M. capitata was exposed to thermal stress during its spawning cycle over the course of 5 weeks, during which polar metabolomic data were collected. We analyzed these data streams individually, and then integrated both data sets using MAGI (Metabolite Annotation and Gene Integration) to investigate molecular transitions and biochemical reactions.

Table of Contents

• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Project Information
• Funding

From the waters of Kāne‘ohe Bay, HI, four colonies of each coral species M. capitata and P. acuta were identified and collected under SAP 2019-60. Each of the four colonies for each species was fragmented into 30 pieces at the Hawaiʻi Institute of Marine Biology, located on Moku o Loʻe in Kāne‘ohe Bay, HI, and hot-glued to labeled plugs. (21N 157 W ; depth 1m).

Metabolite extraction and analysis performed at Rutgers University.

Metabolite extraction from coral nubbins: Metabolites were extracted using a protocol optimized for water-soluble polar metabolite analysis on LC-MS. The extraction buffer was a solution of 40:40:20 (methanol:acetonitrile:water) (v/v/v) + 0.1 M formic acid. The extraction buffer was stored at −20°C before usage. Immediately preceding the metabolite extraction, 1 ml of extraction buffer was added to a 2-ml glass Dounce homogenizer that had chilled on ice. Pieces of the −80°C preserved nubbins were then clipped, weighed, and added to the cold extraction buffer in the Dounce and left to incubate for 5 min. The pestle of the Dounce was then used to homogenize the coral tissue until there was a visible accumulation of coral skeleton at the bottom of the Dounce and the homogenate was visibly pigmented. An additional 500-μl aliquot of cold 40:40:20 + 0.1M formic acid extraction buffer was then used to rinse down the sides of the Dounce and pestle. The total 1.5-ml volume was then strained through a sterile 100-μm cell strainer into a 50-ml receptacle. There was a visible amount of skeleton collected in the strainer. The rest of the homogenate was then transferred to a 1.5-ml Eppendorf tube, vortexed for 10 s, and then centrifuged for 10 min at 16,000g at 4°C. After centrifugation, there was a pellet at the bottom of the tube. A final 500-μl aliquot of the homogenate was then pipetted to a second clean Eppendorf tube, to which 44 μl of 15% NH4HCO3 was added to neutralize the acid in the buffer. This was the final extract and was ready to be loaded to instrument vials for analysis.

Polar metabolite processing: Polar metabolite extractions were based on Lu et al. (2017). In a glass Dounce homogenizer, samples were mechanically ground in one mL of 40:40:20 (methanol: acetonitrile:water) (v/v/v) + 0.1 M formic acid extraction buffer after incubation in the buffer for 5 min. The sample was transferred to a 1.5-mL Eppendorf tube, with an additional 500 mL of extraction buffer used to rinse the Dounce. The samples were then vortexed for 10 s, before a 10-min centrifugation (16,000g) at 4 C. A total of 500 mL of the homogenate was then transferred to another Eppendorf tube and 44 mL of 15% NH4HCO3 was added to neutralize the extraction buffer. The samples were run on an ultra-high–performance LC-MS (UHPLC-MS), consisting of a Vanquish Horizon UHPLC system (Thermo Fisher Scientific, Waltham, MA, USA) with XBridge BEH Amide column (150 mm by 2.1 mm, 2.5-mm particle size; Waters, Milford, MA, USA), and a Thermo Fisher Scientific Q Exactive Plus with a HESI source.

UHPLC conditions: The HILIC separation was performed on a Vanquish Horizon UHPLC system (Thermo Fisher Scientific, Waltham, MA) with XBridge BEH Amide column (150 mm by 2.1 mm, 2.5-μm particle size; Waters, Milford, MA) using a gradient of solvent A [95%:5% H2O:acetonitrile with 20 mM acetic acid and 40 mM ammonium hydroxide (pH 9.4)] and solvent B [20%:80% H2O:acetonitrile with 20 mM acetic acid and 40 mM ammonium hydroxide (pH 9.4)]. The gradient was 0 min, 100% B; 3 min, 100% B; 3.2 min, 90% B; 6.2 min, 90% B; 6.5 min, 80% B; 10.5 min, 80% B; 10.7 min, 70% B; 13.5 min, 70% B; 13.7 min, 45% B; 16 min, 45% B; 16.5 min, 100% B; and 22 min, 100% B. The flow rate was 300 μl/min. The injection volume was 5 μl, and the column temperature was 25°C. The autosampler temperature was set to 4°C, and the injection volume was 5 μl.

Full-scan MS: The full-scan MS analysis was performed on a Thermo Fisher Scientific Q Exactive Plus with a HESI source, which was set to a spray voltage of −2.7 kV under negative mode and 3.5 kV under positive mode. The sheath, auxiliary, and sweep gas flow rates were 40, 10, and 2 (arbitrary unit), respectively. The capillary temperature was set to 300°C, and aux gas heater was 360°C. The S-lens radio frequency (RF) level was 45. The m/z scan range was set to 72 to 1000 m/z under both positive and negative ionization mode. The automatic gain control (AGC) target was set to 3e6, and the maximum injection time (IT) was 200 ms. The resolution was set to 70,000.

Parallel reaction monitoring MS: The MS2 spectra generation was performed on a Thermo Fisher Scientific Q Exactive Plus with a HESI source, which was set to a spray voltage of −2.7 kV under negative mode and 3.5 kV under positive mode. The sheath, auxiliary, and sweep gas flow rates were 40, 10, and 2 (arbitrary unit), respectively. The capillary temperature was set to 300°C, and aux gas heater was 360°C. The S-lens RF level was 45. The m/z scan ranges were specified for the four dipeptides and monitored for the full 22-min run time. The AGC target was set to 2 × 105, and the maximum IT was 100 ms. The resolution was set to 17,500. The isolation window was set to 2.0 m/z. Collision energy was set to a stepwise 30, 50, and 80 normalized collision energy (NCE). Our results suggest that a single MS2 spectrum may contain irrelevant m/z signals and the pseudospectrum, generated by correlating multiple MS2 spectra to the MS1-extracted ion chromatogram, is an effective approach to “clean” the MS2 spectrum. The pseudospectra were generated using a modified version of COVINA (Su et al. 2020).

Metabolite Data analysis: The MS1 data were processed using Maven (Melamud et al. 2010). The compound annotation was based on accurate mass and retention time match to the metabolite standards from the in-house library. The feature detection for untargeted metabolomics was done using Compound Discoverer (Thermo Fisher Scientific, version 3.1). Before the groupwise comparison, the signal intensities were normalized to the sample weight. Significance was determined by the groupwise signal intensity comparison of each metabolite at each time point using a Student’s t test that assumed unequal variance. P values were then adjusted using the Benjamini-Hochberg correction (false discovery rate, <0.05). Of those features with adjusted P < 0.05, the Fisher’s exact test was then applied to determine whether the putative dipeptides were significantly enriched in those features.

Metabolite  Data quality: Before running the samples, the LC-MS system was evaluated for performance readiness by running commercially available standard mixtures and in-house standard mixtures to assess the mass accuracy, signal intensities, and the consistency of retention time. All known metabolites in the mixture were detected within 5 parts per million mass accuracy. Process blanks matching the composition of the extraction solvent were used in every sample batch to assess background signals and ensure that there was no carryover from one run to the next. In addition, the sample queue was randomized with respect to species and treatment to eliminate the potential for batch effects.

Website: https://sites.rutgers.edu/coralbase/

Coverage: Hawaii, Rhode Island, New Jersey, Israel

NSF Abstract:

The remarkable success of coral reefs is explained by interactions of the coral animal with its symbiotic microbiome that is comprised of photosynthetic algae and bacteria. This total organism, or "holobiont", enables high ecosystem biodiversity and productivity in coral reefs. These ecosystems are, however, under threat from a rapidly changing environment. This project aims to integrate information from the cellular to organismal level to identify key mechanisms of adaptation and acclimatization to environmental stress. Specific areas to be investigated include the role of symbionts and of epigenetics (molecular "marks" on coral DNA that regulate gene expression). These aspects will be studied in Hawaiian corals to determine whether they explain why some individuals are sensitive or resistant to environmental perturbation. Results from the proposed project will also provide significant genomic resources that will contribute to fundamental understanding of how complex biological systems generate emergent (i.e., unexpected) properties when faced with fluctuating environments. Broader impacts will extend beyond scientific advancements to include postdoctoral and student training in Science, Technology, Engineering and Mathematics (STEM). Data generated in the project will be used to train university students and do public outreach through live videos of experimental work, and short stop-action animations for topics such as symbiosis, genomics, epigenetics, inheritance, and adaptation. The research approaches and results will be shared with the public in Hawaii through the Hawaii Institute of Marine Biology education department and presentations at Hawaiian hotels, as well as at Rutgers University through its 4-H Rutgerscience Saturdays and 4-H Rutgers Summer Science Programs.

Symbiosis is a complex and ecologically integrated interaction between organisms that provides emergent properties key to their survival. Such is the case for the relationship between reef-building corals and their microbiome, a meta-organism, where nutritional and biogeochemical recycling provide the necessary benefits that fuel high reef productivity and calcification. The rapid warming and acidification of our oceans threatens this symbiosis. This project addresses how relatively stress resistant and stress sensitive corals react to the environmental perturbations of increased temperature and reduced pH. It utilizes transcriptomic, epigenetic, and microbial profiling approaches, to elucidate how corals respond to environmental challenges. In addition to this profiling, work by the BSF Israeli partner will implement powerful analytical techniques such as network theory to detect key transcriptional hubs in meta-organisms and quantify biological integration. This work will generate a stress gene inventory for two ecologically important coral species and a (epi)genome and microbiome level of understanding of how they respond to the physical environment. Acknowledgment of a role for epigenetic mechanisms in corals overturns the paradigm of hardwired genetic control and highlights the interplay of genetic and epigenetic variation that may result in emergent evolutionary and ecologically relevant properties with implications for the future of reefs. Furthermore, clarifying the joint contribution of the microbiome and host in response to abiotic change will provide an important model in metazoan host-microbiome biotic interactions.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.