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Supplement Information
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Experimental design
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This study was designed to clarify the molecular components of heat stress in
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corals with a particular focus on the changes leading to coral bleaching. Previous
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heat stress studies have examined the transcriptomic response of corals during or
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after bleaching had already happened (DeSalvo et al. 2008, 2010; Barshis et al.
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2013), probably missing much of the “upstream” gene regulation responsible for the
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physiological changes characteristic of the bleaching phenomenon (i.e., decreases in
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symbiont density and/or photosynthetic pigment). Here, to increase the chances of
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detecting molecular modulation with consequences for the mechanisms leading to
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bleaching, our experimental design spans a 15 hour time period starting just after
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heat exposure and ending when bleaching begins. We take advantage of a natural
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system in which lagoonal corals can bleach in situ within 24 hours when exposed to
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extreme conditions (i.e., 35 °C at low tide), but survive.
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Symbiont type and chlorophyll a quantification
Symbiont type was determined as in Ladner et al. (2012) first at the time
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nubbins were cut and later verified when the sequencing was performed. Here,
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individual colonies contained mixture of Symbiodinium clade C and D, but no change
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in clade proportions was detected over the seventeen months of field
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acclimatization period. Although the genetic and physiological makeup of the algal
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partner can have an influence over coral susceptibility to bleaching (Stat & Gates
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2011), previous studies on the same population used here found that Symbiodinium
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type did not fully explain thermal tolerance (Oliver and Palumbi 2011; Barshis et al.
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2013; Palumbi et al. 2014). Moreover, in hospite Symbiodinium from this population
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showed no gene expression response to thermal bleaching conditions (Barshis et al.
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2014), indicating that the biology of the host plays an essential role in the thermo-
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tolerance of the holobiont.
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Chlorophyll a was extracted from coral fragments collected at 20 hr and
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stored in 100% ethanol in the dark until spectrophotometric readings. Chlorophyll a
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concentration was calculated as in Tolleter et al. (2013) and normalized to the
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surface area of coral fragments determined by the wax-dipping method.
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Laboratory experiment
Transfers from field to laboratory were accomplished in less than two hours
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with air exposure minimized to three seconds or less per colony. During the
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transfer, a maximum of twenty-four microcolonies were contained at one time in
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shaded 40 liter holding tanks filled with ambient lagoon water before being
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secondarily transferred to experimental tanks. The six experimental tanks were
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identical (engineered by J. Lee at Hopkins Marine Station) and designed to be used
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virtually anywhere electrical power is available. The tanks consisted of portable
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pack-it-all units (15L Igloo Playmate Elite MaxCold® cooler), which contained built-
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in lights, heaters, chillers and pumps. Water temperature in the tanks was
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continuously monitored and controlled to 0.1 degree via a power controller and a
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small laptop computer. Light intensity was measured at ~700 μmol photons m-2s-1
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from two white LEDs (Philips AmbientLED PAR38 16W 4200K) at the bottom of
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each tank with a bulb integral light sensor. In addition to a compact recirculation
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pump (80 gph) inside each tank, flow-through seawater sourced from the lagoon
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was kept constant at 6 L/h throughout the experiment.
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Sample processing
A small fragment (~125 mm-3) of coral skeleton with tissue was transferred
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from RNA stabilizing buffer to a 1.5 mL tube containing TRIzol reagent and silica
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beads. The tissue was removed off the skeleton by vortexing. The coral
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tissue/TRIzol slurry was separated by centrifugation and the remainder of the
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protocol was followed as described in Barshis et al. (2013). Total RNA was
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resuspended in 25 μL of diethylpyrocarbonate treated water and quantified on the
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Qubit® 2.0 Fluorometer (Life Technologies) for quality control. All RNA extractions
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were uncontaminated with reagents and of high quality and concentration.
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Detection of significant gene regulation
For the negative binomial test of a single factor (e.g. treatment or time effect),
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dispersion was estimated by pooling all samples that experienced the same
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treatment using the “pooled” method, “maximum” sharing mode, and “local” fit type
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(see the help document of the “estimateDisperions” function for these arguments’
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description). For the multifactorial negative binomial test taking into account each
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sample’s kinship to a specific genotype, two GLMs were fitted for each gene, one full
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model regressing the genes’ expression to both the genotypes and the treatments
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and another reduced model only regressing to the genotypes. Models were then
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compared to each other in order to infer whether the additional specification of the
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treatment improved the fit and hence, whether the treatment had significant effect.
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The dispersion was estimated using the “pooled-CR” method (for paired design), the
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“gene-est-only” sharing mode (when biological replicates > 4), and the “parametric”
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fit type, as well as specifying the full model formula (count~genotype+treatment or
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count~genotype+timepoint).
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Detection of significant correlation
The Pearson product-moment coefficient correlation (RSQ) was computed
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between the gene expression level for each contig and the bleaching score of the
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colony at 20 hr. Significance was detected at the 0.05 level for a degree of freedom of
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N-2=18. A 2x4 contingency table was generated for RSQ>0.444 and <0.444 from the
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Transient, Slow return, Late and Lingering categories to detect significant difference
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between observed and expected frequencies.
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Detection of functional enrichment
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Mapping against the KEGG Orthology (KO) system provides an informative
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and immediate visualization of the representation of genes potentially involved in
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the same molecular pathway(s). Color codes were used to differentiate between
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genes that were up- (red), and down- (blue) regulated over a background (grey) of
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all the genes present in the transcriptome of A. hyacinthus for a particular KEGG
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map. However, for non model organisms with incomplete genomes using KO is
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restrictive for two reasons: 1) a single KO identifier per gene is used in the analysis,
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reducing the representation of pathways to one KO for multiple contigs (whether
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they belong to the same gene or not), and 2) many genes and KO are not yet curated
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and do not appear on a map. To address these issues, a search across all GO terms
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and keywords was also performed using search keys relevant to the KO pathways
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identified in the transcriptome of A. hyacinthus. Hypergeometric probabilities were
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calculated comparing the number of hits for a search key or KO pathway in each
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DEC list to the overall number of genes associated with the same pathway within the
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whole transcriptome. Significantly enriched pathways were considered at a α=1%
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level.
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