DNA extraction<\/p>\n
Using bleach-cleaned scissors and forceps, each coral fragment (~0.5\u2009cm2) was placed on dry ice, rinsed thoroughly with MilliQ-H2O, gently dried and placed at the bottom of a 2.0\u2009ml reinforced tube kept on ice. Two 3-mm stainless steel beads (acid-cleaned and sterilized) were added. Samples were incubated at 65\u2009\u00b0C for 30\u2009min with 1\u2009\u00b5l of RNase, Proteinase K (Promega) (1\u2009mg\u2009ml\u22121 in MilliQ-H2O), lysozyme (1\u2009mg\u2009ml\u22121 in MilliQ-H2O) (Sigma) and 600\u2009\u00b5l Qiagen kit lysis buffer. Samples were homogenized twice for 10\u2009min at medium speed with a Tissuelyzer (Qiagen). Cell debris\/coral skeleton was removed by centrifugation at 18,000\u2009g for 10\u2009min in a 5430 microcentrifuge (Eppendorf) at room temperature. The resulting supernatant was transferred to a fresh sterile low-bind microcentrifuge tube (Eppendorf) and DNA purified using the Qiagen All-prep-kit (Qiagen) according to manufacturer instructions. DNA was further purified using magnetic beads (AMPure XP system) with two sequential ethanol washing steps.<\/p>\n
16S rRNA gene amplicon sequencing<\/p>\n
DNA samples were prepared at 10\u201350\u2009ng\u2009\u00b5l\u22121 according to Qubit 2.0 (ThermoFisher) measurements and were delivered on dry ice to the Carver Biotechnology Center (University of Illinois-Urbana Champagne, Champaign and Urbana, IL, USA) for sequencing. Briefly, libraries were prepared with the hypervariable region V4 515\u2009F GTGYCAGCMGCCGCGGTAA and V4 806\u2009R GGACTACNVGGGTWTCTAAT primer pair for bacterial 16S rRNA gene amplification55<\/a>,56<\/a>, with spiked PhiX as the positive control, and sequenced with MiSeq 2\u2009\u00d7\u2009250 (Illumina). Sequencing yielded 22 samples with sufficient reads after FASTQ checking and trimming were completed. Sequences were analysed using DADA2 (v.3.6.0)57<\/a> in R. The DADA2 R-script uses raw amplicon sequencing data in FASTQ format as input. Error correction of the reads was performed, where reads were quality filtered, dereplicated, chimaeras were removed, and end pairs were merged to infer amplicon sequence variants (ASVs). For taxonomic assignment of the ASVs, the sequences were aligned against the SILVA database (release 132)58<\/a>. Chloroplasts, mitochondria and eukaryotic reads were removed. An average of 6,972 reads per sample was obtained. The ASV data were then processed using the rANOMALY R package59<\/a>. Beta diversity was assessed and visualized through principal coordinates analysis (PCoA) based on pairwise Bray\u2013Curtis distances and tested using PERMANOVA. All plots were generated using the ggplot60<\/a> and phyloseq61<\/a> R packages, and all statistical analyses were conducted in R v.4.1.1.<\/p>\n Shotgun metagenomic sequencing<\/p>\n DNA samples were prepared following the NovoSeq Nano DNA Sample Preparation Guide. Sequencing was performed using an Illumina Novoseq 6000 (Illumina) with 2\u2009\u00d7\u2009150\u2009bp read length at Novogene. A total of 14 metagenomic libraries (3 replicates \u00d7 5 conditions) were prepared at 50\u2009ng\u2009ml\u22121 concentration. Only one sample was not sequenced for the 27\u2009\u00b0C condition due to an inadequate amount of gDNA. Sequencing produced a total of 590\u2009Gb of data (on average, 42\u2009Gb per sample) and on average, 280 million paired-end (PE) reads per sample (Supplementary Table 3<\/a>).<\/p>\n Quality control and in silico partitioning of coral holobiont metagenomes<\/p>\n Illumina reads were checked for quality using fastqc v.0.11.9 (https:\/\/www.bioinformatics.babraham.ac.uk\/projects\/fastqc\/<\/a>) and MultiQC (v.1.11)62<\/a>. Adapter sequences were trimmed, and sequences of poor quality (below Q-score threshold of 30) were removed using fastp (v.0.23.2)63<\/a>. The \u2018bbsplit.sh\u2019 function within the BBMap alignment tool (v.38.93)64<\/a> was used with default parameters in a multithreaded process that simultaneously mapped the reads into chimaeric coral and algal references obtained by concatenating the available 15 Acropora genomes65<\/a> and three Cladocopium genomes66<\/a>,67<\/a>,68<\/a>. This mapping strategy assigned the reads to coral, algal symbiont, or otherwise unmapped reads that potentially contained the microbial reads and hence were used for downstream analyses.<\/p>\n The metagenomes of coral holobionts produced a total of 589\u2009Gb, averaging 42\u2009Gb per sample (Supplementary Table 3<\/a> and Extended Data Fig. 3<\/a>). We leveraged publicly available genomes for Acropora and Cladocopium to partition the holobiont metagenome before assembling coral and algal contigs (see Methods<\/a>). A total of 257 million reads (~7% of the holobiont reads) were classified as microbial (that is, non-coral, non-algal reads; Fig. 2d<\/a>). These microbial reads were used for downstream read-based and assembly-based metagenomic analyses (Extended Data Figs. 2<\/a>b and 3<\/a>, and Supplementary Table 3<\/a>).<\/p>\n Taxonomic classification and microbial diversity based on metagenomes<\/p>\n The Kraken software suite69<\/a> was used for read-based metagenomic analyses. Kraken2 (v.2.1.2)70<\/a> was used to classify the reads with the \u2018\u2013paired\u2019, \u2018\u2013report-zero-counts\u2019 and \u2018\u2013use-names\u2019 parameters, then BRACKEN (v.2.6.2)71<\/a> was used to compute species abundance using the \u2018-r 150 -l S\u2019 options, and finally, a species abundance matrix was obtained by combining results from all samples. The obtained species abundance raw counts matrix and taxonomy labels were used for downstream analyses. The obtained taxonomic classification data were visualized in Pavian72<\/a>.<\/p>\n Pre-assembly quality control of raw metagenomic reads<\/p>\n The ATLAS metagenomic pipeline (v.2.8.1)73<\/a> was used for read processing, assembly and annotations. Paired reads were processed before de novo assembly using the BBTools suite (v.37.99)74<\/a> to: (1) remove PCR duplicates using \u2018clumpify\u2019, (2) remove potential contaminants by providing the human and PhiX genomes using \u2018BBSplit\u2019, (3) filter reads on the basis of quality and length using \u2018BBduk\u2019 (trimq=10 minlength=51) and (4) correct overlapping paired-end reads based on k-mer coverage using \u2018Tadpole\u2019 and merge with \u2018bbmerge\u2019 before assembly.<\/p>\n Assembly and functional annotation of coral, algal symbiont and potential microbial contigs<\/p>\n Reads that belong to the coral host or algal symbiont, based on read partitioning of the holobiont metagenome, were corrected and processed as above before de novo co-assembly into contigs using MEGAHIT (v.1.2.9)75<\/a> with default parameters. To assess the contribution of the host and algal symbiont to the functional potential of the holobiont, the assembled contigs were annotated with eggNOG mapper76<\/a> and merged with Blastkoala\/Ghostkoala77<\/a> annotations. To ensure that the assembled contigs belonged to either coral or algal symbiont, the contigs were compared against coral and symbiont local databases using BLASTN (e\u2009\u2264\u200910\u22123). Potential microbial reads were de novo assembled individually with metaSPAdes (v.3.15.3)78<\/a>with default parameters. Prodigal (v.2.6.3)79<\/a> was used to predict open reading frames (ORFs) and the translated proteins were clustered using linclust80<\/a> to obtain non-redundant proteins that were mapped to the eggNOG catalogue using eggNOG (v.5)81<\/a>. Any annotations for metazoa and dinophyta were excluded.<\/p>\n Binning, annotation and functional enrichment of metagenome-assembled genomes (MAGs)<\/p>\n The microbial reads were mapped to the assembled microbial contigs using bbmap to obtain alignment files (BAM format) required for calculating contig coverage. metabat2 (v.2.15)82<\/a> uses tetra-nucleotide frequencies, differential abundance and the presence of marker genes and was used for binning the contigs into larger bins with the \u2018sensitive\u2019 and \u2018min_contig_length: 1500\u2019 parameters. The obtained metagenomic bins were checked using the lineage workflow from CheckM (v.1.1.3)83<\/a>. MAGs were finally declared as high quality if they had completeness >75% and contamination <10%. To reduce redundancy, dRep (v.3.2.2)84<\/a> was used to obtain a non-redundant set of MAGs by clustering genomes assembled from all samples at a defined average nucleotide identity (ANI, 0.975) and based on genome size (>5,000\u2009bp), then clusters the genomes using Mash85<\/a> followed by MUMmer86<\/a>. The highest-scoring bin from each cluster was only retained as the winning MAG with the highest dRep score in the dereplicated set. The relative abundance of each genome was quantified across samples on the basis of read recruitment (mapping the reads to the non-redundant MAGs) using BBMap with default parameters. Unmapped reads to the MAGs were further assembled to contigs via metaSPAdes (v.3.15.3)78<\/a> with default parameters and annotated as previously described.<\/p>\n ORFs were predicted using Prodigal as described above, then Distilled and Refined Annotation of Metabolism (DRAM)87<\/a> was used to assign database identifiers, including the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and then curate these annotations into functional categories. Genome annotations of the MAGs are provided on Zenodo (https:\/\/zenodo.org\/deposit\/8210635<\/a>). MAGs were clustered on the basis of KEGG modules (step coverage threshold=0.8) using the \u2018hclust\u2019 function implemented in R. To identify enriched functions (based on complete KEGG modules) in each group of MAGs, we used \u2018anvi-compute-functional-enrichment\u2019 which determines enrichment scores for modules within groups of MAGs by fitting a binomial generalized linear model (GLM) to the occurrence of each module in each group, and then computing a Rao test statistic, uncorrected P values and corrected q-values. The statistical approach for enrichment analysis is defined elsewhere88<\/a>. We considered any function or metabolic module with a q-value\u2009\u2264\u20090.05 to be \u2018enriched\u2019 in its associated group.<\/p>\n Taxonomic placements and phylogenomics of MAGs<\/p>\n The taxonomy of the predicted MAGs was inferred using the genome taxonomy database toolkit (GTDB-Tk v.5.0)89<\/a>. GTDB taxonomy names are used throughout this paper. In addition, the GTDB-guided taxonomy of the MAGs was confirmed by phylogenomic analysis using Anvi\u2019o (v.7.1)32<\/a>. Briefly, a contig database that contains MAGs and other closely related genomes was constructed using the \u2018anvi-gen-contigs-database\u2019 function, before hmm profiles were predicted using \u2018anvi-run-hmms\u2019. Protein sequences of single-copy core genes were extracted and concatenated using \u2018anvi-get-sequences-for-hmm-hits\u2019 and the phylogenomic tree was built using \u2018anvi-gen-phylogenomic-tree\u2019. The Newick-formatted tree was visualized and annotated using iTOL (v.5)90<\/a>.<\/p>\n Endozoicomonas pangenome analyses<\/p>\n Endozoicomonas genomes and MAGs were retrieved from the National Center for Biotechnology Information (NCBI). After filtration based on CheckM completeness cut-off of >89% for genomes and >78% for MAGs, 28 genomes (including the Endozoicomonas MAG assembled in our study, MAG10) were used for the pangenome analyses following the pangenomic workflow in Anvi\u2019o (v.7.1)32<\/a>. Genome statistics, accession numbers and host types are provided in Supplementary Table 4<\/a>. Briefly, header lines were simplified in the 29 FASTA files using \u2018anvi-script-reformat-fasta\u2019, and fasta files were then converted to contig databases using the \u2018anvi-gen-contigs-database\u2019. These databases were annotated using multiple functions. First, contigs were annotated with hidden Markov models (HMMs) by using \u2018anvi-run-hmms\u2019. We then used \u2018anvi-run-ncbi-cogs\u2019 and \u2018anvi-run-kegg-kofams\u2019 to annotate genes in the databases with functions based on the NCBI\u2019s Clusters of Orthologous Groups (COGs) and KEGG databases. A genome storage was created using \u2018anvi-gen-genomes-storage\u2019 to store genomic and amino acid sequences in addition to functional annotations. Next, we used the programme \u2018anvi-pan-genome\u2019 with default parameters to get a pangenome that includes core and accessory genomes as well as singletons. The obtained pangenome was visualized using \u2018anvi-display-pan\u2019. The programme \u2018anvi-compute-similarity\u2019 was used to compute average nucleotide identity across genomes using the \u2018PyANI\u2019 method. The ANI percentage identity data and biosynthetic gene clusters (BGCs) predicted from AntiSMASH were added as layers into the pangenome.<\/p>\n Prediction of BGCs of secondary metabolites<\/p>\n Prediction of BGCs in the 29 genomes within the Endozoicomonas pangenome, including our assembled MAG, was performed using AntiSmash bacterial (v.7)91<\/a> with default parameters and setting the extra features \u2018all on\u2019. Only well-defined clusters were retained by using the \u2018strict\u2019 option. The annotations of BGCs were assessed by matches with the Minimum Information about a Biosynthetic Gene cluster (MIBiG) database92<\/a>.<\/p>\n Endozoicomonas isolation93<\/a> from A. kenti, formerly Acropora tenuis<\/p>\n Samples of three different genotypes of Acropora kenti (separated by 10\u2009m) were collected from Heron Island, Great Barrier Reef, Australia, during the summer of 2020. Fragments of A. kenti were rinsed with autoclaved and filtered (through 0.22\u2009\u03bcm Whatman filters) artificial seawater (AFSW) to remove any seawater-associated bacteria, and then airbrushed to obtain a tissue slurry. Coral tissue slurries of each A. kenti fragment were pooled and homogenized using a TissueRuptor (Qiagen) to liberate bacteria from within the tissue94<\/a>. The pooled slurry was diluted (1\/100) with AFSW, and the diluted slurry was spread on separate plates of Marine Agar 2216 (BD Difco). The agar plates were incubated at 18\u2009\u00b0C (to reduce growth of Vibrio spp.) until visible colonies of bacteria were observed (minimum 3\u2009days). Bacterial colonies were picked and streaked onto new plates and then subsequently inoculated into 3\u2009ml of Marine Broth 2216 (BD Difco) at room temperature (24\u2009\u00b0C) under constant shaking (120\u2009r.p.m.) until maximum growth was observed. The cultured isolates were transferred into marine broth containing 20% glycerol, snap frozen in liquid nitrogen and stored at \u221280\u2009\u00b0C. The bacterial isolates were then Sanger sequenced at the Australian Genomics Research Facility, Sydney. The resulting 16S rRNA gene sequences were matched against the NCBI database using a BLASTn search; the isolate corresponding to Endozoicomonas (sequence identity 96% and e-value 1\u2009\u00d7\u200910\u221267) can be accessed in the NCBI Sequence Read Archive (SRA) under accession number SAMN37396526.<\/p>\n DNA extraction, genome sequencing, assembly and annotations of E. acroporae AT381<\/p>\n Five millilitres of overnight cultures were extracted for gDNA using the DNeasy Blood and Tissue kit (Qiagen) following manufacturer protocol. Libraries for genome sequencing were multiplexed using Nextera XT (Illumina) at the Australian Genome Research Facility and sequenced on an Illumina MiSeq platform (2\u2009\u00d7\u2009150\u2009bp).<\/p>\n Raw reads were quality filtered using fastp v.0.24.0. Quality-filtered and trimmed reads were then assembled into contigs in Unicycler (v.0.5.1)95<\/a> using SPAdes with default parameters. When assembling Illumina reads, Unicycler optimizes SPAdes by testing multiple k-mer sizes to select the best, filtering out low-depth regions to reduce contamination, applying SPAdes repeat resolution directly to the assembly graph, rejecting low-confidence repeat resolutions to minimize mis-assemblies, and trimming graph overlaps to prevent sequence repetition at contig junctions. The newly assembled genome was annotated using Bakta v.1.11 (https:\/\/github.com\/oschwengers\/bakta<\/a>), a rapid and standardized annotation tool for bacterial genomes. Genome completeness and contamination were verified using CheckM (v.1.2.3)83<\/a>.<\/p>\n Metabolite extractions from coral holobionts<\/p>\n All glassware used for sample processing were baked at 420\u2009\u00b0C for 24\u2009h. Coral nubbins were cut into ~1-cm-sized pieces on dry ice and directly transferred into 70% methanol solution on ice in clean 20\u2009ml scintillation vials. These were then extracted in an ultrasonication bath while cooled on ice for 30\u2009min. The resulting slurry was transferred into 15\u2009ml centrifuge vials (Falcon) and centrifuged at 4,000\u2009\u00d7\u2009g in a 5810R microcentrifuge (Eppendorf) for 15\u2009min. The supernatant was removed and transferred into pre-weighed glass test tubes, and the solvent evaporated in a Speedvac (ThermoFisher). Dry weight was determined and samples were kept at \u221280\u2009\u00b0C until measurements.<\/p>\n UHPLC\u2013Q-ToF-MS coral holobiont metabolic profiling<\/p>\n Metabolites were resuspended in 70% methanol with 0.2% formic acid and analysed using an Agilent 1290 HPLC system coupled to a Bruker Impact II HD Q-ToF-LC\/MS (Bruker Daltonics). Metabolites were separated using a reversed-phase (RP) separation method. In RP mode, medium-polarity and non-polar metabolites were separated using an Eclipse Plus C18 column (50\u2009mm\u2009\u00d7\u20092.1\u2009mm ID) (Agilent). Chromatographic mobile phases consisted of MilliQ-H2O\u2009+\u20090.2% formic acid (buffer A) and acetonitrile\u2009+\u20090.2% formic acid (buffer B). The gradient started with 95% A and 5% B, with an initial gradient of 18\u2009min to 100% B and a holding time of 2\u2009min. Every run was followed by a 5\u2009min washing step cycling from buffer B to buffer A to isopropanol and equilibrated for another 2\u2009min. Detection was carried out in positive and negative ionization modes with the following parameters: ESI settings: dry gas temperature 220\u2009\u00b0C, dry gas flow 8.0\u2009l\u2009min\u22121, nebulizer pressure 2.2\u2009bar, capillary 4,500\u2009V, end plate offset \u2212500\u2009V; MS-ToF setting: funnel1 RF 150\u2009Vpp, funnel2 RF 200\u2009Vpp, hexapole RF 50, quadrupole 1\u2009eV, (collision energy for full scan MS\u2009=\u20097\u2009eV, untargeted MSMS\u2009=\u2009stepping 30\u201350\u2009eV); acquisition setting: mass range 50\u20131,300\u2009m\/z, spectra rate 6.0\u2009Hz spectra per second, 1,000\u2009ms per spectrum. Every spectrum was individually calibrated, aligned with an internal lockmass (622.028\u2009m\/z), and peak picking was performed using the T-Rex 3D algorithm of Metaboscape v.4.0 (Bruker Daltonics) with a binning threshold of <5\u2009ppm. Background noise was removed by applying an intensity threshold of 500. Peak picking and integration was accompanied with 13C-cluster detection for molecular feature verification.<\/p>\n Sirius96<\/a>, a mass fragmentation-based compound identification java tool, was used to predict chemical structures and referenced against chemical databases (parameters: m\/z ppm <5\u2009ppm, isotope score = SCORE, MS2 deviation ppm <5, all chemical formula databases, ZODIAC with default settings, CSI-Finger:ID all databases). Further, compound classes were determined using CANOPUS deep neural network fragmentation analysis97<\/a>. Finally, the putative annotations were compared with a fragmentation library consisting of the IROA metabolite library (IROA Technologies). Various single metabolite standards (altogether ~1,200 molecules) measured on the mass spectrometer and the Bruker Personal Library (15,000 molecules, 65,000 MSMS spectra) were used to putatively annotate the metabolites with a cut-off MSMS match factor of >750 (max. 1,000) and manually curated.<\/p>\n For statistical analysis, only features with predicted chemical structure (SIRIUS) (see above) were used in MetaboAnalyst v.5 (GenomeCanada)98<\/a>. Data were normalized according to the internal standard chloramphenicol and range scaled (mean centred and divided by the range of each variable)99<\/a>. PCA was used to estimate differences between sample groups. The statistical significance of PCA was tested using PCAtest (v.0.0.2)100<\/a> in the R package. The significance of multivariate data was tested using a linear model in MetaboAnalyst (v.5)98<\/a>. Molecular features were deemed significant when achieving a Padj\u2009<\u20090.01 (Bonferroni post hoc tests) for FDR detection. A fold change of 1.5 was used to filter for differentially abundant compounds. Dendrograms were clustered using the \u2018Euclidean\u2019 distance measure and \u2018Average\u2019 clustering algorithm. Graphs for metabolomics were created using ggplot2.<\/p>\n Incubation of Endozoicomonas isolates with steroids<\/p>\n Three Endozoicomonas strains, two isolated from Acropora (E. acroporae AT381\/Acr14) and one from Montipora (E. montiporae CL-33), were used. E. acroporae AT381 was islolated in this study while E. acroporae Acr14 and E. montiporae CL-33 were obtained from the Food Industry Research and Development Institute (FIRDI) in Taiwan under accession numbers BRCR 80922 and BCRC 17933. These strains were grown on 0.5% marine broth agar plates (35\u2009g\u2009l\u22121 sea salts, 2.5\u2009g\u2009l\u22121 yeast extract, 0.5\u2009g\u2009l\u22121 protease peptone) at 34\u2009\u00b0C. An overnight culture was grown in minimal marine broth (MMB)35<\/a>. Overnight culture (50\u2009\u03bcl) at an optical density (OD600) of 0.2 was used to inoculate 4\u2009ml of sea salts (35\u2009g\u2009l\u22121) with 0.1% glucose as a carbon source and either cholesterol, testosterone (Cambridge Isotopes) or androstenedione at a final concentration of 100\u2009\u03bcM. All incubations were conducted with 4 replicates, except for controls which were done with 3. The cultures were shaken at 200\u2009r.p.m. at 34\u2009\u00b0C for 24\u2009h. Subsequently, cultures were centrifuged at 4,000\u2009\u00d7\u2009g in a 5810R microcentrifuge (Eppendorf) for 15\u2009min. The supernatant was discarded and the remaining cell pellet was directly frozen at \u221280\u2009\u00b0C. For extraction, the protocol mentioned above was followed.<\/p>\n Measurement of metabolites in Endozoicomonas steroid incubations<\/p>\n Liquid chromatography was performed on a ThermoFisher Vanquish UHPLC (ThermoFisher) with a Kinetex C18 column (100\u2009\u00d7\u20092.1\u2009mm, 1.9\u2009\u00b5m; ThermoFisher) under gradient conditions, using mobile phases A (0.1% formic acid in water) and B (0.1% formic acid in acetonitrile). Flow rate was set at 300\u2009\u00b5l\u2009min\u22121. Mobile phase A was held at 98% for 1\u2009min, followed by an increase to 100% B over 12\u2009min, and kept for 4.9\u2009min. The gradient was then returned to 98% A within 0.1\u2009min and re-equilibrated for 5\u2009min. The injection volume was set at 10\u2009\u00b5l.<\/p>\n Mass spectrometry analyses were performed on an Orbitrap Fusion Lumos mass spectrometer (ThermoFisher) fitted with a HESI source. Source parameters were set as follows: spray voltage at + 3,400\u2009V, sheath gas at 40 (arbitrary units), auxiliary gas at 5 (arbitrary units), sweep gas at 1 (arbitrary units), ion transfer tube temperature at 300\u2009\u00b0C, and vaporizer temperature at 400\u2009\u00b0C. Ion transfer parameters were set as follows: mass range at \u2018normal\u2019, S-lens RF level at 60%. Scan parameters applied were as follows: acquisition time from 0 to 23\u2009min, positive ionization mode, micro scan at \u20181\u2019, data type at \u2018profile\u2019, AGC target at 2\u2009\u00d7\u2009105. For acquisition in MS mode only, data were acquired using the Orbitrap analyser at a resolution of 120,000 at m\/z 200 (full width at half maximum, a spectrum collected at this resolution requires ~0.2\u2009s); maximum injection time was set at 400\u2009ms, and the scan range from m\/z 70 to m\/z 700. Mass fragmentation was performed using stepped collision at 20, 30 and 50\u2009V, and a resolution of 60,000 and 120\u2009ms maximum injection time using a targeted inclusion list for predicted steroid derivatives and complemented with untargeted MS fragmentation analysis.<\/p>\n Raw instrument data (.RAW) were pre-processed using Compound Discoverer 3.3 SP1 (ThermoFisher). Metabolite detection was performed within 5\u2009ppm mass tolerance, peak intensity cut-off of S\/N 1.5. Metabolite annotations were performed using the acquired high-resolution MS\/MS fragments from the samples against the mzCloud fragmentation database of standards (ThermoFisher). Authentic standards were used to identify testosterone peaks in the samples. Accurate [M\u2009+\u2009H]+ m\/z, retention time (min) and additional fragment ions m\/z are shown in Supplementary Table 7<\/a> (MS2 fragments). Statistical significance of fold change was determined by calculating P values using Student\u2019s t-test101<\/a> and using the Benjamini\u2013Hochberg procedure to account for multiple testing102<\/a>.<\/p>\n Field coral collections and incubation with labelled testosterone<\/p>\n Corals were collected from Snoopy Island, Fujairah, the Gulf of Oman (25\u00b0\u200929\u2032\u200931.0\u2033\u2009N, 56\u00b0\u200921\u2032\u200948.0\u2033\u2009E). Six Acropora downingi colonies were sampled at intervals of ~10\u2009m to minimize the likelihood of collecting clonally related individuals and to ensure that each colony represented a distinct genet (genotype). Samples were transported to the aquaria facilities at NYU Abu Dhabi (NYUAD). Colonies were maintained under ambient conditions (28\u2009\u00b0C) and acclimated for 1\u2009week before fragmentation. Colonies were then fragmented into nubbins (2\u20133\u2009cm) for experimental use and allowed to recover for an additional week in experimental aquaria before downstream assays.<\/p>\n To assess coral uptake of steroid compounds, short-term incubation experiments were conducted using 4 glass beakers containing 150\u2009ml of 0.2-\u00b5m-filtered seawater (Millipore), placed within a temperature-controlled water bath at 28\u2009\u00b0C. Beakers were dosed with either 10\u2009nM, 100\u2009nM or 1\u2009\u00b5M of 2,3,4-13C3-labelled testosterone (Cambridge Isotope), or left untreated as controls. Two Acropora fragments were placed into each beaker, with gentle aeration provided via air bubblers. After 24\u2009h and 48\u2009h of incubation, coral fragments were removed, rinsed with 0.2-\u00b5m-filtered seawater for 5\u2009min, immediately snap frozen in liquid nitrogen and stored at \u221280\u2009\u00b0C. An identical experiment was conducted with progesterone, although we were not able to acquire isotopically labelled progesterone. Metabolites were extracted following the holobiont extraction protocol described above; the peaks of the 2,3,4-13C3-labelled testosterone and naturally abundant progesterone in the extracted samples were detected, measured and confirmed using LC\u2013MS and LC\u2013MS\/MS by matching their accurate masses (within 5\u2009ppm), retention time and MS\/MS in the mass range of 250\u2013350\u2009Da with authentic standards as previously described103<\/a>. The peak areas of both testosterone and progesterone were normalized to dry extract weights.<\/p>\n Experimental design for heat stress and steroid exposure<\/p>\n For the heat stress experiment, 5 Acropora downingi colonies were fragmented into a total of 45 nubbins and affixed to ceramic reef plugs using reef-safe adhesive. Following the post-fragmentation acclimation period described above, nubbins were transferred to the experimental aquaria system, which comprised 3 independent 200\u2009l sump systems containing live rock and circulating local seawater. Each sump supplied 2 replicate 50\u2009l experimental tanks. Salinity was maintained at 39\u201340\u2009ppt, reflecting collection-site conditions, and corals were exposed to a 12:12\u2009h light:dark cycle at ~150\u2009\u00b5mol\u2009photons\u2009m\u22122\u2009s\u22121. Before initiating heat stress, coral tissue samples were collected to quantify ambient concentrations of testosterone and progesterone. Targeted analyses indicated that endogenous concentrations of both steroids fell within the range of 10\u2013100\u2009nM, informing the experimental dosing concentrations used in subsequent treatments.<\/p>\n Three nubbins from each colony were assigned to one of three treatments: (1) heat stress control (no steroid addition), (2) heat stress with testosterone or (3) heat stress with progesterone. Nubbins were randomly distributed among replicate tanks within each independent sump system. Maximum photosynthetic efficiency (Fv\/Fm) was measured daily as a proxy for coral health under dark-adapted conditions before light onset using an Aquapen fluorometer (Photon Systems Instruments). Testosterone or progesterone (HPLC grade, Sigma-Aldrich) was added to treatment tanks at a final concentration of 10\u2009nM, and corals were incubated for 3\u2009h before the first sampling timepoint (T1) for RNA sequencing. Seawater temperature was then rapidly increased from 28\u2009\u00b0C to 33\u2009\u00b0C (1.5\u2009\u00b0C above the summer maximum of 31.5\u2009\u00b0C104<\/a>) over 3\u2009h, consistent with CBASS-style heat-stress protocols105<\/a>, after which the second sampling timepoint (T2) samples were collected. Steroid compounds were re-dosed at 10\u2009nM, and heat stress was maintained for an additional 18\u2009h before the final sampling timepoint (T3). Samples were flash frozen in liquid nitrogen and stored at \u221280\u2009\u00b0C. Remaining coral fragments (n\u2009=\u20093\u20135 per treatment) were maintained at 33\u2009\u00b0C for a further 4-day period across all 3 conditions to assess longer-term photophysiological performance. Photochemical efficiency (Fv\/Fm) was analysed using a mixed-effects model, with treatment and time as fixed effects and coral fragment as a random effect, accounting for repeated measures over time. Significant treatment\u2009\u00d7\u2009time interaction was checked, post hoc \u0160\u00edd\u00e1k-corrected comparisons were used to assess treatment differences at biologically relevant timepoints.<\/p>\n Coral transcriptome sequencing, assembly and analysis<\/p>\n Total RNA samples were extracted using the RNeasy mini kit (Qiagen) and sent for transcriptome sequencing at Novogene (Shanghai, PRC). RNA integrity and quality were assessed before the subsequent mRNA enrichment from the coral holobionts. Then, mRNA libraries were prepared from total RNA using the Illumina Stranded mRNA Prep kit and sequenced using the Illumina Novaseq platform with 150-bp paired-end reads (Illumina). Reads are available through the NCBI SRA.<\/p>\n Sequencing produced a total of 1.6 billion reads with an average of 31 million PE reads per library, with 3 replicates per condition. Illumina raw reads were checked for quality using FastQC v.0.11.9 and multiQC v.1.11 for all datasets. Adapter and low-quality reads were removed using fastp v.0.23.2. Clean reads were normalized using the Trinity script \u2018insilico_read_normalization.pl\u2019 before assembling them into de novo transcriptome using Trinity (v.2.15.1)106<\/a> with a minimum contig length of 500. The obtained transcriptome was assessed on the basis of the mapping rate of the filtered reads and completeness based on BUSCO (Benchmarking Universal Single-Copy Orthologues) (v.6)107<\/a>. The obtained transcriptome was filtered by mapping against a local cnidaria database that comprises genomes from several Acropora spp.65<\/a>, Platygyra daedalea108<\/a>, Nematostella vectensis109<\/a> and Exaiptasia pallida110<\/a>. Contigs with BLASTn significant hits (e-value\u2009<\u200910\u22125) were retained as the coral transcriptome and used in downstream analysis. Open reading frames were predicted using TransDecoder v.5.7.1 (https:\/\/github.com\/TransDecoder\/TransDecoder<\/a>). The obtained protein sequences were annotated using KEGG pathways with eggNOG-mapper (v.2.1.13)76<\/a>. We employed the Trinity utility \u2018align_and_estimate_abundance.pl\u2019 in a two-step process. First, it aligns filtered reads against the coral transcriptome using Bowtie2 (v.5.4)111<\/a>, and then calculates transcript abundance based on read alignments using RSEM (v.1.3.3)112<\/a>, utilizing default parameters suitable for paired-end data. The gene-level count matrix was used for differential expression analysis using the R package DESeq2 (v.1.52.0)113<\/a>. Raw count data were modelled using a negative binomial generalized linear model with a design formula that included both treatment and colony identity (~colony\u2009+\u2009treatment), thereby accounting for intercolony variation while testing for treatment effects. This approach enabled the identification of genes that were differentially expressed between treatment groups after controlling for baseline differences among coral colonies. We compared all steroid-incubated and heat-stress samples at T2 and T3 to their corresponding controls at T1. We also compared steroid-treated samples with the negative control at T1. Genes with a Benjamini\u2013Hochberg-adjusted P\u2009\u2264\u20090.05 were considered significantly differentially expressed. Genes related to innate immunity (annotated with KEGG) were investigated, and their normalized expression values were plotted using the R package pheatmap v.1.0.13 and ggplot2 v.4.0.3. A summary of the signalling pathways is illustrated using Biorender and Adobe Illustrator.<\/p>\n Synthesis of (8R,9S,10R,13S,14S)-10,13-dimethyl-1,6,7,8,9,10,11,12,13,14,15,16-dodeca hydro-3H-cyclopenta[a]phenanthrene-3,17(2H)-dione (androstenedione)General experimental methods<\/p>\n All reactions were magnetically stirred and conducted in a flame-dried single-neck round-bottomed flask under a positive pressure of argon (that is, balloon) unless otherwise noted. All purified products were dried under high vacuum. Organic solutions and volatiles were concentrated by rotary evaporation using a water bath at 30\u201335\u2009\u00b0C. Thin layer chromatography was performed with SiliaPlate glass back scored plates (60\u2009\u00c5). Visualization of the spots was achieved by UV irradiation at 245\/365\u2009nm (Analytikjena, 6\u2009W\/0.16\u2009A) or by potassium permanganate staining, then charring. Purification of crude products was done by flash column chromatography (Biotage Selekt) using silica (particle size 230\u2013400 mesh) unless otherwise noted. Yields refer to spectroscopically (1H, 13C) pure materials unless otherwise noted.<\/p>\n Synthetic procedures To a solution of testosterone (600\u2009mg, 2.08\u2009mmol, 1\u2009equiv) in dichloromethane (105\u2009ml, C\u2009=\u20090.020\u2009M), Dess\u2013Martin periodinane (3.53\u2009g, 8.32\u2009mmol, 4.00\u2009equiv) was added. The reaction mixture was stirred for 2\u2009h and diluted with water (150\u2009ml). The diluted product mixture was transferred to a separatory funnel and the layers that formed were separated. The aqueous layer was further extracted with dichloromethane (2\u2009\u00d750\u2009ml), and the combined organic layers were dried over sodium sulfate. The dried solution was filtered and the filtrate was concentrated. The residue was purified by flash column chromatography (eluting with 10% ethyl acetate\u2013hexane initially, then at a linear gradient to 30% ethyl acetate\u2013hexane) to afford the androstenedione as a white solid (540\u2009mg, 91%). Dess\u2013Martin periodinane is potentially explosive and may be sensitive to heat and impact, particularly when impure. It should therefore be handled with appropriate precautions. Characterization data for androstenedione are as follows:<\/p>\n Rf\u2009=\u20090.49 (50% ethyl acetate\u2013hexane; UV)<\/p>\n \\({[{\\rm{\\alpha }}]}_{{\\rm{D}}}^{20}\\): +183 (c 0.55, CHCl3) 1H NMR (500\u2009MHz, CDCl3) \u03b4 5.74 (s, 1H, H12), 2.51\u20132.25 (m, 5H, 2 \u22c5 H1, 2 \u22c5 H11, 1 \u22c5 H6), 2.16\u20132.06 (m, 1H, 1 \u22c5 H2), 2.06\u20132.00 (m, 1H, 1 \u22c5 H2), 1.99\u20131.93 (m, 2H, 1 \u22c5 H7, 1 \u22c5 H10), 1.89\u20131.81 (m, 1H, 1 \u22c5 H5), 1.77\u20131.62 (m, 3H, 1 \u22c5 H4, 1 \u22c5 H6, H9), 1.55 (tt, J\u2009=\u200912.5, 9.1\u2009Hz, 1H, 1 \u22c5 H7), 1.44 (qd, J\u2009=\u200913.2, 4.2\u2009Hz, 1H, 1 \u22c5 H4), 1.34 \u2013 1.22 (m, 2H, 1 \u22c5 H5, H8), 1.19 (s, 3H, H13), 1.10 (qd, J\u2009=\u200912.9, 4.3\u2009Hz, 1H, 1 \u22c5 H10), 0.97 (ddd, J\u2009=\u200912.3, 10.7, 4.2\u2009Hz, 1H, H3), 0.90 (s, 3H, H14).<\/p>\n 13C NMR (126\u2009MHz, CDCl3) \u03b4 199.6 (C), 170.6 (C), 124.2 (CH), 53.9 (CH), 50.9 (CH), 47.6 (CH2), 38.7 (CH2), 35.8 (CH2), 35.8 (CH2), 35.2 (CH), 34.0 (CH2), 32.7 (CH2), 31.4 (CH2), 30.8 (CH2), 21.8 (CH2), 20.4 (CH2), 17.5 (CH3), 13.8 (CH3). One quaternary carbon was not observed. All NMR spectra are provided in Supplementary Figs. 1<\/a>\u20134<\/a>.<\/p>\n HRMS-CI (m\/z): [M\u2009+\u2009H]+ calculated for C19H27O2\u2009=\u2009287.2006; observed peak\u2009=\u2009287.2003.<\/p>\n Reporting summary<\/p>\n![\"\"](\"https:\/\/www.newsbeep.com\/uk\/wp-content\/uploads\/2026\/10\/41564_2026_2494_Figa_HTML.png\"\/)
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