Microbiomes of an oyster are shaped by metabolism and environment

More detailed methods can be found in the supplementary material. Data from this experiment on the characterisation of the microbial community and its response to climate change has been previously published in Scanes et al.¹², therefore, the present study focussed on the interaction of metabolic processes with the microbiome. We examined the links between climate change, metabolism, genotype and microbiome of the Sydney rock oyster, Saccostrea glomerata²⁰. Nine oyster aquacultural breeding lineages (labelled as genotype-lines A–I) of S. glomerata, which are known to differ in their resilience to climate change¹² were exposed to ambient and elevated temperature and PCO₂ treatments. All seawater used in acclimation and experimental exposure was collected from Little Beach, Port Stephens (152°9′30.00″E, 32°42′43.03″S), filtered through canister filters to a nominal 5 µm, and stored onsite in 38,000 L polyethylene tanks as a stock of filtered seawater.

Approximately 72 individual S. glomerata, from each of the nine families (A-I) were collected from intertidal leases in Cromarty Bay, Port Stephens (152° 4′0.69″E, 32°43′19.69″S). Oysters were held on private leases so a collection permit was not required. Oysters were collected in September 2019 for experiments, meaning all oysters were 22 months old when experiments began. Oysters were placed into a 2000 L fibreglass tank and maintained at 24 °C, a salinity of 35 ppt and ambient PCO₂ (pH 8.18) for two weeks to acclimate to laboratory conditions. Following acclimation, oysters from each genotype-line were divided among twelve 750 L polyethylene tanks filled with 400 L of filtered seawater (5 µm) at a density of 54 oysters per tank, with each genotype-line represented by six replicate individuals. Treatments consisted of orthogonal combinations of two PCO₂ concentrations (ambient [400 µatm]; elevated [1000 µatm]) and two temperature treatments (24 and 28 °C). Each combination was replicated across three tanks. Treatments were selected to represent temperatures and PCO₂ concentrations predicted for 2080–2100 by the IPCC²⁷ and reflect measured changes in estuary temperatures reported from south eastern Australia²⁰.

Once oysters were transferred to experimental tanks, the PCO₂ level and temperature were steadily increased in elevated exposure tanks over one week until the experimental treatment level was reached. The elevated CO₂ level was maintained using a pH negative feedback system (Aqua Medic, Aqacenta Pty Ltd, Kingsgrove, NSW, Australia; accuracy ± 0.01 pH units) bubbling food grade CO₂ (BOC Australia) through a mixing chamber and into each tank, previously described in¹⁸. These PCO₂ levels corresponded to a mean ambient pH_NBS of (8.18 ± 0.01) and at elevated CO₂ levels a mean pH_NBS of (7.84 ± 0.01). Temperature was increased and then maintained using 1000 W aquarium heaters in each tank. Oysters were then exposed to their respective treatments for a further four weeks. Oysters were checked daily for mortality; no dead oysters were found in any tanks during the four-week exposure period.

Haemolymph sampling for DNA extraction

Following exposure to experimental conditions, haemolymph was taken from two replicate oysters, from each genotype-line, from each tank for microbial analysis following the methods previously described in Scanes et al.,¹². This amounted to six individuals from each genotype-line, in each treatment. Each oyster was opened using an autoclave sterilised shucking knife, ensuring that the pericardial cavity was not ruptured. Excess fluid was tipped off the tissue surface and 200–300 µL of haemolymph was extracted from the pericardial cavity using a new sterile 1 mL needled syringe (Terumo Co.). Samples from two oysters were transferred to two new pre-labelled DNA/RNA free 1 mL tubes (Eppendorf Co.) and immediately frozen at − 80 °C where they were stored until DNA extraction.

We used 16 s rRNA amplicon sequencing to characterise the bacterial microbiome of S. glomerata haemolymph following the methods previously described in Scanes et al.¹². DNA was extracted from 216 oyster haemolymph samples (9 genotype-lines × 4 treatments × 3 replicate tanks × 2 replicate oysters per tank) using the Qiagen DNeasy Blood and Tissue Kit (Qiagen Australia, Chadstone, VIC), according to the manufacturer’s instructions. The bacterial microbiome of the oyster haemolymph was characterised with 16S rRNA amplicon sequencing, using the 341F (CCTACGGGNGGCWGCAG) and 805R (GACTACHVGGGTATCTAATCC) primer pair²⁸ targeting the V3-V4 variable regions of the 16S rRNA gene with the following cycling conditions: 95 °C for 3 min, 25 cycles of 95 °C for 30 s, 55 °C for 30 s and 72 °C for 30 s, and a final extension at 72 °C for 5 min. Amplicons were sequenced on the Illumina Miseq platform (2 × 300 bp) following the manufacturer’s guidelines at the Ramaciotti Centre for Genomics, University of New South Wales. Raw data files in FASTQ format were deposited in NCBI Sequence Read Archive (SRA) under Bioproject number PRJNA663356.

Sequence analysis

Raw demultiplexed data was processed using the Quantitative Insights into Microbial Ecology (QIIME 2 version 2019.1.0) pipeline. Briefly, paired-end sequences were imported (qiime tools import), trimmed and denoised using DADA2 (version 2019.1.0). Sequences were identified at the single nucleotide threshold (Amplicon Sequence Variants; ASV) and taxonomy was assigned using the classify-sklearn QIIME 2 feature classifier against the Silva v138 database²⁹. Sequences identified as chloroplasts or mitochondria were also removed. Cleaned data were then rarefied at 6,500 counts per sample.

Physiological analysis

We measured physiological variables relating to oyster haemolymph metabolic function. These were: extracellular pH (pH_e), extracellular CO₂ concentrations (PCO_2e) and the whole oyster metabolic rate (MR) measured as a standardised rate of oxygen consumption. Physiological measurements were taken from two oysters from each genotype-line in each tank (methods followed that of Parker et al.^16,30 and Scanes et al.¹⁸). Oysters were immediately opened without rupturing the pericardial cavity. Haemolymph samples were drawn from the interstitial fluid filling the pericardial cavity chamber of an opened oyster using a sealed 1 mL needled syringe. A 0.2 mL sample was drawn carefully to avoid aeration of the haemolymph. Half of the sample was then immediately transferred to an Eppendorf tube where pH_e of the sample was measured at 20 °C using a micro pH probe (Metrohm 827 biotrode). The remaining haemolymph was transferred to a gas analyser (CIBA Corning 965) to determine total CO₂ (CCO₂). The micro pH probe was calibrated prior to use with NBS standards at the acclimation temperature and the gas analyser was calibrated using manufacturer guidelines. Two oysters were sampled per genotype-line in each replicate tank. Partial pressure of CO₂ in haemolymph (PCO_2e) was calculated from the CCO₂ using the modified Henderson-Hasselbalch equation according to Heisler^31,32. Metabolic rate (MR) was determined using a closed respiratory system as previously described in Parker et al.¹⁶ and Scanes et al.¹⁸. Briefly, MR was measured in two oysters per genotype-line, per tank by placing oysters in a closed 500 mL glass chamber containing filtered seawater (5 µm) set at the correct treatment conditions. Oxygen concentrations were then measured within the chamber using a fibre optic dipping probe (PreSens dipping probe DP-PSt3, AS1 Ltd, Regensburg, Germany) and recorded (15 s intervals) until the oxygen concentration had been reduced by 20%, the time taken to reduce oxygen by 20% was recorded. Oysters were removed from the chambers, opened and the tissue was dried at 70 °C for 72 h. Tissue was then weighed on an electronic balance (± 0.001 g), and MR was calculated using Eq. (1):

where MR is oxygen consumption normalised to 1 g of dry tissue mass (mg O₂ g⁻¹ dry tissue mass h⁻¹), V_r is the volume of the respiratory chamber minus the volume of the oyster (L), ΔC_WO₂ is the change in water oxygen concentration measured (mg O₂L⁻¹), Δt is the measuring time (h), bw is the dry tissue mass (g). Equation is modified from Parker et al.¹⁶.

Data analysis

It was not possible to measure all variables in each oyster, but rather three individuals were needed to fulfil one replicate set of measurements. PCO_2e and pH_e could be measured in the same individual however, MR and the microbiome were measured in separate individuals. This meant that measurements were taken from 6 oysters per genotype-line, per replicate tank (each measurement replicated twice). To align physiological data with microbiome data we took a conservative approach where data from PCO_2e and pH_e, MR and the microbiome were randomly matched to individuals from the same genotype-line and replicate tank. This gave us the best approximation and is conservative because it increased variability compared to taking all measurements from the same individual. ANOVA was used to determine the significant (n = 210; P < 0.05) effects of factors on bacterial richness. Estimated marginal means of linear trends were used to determine the source of variation when there was a significant interaction between a physiological variable and fixed factor. Normality was checked using the Shapiro–Wilk normality test. To determine the effects of the physiological variables and whether they interacted with our treatments to alter bacterial communities, PERMANOVA (n = 210) using the Adonis procedure were done on Unifrac and Weighted Unifrac with genotype-line (9 levels), PCO₂ (Ambient and Elevated) and Temperature (24 and 28 °C) as fixed factors, and either PCO_2e and pH_e or MR as a continuous variable.
The homogeneity of dispersion was checked and confirmed for all PERMANOVA. To determine significant differences in the abundance of ASVs dependent on significant physiological variables, the program DESeq2 was used to conduct Generalised Linear Models with a negative binomial distribution and a Benjamini–Hochberg adjusted P value to compare abundances of ASVs among treatments³³. All data analyses downstream of QIIME 2 were done using R v.4.0.1 (R Core team).

Source: Ecology - nature.com