# Revisiting tolerance to ocean acidification: Insights from a new framework combining physiological and molecular tipping points of Pacific oyster

Mathieu Lutier, Carole Di Poi, Frédéric Gazeau, Alexis Appolis, Jérémy Le Luyer, Fabrice Pernet

► To cite this version:

Mathieu Lutier, Carole Di Poi, Frédéric Gazeau, Alexis Appolis, Jérémy Le Luyer, et al. Revisiting tolerance to ocean acidification: Insights from a new framework combining physiological and molecular tipping points of Pacific oyster. Global Change Biology, 2022, 28 (10), pp.3333-3348. 10.1111/gcb.16101. hal-03759707

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Revisiting tolerance to ocean acidification: insights from a
new framework combining physiological and molecular
tipping points of Pacific oyster

Mathieu Lutier¹*, Carole Di Poi¹, Frédéric Gazeau², Alexis Appolis¹, Jérémy Le Luyer³,

Fabrice Pernet¹,*

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1 LEMAR CNRS/UBO/IRD/Ifremer, ZI pointe du diable, CS 10070, F-29280 Plouzané,

France.

2LOV Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche,

Villefranche-sur-Mer, France.

3EIO UPF/IRD/ILM/Ifremer, Labex CORAIL, Unité RMPF, Centre Océanologique du

Pacifique, Vairao - BP 49 Vairao, Tahiti, Polynésie française.

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Correspondence and requests for materials should be addressed to Fabrice Pernet (email:

fpernet@ifremer.fr)

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For submission to global change biology.

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21 Abstract

22 Studies on the impact of ocean acidification on marine organisms involve exposing
23 organisms to future acidification scenarios which has limited relevance for coastal
24 calcifiers living in a mosaic of habitats. Identification of tipping points beyond which
25 detrimental effects are observed is a widely generalizable proxy of acidification
26 susceptibility at the populational level. This approach is limited to a handful of studies
27 that focus on only a few macro-physiological traits, thus overlooking the whole organism
28 response. Here we develop a framework to analyze the broad macro-physiological and
29 molecular responses over a wide pH range in juvenile oyster. We identify low tipping
30 points for physiological traits at pH 7.3-6.9 that coincide with a major reshuffling in
31 membrane lipids and transcriptome. In contrast, a drop in pH affects shell parameters
32 above tipping points, likely impacting animal fitness. These findings were made possible
33 by the development of an innovative methodology to synthesize and identify the main
34 patterns of variations in large -omic datasets, fitting them to pH and identifying molecular
35 tipping-points. We propose the broad application of our framework to the assessment of
36 effects of global change on other organisms.

37

38 Keywords

39 Acidification, Lipidomic, Mollusk, Reaction norm, Threshold, Transcriptomic

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## 41 Introduction

The exponential increase in the atmospheric emission of carbon dioxide ($CO_2$) from anthropogenic activities is mitigated by ocean absorption that leads to decreasing ocean pH and changes in carbonate chemistry, a phenomenon known as ocean acidification (OA) (Caldeira & Wickett, 2003; Orr et al., 2005). This represents a tremendous challenge for marine organisms, especially for calcifiers that produce calcium carbonate ($CaCO_3$)-based exoskeletons. OA not only induces internal acidosis that impacts metabolism, behavior, growth and reproduction, but also decreases carbonate ion ($CO_3^{2-}$) concentration in the ocean, the elemental constituent of calcifier exoskeletons (Gazeau et al., 2013; IPCC, 2019; Kroeker et al., 2013; Tresguerres & Hamilton, 2017 for reviews).

OA has been the most-studied topic in marine science in recent times (Browman, 2017). As such, many OA experiments have been conducted, usually exposing organisms to experimental conditions based on scenarios modelled for open ocean waters, typically simulating present and near-future surface ocean pH levels. However, many calcifying species thrive in coastal areas where pH levels vary far more than in the open ocean on both daily and seasonal scales (Vargas et al., 2017; Waldbusser & Salisbury, 2014). As a result, several authors have applied pH offsets or conditions that are more locally relevant (Dineshram et al., 2015; Gazeau et al., 2014; Ko et al., 2014), but the results cannot be extrapolated to populations or species that are distributed in a mosaic of habitats. We need to implement experimental approaches whose results can be applied in a wide range of environments at the scale of species (Vargas et al., 2017).

Reaction norm, i.e. the response of an organism to changing environmental parameters, allows the identification of tipping points that beyond small variations will have major impacts. According to the last IPCC reports, identification of tipping-points
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is a key knowledge gap in environmental change research (IPCC, 2019, 2021). Tipping-point is valid for a whole population regardless of habitat. Also, this is a single, easily communicable value that reflects susceptibility to OA (Bednaršek et al., 2019, 2021). To date, only four studies have experimentally established reaction norms for marine calcifiers in relation to pH. These studies focused on the assessment of tipping points for a few selected traits measured at the organism level (i.e. growth, survival) and provide a limited view of the whole organism response, ignoring molecular responses (Comeau et al., 2013; Dorey et al., 2013; Lee et al., 2019; Ventura et al., 2016). We therefore identify the need to integrate macro-physiology and omics together with reaction norms, for understanding the mechanisms responsible for individual organism success under a changing environment. This however requires developing an innovative methodology to synthesize and identify the main patterns of variations in large -omics datasets (Strader et al., 2020) and fit them to reaction norms.

Lipidomic and transcriptomic approaches provide complementary and holistic views of organismal responses to environmental changes at the molecular and biochemical levels. Membrane lipids modulate exchanges between the intracellular milieu, which is highly-regulated, and the extracellular milieu which reflects environmental changes, and therefore play a key role in metabolic regulation and ion exchanges (Hazel & Williams, 1990; Hochachka & Somero, 2002; Hulbert & Else, 1999). However, the role of membrane lipids in the regulation of acid-base equilibrium and biomineralization under OA has never been investigated. Here we expect that membrane lipids play a key role in the maintenance of electrochemical gradient across membranes, a parameter that is essential for organism's persistence under OA (Cyronak et al., 2016).

The transcriptomic response is one of the first level of metabolic regulation that determine shifts in acid-base ion regulation, metabolic processes, calcification and stress
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response mechanisms (Matz, 2018; Strader et al., 2020). Although an increasing number of studies investigate the transcriptomic responses of marine calcifiers under OA, they generally focus on a few targeted genes that play a central role in physiological processes and they assume that gene functions are conserved among taxa (Strader et al., 2020). This creates a vicious cycle limiting the opportunities to uncover new mechanisms and cellular pathways involved in organism responses to OA. New methodologies are therefore required to provide new insight on organism's response to OA at the transcriptional level (Rajan et al., 2021; Yarra et al., 2021).

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Here we determine the reaction norm of juvenile Pacific oyster *Crassostrea gigas*, one of the most cultivated invertebrate species in the world (FAO, 2020), over a wide range of pHs for macro-physiological traits, membrane lipids and gene expression. Although the impacts of OA on this model species have been intensively studied using scenario approaches, there is currently no consensus on its robustness to low pH (Ducker & Falkenberg, 2020). The establishment of reaction norm with biochemical and molecular data is a novel approach that provides new insights on the sensitivity of marine calcifiers to OA.

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## Material and methods

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## Animals and maintenance

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The oysters were produced at the Ifremer hatchery facilities in Argenton (Brittany, France) in late August 2018 according to Petton et al. (2015). The broodstock consisted of 139 females and 40 males originating from six different cohorts collected in the natural environment between 2011 and 2016, off Fouras (Ile d'Aix, France). At 40 days old, the juveniles were moved to the Ifremer growing facilities in Bouin (Vendée, France). On January 10th 2019, oysters were returned to Argenton and kept for 8 d in a 500 L flow-
through tank. Seawater temperature was gradually increased from 7 °C to 22 °C, the optimal temperature for *C. gigas* (Bayne, 2017), at a rate of *ca.* 2 °C d⁻¹. During the experiment, oysters were continuously supplied with natural seawater originating from a pool (~9000 m³) which is renewed with each spring tide, filtered at 5 µm and UV-treated. The oysters were fed continuously on a mixed diet of *Isochrysis affinis galbana* (CCAP 927/14) and *Chaetoceros gracilis* (UTEX LB2658) (1:1 in dry weight). Food concentration was maintained at 1500 µm³ mL⁻¹ of phytoplankton cells at the outlet of the tank – *ad libitum* (Rico-Villa et al., 2009) – and controlled twice daily using an electronic particle counter (Multisizer 3, Beckman Coulter, USA) equipped with a 100 µm aperture tube. On the eve of the experiment on January 14th 2019, at which point they were 5-month-old, oysters were divided into 15 batches containing 292 ± 20 individuals (95.2 ± 0.2 g).

## 127 Experimental design

Each batch of oysters was exposed to one constant nominal pH condition ranging from pH 7.8 to 6.4 with a step of 0.1 between two levels. The upper pH condition (pH ~7.8) was obtained by running seawater with oysters without pH regulation. The lowest pH values encountered by oysters today are presumably between pH 7.4 and 7.0 (Frankignoulle et al., 1996; Melzner et al., 2013; Proum et al., 2017; Wallace et al., 2014). Geochemical models using the record of atmospheric CO₂ levels over the last 300 Myr suggest that the ocean pH was at most 0.6 units lower than today (Caldeira 2003). Continued release of CO₂ from fossil fuels into the atmosphere (worst case scenario) could result in a reduction in pH of 0.7 units at the ocean surface (Caldeira 2003).
Therefore, the lowest pH values used in our study are probably unrealistic but expand the reaction norms and increase the statistical power of the tipping point.

The experimental system consisted of 18 experimental units that were randomly assigned to one pH condition (n = 15, no pH replication) or to control blanks without animals (n = 3) (Supplementary Figure 1). Each experimental unit consisted of a header tank in which seawater was acidified (except for the ambient pH and blanks) and then delivered by a pump to a holding tank containing the oysters. These tanks were 45 L and their entire volume was renewed every 82 min. pH was regulated by means of pure-CO₂ bubbling, controlled by a pH-regulator (ProFlora® u403 JBL). The pH-regulator was connected to pH-probes installed in each header tank (pH-sensor+Cal, JBL), checked at the start of the experiment using Certipur® Merck NBS buffers (pH = 4.00, pH = 7.00). pH electrodes from the pH-regulator were inter-calibrated twice daily against a pH probe (Sentix® 940-3 WT) connected to a Multiline® Multi 3630 IDS-WTW. The pH-probe was checked once a week using Certipur® Merck NBS buffers (pH = 4.00, 7.00, and 9.00) and calibrated twice a week on the total scale with a certified Tris/HCl buffer (salinity 35; provided by A. Dickson, Scripps University, USA). Throughout the text, pH levels are therefore expressed on the total scale (pHₜ). In each experimental tank, air bubbling and a small homogenisation pump (3 W) ensured an efficient mixing of seawater surrounding the oysters. Photoperiod was 10 h light: 14 h dark. During the entire experimental period, oysters were fed as described above.
On January 18th 2019, each batch of oysters was randomly assigned to one tank.

Oysters were first held at ambient pH for 3 d. Then, pH was progressively decreased in each pH-regulated tank at a rate of 0.2 unit d⁻¹. The decrease in pH lasted for 7 d for the lowest condition. Experimental pH conditions were all reached on January 27th 2019.

The pH exposure lasted 23 days, the time required for critical shell alteration (bleaching, perforation, and dissolution) under the lowest pH conditions. No mortality was recorded during the experiment.

## Seawater carbonate chemistry

In each oyster tank, temperature, salinity, dissolved oxygen (O₂) saturation levels and pH were measured twice a day using a Multiline® Multi 3630 IDS-WTW (pH probe Sentix® 940-3 WTW, O₂ probe FDO® 925 WTW, salinity probe TetraCon® 925 WTW). Seawater samples were collected weekly, filtered through 0.7 µm (GF/F, Whatman®) and poisoned with 0.05% mercury (II) chloride. Total alkalinity (TA) was then measured in triplicate 50 mL subsamples by potentiometric titration at 22 °C, using a Titrando 888 (Metrohm®) titrator coupled to a glass electrode (ecotrode plus, Metrohm®) and a thermometer (pt1000, Metrohm®). TA was calculated following the protocol described in Dickson et al. (2007). At the time of sampling, pH, salinity and temperature were measured, and then used together with TA value to determine carbonate chemistry parameters using the package seacarb v 3.2.16. of the R software.

## Biometry

The total fresh weight (shell + tissue) of each batch of oysters was measured twice weekly and interpolated between measurements to estimate daily weight. In
addition, shell length and total fresh weight were measured individually on 30 oysters from each condition at the onset (1 d) and at the end of the experiment (23 d). The measurements relate to different individuals taken over time. These oysters were then dissected and pooled to determine the total weight of shell and tissue, separately. The tissues were then lyophilized and weighted to obtain dry weight. Growth rate was calculated as:

$$G = (\overline{X}_{23} - \overline{X}_{1}) \div 23,$$

where G is growth rate as expressed as increase in shell length or total body weight per day (mm d⁻¹, mg TW d⁻¹), X̄₁ and X̄₂₃ are the average parameter values for shell length and total weight measured at the onset (1 d) and the end of the experiment (23 d).

Shell thickness was measured on the left valve of five individuals collected per pH condition at the end of the experiment. The left valve was more susceptible to acidification than the right valve. We observed holes in the shell under low pH condition (pH < 6.6) that always occurred on the left valves, close to the umbo. Shells were dried for 24 h at 45 °C, embedded in polyester resin and cross-sectioned from umbo to opposite shell margin along the longitudinal growth axis using a precision saw (Secotom-10 Struers). Sections were glued on a microscope slide and polished with silicon carbide abrasive disks (1200 and 2500 grains cm⁻²). Images of the section were captured under a Lumar V12 stereoscope (Zeiss) at 30x magnification, and the entire section was reconstructed using an image acquisition software (AxioVision SE 64 –
201 v4.9.1, Zeiss). The minimal shell thickness was measured within the first third of the
202 shell starting from umbo using ImageJ software (Schneider et al., 2012).

203 Physiological rates

204 Seawater was sampled twice a day at the inlet and outlet of each oyster tank
205 and phytoplankton cell concentrations were measured using an electronic particle
206 counter (see previous section). No pseudofaeces production was detected throughout
207 the experiment. Ingestion rate was determined as:

208 $I = \frac{\Delta_{phyto} \times \text{flow rate}}{W},$

209 where *I* is the ingestion rate expressed as cm³ min⁻¹ g⁻¹, *Δ*<sub>phyto</sub> is difference in
210 phytoplankton concentrations between the inlet and the outlet of the oyster tanks
211 (cm³ min⁻¹), *flow rate* is the water flow at the inlet of the oyster tanks (mL min⁻¹), and
212 *W* is the total fresh weight of oyster batch (g). The three control blanks were used to
213 check that there was no sedimentation of algae (no differences in cell concentrations
214 between the inlet and the outlet, data not shown).

215 Net calcification and respiration rates were measured after 22 d of exposure.
216 Food supply was stopped 17 h before the assay and tanks were emptied, cleaned and
217 refilled. Again, 1 h before the assay, the tanks were emptied, cleaned and refilled. At
218 the onset of the incubation, water flow and air bubbling were stopped. Gentle mixing
219 of the seawater was maintained by homogenization pump but air bubbling was
220 stopped. Incubations lasted for 90 min. This duration allowed to keep the pH close to
221 the setpoint (< 0.1 pH unit variation) despite oyster respiration. At the onset and at
222 the end of the incubation period, temperature, salinity, pH and O₂ concentration (mg
L⁻¹) were measured, and seawater samples were filtered and poisoned for TA analyses (see above) or were immediately frozen at -20 °C for ammonium (NH₄⁺) measurements (Aminot & Kérouel, 2007) (see Supplementary Table 1). Empty tanks were used as controls to check that there was no change in any of the parameters due to evaporation or other potential biological processes in the water itself. Net calcification rate was determined following a modified procedure from Gazeau et al. (2015) using the alkalinity anomaly technique (Smith & Key, 1975):

$$230 \quad NC = \frac{(\Delta_{TA} \times \rho - \Delta_{NH_4^+}) \times V}{2 \times t \times W \times \frac{DW}{TW}},$$

where NC is the net calcification rate expressed as µmol CaCO₃ h⁻¹ g⁻¹, ΔTA and ΔNH₄⁺ are differences in TA (µmol Kg⁻¹) and NH₄⁺ (µmol L⁻¹) between the onset and the end of the incubation period, ρ is seawater density (kg L⁻¹) calculated based on temperature and salinity during the incubation, V is the volume of the tanks (L), t is the duration of the incubations (h), DW is the ratio of dry weight and total weight determined after lyophilization of a pool of tissues from 30 oysters (see above).

Respiration rate was determined following:

$$238 \quad R = \frac{(\Delta_{O_2} - \Delta_{O_2 \text{ control}} \times \rho) \times V}{t \times W \times \frac{DW}{TW}},$$

where R is the respiration rate expressed as mg O₂ h⁻¹ g⁻¹, ΔO₂ and ΔO₂ control are differences in O₂ concentration (mg L⁻¹) between the onset and the end of the incubation period in the oyster tank and in the three control tanks (average) respectively. Negative values mean that oxygen consumption was negligible and similar to control blanks.
244 Biochemistry

245 Soft-tissues of five individuals from each pH condition were collected at 23 d,
246 flash-frozen in liquid nitrogen, pooled, grounded with a ball mill and stored at -80 °C
247 pending analyses. Oyster powder was diluted with chloroform/methanol (2:1, v/v) for
248 the determination of neutral lipids (triacylglycerol: TAG, and sterols: ST) using high
249 performance thin layer chromatography. TAG-ST ratio was used as a proxy of the
250 relative contribution of lipid reserve to structure (membrane). Polar lipids were
251 purified on silica gel micro column, transesterified using methanolic H2SO4 at 100 °C,
252 and the resulting fatty acid methyl esters (FAME) were analyzed using a gas-
253 chromatography flame ionization detection system equipped with a DB-Wax capillary
254 column. Peaks were analyzed by comparison with external standards. Each fatty acid
255 was expressed as the peak area percentage of total polar fatty acids. Carbohydrate
256 content (µg mg-1) was determined according to the colorimetric method described in
257 DuBois et al. (1956).

258 Transcriptomics

259 Soft-tissues of five individuals from each tank were collected at 23 d, flash-
260 frozen in liquid nitrogen and individually grounded (n = 75). Total RNA was then
261 extracted with Extract-All (Eurobio) at a ratio of 5 mL per 100 mg of tissue. RNA
262 quantity/integrity and purity were verified with a NanoDrop 2000 spectrophotometer
263 (Thermoscientific®) and a Bioanalyzer 2100 (Agilent Technologies®) respectively.
264 Samples were then DNase-treated using a DNase Max™ Kit (MO BIO Laboratories,
265 Inc.) and analyzed at the Genotoul sequencing platform (INRAE US 1426 GeT-PlaGe,
266 Centre INRAE de Toulouse Occitanie, Castanet-Tolosan, France). TruSeq RNA
267 libraries were multiplexed and sequenced on a single lane of NovaSeq6000 Illumina
268 S4 150-bp paired-end.

269 Raw reads were first filtered using Trimmomatic v.0.36 for a minimum length (60 bp),
270 a minimum quality (trailing = 20, leading = 20) and the presence of potential
271 contaminants and remaining adaptors (https://ftp.ncbi.nlm.nih.gov/pub/UniVec;
272 08/17/20). The quality of the reads was monitored before and after this trimming
273 process with FastQC v.0.11.5

274 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The *C. gigas* reference
275 genome (Peñaloza et al., 2021) was downloaded from NCBI (GCF_902806645.1) and
276 indexed with Gmap v2020.06.01. Filtered reads were mapped against the reference
277 genome using GSNAP v2020-06-30 keeping default parameters but allowing a
278 minimum mismatch value of 2 and a minimum read coverage of 90%. Finally, the
279 gene expression levels were assessed using HTSEQ v0.6.1. The DESeq2 v1.30.0 R
280 package was used to process the expression with a first step of normalization using the
281 variance-stabilizing transformation implemented in the 'vst' function. The vst matrix
282 was used to build a signed weighted co-expression network analysis implemented in
283 the WGCNA v1.69 R package. First, genes with low overall variance (< 5%) were
284 removed for the analysis as recommended (Langfelder & Horvath, 2008). We fixed
285 the "soft" threshold powers of 13 the scale-free topology criterion to reach a model fit
286 (|R|) of 0.90. Clusters were defined using the "cutreeDynamic" function (minimum of
287 50 genes by cluster and default cutting height of 0.99) based on the topological overlap
matrix, and an automatic merging step with the threshold fixed at 0.25 (default) 288
allowed merging correlated clusters. For each cluster, we defined the cluster 289
membership (kME; Eigengene-based connectivity) and only clusters showing 290
significant correlation (p < 0.01) to pH were kept for downstream functional analysis. 291

## Statistical analysis

All statistical analyses were performed using the R software v4.0.3 and the 389
threshold of statistical significance was fixed at 0.05 unless specifically stated. 390
Relationships between dependent variables and average pH recorded during the 391
exposure period (or the incubation period for net calcification and respiration rates) 392
were computed using regression models. Dependent variables were biometrics (shell 393
length, total body weight, and growth), physiological rates (ingestion, net calcification 394
and respiration), biochemistry (lipid reserves, carbohydrates) and gene expression. 395
Each gene included in the WGCNA clusters were individually tested against pH. Fatty 396
acids were summarized using principal component analysis and separated into two 397
groups according to their correlation with first principal component (positive or 398
negative). Each group was then summed and tested against pH. Piecewise, linear and 399
log-linear regression models were tested and compared for each individual variable 400
(or set of variables). In each case, the model that had the highest R², the lowest Akaike 401
and Bayesian information criteria (AIC, BIC) and the lowest residual sum of squares 402
was selected. For each piecewise regression model, we estimated the tipping point, 403
defined as the values of the pH where the dependent variables tip, by implementing 404
the bootstrap restarting algorithm (Wood, 2001). For physiological rates, we also 405
determined the critical-point defined as the pH at which the dependent variable was equal to zero. Normality of residual distribution was tested using Shapiro-Wilk test and homogeneity of variances was graphically checked. Significance of each slope was tested according to Student's *t* test. Piecewise regression models were built using the segmented v1.3-4 R package.

For transcriptomic analysis, only genes that are significantly correlated to pH were tested among the retained clusters (Pearson correlation). The frequency distribution of pH tipping-point values was plotted for each cluster of genes. Groups of genes which exhibit neighboring tipping points with distribution frequencies > 5%, were grouped together and used for GO enrichment analysis using GOA-tools v0.7.11 (Klopfenstein et al., 2018), implemented in the Github repository 'go_enrichment' (https://github.com/enormandeau/go_enrichment) and the go-basicobo database (release 2019-03-19) with Fisher's extract tests. Only significant GO terms that included a minimum of three genes and with Bonferroni adjusted P < 0.01 were kept.

For complementarity with previous studies, we also specifically examined the genes that are commonly reported as involved in the calcification process and in the formation of the organic matrix of the shell and periostracum (Supplementary File 1).

## 423 Results

### 424 Acclimation of oysters to 15 different pH conditions

During the experiment, pH levels in the oyster tanks were stable and reached the targeted values, except for the lowest pH condition that was 6.5 ± 0.3 instead of 6.4 (Supplementary Note 1, Supplementary Table 2, Supplementary Figure 2). The effects of acidification were clearly visible at the end of the experiment on the color and the
size of the shells, as shown in Figure 1. Shell pigmentation apparently decreased with decreasing pH. Total body weight and shell length of oysters ranged from 0.4 ± 0.1 to 2.0 ± 0.8 g and from 12.9 ± 2.1 to 25.4 ± 4.5 mm respectively (mean ± SD; Figure 2A, Supplementary Figure 3).

### Most macro-physiological traits tip at low pH values between 7.1-6.9

We measured biometric parameters like the length, thickness, and weight of the shell and the total body weight and the dry flesh weight at the end of the 23-day exposure to the 15 different pH conditions. We calculated growth rates for shell length and total body weight, and we measured physiological rates like calcification, ingestion and respiration rates. From these data, we identified an overall tipping point for macro-physiological traits at pH ~7.1-6.9 below which they declined sharply (Figures 2A-H). Calcification rate and growth rates exhibited critical points at pH 6.8 and 6.6, respectively, below which they became negative and turned to net dissolution of the shell and weight and length losses (Figures 2B-D). Concomitantly, respiration rate was arrested at pH 6.7 and ingestion rate was near-zero at pH 6.5 (Figure 2E-F).

### Shell parameters and respiration rate are impacted above tipping point

Although macro-physiological traits were generally unaffected by reductions in pH above tipping points, this was not the case for shell length, shell weight and respiration rate. Both shell parameters indeed decreased with decreasing pH while the respiration rate increased before reaching the tipping point (Figures 2A, 2E and 2G). In addition, the shell thickness was the only parameter that decreased linearly over the entire pH range without demonstrating a measurable tipping point (Figure 2I).
451 A major remodeling of membrane lipids occurs at pH 6.9

452 To provide an in-depth characterization of the reaction norms at the whole
453 organism level, we aimed to link the oyster reaction norm established for the macro-
454 physiological traits described previously to the molecular responses of oysters. We first
455 analyzed the fatty acid composition of membrane lipids that play an important role in
456 exchanges between intracellular and extracellular compartments. Principal component
457 analysis of all fatty acids showed that the first axis (PC1) alone explained 66% of the total
458 variance in relation to pH (Figure 3A). The set of fatty acids was reduced to two terms
459 defined by the sums of fatty acids that correlated with PC1 either positively or negatively,
460 and each term was plotted against pH (Figure 3B-C). Positively correlated fatty acids
461 mainly consisted of docosahexaenoic acid (DHA, 22:6n-3) and palmitic acid (PA, 16:0)
462 contributing 43% and 14%, respectively, to PC1 (Supplementary Table 3). This group of
463 fatty acids exhibited a tipping point at pH 6.9, below which their contribution to
464 membranes decreased markedly (Figure 3B). In contrasts, the contribution to membrane
465 of the fatty acids that were negatively correlated with PC1 increased markedly below pH
466 6.9. These negatively correlated fatty acids consisted of arachidonic acid (ARA, 20:4n-
467 6), eicosapentaenoic acid (EPA, 20:5n-3) and non-methylene interrupted fatty acids
468 (22:2NMIᵢⱼ), which contributed 12%, 6% and 6%, respectively, to PC1 (Figure 3C)
469 (Supplementary Table 3).

470 Expression of most genes shows tipping points at pH 7.3-6.9

471 To further assess the molecular effects of pH change, we compared the
472 transcriptome responses of oysters exposed to the 15 pH conditions for 23 days using
473 RNA-seq. To do this, we first clustered the differentially expressed genes that co-vary
474 together using WGCNA analysis and retained the clusters that were correlated with pH
475 (Supplementary Table 4). Then, we plotted the frequency distribution of pH tipping-point
476 for each cluster of genes (Figure 4). Owing to this original method, we found that 1054 genes, distributed in three clusters, showed linear (or loglinear) or piecewise regression with pH (Supplementary Table 4). Among them, 49% showed tipping-points at pH 7.3-6.9 (Figure 4A-C). Expression level of most genes were unchanged between pH conditions down to the pH tipping point, below which it increased for genes in clusters 1 and 2 (Figure 4A-B), or decreased for genes in cluster 3 (Figure 4C). Some genes linearly increased for clusters 1 and 2, or decreased for cluster 3 with decreasing pH.
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483 Gene ontology analysis (GO) showed that most of the genes from cluster 1 (42%) exhibited tipping-points at pH 7.3-6.9 (Figure 4A) and were associated with GO terms corresponding to the regulation of RNA-transcription, cellular metabolism, macromolecule biosynthesis and negative regulation of cell-cell adhesion (Table 1). The expression of another group of genes from cluster 1 (14%) increased linearly (or loglinearly) with decreasing pH and was associated with GO terms corresponding to GTP-binding, GTPase activity and ribonucleotide metabolism (Table 1). The expression of most of the genes from cluster 2 (44%) exhibited tipping-points at pH 7.3-7.0, whereas another large group of genes (18%) increased linearly (or loglinearly) with decreasing pH (Figure 4B). These genes were associated with GO terms corresponding to ribosome synthesis, RNA-binding, translation and protein/amino acid synthesis (Table 1). The expression of 62% of the genes from cluster 3 exhibited tipping-points at pH 7.2-6.9 and decreased thereafter (Figure 4C). These genes were associated with GO terms corresponding to ion transport and more specifically to transmembrane cation transport (Table 1).
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Expression of genes related to biomineralization shows tipping point at pH 7.1-6.9

We specifically examined the genes that are commonly reported in the scientific literature as being involved in the calcification process and in the formation of the organic matrix of the shell and periostracum. We found that the expression of 60% of the 38 genes involved in the calcification process that were retained by our models showed pH tipping points between 6.9 and 7.1 (Table 2). These genes encode for calcium-binding proteins, Ca²⁺ signaling pathway, amorphous calcium carbonate-binding proteins and ion transmembrane transporters. Most of these genes decreased below the tipping-point (n = 14) while others increased (n = 8). The expression of genes associated with the regulation of the synthesis of the shell organic matrix and the periostracum (n = 11) generally increased with decreasing pH (Table 2). The relationships were loglinear (n = 3) or piecewise (n = 8).

Among these 38 genes, the family of genes that are the most represented are coding for acetylcholine receptors, monocarboxylate transporters and tyrosinase synthesis (Table 2). The expression of four genes coding for acetylcholine receptors decreased below tipping points at pH 6.9-7.0. The expression of five genes coding for monocarboxylate transporters increased with decreasing pH, and four of them showed pH tipping points between 7.3 and 6.6. The expression of four genes associated to tyrosinase synthesis early increased with decreasing pH and three of them showed tipping points between pH 7.4 and 7.1.

Discussion

Here we provide the first reaction norm of juvenile oyster *C. gigas* assessed by combining macro-physiological traits and micro-molecular characteristics. This novel approach applied to juvenile oysters exposed to a wide range of pH conditions revealed
that macro-physiological parameters such as growth, calcification, food intake, and
respiration exhibited pH tipping points that coincide with a major reshuffling in
membrane lipids and transcriptome that was previously unappreciated, but others like
shell parameters seemed uncoupled from global effects (Figure 5). This comprehensive
view of the oyster response to pH sheds new light on adaptation capacity of this species
to OA. We indeed identify low tipping points for most physiological traits that suggest
the species is robust to OA, but reveal sensitivity of shell parameters above the tipping
points that potentially impact animal fitness. We therefore determine the true sensitivity
of oyster to pH and identify the most relevant and sensitive response variables.

We first identify a global tolerance threshold for juvenile *C. gigas* at pH 6.9-7.3, corresponding to the tipping points identified for the majority of the examined parameters. In line with this, several studies based on IPCC scenario assumed the existence of tipping points below pH 7.5-7.4 for growth, calcification, acid-base equilibrium, and reproduction in oysters and mussels (Boulais et al., 2017; Fitzer et al., 2014; Lannig et al., 2010). Our results are also in agreement with previous studies showing that there are no significant changes in growth, respiration, mortality, metabolic rate, ion regulation, energy reserves, gene expression and fatty acid profile in juveniles and adults of *C. gigas* for pH values above 7.3. (Clark et al., 2013; Lannig et al., 2010; Lemasson et al., 2018; Timmins-Schiffman et al., 2014; Wang et al., 2020). Below tipping points, growth, calcification, respiration and feeding rate decreased markedly, likely reflecting increasing maintenance cost up until reaching critical points that are representative of metabolic depression (Michaelidis et al., 2005; Sokolova, 2021). It is noteworthy that longer exposure time might have revealed an impact of the pH on survival or reproduction, two important parameters for fitness. Survival should be compromised
below the tipping point as the animal shift to metabolic depression, i.e., a short-term survival strategy (Michaelidis et al., 2005).

The tipping points we determined at pH 7.3-6.9 (pCO₂~2600-8200 µatm) are well below IPCC projections for 2100 in open ocean waters (pH=7.8 according to scenario RCP 8.5 IPCC, 2019). However, acidification events with pH values as low as 7.4-7.0 (pCO₂ > 3000 µatm) currently occur transiently in estuarine ecosystems in relation to eutrophication and hypoxia (Frankignoulle et al., 1996; Melzner et al., 2013; Proum et al., 2017; Wallace et al., 2014) and may explain why the physiological tipping point of oysters is so low. These acidification events are expected to become increasingly frequent and intense in the future (IPCC, 2019), such that the physiological tipping points defined here for juvenile oysters may be reached.

This point outs the importance of tipping point for coastal management and conservation in a changing world. Tipping-point is valid for a whole population regardless of habitat. Here we used juveniles that originate from a large genetic pool and *C. gigas* is a weakly genetically-structured species (Meistertzheim et al., 2013). However, it was recently shown that some wild and domesticated oysters populations vary in their adaptive response to OA (Durland et al., 2021). We therefore need to verify that pH tipping points defined in our study are valid in genetically differentiated populations before integrating them in biogeographic models of species distribution. Finally, tipping point is a single value that reflects susceptibility to stressors that is easily communicable to end-users and society (Bednaršek et al., 2019, 2021).

We show here that shell parameters, i.e. thickness, weight and length, were altered as soon as pH decreased from ambient levels, as already reported for many shellfish species (Byrne & Fitzer, 2019). Shell thickness is generally related to shell strength and therefore with protection against predation and resistance to mechanical stress related to
wave exposure and aquaculture processes. Thus, moderate acidification increases predation risk of young *C. gigas* through reduction of shell strength (Wright et al., 2018). We also find that respiration rate first increased when pH decreased, suggesting that metabolism was readily impacted. However, body condition (dry flesh weight) and energy reserves (TAG-ST and carbohydrate, Supplementary Figure 4) were not affected by pH, likely because oysters were fed *ad libitum* (Leung et al., 2019; Thomsen et al., 2013). Finally, we report here a linear increase in the expression level of genes related to GTPase activity and GTP binding, which are characteristics of stress response in *Crassostrea* spp. (Yan et al., 2018). Overall, these changes suggest that longer exposure to moderate acidification (above tipping point) could impair overall oyster fitness.

We further show that this reduction in shell parameters was not related to metabolic depression or to changes in net calcification or global gene expression. In contrast, a recent meta-analysis on *C. gigas* suggests that OA induces a reduction in shell thickness that is related to an overall transcriptional change inducing metabolic depression and alteration of calcification rate (Ducker & Falkenberg, 2020). Here we revisit these assumptions and find in agreement with other prior studies that no metabolic disruptions occurred while shell strength and thickness decreased at pH 7.3-7.7 (Timmins-Schiffman et al., 2014; Wright et al., 2018).

Concomitantly, a delamination of the periostracum, the organic coat covering the shell, may have occurred at pH levels above the tipping point, probably altering shell protection. This was supported by an increase in shell bleaching (not measured) and expression of four genes coding for tyrosinase-like proteins. Tyrosinases are involved in periostracum synthesis and the increase in expression of their related genes is often considered as a mechanism to limit the damage to the periostracum and the corrosion of the shell in reaction to OA (Hüning et al., 2013). Delamination of the periostracum due
to moderate OA was frequently associated with alteration of shell properties such as weakening and thinning (Alma et al., 2020; Auzoux-Bordenave et al., 2019; Bressan et al., 2014; Coleman et al., 2014; Peck et al., 2016, 2018; Zhao et al., 2020). Our results agree with the idea that OA is more a dissolution problem than a biomineralization problem (Rajan et al., 2021).

This investigation of tipping points at the micro-scale also provides new insights on the physiological response of oysters to OA. For the first time, we find a major remodeling of membrane lipids in response to OA and observed a tipping point at pH 6.9. This remodeling consisted of decreasing the long chain PUFA DHA (22:6n-3) that is essential for growth and survival (Knauer & Southgate, 1999; Langdon & Waldock, 1981), at the benefit of eicosanoid precursors (ARA, 20:4n-6 and EPA, 20:5n-3) involved in the stress response (Delaporte et al., 2003). Since membrane fatty acid composition influences the activity of transmembrane proteins involved in ion transport (Hazel & Williams, 1990; Hochachka & Somero, 2002), it could also have been related to regulation of acid-base homeostasis and perhaps calcification under OA.

In line with this, we find an overall decrease in expression of genes related to cation transmembrane transport below tipping points at pH 6.9-7.2 that can be related to alteration of calcification and acid-base homeostasis (Zhao et al., 2020). Concomitantly, we observe an overall decrease in the expression of genes coding for proteins involved in the regulation of calcification through calcium signaling pathway, calcium homeostasis, calmodulin signaling pathway and regulation of calcium carbonate crystal growth (Feng et al., 2017; Wang et al., 2017; Zhao et al., 2020).

In our analysis, we also identify families of genes that must play an important role in oyster response to OA. Among these genes, we identify four genes coding for acetylcholine receptors that are known for regulating and ordering the formation of the
shell micro-structure (Feng et al., 2017). We also find five monocarboxylate transporters coding genes, members of a family of proteins that act as co-transporters of protons H⁺, that may be involved in the regulation of acid-base homeostasis and calcification process (Tresguerres et al., 2020; Wang et al., 2020). These proteins also co-transport monocarboxylates such as lactate or pyruvate and were reported to be involved in the stress response of oysters through induction of anaerobiosis under metabolic depression (Ertl et al., 2019).

In conclusion, we show that juvenile *C. gigas* have a broad tolerance to ocean acidification, exhibiting tipping points around pH 7.3-6.9 for most parameters (Figure 5). Nonetheless, we observe that shell parameters change as soon as pH drops, well before tipping points are reached, suggesting animal fitness is likely to be affected. This thus raises concerns about the future of natural and farmed oyster populations in a high-CO₂ world. This new framework for identification of tolerance threshold in organisms represents a breakthrough in the field of global change research. It was made possible by (1) combining reaction norm assessment and thorough molecular and biochemical analyses of animal responses, and (2) developing a procedure to analyze and synthesize omics data measured over an environmental range.

We believe that such an integrative and holistic approach could now be applied to other organisms and integrate intra-specific variation, life-stages, generations, and other stressors such as temperature, nutrition, pollutants or oxygen levels. Although this approach requires significant financial resources, it allows to determine the true sensitivity of a species to a stressor and to identify the most relevant and sensitive response variables to study in the future.

644

645 Acknowledgments
We are grateful for K. Lugue, J. Veillet, C. Quéré, V. Le Roy and I. Queau for their participation to data recording and the Ifremer staff for their involvement in oyster and algae production. We thank J. Thebault and E. Dabass for their advices in shell analysis. We are also grateful for G. Le Moullac for project conception and funding. This work was funded by the Ocean Acidification Program of the French Foundation for Research on Biodiversity (FRB - www.fondationbiodiversite.fr) and the French Ministère de la transition écologique.

## Author contributions

ML, CDP, and FP designed and conducted the experiment. All authors analyzed the data. ML performed statistics. ML and FP wrote the first draft of the document and all authors contributed and accepted it. FP and JL obtained the funding. This work is part of the PhD thesis of ML.

## Data availability

RNA-seq data have been made available through the SRA database (BioProject accession number PRJNA735889). Other data analyzed during this study are included in this published article and its supplementary information files or available through the SEANOE database (https://doi.org/10.17882/83294). Complementary information is available from the corresponding authors on reasonable request.

## Competing interests

The authors declare no competing interests.

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## Figure captions

**Figure 1.** Oysters exposed to 15 pH conditions for 23 d. Five oysters were selected from each condition, and sorted from the smallest to the biggest. Corresponding pH (total scale) and saturation states of seawater with respect to calcite ($\Omega_{CA}$) are shown.

**Figure 2.** Biometry and physiological rates of oysters as a function of pH (total scale). (a) shell length, (b) net calcification, (c) growth rate in shell length, (d) growth rate in total body weight (TW), (e) respiration rate, (f) ingestion rate, (g) shell weight, (h) dry flesh weight and (i) minimal shell thickness. Data are means ± SD when available. Tipping points and critical points and their 95% confidence intervals are shown in grey and red, respectively. The significance levels of the slopes are presented using symbols ($P<0.001^{***}$, $<0.01^{**}$, $<0.05^{*}$, $<0.1^{\cdot}$).

**Figure 3.** Membrane fatty acid (FA) composition of oysters as a function of pH (total scale). (a) Principal component analysis of polar fatty acid classes for oysters (n = 15 pools of five oysters) exposed to 15 pH levels. Arrows represent fatty acids contributing to more than 5% of the first principal component (PC1). Individuals are colored according to the average pH. Contribution to membrane of the sum of fatty acids that are (b) positively or (c) negatively correlated to PC1 as a function of pH. Tipping points and their 95% confidence intervals are shown in grey. The significance levels of the slopes are presented using symbols ($P<0.001^{***}$, $<0.01^{**}$, $<0.05^{*}$, $<0.1^{\cdot}$).

**Figure 4.** Tipping points of oyster transcriptome. (a-c) Frequency distribution of tipping-point for piecewise linear relationships (left side). Linear and loglinear models (no tipping-point) are under “Lin” name. Genes are grouped into three clusters of genes that co-vary together. The grey line indicates the distribution frequency of all genes irrespective of clusters. Groups of genes which exhibit neighboring tipping points with distribution frequencies > 5% (shown as a dotted line), were grouped together. The
990 segments above the bars indicate the groups of genes on which GO analyses were
991 conducted. In each case, the gene that best represents the cluster according to module
992 membership, gene significance for pH and R² is presented as a function of pH as an
993 example (right side). Tipping points and their 95% confidence intervals are shown in
994 grey. The significance levels of the slopes are presented using stars (P <0.001 ***, p<0.01
995 **, p<0.05 *). Gene names are: LOC117690205: monocarboxylate transporter 12-like,
996 LOC105317113: 60S ribosomal protein L10a, LOC105331560: protocadherin Fat 4
997 **Figure 5.** Graphical summary of the reaction norm of juvenile oysters *Crassostrea gigas*
998 over a wide range of pH conditions. Abbreviations: GO, gene ontology term; ARA,
999 arachidonic acid, EPA, ecosapentaenoic acid, DHA, docosahexaenoic acid, NMI, non-
1000 methylene interrupted.
1001

1002 **Figures**

<figure><img src="image_2.png" /></figure>

1003
<figure><img src="image_3.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>G</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>H</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>I</figcaption></figure>

<figure><img src="image_11.png" /></figure>
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>
<figure><img src="image_15.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_17.png" /></figure>

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PHYSIOLOGICAL PARAMETERS

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

GLOBAL PHYSIOLOGICAL RESPONSE

<figure><img src="image_20.png" /></figure>
<table><caption>**Table 1** Gene ontology term enrichment and tipping point. Model characteristics, physiological function and gene ontology for each cluster. For each GO term, the P-value of Bonferroni test is displayed using symbols (P < 0.001 **, < 0.01 **, < 0.05 *, < 0.1 *). Abbreviations: TP: tipping point</caption><thead><tr><th>Cluster</th><th colspan="3">Models</th><th>Physiological function</th><th>GO</th><th>Gene Ontology</th><th>number of genes</th><th>p bonferroni</th></tr><tr><th></th><th>shape of the relationship</th><th>TP range</th><th>number of genes</th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td rowspan="10">1</td><td rowspan="5">↔</td><td rowspan="5">6.9-7.3</td><td rowspan="5">283</td><td>regulation of</td><td>......GO:1903506</td><td>regulation of nucleic acid-templated transcription</td><td>38</td><td>**</td></tr><tr><td>RNA-transcription</td><td>......GO:0006357</td><td>regulation of transcription by RNA polymerase II</td><td>30</td><td>***</td></tr><tr><td>cellular metabolism & macromolecule</td><td>......GO:2000112</td><td>regulation of cellular macromolecule</td><td>41</td><td>*</td></tr><tr><td>byosynthesis</td><td>......GO:0031325</td><td>biosynthetic process positive regulation of cellular metabolic process</td><td>39</td><td>*</td></tr><tr><td>cell-cell adhesion</td><td>......GO:0022408</td><td>negative regulation of cell-cell adhesion</td><td>8</td><td>□</td></tr><tr><td rowspan="3">↔</td><td rowspan="3">n.a.</td><td rowspan="3">96</td><td>GTP metabolism</td><td>......GO:0003924</td><td>GTPase activity</td><td>8</td><td>**</td></tr><tr><td>ribonucleotide metabolism</td><td>......GO:0005525</td><td>GTP binding guanyl ribonucleotide binding</td><td>9</td><td>*</td></tr><tr><td></td><td>......GO:0032561</td><td>purine ribonucleoside binding</td><td>9</td><td>□</td></tr><tr><td rowspan="10">2</td><td rowspan="5">↔</td><td rowspan="5">7.0-7.3</td><td rowspan="5">62</td><td>ribosome synthesis / ribosomal subunit assembly</td><td>......GO:0022625</td><td>ribosomal large subunit assembly</td><td>4</td><td>**</td></tr><tr><td>cytosolic large ribosomal subunit</td><td>......GO:0022627</td><td>cytosolic large ribosomal subunit</td><td>10</td><td>***</td></tr><tr><td>cytosolic small ribosomal subunit</td><td>......GO:0022618</td><td>cytosolic small ribosomal subunit</td><td>6</td><td>***</td></tr><tr><td>ribonucleoprotein complex assembly</td><td>......GO:1990932</td><td>ribonucleoprotein complex assembly</td><td>6</td><td>*</td></tr><tr><td>RNA-binding</td><td>......GO:0019843</td><td>5.8S rRNA binding</td><td>3</td><td>***</td></tr><tr><td rowspan="3">↔</td><td rowspan="3">n.a.</td><td rowspan="3">62</td><td>RNA-binding</td><td>......GO:0003729</td><td>rRNA binding mRNA binding</td><td>8</td><td>***</td></tr><tr><td>translation</td><td>......GO:0002181</td><td>cytoplasmic translation</td><td>6</td><td>*</td></tr><tr><td></td><td>......GO:0002181</td><td>cytoplasmic translation</td><td>8</td><td>***</td></tr></tbody></table>
<table><caption>**Table 2** Tipping points of genes related to calcification and production of the shell organic matrix. Model characteristics and physiological function are reported for each gene. The slope inclination is displayed using arrows only if it is significant according to Student t tests (P < 0.05). Abbreviations: TP: tipping point, CBP: calcium-binding proteins, ACCBP: amorphous calcium carbonate-binding proteins, SMP: shell matrix proteins.</caption><thead><tr><th rowspan="2">Physiological function</th><th rowspan="2"></th><th colspan="4">Model</th><th colspan="2">Gene</th></tr><tr><th>Model</th><th>TP</th><th>Slope before TP</th><th>Slope after TP</th><th>R<sup>2</sup></th><th>Cluster</th><th>Name</th></tr></thead><tbody><tr><td rowspan="10">Calcification</td><td>Ion transport</td><td>piecewise</td><td>6.6±0.1</td><td>κ</td><td>n.s.</td><td>0.33</td><td>1</td><td>sodium-dependent phosphate transport protein 2B</td></tr><tr><td></td><td></td><td>6.6±0.0</td><td>κ</td><td>n.s.</td><td>0.37</td><td>1</td><td>monocarboxylate transporter 5</td></tr><tr><td>CBP</td><td></td><td>6.6±0.0</td><td>κ</td><td>n.s.</td><td>0.20</td><td>1</td><td>fibrillin-2-like</td></tr><tr><td></td><td></td><td>6.6±0.0</td><td>κ</td><td>n.s.</td><td>0.48</td><td>3</td><td>EF-hand Ca-binding domain-containing protein 6</td></tr><tr><td>Ca<sup>2+</sup> signaling pathway</td><td></td><td>6.7±0.2</td><td>n.s.</td><td>κ</td><td>0.20</td><td>2</td><td>neurocalcin-delta</td></tr><tr><td>CBP</td><td></td><td>6.7±0.1</td><td>κ</td><td>n.s.</td><td>0.32</td><td>3</td><td>ankyrin repeat and EF-hand domain-containing protein 1</td></tr><tr><td>Ca<sup>2+</sup> signaling pathway</td><td></td><td>6.8±0.1</td><td>κ</td><td>n.s.</td><td>0.40</td><td>3</td><td>polycystin-2</td></tr><tr><td></td><td></td><td>6.9±0.3</td><td>n.s.</td><td>κ</td><td>0.09</td><td>1</td><td>neurocalcin-like protein</td></tr><tr><td>Ion transport</td><td></td><td>6.9±0.1</td><td>κ</td><td>n.s.</td><td>0.71</td><td>1</td><td>monocarboxylate transporter 12-like</td></tr><tr><td>CBP</td><td></td><td>6.9±0.1</td><td>κ</td><td>n.s.</td><td>0.17</td><td>3</td><td>D-galactoside-specific lectin</td></tr><tr><td></td><td></td><td>6.9±0.1</td><td>κ</td><td>n.s.</td><td>0.31</td><td>3</td><td>sarcoplasmic calcium-binding protein</td></tr><tr><td></td><td></td><td>6.9±0.1</td><td>κ</td><td>n.s.</td><td>0.42</td><td>3</td><td>calmodulin-like protein 12</td></tr><tr><td>Ion transport</td><td></td><td>6.9±0.1</td><td>κ</td><td>κ</td><td>0.45</td><td>3</td><td>sodium/calcium exchanger 3</td></tr><tr><td>ACCBP</td><td></td><td>6.9±0.1</td><td>κ</td><td>n.s.</td><td>0.49</td><td>3</td><td>neuronal acetylcholine receptor subunit alpha-6</td></tr><tr><td></td><td></td><td>6.9±0.1</td><td>κ</td><td>n.s.</td><td>0.36</td><td>3</td><td>acetylcholine receptor subunit beta</td></tr><tr><td></td><td></td><td>6.9±0.1</td><td>κ</td><td>n.s.</td><td>0.31</td><td>3</td><td>neuronal acetylcholine receptor subunit alpha-3</td></tr><tr><td>Ca<sup>2+</sup> signaling pathway</td><td></td><td>7.0±0.2</td><td>κ</td><td>n.s.</td><td>0.17</td><td>1</td><td>metabotropic glutamate receptor 5</td></tr><tr><td>Signaling pathway</td><td></td><td>7.0±0.2</td><td>κ</td><td>n.s.</td><td>0.30</td><td>1</td><td>toll-like receptor 4</td></tr><tr><td>Ion transport</td><td></td><td>7.0±0.2</td><td>n.s.</td><td>κ</td><td>0.19</td><td>1</td><td>G protein-activated inward rectifier potassium channel 2</td></tr><tr><td></td><td></td><td>7.0±0.4</td><td>n.s.</td><td>κ</td><td>0.15</td><td>1</td><td>monocarboxylate transporter 9</td></tr><tr><td></td><td></td><td>7.0±0.1</td><td>κ</td><td>n.s.</td><td>0.29</td><td>3</td><td>organic cation transporter protein</td></tr><tr><td></td><td></td><td>7.0±0.1</td><td>κ</td><td>n.s.</td><td>0.32</td><td>3</td><td>small conductance calcium-activated potassium channel protein 2</td></tr><tr><td></td><td></td><td>7.0±0.1</td><td>κ</td><td>n.s.</td><td>0.24</td><td>3</td><td>sodium/glucose cotransporter 4</td></tr></tbody></table>
<table><tbody><tr><td>Ca<sup>2+</sup> signaling pathway</td><td>7.0±0.1</td><td>κ</td><td>n.s.</td><td>0.65</td><td>3</td><td>protocadherin FAT 4</td></tr><tr><td>ACCBP</td><td>7.0±0.1</td><td>κ</td><td>n.s.</td><td>0.43</td><td>3</td><td>acetylcholine receptor subunit beta-type unc-29</td></tr><tr><td>CBP</td><td>7.1±0.3</td><td>κ</td><td>n.s.</td><td>0.28</td><td>1</td><td>neurogenic locus notch homolog protein 1</td></tr><tr><td>Ca<sup>2+</sup> signaling pathway</td><td>7.1±0.2</td><td>κ</td><td>n.s.</td><td>0.26</td><td>1</td><td>protocadherin gamma-A4</td></tr><tr><td>CBP</td><td>7.1±0.1</td><td>κ</td><td>κ</td><td>0.42</td><td>3</td><td>delta-like protein 4 / delta-like protein C</td></tr><tr><td>Ion transport</td><td>7.1±0.1</td><td>κ</td><td>n.s.</td><td>0.28</td><td>3</td><td>calcium-activated potassium channel slowpoke</td></tr><tr><td>Ca<sup>2+</sup> signaling pathway</td><td>7.2±0.1</td><td>κ</td><td>n.s.</td><td>0.18</td><td>1</td><td>protocadherin-11 X-linked</td></tr><tr><td>Ion transport</td><td>7.2±0.2</td><td>κ</td><td>n.s.</td><td>0.14</td><td>3</td><td>cadherin-87A</td></tr><tr><td>CBP</td><td>7.3±0.2</td><td>κ</td><td>n.s.</td><td>0.20</td><td>1</td><td>monocarboxylate transporter 12-like</td></tr><tr><td>Ion transport</td><td>7.4±0.1</td><td>κ</td><td>n.s.</td><td>0.29</td><td>1</td><td>calmodulin-A</td></tr><tr><td>Ca<sup>2+</sup> signaling pathway</td><td>7.5±0.1</td><td>κ</td><td>n.s.</td><td>0.21</td><td>1</td><td>protocadherin gamma-B1</td></tr><tr><td>Ion transport</td><td>7.6±0.1</td><td>κ</td><td>κ</td><td>0.33</td><td>1</td><td>potassium channel GORK</td></tr><tr><td>CBP</td><td>loglinear</td><td>n.a.</td><td>κ</td><td>n.a.</td><td>0.42</td><td>monocarboxylate transporter 9-like</td></tr><tr><td>CBP</td><td>n.a.</td><td>κ</td><td>n.a.</td><td>0.25</td><td>3</td><td>fibrillin-1</td></tr><tr><td rowspan="10">Shell organic matrix &amp; periostracum</td><td>SMP</td><td>piecewise</td><td>6.6±0.0</td><td>κ</td><td>κ</td><td>0.66</td><td>asparagine-rich protein</td></tr><tr><td>SMP, periostracum formation</td><td>7.0±0.1</td><td>κ</td><td>n.s.</td><td>0.21</td><td>1</td><td>perducin-like protein</td></tr><tr><td>SMP, periostracum formation</td><td>7.0±0.1</td><td>κ</td><td>n.s.</td><td>0.37</td><td>3</td><td>putative tyrosinase-like protein tyr-1</td></tr><tr><td>SMP, periostracum formation</td><td>7.1±0.2</td><td>κ</td><td>n.s.</td><td>0.20</td><td>1</td><td>tyrosinase-like protein 2</td></tr><tr><td>SMP, periostracum formation</td><td>7.4±0.2</td><td>κ</td><td>n.s.</td><td>0.24</td><td>1</td><td>tyrosinase-like protein 2</td></tr><tr><td>SMP, periostracum formation</td><td>7.4±0.2</td><td>κ</td><td>n.s.</td><td>0.30</td><td>1</td><td>tyrosinase-like protein 2</td></tr><tr><td>SMP, carbonic anhydrase</td><td>7.5±0.2</td><td>κ</td><td>n.s.</td><td>0.19</td><td>1</td><td>nacrein-like protein F2</td></tr><tr><td>SMP, carbonic anhydrase</td><td>7.5±0.2</td><td>κ</td><td>n.s.</td><td>0.25</td><td>1</td><td>nacrein-like protein / carbonic anhydrase 6</td></tr><tr><td>SMP</td><td>loglinear</td><td>n.a.</td><td>κ</td><td>n.a.</td><td>0.23</td><td>leucine-rich repeat and fibronectin type-III domain-containing protein 5-like</td></tr><tr><td>SMP, periostracum formation</td><td>n.a.</td><td>κ</td><td>n.a.</td><td>0.43</td><td>1</td><td>tyrosinase-like protein 1</td></tr><tr><td>SMP</td><td>n.a.</td><td>κ</td><td>n.a.</td><td>0.28</td><td>1</td><td>circumsporozoite protein-like</td></tr></tbody></table>
1035 Supplementary information

1036 Revisiting tolerance to ocean acidification: insights from a

1037 new framework combining physiological and molecular

1038 tipping points of Pacific oyster

1039 Mathieu Lutier¹,*, Carole Di Poi¹, Frédéric Gazeau², Alexis Appolis¹, Jérémy Le Luyer³,

1040 Fabrice Pernet¹,*

1041

1042 ¹ LEMAR UMR6539, CNRS/UBO/IRD/Ifremer, ZI pointe du diable, CS 10070, F-29280

1043 Plouzané, France.

1044 ²LOV UMR7093, Sorbonne Université, CNRS, Laboratoire d'Océanographie de

1045 Villefranche, Villefranche-sur-Mer, France.

1046 ³EIO UMR24, UPF/IRD/ILM/Ifremer, Labex CORAIL, Unité RMPF, Centre

1047 Océanologique du Pacifique, Vairao – BP 49 Vairao, Tahiti, Polynésie française.

1048

1049 Correspondence and requests for materials should be addressed to M.L. (email:

1050 mathieu.lutier@gmail.com) or to F.P. (email: fpernet@ifremer.fr)

1051
1052

1053 **Supplementary Note 1.**

1054 Temperature, salinity and O₂ saturation levels were stable across the experimental period and similar among pH conditions (Supplementary Table 1). Seawater was undersaturated with respect to calcite and aragonite when pH was below 7.3 and 7.6, respectively (Supplementary Table 1). However, parameters of carbonate chemistry were sometimes similar for different pH conditions. This reflects the difference in the frequency of measurements between the parameters (twice daily for pH and weekly for TA). Also, TA decreased slightly but linearly with increasing pH (R² = 0.82, slope = -0.08, P < 0.001) suggesting that seawater renewal was likely not sufficient to counteract both the uptake and the release of carbonate ions from net calcification at high pH and net dissolution at low pH, respectively (Supplementary Figure 2).

1064
<figure><img src="image_21.png" /><figcaption>**Supplementary Figure 1.** Experimental design for the determination of oyster's reaction norms to pH. Each batch of oysters was exposed 23 d to one nominal pH condition after 7 d of gradual pH decrease at a rate of 0.2 unit d⁻¹. There were 15 nominal pH conditions. The aspect of 5 oysters is shown for each pH conditions at the end of the exposure period (23rd day) in the lower panel.</figcaption></figure>
<figure><img src="image_22.png" /><figcaption>**Supplementary Figure 2.** Total alkalinity as a function of pH (total scale). Data are means ± SD values recorded during the 23 d of exposure. The significance level of the slope is presented using stars (P <0.001 ***, <0.01 **, <0.05 *, <0.1 *).</figcaption></figure>
<figure><img src="image_23.png" /></figure>

1071

1072

1073

1074

1075

**Supplementary Figure 3.** Total body weight of oysters as a function of pH (pH on the total scale). Data are means ± SD. Tipping-point and its confidence intervals is shown in grey. The significance levels of the slopes are presented using stars (P < 0.001 ***, < 0.01 **, < 0.05 *, < 0.1 ).
<figure><img src="image_24.png" /><figcaption>**Supplementary Figure 4.** Energy reserves of oysters as a function of pH (total scale).</figcaption></figure>

(A) total carbohydrates and (B) triacylglycerol/sterol ratio (TAG-ST). Relationships were not significant (P > 0.05).
1080 **Supplementary Table 1** Seawater parameters during the incubations. pH on the total scale (pH<sub>T</sub>), calcite saturation state (Ω<sub>CA</sub>), total alkalinity (TA), oxygen saturation (O<sub>2</sub>), salinity (S) and temperature (T). *Average* represents the mean values between the onset and the end of the incubations. Δ represents the difference between the end and the onset of the incubations. Values are mean ± SD.

<table><thead><tr><th>Nominal pH<sub>T</sub></th><th>Average pH<sub>T</sub></th><th>Δ pH<sub>T</sub></th><th>Average Ω<sub>CA</sub></th><th>Δ Ω<sub>CA</sub></th><th>Δ TA (μmol.kg<sup>-1</sup>)</th><th>Δ O<sub>2</sub> (%sat)</th><th>Δ NH<sub>4</sub><sup>+</sup> (μmol.L<sup>-1</sup>)</th><th>Average S</th><th>Average T (°C)</th></tr></thead><tbody><tr><td>Blank</td><td>8.0±0.0</td><td>0.0</td><td>4.4±0.0</td><td>0.0</td><td>-3</td><td>2</td><td>0.0</td><td>35.7±0.0</td><td>21.7±0.1</td></tr><tr><td>Blank</td><td>8.0±0.0</td><td>0.0</td><td>4.3±0.0</td><td>0.0</td><td>1</td><td>1.7</td><td>-0.1</td><td>35.7±0.0</td><td>21.6±0.1</td></tr><tr><td>Blank</td><td>8.0±0.0</td><td>0.1</td><td>4.4±0.0</td><td>0.0</td><td>0</td><td>1.9</td><td>0.3</td><td>35.7±0.0</td><td>21.7±0.1</td></tr><tr><td>7.8</td><td>7.9±0.1</td><td>0.0</td><td>3.1±0.5</td><td>0.7</td><td>80</td><td>3</td><td>-1.1</td><td>35.7±0.0</td><td>21.7±0.1</td></tr><tr><td>7.7</td><td>7.6±0.1</td><td>0.1</td><td>1.7±0.3</td><td>0.4</td><td>65</td><td>6.5</td><td>-1.2</td><td>35.7±0.0</td><td>21.6±0.1</td></tr><tr><td>7.6</td><td>7.5±0.1</td><td>0.0</td><td>1.4±0.2</td><td>0.3</td><td>60</td><td>5</td><td>-1.0</td><td>35.7±0.0</td><td>21.7±0.1</td></tr><tr><td>7.5</td><td>7.4±0.1</td><td>-0.1</td><td>1.1±0.2</td><td>0.3</td><td>72</td><td>7.9</td><td>1.4</td><td>35.7±0.0</td><td>21.7±0.1</td></tr><tr><td>7.4</td><td>7.3±0.1</td><td>0.1</td><td>0.9±0.2</td><td>0.2</td><td>72</td><td>7.3</td><td></td><td>35.7±0.0</td><td>21.5±0.1</td></tr><tr><td>7.3</td><td>7.3±0.0</td><td>-0.2</td><td>1.0±0.0</td><td>0.0</td><td>61</td><td>6.1</td><td>-1.0</td><td>35.7±0.0</td><td>21.7±0.1</td></tr><tr><td>7.2</td><td>7.1±0.0</td><td>0.0</td><td>0.6±0.0</td><td>0.0</td><td>38</td><td>6.7</td><td>-0.8</td><td>35.7±0.0</td><td>21.7±0.1</td></tr><tr><td>7.1</td><td>7.1±0.0</td><td>0.0</td><td>0.6±0.0</td><td>0.0</td><td>44</td><td>5.5</td><td>-1.1</td><td>35.7±0.0</td><td>21.6±0.1</td></tr><tr><td>7.0</td><td>7.0±0.0</td><td>0.0</td><td>0.5±0.0</td><td>0.0</td><td>36</td><td>6.1</td><td>-2.0</td><td>35.7±0.0</td><td>21.7±0.1</td></tr><tr><td>6.9</td><td>6.9±0.0</td><td>0.1</td><td>0.4±0.0</td><td>0.0</td><td>19</td><td>5.5</td><td>-0.9</td><td>35.7±0.0</td><td>21.7±0.1</td></tr><tr><td>6.8</td><td>6.9±0.1</td><td>-0.1</td><td>0.4±0.1</td><td>-0.1</td><td>2</td><td>4.9</td><td>-1.0</td><td>35.7±0.0</td><td>21.6±0.1</td></tr><tr><td>6.7</td><td>6.8±0.1</td><td>-0.1</td><td>0.3±0.0</td><td>-0.1</td><td>13</td><td>3.1</td><td>-1.1</td><td>35.7±0.0</td><td>21.7±0.1</td></tr><tr><td>6.6</td><td>6.8±0.1</td><td>-0.2</td><td>0.3±0.1</td><td>-0.2</td><td>0</td><td>2.7</td><td>-0.6</td><td>35.7±0.0</td><td>21.6±0.1</td></tr><tr><td>6.5</td><td>6.7±0.1</td><td>0.1</td><td>0.3±0.1</td><td>-0.1</td><td>-12</td><td>2.4</td><td>-0.6</td><td>35.7±0.0</td><td>21.7±0.1</td></tr><tr><td>6.4</td><td>6.6±0.1</td><td>0.0</td><td>0.2±0.0</td><td>0.0</td><td>-18</td><td>1.5</td><td>-0.6</td><td>35.7±0.0</td><td>21.6±0.1</td></tr></tbody></table>

1085

1086
1087 Supplementary Table 2 Seawater parameters measured during the experiment in each
1088 experimental tank, including three blanks without oysters. pH on the total scale (pH<sub>T</sub>),
1089 total alkalinity (TA), oxygen saturation (O<sub>2</sub>), salinity (S), temperature (T), calcite
1090 saturation state (Ω<sub>CA</sub>), aragonite saturation state (Ω<sub>AR</sub>), CO<sub>2</sub> partial pressure (pCO<sub>2</sub>) and
1091 dissolved inorganic carbon (DIC) concentrations are shown (mean ± SD). pH, O<sub>2</sub>, S and
1092 T were measured twice daily and TA was measured once a week.

<table><thead><tr><th colspan="2">Nominal</th><th colspan="4">Measured</th><th colspan="4">Calculated</th></tr><tr><th>pH<sub>T</sub></th><th>pH<sub>T</sub></th><th>TA<br>(µmol.kg<sup>-1</sup>)</th><th>O<sub>2</sub><br>(%sat)</th><th>S</th><th>T<br>(°C)</th><th>Ω<sub>CA</sub></th><th>Ω<sub>AR</sub></th><th>pCO<sub>2</sub><br>(µatm)</th><th>DIC<br>(µmol.kg<sup>-1</sup>)</th></tr></thead><tbody><tr><td>Blank</td><td>8.0±0.1</td><td>2331±7</td><td>101.6±0.5</td><td>35.7±0.1</td><td>21.8±0.1</td><td>3.9±0.4</td><td>2.5±0.3</td><td>552±82</td><td>2111±31</td></tr><tr><td>Blank</td><td>8.0±0.1</td><td>2328±3</td><td>101.5±0.4</td><td>35.7±0.1</td><td>21.7±0.1</td><td>3.9±0.4</td><td>2.5±0.3</td><td>551±82</td><td>2108±29</td></tr><tr><td>Blank</td><td>7.9±0.2</td><td>2331±6</td><td>101.4±0.4</td><td>35.7±0.1</td><td>21.7±0.1</td><td>3.9±0.4</td><td>2.5±0.3</td><td>552±82</td><td>2111±29</td></tr><tr><td>7.8</td><td>7.8±0.1</td><td>2251±8</td><td>97.6±1.8</td><td>35.7±0.1</td><td>21.8±0.1</td><td>3.1±0.3</td><td>2.0±0.2</td><td>695±103</td><td>2082±33</td></tr><tr><td>7.7</td><td>7.7±0.1</td><td>2255±2</td><td>97.6±1.6</td><td>35.7±0.1</td><td>21.8±0.1</td><td>2.2±0.3</td><td>1.4±0.2</td><td>1070±161</td><td>2152±19</td></tr><tr><td>7.6</td><td>7.6±0.1</td><td>2262±2</td><td>96.7±2.6</td><td>35.7±0.1</td><td>21.7±0.1</td><td>1.5±0.0</td><td>1.0±0.0</td><td>1617±1</td><td>2217±2</td></tr><tr><td>7.5</td><td>7.5±0.1</td><td>2289±54</td><td>96.5±2.2</td><td>35.7±0.1</td><td>21.7±0.1</td><td>1.3±0.0</td><td>0.8±0.0</td><td>2091±49</td><td>2276±55</td></tr><tr><td>7.4</td><td>7.4±0.1</td><td>2222±54</td><td>97.1±2.1</td><td>35.7±0.1</td><td>21.7±0.1</td><td>1.1±0.1</td><td>0.7±0.1</td><td>2215±352</td><td>2219±66</td></tr><tr><td>7.3</td><td>7.3±0.0</td><td>2273±3</td><td>96.7±2.3</td><td>35.7±0.1</td><td>21.7±0.1</td><td>1.0±0.0</td><td>0.7±0.0</td><td>2644±4</td><td>2292±3</td></tr><tr><td>7.2</td><td>7.2±0.1</td><td>2279±3</td><td>96.8±2.2</td><td>35.7±0.1</td><td>21.8±0.1</td><td>0.6±0.0</td><td>0.4±0.0</td><td>4276±7</td><td>2371±3</td></tr><tr><td>7.1</td><td>7.1±0.1</td><td>2293±9</td><td>96.8±1.7</td><td>35.7±0.1</td><td>21.7±0.1</td><td>0.6±0.1</td><td>0.4±0.0</td><td>5067±671</td><td>2414±30</td></tr><tr><td>7.0</td><td>7.0±0.0</td><td>2292±3</td><td>97.2±1.5</td><td>35.7±0.1</td><td>21.7±0.1</td><td>0.6±0.1</td><td>0.4±0.0</td><td>5062±664</td><td>2413±27</td></tr><tr><td>6.9</td><td>6.9±0.1</td><td>2308±2</td><td>97.7±1.6</td><td>35.7±0.1</td><td>21.7±0.1</td><td>0.4±0.0</td><td>0.2±0.0</td><td>8160±1063</td><td>2536±38</td></tr><tr><td>6.8</td><td>6.8±0.1</td><td>2311±7</td><td>97.6±1.9</td><td>35.7±0.1</td><td>21.7±0.2</td><td>0.3±0.0</td><td>0.2±0.0</td><td>8777±26</td><td>2560±8</td></tr><tr><td>6.7</td><td>6.7±0.1</td><td>2329±12</td><td>98.2±1.3</td><td>35.7±0.1</td><td>21.7±0.1</td><td>0.3±0.0</td><td>0.2±0.0</td><td>11172±60</td><td>2656±13</td></tr><tr><td>6.6</td><td>6.6±0.1</td><td>2343±16</td><td>98.9±1.0</td><td>35.7±0.1</td><td>21.7±0.2</td><td>0.2±0.1</td><td>0.1±0.0</td><td>15691±3912</td><td>2813±135</td></tr><tr><td>6.5</td><td>6.5±0.2</td><td>2353±20</td><td>99.2±0.8</td><td>35.7±0.1</td><td>21.7±0.2</td><td>0.2±0.0</td><td>0.1±0.0</td><td>19865±4915</td><td>2953±164</td></tr><tr><td>6.4</td><td>6.5±0.3</td><td>2353±18</td><td>99.2±0.9</td><td>35.7±0.1</td><td>21.7±0.2</td><td>0.1±0.0</td><td>0.1±0.0</td><td>23079±5471</td><td>3054±183</td></tr></tbody></table>

1093

1094
<table><caption>1095 Supplementary Table 3 Relative contribution of fatty acids to membrane. Their
1096 contribution (%) and Pearson's correlation to the first principal component (PC1) of the
1097 PCA are shown.</caption><thead><tr><th rowspan="2">Average pH<sub>r</sub></th><th colspan="8">Fatty acid (% total fatty acids)</th></tr><tr><th>14:0</th><th>16:0</th><th>16:1n-7</th><th>18:0<br>dma</th><th>18:0</th><th>18:1n-9</th><th>18:1n-7</th><th>18:2n-6</th><th>18:3n-3</th><th>18:4n-3</th></tr></thead><tbody><tr><td>7.8</td><td>2.7</td><td>14.1</td><td>1.7</td><td>9.7</td><td>5.6</td><td>1.8</td><td>5.3</td><td>2.2</td><td>1.3</td><td>1.7</td></tr><tr><td>7.7</td><td>2.7</td><td>12.5</td><td>2.1</td><td>9.2</td><td>3.6</td><td>1.9</td><td>6.0</td><td>2.3</td><td>1.4</td><td>1.8</td></tr><tr><td>7.6</td><td>2.5</td><td>13.7</td><td>1.6</td><td>9.8</td><td>4.0</td><td>1.9</td><td>5.4</td><td>2.4</td><td>1.3</td><td>1.6</td></tr><tr><td>7.5</td><td>2.0</td><td>12.5</td><td>1.6</td><td>10.8</td><td>4.4</td><td>1.6</td><td>5.6</td><td>1.8</td><td>1.2</td><td>1.4</td></tr><tr><td>7.4</td><td>2.2</td><td>13.0</td><td>1.7</td><td>10.8</td><td>4.7</td><td>1.6</td><td>5.5</td><td>1.9</td><td>1.3</td><td>1.5</td></tr><tr><td>7.3</td><td>2.2</td><td>13.7</td><td>1.6</td><td>9.4</td><td>4.3</td><td>1.8</td><td>5.1</td><td>2.2</td><td>1.3</td><td>1.6</td></tr><tr><td>7.2</td><td>2.5</td><td>12.8</td><td>1.8</td><td>9.9</td><td>3.9</td><td>1.7</td><td>5.5</td><td>2.2</td><td>1.4</td><td>1.9</td></tr><tr><td>7.1</td><td>2.3</td><td>12.0</td><td>1.8</td><td>11.2</td><td>4.6</td><td>1.7</td><td>5.2</td><td>1.9</td><td>1.4</td><td>1.7</td></tr><tr><td>7.0</td><td>2.4</td><td>12.7</td><td>1.8</td><td>10.7</td><td>4.3</td><td>1.6</td><td>4.4</td><td>1.9</td><td>1.2</td><td>1.6</td></tr><tr><td>6.9</td><td>2.0</td><td>11.6</td><td>1.7</td><td>11.3</td><td>4.8</td><td>1.8</td><td>6.0</td><td>2.0</td><td>1.3</td><td>1.6</td></tr><tr><td>6.8</td><td>2.0</td><td>12.0</td><td>1.7</td><td>9.8</td><td>4.7</td><td>1.8</td><td>5.9</td><td>2.0</td><td>1.1</td><td>1.4</td></tr><tr><td>6.7</td><td>1.5</td><td>11.9</td><td>1.7</td><td>10.6</td><td>6.3</td><td>1.7</td><td>6.2</td><td>1.6</td><td>0.8</td><td>1.0</td></tr><tr><td>6.6</td><td>2.0</td><td>11.8</td><td>1.9</td><td>9.3</td><td>4.6</td><td>2.0</td><td>6.7</td><td>2.1</td><td>1.0</td><td>1.4</td></tr><tr><td>6.5</td><td>1.8</td><td>10.8</td><td>2.3</td><td>10.0</td><td>2.4</td><td>1.7</td><td>6.7</td><td>1.4</td><td>0.7</td><td>1.0</td></tr><tr><td>6.5</td><td>1.5</td><td>11.3</td><td>2.0</td><td>9.0</td><td>5.7</td><td>1.7</td><td>6.5</td><td>1.3</td><td>0.7</td><td>0.7</td></tr><tr><td>Contribution to<br>PC1 (%)</td><td>1.9</td><td>13.6</td><td>0.5</td><td>0.7</td><td>0.0</td><td>0.0</td><td>4.6</td><td>1.7</td><td>1.1</td><td>1.8</td></tr><tr><td>Correlation to<br>PC1 (Pearson)</td><td>0.8</td><td>0.9</td><td>-0.8</td><td>0.2</td><td>0.0</td><td>0.0</td><td>-0.7</td><td>0.8</td><td>0.9</td><td>0.9</td></tr></tbody></table>

<table><thead><tr><th rowspan="2">Average pH<sub>r</sub></th><th colspan="8">Fatty acid (% total fatty acids)</th></tr><tr><th>20:1<br>dma</th><th>20:0</th><th>20:1n-<br>11</th><th>20:1n-7</th><th>20:4n-6</th><th>20:5n-3</th><th>22:2<br>NMI<sub>ij</sub></th><th>22:5n-6</th><th>22:6n-3</th></tr></thead><tbody><tr><td>7.8</td><td>1.9</td><td>1.1</td><td>1.7</td><td>3.8</td><td>4.5</td><td>8.7</td><td>5.0</td><td>3.2</td><td>15.8</td></tr><tr><td>7.7</td><td>1.9</td><td>1.1</td><td>1.9</td><td>3.7</td><td>4.9</td><td>9.9</td><td>5.0</td><td>3.0</td><td>15.2</td></tr><tr><td>7.6</td><td>1.9</td><td>1.2</td><td>1.7</td><td>4.1</td><td>4.4</td><td>9.5</td><td>4.5</td><td>3.0</td><td>16.3</td></tr><tr><td>7.5</td><td>2.1</td><td>1.1</td><td>2.0</td><td>4.0</td><td>4.5</td><td>9.6</td><td>5.4</td><td>3.1</td><td>16.1</td></tr><tr><td>7.4</td><td>2.1</td><td>1.1</td><td>2.0</td><td>4.1</td><td>4.4</td><td>9.2</td><td>5.4</td><td>3.2</td><td>16.6</td></tr><tr><td>7.3</td><td>1.8</td><td>1.2</td><td>1.8</td><td>3.9</td><td>4.7</td><td>10.1</td><td>4.7</td><td>2.9</td><td>16.0</td></tr><tr><td>7.2</td><td>1.9</td><td>1.1</td><td>2.0</td><td>4.1</td><td>4.5</td><td>9.6</td><td>5.1</td><td>3.0</td><td>16.3</td></tr><tr><td>7.1</td><td>2.1</td><td>1.2</td><td>2.2</td><td>4.2</td><td>4.6</td><td>9.6</td><td>6.0</td><td>2.8</td><td>14.0</td></tr><tr><td>7.0</td><td>2.1</td><td>1.1</td><td>2.1</td><td>4.3</td><td>4.9</td><td>9.9</td><td>5.7</td><td>3.0</td><td>15.7</td></tr><tr><td>6.9</td><td>1.9</td><td>1.1</td><td>2.1</td><td>4.0</td><td>4.8</td><td>9.7</td><td>5.4</td><td>3.1</td><td>15.5</td></tr><tr><td>6.8</td><td>2.0</td><td>1.2</td><td>2.0</td><td>4.4</td><td>5.1</td><td>9.9</td><td>5.7</td><td>3.0</td><td>14.7</td></tr><tr><td>6.7</td><td>2.3</td><td>1.2</td><td>2.1</td><td>4.5</td><td>5.6</td><td>10.3</td><td>5.9</td><td>2.5</td><td>13.7</td></tr><tr><td>6.6</td><td>1.9</td><td>1.4</td><td>1.9</td><td>4.3</td><td>5.4</td><td>9.7</td><td>5.9</td><td>2.7</td><td>14.3</td></tr><tr><td>6.5</td><td>2.8</td><td>1.2</td><td>2.2</td><td>4.9</td><td>6.5</td><td>11.1</td><td>6.6</td><td>2.0</td><td>12.5</td></tr><tr><td>6.5</td><td>2.5</td><td>1.4</td><td>2.1</td><td>5.4</td><td>6.9</td><td>11.1</td><td>6.4</td><td>2.0</td><td>11.8</td></tr><tr><td>Contribution to<br>PC1 (%)</td><td>1.2</td><td>0.1</td><td>0.2</td><td>3.2</td><td>11.8</td><td>6.1</td><td>5.8</td><td>2.8</td><td>43.0</td></tr><tr><td>Correlation to<br>PC1 (Pearson)</td><td>-0.8</td><td>-0.7</td><td>-0.6</td><td>-0.9</td><td>-1.0</td><td>-0.9</td><td>-0.9</td><td>0.9</td><td>1.0</td></tr></tbody></table>
<table><caption>**Supplementary Table 4** Number of genes modelled against pH. Each step of filtering and the corresponding number of selected genes are specified.</caption><thead><tr><th>WGCNA cluster of gene</th><th>correlated with pH</th><th>modelled &amp; slope ≠ 0</th><th>model</th><th></th></tr></thead><tbody><tr><td rowspan="2">1</td><td rowspan="2">3380</td><td rowspan="2">1691</td><td>piecewise</td><td>567</td></tr><tr><td>linear</td><td>97</td></tr><tr><td rowspan="2">2</td><td rowspan="2">723</td><td rowspan="2">343</td><td>piecewise</td><td>130</td></tr><tr><td>linear</td><td>18</td></tr><tr><td rowspan="2">3</td><td rowspan="2">2073</td><td rowspan="2">623</td><td>piecewise</td><td>221</td></tr><tr><td>linear</td><td>21</td></tr></tbody></table>

1099

1100

1101

1102

1103