs with Bd on two consecutive days. On each day, we prepared a zoospore suspension of 1 x 106 zoospores per ml. We inoculated frogs at room temperature on three consecutive days. To inoculate, we placed each frog into an individual 70-ml plastic container and added 3 ml of zoospore inoculant or sham inoculant (enough to cover the bottom of the container) to each container using a syringe. We left frogs in inoculant baths for eight hours per day. To ensure regular contact of frogs with the inoculant, we monitored frogs every 15 minutes during each inoculation period. If a frog had climbed out of the inoculant onto the wall of the container, we gently tilted the container to bathe the frog in the inoculant. After each inoculation period, we returned frogs with their inoculant to individual permanent enclosures comprising 70 x 120 x 170 mm plastic containers lined with tap water-saturated paper towel. We allocated frogs in their individual enclosures to temperature-controlled chambers on the day after the last inoculation. To monitor Bd infection status and intensity, we swabbed frogs upon delivery from the captive breeding facility and every eight days thereafter and determined the number of Bd zoospore genome equivalents (ZGE) per swab with a real-time quantitative PCR protocol. After the last inoculation period, frogs were allocated to temperature-controlled chambers that performed the temperature treatments.

We prepared cultured Bd at a concentration of 5 x 105 zoospores per ml and pipetted 100 μl of the suspension into wells of 96-well plates. We placed the plates in temperature-controlled chambers that performed the temperature treatments and measured Bd growth by measuring the optical density of each well daily for 7 d with a Multiskan Ascent 96/384 plate reader (MTX Lab Systems Inc., Vienna, Virginia, USA) at an absorbance of 492 nm. We then used the daily measurements of optical density to construct population growth curves for each well. For each growth curve, we used the grofit package in Program R to fit the curve to a set of conventional, parametric growth functions (logistic, Gompertz, modified Gompertz, and Richards) and a model free spline function, select the best-fitting function according to Akaike’s information criterion, and estimate parameters of the best-fitting growth curve. The parameters were lag duration (time preceding the exponential growth phase), maximum slope of the curve (representative of the maximum rate of exponential growth), and two measures of total growth: maximum height of the curve and area under the curve.

This dataset shows our data for the Bd culture component of this study. Column headings are explained below.

Rep.id = replicate well
Plate = individual plate identifier
Treat = temperature treatment
Mu.model = maximum slope of curve
Lambda.model = lag duration of curve
A.model = maximum height of curve
Integral.model = area under curve