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Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

A coral transcriptome in the Anthropocene as an“alternative stable state”
Anderson B. Mayfield1,2* and Chii-Shiarng Chen3,5
1Cooperative Institute for Marine & Atmospheric Studies, University of Miami, Miami, FL 33149, USA
2Atlantic Oceanographic & Meteorological Laboratory, National Oceanic & Atmospheric Administration, Miami, FL

33149, USA
3National Museum of Marine Biology & Aquarium, Checheng, Pingtung 944, Taiwan
4Graduate Institute of Marine Biotechnology, National Dong-Hwa University, Checheng, Pingtung 944, Taiwan
5Department of Marine Biotechnology & Resources, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan*Corresponding author. Email: anderson.mayfield@noaa.gov or abm64@miami.edu

Abstract
In contrast to many coral reefs across the globe, those of Taiwan’s deep south had,
until the summer of 2020, not generally been greatly affected by climate change-driven
seawater temperature rise. This has been attributed to the effects of cold-water
upwelling, which naturally cools reefs within Nanwan Bay during the warmest timesof the year; episodic upwelling has also thermally “hardened” the resident corals, andprior works sought to uncover the molecular basis of such thermotolerance in coloniesof the model coral Pocillopora acuta collected from Nanwan Bay by exposing
fragments to sustained high temperatures (30°C) in laboratory mesocosms for ninemonths. Although all corals ultimately acclimated to this hypothetically stress-inducing
temperature ex situ, the dinoflagellate endosymbionts residing within the host corals’
gastrodermal cells collectively displayed a concerted, sustained mRNA-level response
to prolonged high-temperature exposure. Herein we used univariate, multivariate, andmodeling approaches with the same transcriptomic dataset and identified numerous
genes only expressed by corals at high temperatures; although this finding was not
generally supported by real-time PCR, these genes may nevertheless be of use to thoseseeking to develop biomarker assays or molecularly biology-trained predictive models
for identifying corals displaying “alternative stable states,” in which their cellularbiology has fundamentally changed on account of having acclimatized to chronicallyhigh temperatures.
Keywords: Acclimation, Coral reefs, Dinoflagellate, Gene expression, Molecularbiology, Taiwan
1Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

Introduction

Safaie et al., 2018). Unfortunately, even

Although global climate change

the presumed thermal conditioning as a

(GCC; Mayfield & Gates, 2007; Hoegh-

resultGuldberg et al., 2017) and other, localized

insufficient to safeguard Nanwan corals

anthropogenic

seawateragainst bleaching in the summer of 2020;

pollution (Huang et al., 2011; Lin et al.,whether these corals recover and are then2018; Mayfield et al., 2019b) threaten

even further able to combat future high-coral reefs (Gates, 1990; Brown, 1997)

temperature episodes (sensu Brown et al.,and2000) remains to be determined. If no

otherinsults

marinelikeecosystems(e.g.,

ofhabitual

upwellingwasseagrass beds [Liu et al., 2020] & tidal flatsacclimatization or

adaptation occurs,[Chen et al., 2017, 2019]), coral reefs of

Taiwan’s coral reefs are projected toSouthern Taiwan have generally thrived

bleach annually by 2023 according to awith respect to coral abundance andnewly released report from the United

diversity (Ribas-Deulofeu et al., 2016, but

Nations Environmental Programme (sp;see Liu et al., 2009, 2012 for information

UNEP, 2020); only ~10% of Taiwan’s

on local stressor regimes.). The marked

reefs are anticipated to resist “annual

resilience noted in the framework-building

severe bleaching” beyond 2030.

scleractinians has been hypothesized to be

Although highly GCC-tolerant corals

due to the thermodynamic nature ofexist across the globe (e.g., Krueger et al.,Nanwan Bay (Mayfield et al., 2012a),2017; Putnam et al., 2017; Enochs et al.,which experiences extreme upwelling2020),

events in the boreal summer (Lee et al.,“pristine” locales hundreds of kilometers

1997) that result in large shifts infrom human settlements (Fig. 1) display

temperature (6-9°C/day), nutrient levels,

perceivably “stressed” cellular phenotypes

and other seawater quality parameters. On(Mayfield et al., 2016a, 2017a-c, 2019a,

a global scale, reef cooling via upwelling

2018b-c, in prep; Mayfield, in review). For

has indeed been shown to protect, or eveninstance, heat shock proteins, oxidative

rescue,bleaching,stress markers, and other genes/proteinsespecially when upwelling occurs during

involved in the cellular stress response

summer (Randall et al., 2020; Storlazzi et(CSR) are constitutively expressed evenal., 2020; but see Chollott et al., 2010).

during low-temperature periods (Mayfield

More generally, highly variable seawater

et al., in review). Instead, proteins

quality conditions have been shown to

involved in other cellular processes,

coral

reefs

from

stress-harden corals (Mayfield et al., 2011,
2

even corals

from seeminglyPlatax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

Fig. 1. A recently consolidated lava flow covered with scleractinian corals. “Lava
Flow” dive site off Bandaneira, Maluku, Indonesia. Photo credit=Anderson
Mayfield (a.k.a. 珊瑚醫生).
namely lipid trafficking, undergo a greater(Hochachka & Somero, 2002). Canonicaldegree of regulation in response to

CSR should be ephemeral, though perhaps

changes in temperature (Mayfield, 2020;

an “alternative stable state” may be

Mayfield et al., 2016b, 2018a). Barshis etreached in which, while still technically fit

al. (2013) interpreted this finding to reflect(growing

a desirable trait: the CSR is engaged

dinoflagellate cell biology may undergo

perpetually to effectively thwart future,

profound alterations as a result of prior (orpredictable shifts in the abiotic milieu.

present)

This indeed occurs in corals exposed tooceanographic conditions.

variable temperatures (Mayfield et al.,&reproducing),

exposuretocoral-

aberrant

Thankfully, there is a wealth of

2011, 2012a), though why corals inknowledgeontherelatively stable seawater temperaturephysiologyofpocilloporid

habits (1-2°C change/day) would adopt

particularly in Southern Taiwan (e.g.,

thisMayfield et al., 2010, 2012b, 2013a,c,strategy is

consideringthepuzzling,
highespeciallyenergyenvironmental
corals,costs

2014a-b, 2016b-c, 2018a; Putnam et al.,associated with maintaining the CSR

2013; Mayfield, 2016; McRae et al., in
3

Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

review), and a plethora of cellular andTaiwan) were fragmented into nubbins,

molecular tools for probing the cellularsuspended on fishing lines, and acclimatedphysiology of marine organism- (e.g.,

in flow-through seawater mesocosms (n=6)

anthozoans, forams, & giant clams)-

for several weeks prior to initiating a long-

dinoflagellate (family Symbiodiniaceae)

term high-temperature exposure of 29.7°C

endosymbioses is currently at our disposal(rounded to 30°C hereafter). Control(e.g., Peng et al., 2011; Chen et al., 2012,

mesocosms (n=3) were instead maintained

2015, 2017; Doo et al., 2012, 2014; Wangat 27°C. These customized, mesocosm-

et al., 2013; Mayfield et al., 2014c; Lin etlike aquarium systems have recently beenal., 2015, 2019; Monteiro et al., 2020). We

found to result in the highest pocilloporidsought herein to use the transcriptomic

coral growth rates ever documented

dataset from the “Pocillopora acuta long-(Huang et al., 2020). Corals were sampledterm temperature experiment” (PALTTE;

prior to the start of the temperature

Mayfield et al., 2014d) to explore the idea

ramping, as well as after 2, 4, 8, 24, and 36

of high-temperature-mediated alternative

weeks. A subset of three nubbins fromstable states by employing a number of

each of the two treatments was sacrificed

bothmultivariate

after 2 and 36 weeks to assess the effect ofapproaches not featured in the initial

relatively short- and long-term high-analysis. We specifically hypothesized

temperature exposure on gene expressionthat we could develop a molecular(n=12 samples analyzed by RNA-Seq

biology-trained model (sensu Mayfield &[Illumina Tru-Seq™ technology]). The

Chen, 2019) that could be used predict thegene expression findings were described indegree to which coral biology has shifted

Mayfield et al. (2014d), with a subset ofas a result of GCC-induced stress.

samples also analyzed by two-dimensional

univariate

andgel electrophoresis followed by mass

Materials & Methods
The

experiment

et al., 2018b). Although it was found that

since

P.

there was no congruency between thedamicornis & P. acuta were synonymized

mRNA- and protein-level responses (asat the time of analysis [2010-2011]; see

was the case in other corals [Mayfield et

Schmidt-Roach et al., 2014 & Mayfield etal., 2016c, in review; Mayfield, 2020]), weal., 2015.) was originally described bynevertheless hypothesized that the geneMayfield et al. (2013b). Briefly, replicate

expression data could be useful incolonies from Houbihu (Nanwan Bay,identifying(formerly

PALTTE.
theThe

spectrometry-based proteomics (Mayfield“PDLTTE”

4biomarkers(univariate

Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

approach)and/ordeveloping

modelsof genes is differentially regulated in(multivariate approach) that would enable

response to temperature stress in one

us to understand whether to-be-sampledcompartment vs. the other.

conspecifics in situ are exhibiting cellular

MSA. Principal components analysis

hallmarks of chronic high-temperature(PCA)

exposuredimensional scaling (MDS) were carried(i.e.,

theaforementioned

alternative stable states).

oncorrelations

andmulti-

out with JMP® Pro (ver. 15, USA) with

Meta-analysis of the P. acuta-

raw

and

standardized

FPKM

data,

Cladocopium spp. transcriptome. The

respectively; JMP Pro was used for all

holobiontremaining analyses unless noted otherwise.

(hostcoral+dinoflagellates+bacteria & other microbes) transcriptome

Because the “robustof Mayfield et al. (2014d) was downloaded

uncovered an outlier (C36T2S1), thefrom our interactive transcriptome server

approaches(http://symbiont.iis.sinica.edu.tw/coral_pd

excluded this sample. This resulted in an

ltte/static/html/index.html#home), and theunbalanced design in which there were

fragments per kilobases mapped (FPKM)

three, three, two, and three control-2-weekdata were re-analyzed using a series of(C2), high-temp.-2-week (H2), control-36-

both univariate and multivariate statisticalweek (C36), and high-temp.-36-weekapproaches (MSA). Please note that,(H36) samples, respectively. A Venn

althoughthefeaturesdiagram was used to depict overlap in the236,435

genes,

analysestranscriptomes of these samples, andtranscriptome
certain

PCA”

methodoutlined below generally

incorporated subsets of this total. A

permutational

diagnostic analysis was first undertaken in(PERMANOVA; PRIMER, ver. 6, UK)

which normal quantile box plots of log2-

was used to assess the effects oftransformed expression levels (n=202,283

temperature (control vs. high), time (2 vs.

genes only since bacterial, viral, & non-

36 weeks), and their interaction on theexpressed genes were excluded.) weresimilarity

produced to depict general temperature x(Euclidean

distancetime trends for each compartment (coralstandardizeddata).

host vs. dinoflagellates); the specific goals

permutation of raw data (sum of squares,of this analysis were to determine whether

type1) the hosts or their endosymbionts(alpha=0.05). A repeated measures design

demonstrate higher mean gene expressionwaslevels and 2) a relatively higher proportion

nubbins were sacrificed within each
5

III)
not

multivariate

among

model

the

ANOVA

11

samplesmatrix

using

An
was

unrestrictedemployedadopted since independent

Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

mesocosm

ateachsampling

time.

state). Ideally, stressed samples would be

PERMDISP was conducted to ensure that

included in such an analysis, but, based on

multivariate dispersion did not differ

our physiological (Mayfield et al., 2013b)

significantly across treatments. Because

and molecular (Mayfield et al., 2014d)

PRIMER can only analyze <65,000 datadata, these coral nubbins appeared to havepoints, simple random samples (SRS) of

acclimated to 30°C. JMP Pro 16 beta’s~65,000 genes were taken from the“model screening” algorithm was usedtranscriptome, and analyses were run 3-4

with temperature (control vs. high) as thetimes.

Y and the 236,435 genes as predictors (X).

Transcriptomic predictive modeling.

Sevendifferent

modelscapableIn addition to uncovering 1) multivariate

accommodating

treatmentvariables (binomial in this case: control vs.

effects on the P. acuta-

Cladocopium

spp.

categoricalof

response

dinoflagellatehigh) were built in tandem (n=4 uniquetranscriptome (above) and 2) differentially

iterations), with seven and four training

expressed genes (DEGs; below), there wasand validation (i.e., hold-back) samples,

an interest in developing models that could

respectively.

be used to predict the susceptibility of anominal logistic, support vector machines,

coral sample to high temperatures based

bootstrap

on its gene expression profiles. In thisgeneralized

particular experiment, in which all corals

neighbors, and naïve Bayes. The resulting

ultimately acclimated to temperatures

validation model with the lowest 1)

approachingendeavor

misclassification rate and 2) root mean

additionally sought to characterize thesquare error (RMSE) was chosen as“acclimatome” of this model coral species.

having the highest power to predict in situ

If we can characterize the diagnostic

coral temperature effects in future “test”

features of a coral that has effectively

samples (i.e., those not used in the model-

acclimated to high temperatures in thebuilding exercise and for which we knowlaboratory, then perhaps we could sample

nothing). The mean misclassification rates

a coral from the field in which we know

and RMSE across the four iterations of thenothing about its physiology, input thesuperior model were calculated and are thedata into the experimental model derived

main model parameters discussed herein.

herein, and make predictions as to its

DEGs. A univariate approach was

acclimatization potential (or determine

taken to uncover genes differentiallywhether it has entered an alternative stable

expressed across temperatures, times, and

30°C,

this

6

These
forest,modelsneuralregression,

included(boosted),K

nearestPlatax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

their interaction. Rather than use a more

forward and reverse primers (Tab. 1),

traditional false discovery rate (FDR)-

which were synthesized (salt-free) bybased “response screen” exclusively, weEurofins Scientific (USA; 10-µM scale).

instead first identified only those genes

qPCRs were carried out in triplicate

expressed by all samples in one treatment

for each sample and gene on an Applied

and in no samples of the opposing

Biosystems 7500 real-time PCR machine

treatment. For instance, if a gene was

as described in Tab. 1, using SYBR Green

expressed by all six control samples

I and ROX as the target and reference dyes,(including the outlier), and in none of therespectively. Serial dilutions (1/1, 1/10, &six high-temperature samples, it was

1/100-fold of a control sample made byconsidered a DEG. Response screening

mixing cDNA from a multitude of adultand predictor screening analyses were thenpocilloporid corals

used

these

equivalent in total]), as well as no-template

presence/absence DEGs. Of the 108 genes

controls, were run in triplicate on each 96-

only expressed by H2 samples (online

well

supplemental data file [OSDF]), real-time

experimental samples to optimize primer

PCR (qPCR) assays were designed for aefficiency (sensu Mayfield et al., 2013d,

subset of 10: 9 from the dinoflagellates and2014a). All assays described in Tab. 1

1 from the coral host (Tab. 1). Unlike forwere characterized by 1) single peaks in

the MSA, all 12 mRNA samples were

the post-run melting curve analysis (65-

analyzed

reverse95°C) and 2) PCR efficiencies between 90

transcription of 200 ng of RNA to cDNA

and 110% (data not shown). Since

using

cDNA

endosymbiont densities did not varysynthesis kit from Applied Biosystems

dramatically across samples (Mayfield et(USA; 20 µL reactions using randomal., 2013b), nor did the host/endosymbiont

primers following the manufacturer’s

contig ratio differ significantly across therecommendations). It should be noted thattemperature x time

the same mRNA aliquots were used for(Mayfield et al., 2014d & Fig. 2), the

RNA-Seq (Mayfield et al., 2014d) andbiological

qPCR. Reactions (20 µL) comprised 10 µL

featured in our prior analyses (e.g.,of 2X EZ-Time™ SYBR® Green I premix

Mayfield et al., 2009) was not undertaken;

withinstead, the inverse of the qPCR thresholdtocorroborate

by

the

ROX®

qPCR

High

after

Capacity™

passivereferencedye(Yeastern, Taiwan), 2 µL of 4-fold diluted

plate

before7analyzingtheinteraction groups

composition

cycle (Ct) values was

cDNA, and variable quantities of the[200 ng mRNA

normalization

8

comp69860_c1_seq1

27

comp30872_c0_seq1
comp44179_c0_seq1

5

comp28643_c0_seq1

comp22663_c0_seq1

comp768642_c0_seq1

comp56753_c1_seq1

13*

70*

6*

NSa

23

comp76579_c2_seq1

comp22007_c0_seq1

90*

30*

Transcriptome
accessioncomp20944_c0_seq1

H2>C2 foldchange
16*

73

51

148

132

141

110

156

124

59

Ampliconsize (bp)108

Finding instead contradicted: C2>H2 (no statistically significant Illumina vs. qPCR correlation).

a

TNF receptor
5I
CHAT domain
6

Sym-TNFrecep-F1
Sym-TNFrecep-R1
PD-CHAT-F1
PD-CHAT-R1

Sym-spatacsin-F1
Sym-spatacsin-R1

Sym-largeATP-R1
Sym-lrrcp-F1
Sym-lrrcp-R1

Sym-largeATP-F1

E3 ubiquitin ligase
5D
flagellar hook-length
control protein
5E
large ATP-binding
protein5F
leucine-rich repeatcontaining protein
5G
spatacsin
5H

Gene name/Fig.
unknown
5A
ciliary dynein 6
5B
DNAJ
5C

Sym-E3-ubiq-lig-F1
Sym-E3-ubiq-lig-R1
Sym-flik-F1
Sym-flik-R1

Sym-20944-F1
Sym-20944-R1
Sym-cildyn6-F1
Sym-cildyn6-R1
Sym-DNAJ-F1
Sym-DNAJ-R1

Primer name

CCG-GCA-CTT-GGA-TGG-ATA-GA
CCT-GGG-CAT-TAT-GTG-GGA-C
CAG-GGA-AGG-AAG-CAC-ACG-A
TTA-CGC-GGC-ATT-GGA-GGA-C

GAT-GTT-TGC-GCC-TGT-GTC-TTC
TCG-GCT-TCC-CTC-TAC-TCC-T

GGA-ACA-AAT-CAA-GTC-GCG-GA
ACA-GCA-ACA-AGC-CCG-AAG
TCC-ACG-TCG-CCC-ATT-TC

ACG-TGG-TTT-TCG-GAA-GAT-GG

GCA-CAG-GTG-GCA-GAC-ATT
GTC-AGA-GAC-AAA-GCC-CAC-A
CTC-TCC-TTG-GCT-CCA-CTT-T
AAG-GCA-TTT-CAA-CAC-TAC-AGG-TC

AGG-ACA-CCC-TGC-CGA-AAG-A
CCA-GCA-CAC-CAA-ACG-CAC-AA
GTA-TGA-GTT-TGA-GAT-TGT-GCC-GT
AGA-TGG-TCA-TGT-AAT-CGT-TGG-G
CTC-AGT-TCC-TCC-AGC-GCA-T
CAG-ACG-GTC-TTT-TTG-GTG-CCA-T

Sequence (‘5-3’)

500

150

500

500

250

500

500

250

400

[primer]
nM
250

61

61

60

59

61

59

60

61

60

Annealing
temp. (ºC)
61

35

40

40

40

40

40

35

40

40

Cycle
#
40

Tab. 1. Real-time PCR (qPCR) assays for select genes found by RNA-Seq to be expressed only by high-temperature samples at the 2-week sampling time (H2). All
genes with the exception of the tetratricopeptide repeat protein 28-like protein with a CHAT (“caspase HetF associated with Tprs”) domain (hereafter “CHAT domain;”
Fig. 6) were targeted in the Symbiodiniaceae (“Sym”) endosymbionts exclusively. No Illumina-derived trends (i.e., expressed by H2 only) were supported entirely; at“H2>C2 fold change” values have beenleast some detectable level of expression was documented in 2-week control samples (C2) by qPCR. Instead, the qPCR-derived
2
shown except for when they were not statistically significant (NS), and those for which the Illumina vs. qPCR R (Fig. 5) values were statistically significant (p<0.01)
have been denoted by asterisks (*). Genes involved the cellular stress response have been emphasized in bold font. Please note that the “PD” in the host primer names
corresponds to Pocillopora damicornis, as P. acuta and P. damicornis were synonymized at the time of study. Bp=base pairs. TNF=tumor necrosis factor.

doi: 10.29926/platax.202012_17.0002

Platax 17: 1-26, 2020

Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

calculated as: (1/(ECt))*(109), where “E” isfrom the qPCR data to determine whether,

the PCR efficiency (e.g., 2=100%). Thesein addition to the statistical significance ofvalues were directly regressed against thethe correlation itself, the two mRNA

respective Illumina-derived FPKM values,

quantification methods yielded congruent

and the statistical significance of theresults. All in-text error terms represent

correlations was assessed with linear

standard error of the mean (SEM) unless

regression t-tests (alpha=0.01). An H2/C2

stated otherwise.

relative fold difference was also calculatedA

log2 gene expression

10
5

f

0

g

e

a

b

d

c

h

-5

time (weeks)
2 (n=100,991 contigs)
36 (n=101,292 contigs)
10

5

0

f

g

b

a

e

d
h

c

-5

2
treatment
controlhigh C
controlhigh36log2 gene expression

log2 gene expression

compartment
Bcoral host (n=147,497) Symbiodiniaceae (n=54,786)

n=37,733

5
global
mean
0

-5
-10

2

36

time (weeks)

n=37,272
2.67

2.86

n=35,756
2.55

coral hostcoral hosthigh-2-wk

compartment

n=36,736

2.70

host/Symbio
ratio

coral hostcoral hostcontrol-2-wk control-36-wk

Symbiodiniaceae coral host Symbiodiniaceae

coral hosthigh-36-wk

n=13,962

n=14,011
n=13,211

SymbioSymbiocontrol-2-wk control-36-wk

Symbiohigh-2-wk

n=13,612

Symbiohigh-36-wk

Fig. 2. PALTTE transcriptome diagnostic plots. Normal quantile box plots of log2transformed fragments per kilobases mapped values assessed across compartments(host coral vs. Symbiodiniaceae dinoflagellates) over time (A-C). Control (27ºC) andhigh-temperature (30ºC) samples have been colored green and red, respectively. In A
and C, the sample sizes (“n=”) reflect the number of sequenced contigs, whereas thelowercase letters within the box plots in A-B reflect Tukey’s post-hoc inter-meandifferences (p<0.05) across the eight temperature (df=1) x time (df=1) x compartment(df=1) interaction groups (Tab. 2). The values in the lower halves of the first four box
plots (host coral) in C instead reflect the host coral/Symbiodiniaceae (“Symbio”)
contig ratios. In the x-axis label of C, “wk” corresponds to week, and the “globalmean” log2 gene expression level of ~1.1 has been plotted as a horizontal black line.

9Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

Tab. 2. Three-way ANOVA of the effects of temperature, time, and compartment
on global coral holobiont gene expression. Fragments per kilobases mapped(FPKM) data from a subset of 227,615 contigs (FPKM>0; OSDF) were log2transformed prior to analysis, and the results of Tukey’s post-hoc tests across alleight interaction groups (2 temperatures x 2 times x 2 compartments) can beseen in Fig. 2A-B. C2, H2, C36, and H36 correspond to the control and hightemperature samples at the 2- and 36-week sampling times, respectively.
SS=sumofsquares.
Sym=Symbiodiniaceae
dinoflagellates.
Treatment=temperature treatment. ***p<0.001.
SourceSS

F

Multiple comparisonscompartment
treatment
compartment x treatment

14350
2898
3032.13048***
615.5***
644.0***

time

8969

1905.1***compartment x timetreatment x time

18601
599.3

3951***
127.3***

Sym>host
high>control
Sym-high(a)>Sym-control(b)>host-control(c)=host-high(c)2>36 weeks
Sym-2(a)>host-36(b)>Sym-36(c)>host-2(d)compartment x treatment x time

125.5

26.66***

H2(a)>C2(b)>H36(c)>C36(d)Fig. 2

Results & Discussion

level of 1.04±0.01 was significantly higher

Meta-analysis. Although there werethan that of the 2-week time (0.83±0.01;

approximately 3-fold more contigs of hostsee post-hoc groups in Fig. 2A.). Thiscoral

signifies(~150,000)

thanendosymbiont

thatsampling

time

more

influenced

coral

gene

(~50,000) origin (Fig. 2A), the mean log2-

dramaticallytransformed FPKM level of 1.55±0.01 of

expression levels than did temperature.

the latter compartment was significantly

Although the primary goal of this work

higher than that of the former (0.94±0.01;

was to assess temperature-mediated, rather

Tab. 2). In fact, all effects in the 3-way

than exclusively temporal, effects, the

ANOVA (Tab. 2) were statisticallyseasonal (2-week time=winter & 36-weeksignificant,xtime=summer) difference in global genetreatment x time interaction effects haveexpression is interesting and possibly due

been depicted in Fig. 2. Within the hostto changes in the light environment: mean

fraction,

log2-transformed

summer levels>mean winter levels on

expression level did not differ between theaccount of the mesocosms being exposed

75,005high-to ambient, albeit shaded, light (Mayfield

temperature genes when pooled acrosset al., 2013b, 2014a). Indeed, others (e.g.,times (though post-hoc interaction effects

Fitt et al., 2000) have documented

were nevertheless evident; Fig. 2A). In

seasonal changes in tissue

contrast, the mean 36-week expression

the biochemical makeup of the holobiont,

and the compartment

thecontrolmean
and72,49210

biomass andPlatax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

which would likely be associated with

Symbiodiniaceae

changes in expression of numerous genes

successfully annotated herein given theof both host and endosymbiont origin.

recent growth in bioinformatics databases

GlobalSymbiodiniaceae

contigs

were

gene

(Lin et al., 2015). In other words, P. acuta

expression levels were instead higher at

is approximately 2/3 animal, and 1/3 plantthe 2-week sampling time (Fig. 2A-B &from an mRNA (& likely functional)

Tab. 2), and dinoflagellate gene expression

perspective. However, in this analysis, thewas higher at high temperature at both

identity of the genes was not considered,sampling times (Fig. 2A-B), as well as

only their concentrations. To gain insightwhen pooled across both (Tab. 2). Because

into the thermo-biology of the holobiont, it

there

differences

is actually preferable to consider eachbetween the host-high-temperature andindividual gene in the analysis in a strictly

host-control temperature groups in the 3-

multivariate

way ANOVA (Tab. 2), whereas such atranscriptomic similarity among samples

temperature-related

or groups of samples may be discerned;

were

no

post-hoc

difference

wasdocumented for Symbiodiniaceae, we takeframework

so

that

this is the topic of the next section.

this to signify that high-temperature

Multivariate effects. As for the

exposure more significantly influenced theglobal mean expression analysis of theSymbiodiniaceae transcriptome than thathost coral genes (Fig. 2 & Tab. 2), timeof their coral hosts; this is the same

more

conclusion made in our initial analysis ofmultivariate

this dataset (Mayfield et al., 2014d), which

temperature when using a more rigorous

washavingmultivariate approach that considers the

documented a relatively higher number ofconcentrations of each individual gene,

FDR-governed DEGs in the endosymbiont

PERMANOVA, with ~65,000-contig SRS

fractionTheof the data (Tab. 3). In no iteration was

host/endosymbiont contig ratio (a proxy

there a significant effect of temperature on

for the biomass ratio) was approximately

the holobiont transcriptome, whereas there

2.5-3:1 (Fig. 2C) and did not varywassignificantly in response to temperature.

temperature effect. This seasonal effect isThis is slightly lower than the mean ratio

not evident in the Venn diagram (Fig. 3A),

value of 3.3:1 reported by Mayfield et al.

in which ~45-50% of genes wereinstead

vs.

based

their

on

our

hosts.

(2014d) for this same dataset and may be
due to the fact that a larger percentage of11significantly

alwaysaffectedtranscriptomeahighly

than

the
did

significant

Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

Tab. 3. Permutational multivariate ANOVA (PERMANOVA) of the effects oftemperature, time, and their interaction on the Pocillopora acutaCladocopium spp. transcriptome. Data were standardized prior to creating the
Euclidean distance matrix such that each protein was weighted evenly. Please
note that, because the experimentally fragmented nubbins were independent
from one another (& not repeatedly sampled), a 2-way PERMANOVA (rather
than a repeated measures ANOVA) was performed. Since PRIMER (ver. 6) canonly analyze ~65,000 analytes in a single analysis, simple random samples(SRS) of 65,000 contigs were taken thrice from the entire transcriptome. There
were no statistically significant PERMDISP effects for temperature, time, or
their interaction (p>0.05) in any of the three SRS.
Source of variationdf

Pseudo-F#permutationspTemperature (temp.)

1

1.320

991

0.103

Time
Temp. x time

1
1

2.448
0.976

986
985

0.001
0.423

Residual (SRS-1)

7

SRS-1 (n=63,364 contigs)

SRS-2 (n=63,781 contigs)
Temp.

1

1.329

988

0.099

Time
Temp. x time

1
1

2.443
0.977

981
981

0.001
0.403

Residual (SRS-2)

7

SRS-3 (n=63,757 contigs)
Temp.
Time

1
1

1.319
2.443

985
990

0.076
0.001

Temp. x time

1

0.973

987

0.415

Residual (SRS-3)

7expressed

all

four

difference cannot be interpreted with

However,theconfidence. Furthermore, there was no

multivariate time effect can be seen in theinteraction of temperature and time in the

MDS plot (Fig. 3B). Although the ellipses

PERMANOVA (Tab. 3).

interaction

bysamplesgroups.

ofthere is

Predictive modeling. JMP Pro’s

nevertheless separation between the short-model screening platform found that only(“S;” 2-week) and long-term (“L;” 36-

the neural boosted algorithm, which isweek) sampling times. In contrast, thebased on artificial intelligence, was able tocontrol (green) and high-temperaturedevelop a model for which the R2 did not

samples (red) are only dissimilar after 36vary dramatically between the training andweeks. However, since one C36 sample

validation incarnations; this resulted inwas excluded, this 36-week temperature

low misclassification rates and RMSE in

do notsignify clustering,12Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

A C36

6,684

13,6629,

3

8,6 14,792
62

02

111,716 (all four)

9,153

H36

10 8,694
, 40
9

B 10
Dimension 2

C2

5
0
-5
-10

8,237

9,580
9,995

H2

5,029

stress=0.12

ongSS Lterm
S
SL
L
Sterm
horthigh
S L L control
-10

0
10
Dimension 1

2030

Fig. 3. Venn diagram (A) depicting overlap in expressed contigs across the control
and high-temperature samples of the 2- and 36-week sampling times (C2,
H2, C36, & H36, respectively) and a multi-dimensional scaling (MDS) plot(B) of 11 of the 12 transcriptome samples. Please note that several overlap
groups are obscured in A and so the corresponding values do not sum to thecontig total of 236,435. For MDS (B), one control sample at the 36-weeksampling time (C36T2S1) was found to be a “robust PCA” (JMP® Pro) outlier
and was excluded from analysis. Green and red icons represent control and hightemperatures samples, respectively, and the short (2-week) and long-term (36week) sampling times are represented by “S” and “L,” respectively. A principal
coordinates ordination (not shown; PRIMER) found that the first and second
dimensions captured 23.7 and 11.8% of the collective variation, respectively.

13

Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

the four iterations (Fig. 4). The actual

response plateau whereby, while notmodels are complex, though can be sharedtechnicallyupon request. The fact that the neuraldemonstrating an aberrant transcriptomic

boosted model could call 100% of the

profile that may have underlaid theirsamplestocapacity to survive at 30ºC over such atreatment means that, were we to analyze

prolonged duration. Similar results (albeit

a coral biopsy for which nothing is knownat the protein level) were obtained inwith respect to its resilience, the gene

Caribbean Orbicella faveolata specimens

expression data could be input into theafter a multi-week exposure to an ambientmodel to make a guess as to whether that(presumably “control”) temperature ofcoral was healthy (i.e., predicted as being

30ºC (Mayfield et al., in review).

accurately

withrespect

stressed,

they

were

a control) or potentially in an alternative

DEGs. For the purposes of biomarker

stable state with respect to chronic high-profiling, binary (presence/absence) data

temperature exposure. Since the corals

are preferable; this is because geneused in model training were presumably

expression data are nearly always “noisy,”

unstressed, this model cannot determine

highly variable, and skewed. If a thermal

whether a coral is stress-susceptible or

stress-associated gene, for instance, isresilient; instead, if a coral is predicted toexpressed at 2-fold higher levels in corals

be a high-temperature sample by the

exposed to high temperatures, and themodel, this could actually signify that theexpression levels

coral has been acclimating to highmeasured in test (i.e. field) samples, it willtemperatures for at least 2, or upwards of

be difficult to discern based on expression

36 weeks. In theory, the stress orof this gene alone whether the coral isacclimation/acclimatization

response

undergoing cellular stress given saidshould be ephemeral (Cruz-Garcia et al.,variability (not to mention the generally2020); however, since the stressor washigh concentrations of CSR genes/proteinsnever eliminated at any point over the

discussed above). In contrast, if a gene iscourse of the study (e.g., no return to

identified that is only expressed at highambient temperatures at night), yet thetemperatures, and never in control samples

expression

genes(or vice versa), this would represent a

remained distinct between control andsuperior biomarker. For that reason, wehigh-temperature samples at the final

used conditional arguments to identify

sampling time, this could signify that thethose genes in the transcriptome that were

corals1) only in the H2 treatment (& not in the 2-

hadlevelsofreachedcertainaphysiological
14of thisgene arePlatax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

Fig. 4. Results of four iterations of a boosted neural networking model capable ofpredicting coral temperature dosing (control vs. high). In this simulation,
four samples were randomly left out for validation with the other seven used
for training (the outlier mentioned above was not considered.). Three of the fourmodels were characterized by 0% misclassification rates in the validation stage(mean=6% of samples misclassified with respect to temperature across all four
iterations), and the mean root mean square error (RMSE) was 0.16±0.10 (SEM).

15

Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

week controls; n=108; OSDF), 2) only inal. (2014d).

the H36 treatment (& in none of the 36-

For a subset of DEGs expressed only

week controls; n=105; OSDF), 3) only inby H2, qPCR assays were developed (Tab.

the six high-temperature samples (& in1), and the presence/absence data were notnone of the six control samples; n=10; Tab.

corroborated (Figs. 5-6); at least some4), and 4) only in the six control-level

temperature samples (& in none of the six

documented

high-temperature samples; n=12; Tab. 4).

endosymbiont (Fig. 5A-I) and one coral

Of these 213 new presence/absence DEGs,(Fig. 6) gene. That said, the H2>C2 effecta SRS of 40 H2-only and 53 H36-only

was verified for all genes except for the

DEGs were annotated, and 17 (43%) andlarge ATP-binding protein (Fig. 5F).

40 (75%), respectively, failed to align toFurthermore, given the generally large

any published sequence on the NCBI

fold differences over controls (~25; Tab.

database. Of the 40 H2-only DEGs

1), it is still possible that these DEGs might(OSDF),

ofbe used as biomarkers for assessing sub-Symbiodiniaceae origin; this represents alethal thermal stress, or a shift to an

marginally statistically significant over-alternative stable state, for those without

representation versus the overall holobiont

sufficientcomposition of ~70/30 host/endosymbiont(~$150/sample as of September 2020). If

18

(45%)

were

2

of

expression(Ct<35)

was

for

of

nine

each

funds

for

the

RNA-Seq(X test, p=0.05). In contrast, only 3 of thewe can ultimately rank corals along a

53 annotated DEGs of the 36-week

spectrum (Fig. 7), from highly bleaching-sampling time were of endosymbiont

prone to markedly GCC-resistant (i.e.,

origin (6%). Although it is tempting tosuccessfully acclimatizing &/or adapting),

take the 18 endosymbiont vs. 9 host coral

the resulting triage capacity will allow usannotated

1:2

inform managers as to which reefs may be

host:endosymbiont ratio) to signify thatbeyond saving, as well as which may

the endosymbiont compartment is more

instead be refugia (e.g., Cacciapaglia &thermo-sensitive (as discussed above

van Woesik, 2016; Tortolero-Langarica etusing alternative approaches), it is possibleal., 2016; Rodriguez-Troncoso et al.,that the vast majority of the unknown2019).

DEGs(i.e.,

contigs might be of host coral origin, in

Acknowledgements

which case the DEG breakdown might
actually not differ significantly from theThanks are given to those who

2:1 host:endosymbiont ratio of Mayfield etmade charitable contributions to the16

17

genome accession
OLP90795.1
no hitno hitXP_027057486
XP_027060124.1
XP_027057972
XP_027043126
no hitno hitXP_027056940
weak hit
OLQ04308
no hitno hitXP_027050742
XP_027043046
no hitXP_027051642.1
XP_027040302
weak hitno hitcompartment
endosymbiont
unknown
unknowncoral hostcoral hostcoral hostcoral host
unknown
unknown
weak bacterial hit
coral hostendosymbiont
endosymbiont
unknown
unknowncoral hostcoral host
unknowncoral hostcoral hostendosymbiont
unknownfull name: probable dolichyl pyrophosphate Glc1Man9GlcNAc2 alpha-1,3-glucosyltransferase isoform X3.

aaccession
identity
functionHigh temp.-only: all 6 high-temp. samples & 0 of the 6 control samples (n=10)
comp80172_c0_seq2*
GMP synthase glutamine-hydrolyzing
nucleotide metabolism
comp82751_c0_seq1*
unknown
unknown
comp86677_c0_seq1
unknown
unknown
comp111636_c0_seq10
unknown with CUB domain
unknown
comp112485_c0_seq4
serine/threonine-protein phosphatase 6
signaling
regulatory ankyrin repeat subunit C-likecomp114802_c0_seq3
unknown
unknown
comp123755_c0_seq19
oncoprotein-induced transcript 3 protein-likeunknown
comp265791_c0_seq1
unknown
unknown
comp331820_c0_seq1
unknown
unknown
comp390759_c0_seq1
unknown
unknown
Control temp.-only: all 6 control samples & 0 of the 6 high-temp. samples (n=12)
comp49375_c0_seq1
glucosyltransferasea
protein modification
comp55964_c0_seq1
unknown
unknown
comp62421_c1_seq1
protein GRPE
transport
comp64413_c1_seq1
unknown
unknown
comp98703_c0_seq1
unknown
unknown
comp115002_c0_seq2
3'5'-cyclic nucleotide phosphodiesterase
nucleotide metabolism
comp115250_c3_seq2
sphingosine 1-phosphate receptor 3-likereceptorcomp120597_c2_seq1
unknown
unknown
comp120797_c2_seq11
neuropeptide Y receptor type 1-likereceptorcomp121122_c1_seq29
cAMP & cAMP-inhibited cGMP 3',5'-cyclic
nucleotide metabolism
phosphodiesterase 10A
comp123092_c0_seq22
unknown
unknown
comp661593_c0_seq1
unknown
unknown

Tab. 4. Differentially expressed genes (DEGs). Please note that the multivariate outlier, C36T2S1, was maintained in thisanalysis. Samples in bold and denoted by asterisks (*) were identified by response screening (FDRlogworth≥1) andpredictor screening (>2% of the variation due to temperature, time, or their interaction explained), respectively. Of the
22 DEGs, 9, 4, 1, and 8 were of host coral (41%), endosymbiont (18%), bacterial (5%), and unknown (36%) origin,respectively. The functions of only 8 (36%) could be inferred via homology searches against public databases (e.g., NCBI,
KEGG, pFAM), and 3 such DEGs (14%) were involved in nucleotide metabolism. The second most common cellular
process identified was receptor (2/22=9%). Temp.=temperature.

doi: 10.29926/platax.202012_17.0002

Platax 17: 1-26, 2020

Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

serviceexpressed herein are the authors’ own and(http://coralreefdiagnostics.com/fbes), as

do not necessarily reflect those of thewell as the MacArthur Foundation for

United States government.

Friendly

Bear

Editorial

funding the laboratory analyses. The views

100

Symbiodiniaceae gene mRNAs
A
20944***
B
cildyn***C
DNAJ
2
30 R2=0.692
R2=0.768
30 R =0.288
6

50

15
0

10

3
0

2

0
0

2

4

0

6

4

8

12

4

0

qPCR

100

D
E3 ubiq-lig** E
flik2*
2
2
R =0.554
6 R =0.441

F

5
0
-5

3
0

50

0

large ATP
10 R2=0.152
0

5

4

8

0
G

lrrcp***
10 R2=0.695
5

H spatacsin*** I
R2=0.798

5
0

100

0

50

4

TNF***
4 R2=0.833
2
0

8

0

2

4

0
0

5

10

0

5

10

Illumina

0

5

10Fig. 5. Correlations between RNA-Seq (Illumina) and qPCR data for nine
Symbiodiniaceae genes found by the former approach to be only expressedby high-temperature samples sacrificed after two weeks. Since all qPCR
data (y-axes) were plotted on the same scale, insets have been placed within all
but two panels to better demonstrate the linear relationships. Statisticallysignificant linear correlations have been denoted by *(p<0.05), **(p<0.01), or***(p<0.001). The blue bands about the trend lines represent 95% confidence.

18

Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

Fig. 6. Correlation between qPCR and RNA-Seq (Illumina) data for a host coral
gene found by the latter approach to only be expressed by hightemperature samples at the 2-week sampling time: tetratricopeptide repeat
protein 28-like protein with CHAT domain. An inset of an experimental
coral from which “nubbins” were made for the PALTTE has been displayed in
an inset. FPKM=fragments per kilobases mapped.

19

Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

Fig. 7. A hypothetical coral health index. This index incorporates response variablesfrom the anthozoan host alone (“Coral host biology”), the dinoflagellatecompartment alone (“Dinoflagellate biology”), and the holobiont as a composite
unit (“Holobiont properties”). The latter includes not only microbes (e.g.,bacteria & viruses) but also larger invertebrates that associate with corals, like
copepods (Cheng et al., 2016). A fourth wedge, “Phenotypic plasticity” reflects
both spatio-temporal and environmental variation in physiological performance,
as well as deviance from the mean phenotype established for the species inquestion (i.e., degree of aberrant behavior).

20

Platax 17: 1-26, 2020
doi: 10.29926/platax.202012_17.0002

Coral Reefs, 31:521-534.
Cheng, Y.R., P.J. Meng, A.B. Mayfield & C.F.
Dai. 2016. Copepods associated withscleractinian corals: a worldwide
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on the reef-building coral Pocillopora
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