Dataset descriptionSample collection

Our research complies with all relevant ethical regulations following policies at the University of California, San Diego (UCSD). Animal samples that were sequenced were not collected at UCSD and are not for vertebrate animals research at UCSD following the UCSD Institutional Animal Care and Use Committee (IACUC). Samples were contributed by 34 principal investigators of the Earth Microbiome Project 500 (EMP500) Consortium and are samples from studies at their respective institutions (Supplementary Table 1). Relevant permits and ethics information for each parent study are described in the ‘Permits for sample collection’ section below. Samples were contributed as distinct sets referred to here as studies, where each study represented a single environment (for example, terrestrial plant detritus). To achieve more even coverage across microbial environments, we devised an ontology of sample types (microbial environments), the EMP Ontology (EMPO) (http://earthmicrobiome.org/protocols-and-standards/empo/)1, and selected samples to fill out EMPO categories as broadly as possible. EMPO recognizes strong gradients structuring microbial communities globally, and thus classifies microbial environments (level 4) on the basis of host association (level 1), salinity (level 2), host kingdom (if host-associated) or phase (if free-living) (level 3) (Fig. 1a). As we anticipated previously1, we have updated the number of levels as well as states therein for EMPO (Fig. 1b) on the basis of an important additional salinity gradient observed among host-associated samples when considering the previously unreported shotgun metagenomic and metabolomic data generated here (Fig. 3c,d). We note that although we were able to acquire samples for all EMPO categories, some categories are represented by a single study.

Samples were collected following the Earth Microbiome Project sample submission guide50. Briefly, samples were collected fresh, split into 10 aliquots and then frozen, or alternatively collected and frozen, and subsequently split into 10 aliquots with minimal perturbation. Aliquot size was sufficient to yield 10–100 ng genomic DNA (approximately 107–108 cells). To leave samples amenable to chemical characterization (metabolomics), buffers or solutions for sample preservation (for example, RNAlater) were avoided. Ethanol (50–95%) was allowed as it is compatible with LC–MS/MS although it should also be avoided if possible.

Sampling guidance was tailored for four general sample types: bulk unaltered (for example, soil, sediment, faeces), bulk fractionated (for example, sponges, corals, turbid water), swabs (for example, biofilms) and filters. Bulk unaltered samples were split fresh (or frozen), sampled into 10 pre-labelled 2 ml screw-cap bead beater tubes (Sarstedt, 72.694.005 or similar), ideally with at least 200 mg biomass, and flash frozen in liquid nitrogen (if possible). Bulk fractionated samples were fractionated as appropriate for the sample type, split into 10 pre-labelled 2 ml screw-cap bead beater tubes, ideally with at least 200 mg biomass, and flash frozen in liquid nitrogen (if possible). Swabs were collected as 10 replicate swabs using 5 BD SWUBE dual cotton swabs with wooden stick and screw cap (281130). Filters were collected as 10 replicate filters (47 mm diameter, 0.2 um pore size, polyethersulfone (preferred) or hydrophilic PTFE filters), placed in pre-labelled 2 ml screw-cap bead beater tubes, and flash frozen in liquid nitrogen (if possible). All sample types were stored at –80 °C if possible, otherwise –20 °C.

To track the provenance of sample aliquots, we employed a QR coding scheme. Labels were affixed to aliquot tubes before shipping when possible. QR codes had the format ‘name.99.s003.a05’, where ‘name’ is the PI name, ‘99’ is the study ID, ‘s003’ is the sample number and ‘a05’ is the aliquot number. QR codes (version 2, 25 pixels × 25 pixels) were printed on 1.125’ × 0.75’ rectangular and 0.437’ circular cap Cryogenic Direct Thermal labels (GA International, DFP-70) using a Zebra model GK420d printer and ZebraDesigner Pro 3 software for Windows. After receipt but before aliquots were stored in freezers, QR codes were scanned into a sample inventory spreadsheet using a QR scanner.

Sample metadata

Environmental metadata were collected for all samples on the basis of the EMP Metadata Guide, which combines guidance from the Genomics Standards Consortium MIxS (Minimum Information about any Sequence) standard74 and the Qiita Database (https://qiita.ucsd.edu)51. The metadata guide provides templates and instructions for each MIxS environmental package (that is, sample type). Relevant information describing each PI submission, or study, was organized into a separate study metadata file (Supplementary Table 1).

MetabolomicsLC–MS/MS sample extraction and preparation

To profile metabolites among all samples, we used LC–MS/MS, a versatile method that detects tens of thousands of metabolites in biological samples. All solvents and reactants used were LC–MS grade. To maximize the biomass extracted from each sample, the samples were prepared depending on their sampling method (for example, bulk, swabs, filter and controls). The bulk samples were transferred into a microcentrifuge tube (polypropylene, PP) and dissolved in 7:3 MeOH:H2O using a volume varying from 600 µl to 1.5 ml, depending on the amounts of sample available, and homogenized in a tissue lyser (QIAGEN) at 25 Hz for 5 min. Then, the tubes were centrifuged at 2,000 × g for 15 min, and the supernatant was collected in a 96-well plate (PP). For swabs, the swabs were transferred into a 96-well plate (PP) and dissolved in 1.0 ml of 9:1 ethanol:H2O. The prepared plates were sonicated for 30 min, and after 12 h at 4 °C, the swabs were removed from the wells. The filter samples were dissolved in 1.5 ml of 7:3 MeOH:H2O in microcentrifuge tubes (PP) and sonicated for 30 min. After 12 h at 4 °C, the filters were removed from the tubes. The tubes were centrifuged at 2,000 × g for 15 min, and the supernatants were transferred to 96-well plates (PP). The process control samples (bags, filters and tubes) were prepared by adding 3.0 ml of 2:8 MeOH:H2O and recovering 1.5 ml after 2 min. After the extraction process, all sample plates were dried with a vacuum concentrator and subjected to solid phase extraction (SPE). SPE was used to remove salts that could reduce ionization efficiency during mass spectrometry analysis, as well as the most polar and non-polar compounds (for example, waxes) that cannot be analysed efficiently by reversed-phase chromatography. The protocol was as follows: the samples (in plates) were dissolved in 300 µl of 7:3 MeOH:H2O and put in an ultrasound bath for 20 min. SPE was performed with SPE plates (Oasis HLB, hydrophilic-lipophilic-balance, 30 mg with particle sizes of 30 µm). The SPE beds were activated by priming them with 100% MeOH, and equilibrated with 100% H2O. The samples were loaded on the SPE beds, and 100% H2O was used as wash solvent (600 µl). The eluted washing solution was discarded, as it contains salts and very polar metabolites that subsequent metabolomics analysis is not designed for. The sample elution was carried out sequentially with 7:3 MeOH:H2O (600 µl) and 100% MeOH (600 µl). The obtained plates were dried with a vacuum concentrator. For mass spectrometry analysis, the samples were resuspended in 130 µl of 7:3 MeOH:H2O containing 0.2 µM of amitriptyline as an internal standard. The plates were centrifuged at 30 × g for 15 min at 4 °C. Samples (100 µl) were transferred into new 96-well plates (PP) for mass spectrometry analysis.

LC–MS/MS sample analysis

The extracted samples were analysed by ultra-high performance liquid chromatography (UHPLC, Vanquish, Thermo Fisher) coupled to a quadrupole-Orbitrap mass spectrometer (Q Exactive, Thermo Fisher) operated in data-dependent acquisition mode (LC–MS/MS in DDA mode). Chromatographic separation was performed using a Kinetex C18 1.7 µm (Phenomenex), 100 Å pore size, 2.1 mm (internal diameter) × 50 mm (length) column with a C18 guard cartridge (Phenomenex). The column was maintained at 40 °C. The mobile phase was composed of a mixture of (A) water with 0.1% formic acid (v/v) and (B) acetonitrile with 0.1% formic acid. Chromatographic elution method was set as follows: 0.00–1.00 min, isocratic 5% B; 1.00–9.00 min, gradient from 5% to 100% B; 9.00–11.00 min, isocratic 100% B; followed by equilibration 11.00–11.50 min, gradient from 100% to 5% B; 11.50–12.50 min, isocratic 5% B. The flow rate was set to 0.5 ml min−1.

The UHPLC was interfaced to the orbitrap using a heated electrospray ionization source with the following parameters: ionization mode, positive; spray voltage, +3,496.2 V; heater temperature, 363.90 °C; capillary temperature, 377.50 °C; S-lens RF, 60 arbitrary units (a.u.); sheath gas flow rate, 60.19 a.u.; and auxiliary gas flow rate, 20.00 a.u. The MS1 scans were acquired at a resolution (at m/z 200) of 35,000 in the m/z 100–1500 range, and the fragmentation spectra (MS2) scans at a resolution of 17,500 from 0 to 12.5 min. The automatic gain control target and maximum injection time were set at 1.0 × 106 and 160 ms for MS1 scans, and set at 5.0 × 105 and 220 ms for MS2 scans, respectively. Up to three MS2 scans in data-dependent mode (Top 3) were acquired for the most abundant ions per MS1 scans using the apex trigger mode (4–15 s), dynamic exclusion (11 s) and automatic isotope exclusion. The starting value for MS2 was m/z 50. Higher-energy collision induced dissociation (HCD) was performed with a normalized collision energy of 20, 30 and 40 eV in stepped mode. The major background ions originating from the SPE were excluded manually from the MS2 acquisition. Analyses were randomized within plate and blank samples analysed every 20 injections. A quality control mix sample assembled from 20 random samples across the sample types was injected at the beginning, the middle and the end of each plate sequence. The chromatographic shift observed throughout the batch was estimated as less than 2 s, and the relative standard deviation of ion intensity was 15% per replicate.

LC–MS/MS data processing

The mass spectrometry data were centroided and converted from the proprietary format (.raw) to the m/z extensible markup language format (.mzML) using ProteoWizard (ver. 3.0.19, MSConvert tool)75. The mzML files were then processed with MZmine 2 toolbox76 using the ion-identity networking modules77 that allow advanced detection for adduct/isotopologue annotations. The MZmine processing was performed on Ubuntu 18.04 LTS 64-bits workstation (Intel Xeon E5-2637, 3.5 GHz, 8 cores, 64 Gb of RAM) and took ~3 d. The MZmine project, the MZmine batch file (.XML format) and results files (.MGF and .CSV) are available in the MassIVE dataset MSV000083475. The MZmine batch file contains all the parameters used during the processing. In brief, feature detection and deconvolution was performed with the ADAP chromatogram builder78 and local minimum search algorithm. The isotopologues were regrouped and the features (peaks) were aligned across samples. The aligned peak list was gap filled and only peaks with an associated fragmentation spectrum and occurring in a minimum of three files were conserved. Peak shape correlation analysis grouped peaks originating from the same molecule and annotated adduct/isotopologue with ion-identity networking77. Finally, the feature quantification table results (.CSV) and spectral information (.MGF) were exported with the GNPS module for feature-based molecular networking analysis on GNPS79 and with SIRIUS export modules.

LC–MS/MS data annotation

The results files of MZmine (.MGF and .CSV files) were uploaded to GNPS (http://gnps.ucsd.edu)52 and analysed with the feature-based molecular networking workflow79. Spectral library matching was performed against public fragmentation spectra (MS2) spectral libraries on GNPS and the NIST17 library.

For the additional annotation of small peptides, we used the DEREPLICATOR tools available on GNPS80,81. We then used SIRIUS82 (v. 4.4.25, headless, Linux) to systematically annotate the MS2 spectra. Molecular formulae were computed with the SIRIUS module by matching the experimental and predicted isotopic patterns83, and from fragmentation trees analysis84 of MS2. Molecular formula prediction was refined with the ZODIAC module using Gibbs sampling85 on the fragmentation spectra (chimeric spectra or those with poor fragmentation were excluded). In silico structure annotation using structures from biodatabase was done with CSI:FingerID86. Systematic class annotations were obtained with CANOPUS41 and used the NPClassifier ontology87.

The parameters for SIRIUS tools were set as follows, for SIRIUS: molecular formula candidates retained, 80; molecular formula database, ALL; maximum precursor ion m/z computed, 750; profile, orbitrap; m/z maximum deviation, 10 ppm; ions annotated with MZmine were prioritized and other ions were considered (that is, [M+H3N+H]+, [M+H]+, [M+K]+, [M+Na]+, [M+H-H2O]+, [M+H-H4O2]+, [M+NH4]+); for ZODIAC: the features were split into 10 random subsets for lower computational burden and computed separately with the following parameters: threshold filter, 0.9; minimum local connections, 0; for CSI:FingerID: m/z maximum deviation, 10 ppm; and biological database, BIO.

To establish putative microbially related secondary metabolites, we collected annotations from spectral library matching and the DEREPLICATOR+ tools and queried them against the largest microbial metabolite reference databases (Natural Products Atlas88 and MIBiG89). Molecular networking79 was then used to propagate the annotation of microbially related secondary metabolites throughout all molecular families (that is, the network component).

LC–MS/MS data analysis

We combined the annotation results from the different tools described above to create a comprehensive metadata file describing each metabolite feature observed. Using that information, we generated a feature-table including only secondary metabolite features determined to be microbially related. We then excluded very low-intensity features introduced to certain samples during the gap-filling step described above. These features were identified on the basis of presence in negative controls that were universal to all sample types (that is, bulk, filter and swab) and by their relatively low per-sample intensity values. Finally, we excluded features present in positive controls for sampling devices specific to each sample type (that is, bulk, filter or swab). The final feature-table included 618 samples and 6,588 putative microbially related secondary metabolite features that were used for subsequent analysis.

We used QIIME 2’s90 (v2020.6) ‘diversity’ plugin to quantify alpha-diversity (that is, feature richness) for each sample and ‘deicode’91 to quantify beta-diversity (that is, robust Aitchison distances, which are robust to both sparsity and compositionality in the data) between each pair of samples. We parameterized our robust Aitchison principal components analysis (RPCA)91 to exclude samples with fewer than 500 features and features present in fewer than 10% of samples. We used the ‘taxa’ plugin to quantify the relative abundance of microbially related secondary metabolite pathways and superclasses (that is, on the basis of NPClassifier) within each environment (that is, for each level of EMPO 4), and ‘songbird’ v1.0.492 to identify sets of microbially related secondary metabolites whose abundances were associated with certain environments. We parameterized our ‘songbird’ model as follows: epochs, 1,000,000; differential prior, 0.5; learning rate, 1.0 × 10−5; summary interval, 2; batch size, 400; minimum sample count, 0; and training on 80% of samples at each level of EMPO 4 using ‘Animal distal gut (non-saline)’ as the reference environment. Environments with fewer than 10 samples were excluded to optimize model training (that is, ‘Animal corpus (non-saline)’, ‘Animal proximal gut (non-saline)’, ‘Surface (saline)’). The output from ‘songbird’ includes a rank value for each metabolite in every environment, which represents the log fold change for a given metabolite in a given environment92. We compared log fold changes for each metabolite from this run to those from (1) a replicate run using the same reference environment and (2) a run using a distinct reference environment: ‘Water (saline)’. We found strong Spearman correlations in both cases (Supplementary Table 8), and therefore focused on results from the original run using ‘Animal distal gut (non-saline)’ as the reference environment, as it has previously been shown to be relatively unique among other habitats. In addition to summarizing the top 10 metabolites for each environment (Supplementary Table 3), we used the log fold change values in our multi-omics analyses described below.

We used the RPCA biplot and QIIME 2’s90 EMPeror93 to visualize differences in composition among samples, as well as the association with samples of the 25 most influential microbially related secondary metabolite features (that is, those with the largest magnitude across the first three principal component loadings). We tested for significant differences in metabolite composition across all levels of EMPO using PERMANOVA implemented with QIIME 2’s ‘diversity’ plugin90 and using our robust Aitchison distance matrix as input. In parallel, we used the differential abundance results from ‘songbird’ described above to identify specific microbially related secondary metabolite pathways and superclasses that varied strongly across environments. We then went back to our metabolite feature-table to visualize differences in the relative abundances of those pathways and superclasses within each environment by first selecting features and calculating log-ratios using ‘qurro’94, and then plotting using the ‘ggplot2’ package95 in R96 v4.0.0. We tested for significant differences in relative abundances across environments using Kruskal–Wallis tests implemented with the base ‘stats’ package in R96.

GC–MS sample extraction and preparation

To profile volatile small molecules among all samples in addition to what was captured with LC–MS/MS, we used gas chromatography coupled with mass spectrometry (GC–MS). All solvents and reactants were GC–MS grade. Two protocols were used for sample extraction, one for the 105 soil samples and a second for the 356 faecal and sediment samples that were treated as biosafety level 2. The 105 soil samples were received at the Pacific Northwest National Laboratory and processed as follows. Each soil sample (1 g) was weighed into microcentrifuge tubes (Biopur Safe-Lock, 2.0 ml, Eppendorf). H2O (1 ml) and one scoop (~0.5 g) of a 1:1 (v/v) mixture of garnet (0.15 mm, Omni International) and stainless steel (0.9–2.0 mm blend, Next Advance) beads and one 3 mm stainless steel bead (Qiagen) were added to each tube. Samples were homogenized in a tissue lyser (Qiagen) for 3 min at 30 Hz and transferred into 15 ml polypropylene tubes (Olympus, Genesee Scientific). Ice-cold water (1 ml) was used to rinse the smaller tube and combined into the 15 ml tube. Chloroform:methanol (10 ml, 2:1 v/v) was added and samples were rotated at 4 °C for 10 min, followed by cooling at −70 °C for 10 min and centrifuging at 150 × g for 10 min to separate phases. The top and bottom layers were combined into 40 ml glass vials and dried using a vacuum concentrator. Chloroform:methanol (1 ml, 2:1) was added to each large glass vial and the sample was transferred into 1.5 ml tubes and centrifuged at 1,300 × g. The supernatant was transferred into glass vials and dried for derivatization.

The remaining 356 samples received from UCSD that included faecal and sediment samples were processed as follows: 100 µl of each sample was transferred to a 2 ml microcentrifuge tube using a scoop (MSP01, Next Advance). The final volume of the sample was brought to 1.5 ml, ensuring that the solvent ratio is 3:8:4 H2O:CHCl3:MeOH by adding the appropriate volumes of H2O, MeOH and CHCl3. After transfer, one 3 mm stainless steel bead (QIAGEN), 400 µl methanol and 300 µl H2O were added to each tube and the samples were vortexed for 30 s. Then, 800 µl chloroform was added and samples were vortexed for 30 s. After centrifuging at 150 × g for 10 min to separate phases, the top and bottom layers were combined in a vial and dried for derivatization.

The samples were derivatized for GC–MS analysis as follows: 20 µl of a methoxyamine solution in pyridine (30 mg ml−1) was added to the sample vial and vortexed for 30 s. A bath sonicator was used to ensure that the sample was completely dissolved. Samples were incubated at 37 °C for 1.5 h while shaking at 1,000 r.p.m. N-methyl-N-trimethylsilyltrifluoroacetamide (80 µl) and 1% trimethylchlorosilane solution was added and samples were vortexed for 10 s, followed by incubation at 37 °C for 30 min, with 1,000 r.p.m. shaking. The samples were then transferred into a vial with an insert.

An Agilent 7890A gas chromatograph coupled with a single quadrupole 5975C mass spectrometer (Agilent) and an HP-5MS column (30 m × 0.25 mm × 0.25 μm; Agilent) was used for untargeted analysis. Samples (1 μl) were injected in splitless mode, and the helium gas flow rate was determined by the Agilent Retention Time Locking function on the basis of analysis of deuterated myristic acid (Agilent). The injection port temperature was held at 250 °C throughout the analysis. The GC oven was held at 60 °C for 1 min after injection, and the temperature was then increased to 325 °C at a rate of 10 °C min−1, followed by a 10 min hold at 325 °C. Data were collected over the mass range of m/z 50–600. A mixture of FAMEs (C8–C28) was analysed each day with the samples for retention index alignment purposes during subsequent data analysis.

GC–MS data processing and annotation

The data were converted from vendor’s format to the .mzML format and processed using GNPS GC–MS data analysis workflow (https://gnps.ucsd.edu)97. The compounds were identified by matching experimental spectra to the public libraries available at GNPS, as well as NIST 17 and Wiley libraries. The data are publicly available at the MassIVE depository (https://massive.ucsd.edu); dataset ID: MSV000083743. The GNPS deconvolution is available in GNPS (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=d5c5135a59eb48779216615e8d5cb3ac), as is the library search (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=59b20fc8381f4ee6b79d35034de81d86).

GC–MS data analysis

For multi-omics analyses including GC–MS data, we first removed noisy (that is, suspected background contaminants and artifacts) features by excluding those with balance scores <50%. Balance scores describe compositional consistency of deconvoluted spectra across the dataset, where high values indicate reproducible spectral patterns and thus high-quality spectra. We then used QIIME 2’s ‘deicode’91 plugin to estimate beta-diversity for each dataset using robust Aitchison distances. The final feature-table for GC–MS beta-diversity analysis included 460 samples and 216 features.

MetagenomicsDNA extraction

For each round of DNA extractions described below for both amplicon and shotgun metagenomic sequencing, a single aliquot of each sample was processed for DNA extraction. DNA was extracted following the EMP 96-sample, magnetic bead-based DNA extraction protocol98 following refs. 99,100,101 and using the QIAGEN MagAttract PowerSoil DNA KF kit (384-sample) (that is, optimized for KingFisher, 27100-EP). Importantly, material from each sample was added to a unique bead tube (containing garnet beads) for single-tube lysis, which has been shown to reduce sample-to-sample contamination common in plate-based extractions101. For bulk samples, 0.1–0.25 g of material was added to each well; for filtered samples, one entire filter was added to each well; for swabbed samples, one swab head was added to each well. The lysis solution was dissolved at 60 °C before addition to each tube, then capped tubes were incubated at 65 °C for 10 min before mechanical lysis at 6,000 r.p.m. for 20 min using a MagNA lyser (Roche). Lysate from each bead tube was then randomly assigned and added to wells of a 96-well plate, and then cleaned-up using the KingFisher Flex system (Thermo Fisher). Resulting DNA was stored at –20 °C for sequencing. We note that whereas QIAGEN does not offer a ‘hybrid’ extraction kit allowing for single-tube lysis and plate-based clean-up, the Thermo MagMAX Microbiome Ultra kit does, and was recently shown to be comparable to the EMP protocol used here102.

Amplicon sequencing

We generated amplicon sequence data for variable region four (V4) of the bacterial and archaeal 16S rRNA gene, variable region nine (V9) of the eukaryotic 18S rRNA gene, and the fungal internal transcribed spacer one (ITS1). For amplifying and sequencing all targets, we used a low-cost, miniaturized (that is, 5 µl volume), high-throughput (384-sample) amplicon library preparation method implementing the Echo 550 acoustic liquid handler (Beckman Coulter)103. The same protocol was modified with different primer sets and PCR cycling parameters depending on the target. Two rounds of DNA extraction and sequencing were performed for each target to obtain greater coverage per sample. For a subset of 500 samples, we also generated high-quality sequence data for full-length bacterial rRNA operons following a previously published protocol104, which is briefly outlined below.

The protocol for 16S is outlined fully in ref. 105. To target the V4 region, we used the primers 515F (Parada) (5’-GTGYCAGCMGCCGCGGTAA-3’) and 806R (Apprill) (5’-GGACTACNVGGGTWTCTAAT-3’). These primers are updated from the original EMP 16S-V4 primer sequences106,107 to (1) remove bias against Crenarchaeota/Thaumarchaeota108 and the marine freshwater clade SAR11 (Alphaproteobacteria)109, and (2) enable the use of various reverse primer constructs (for example, the V4-V5 region using the reverse primer 926R110) by moving the barcode/index to the forward primer108. We note that while we previously named these updated primers ‘515FB’ and ‘806RB’ to distinguish them from the original primers, the ‘B’ may be misinterpreted to indicate ‘Barcode’. To avoid ambiguity, we now use the original names suffixed with the lead author name (that is, ‘515F (Parada)’, ‘806R (Apprill)’ and ‘926R (Quince)’). We highly recommend to always check the primer sequence in addition to the primer name. For Qiita users, studies with ‘library_construction_protocol’ as ‘515f/806rbc’ used the original primers, whereas ‘515fbc/806r’ indicates use of updated primers, where ‘bc’ refers to the location of the barcode.

To facilitate sequencing on Illumina platforms, the following primer constructs were used to integrate adapter sequences during amplification106,107,111. For the barcoded forward primer, constructs included (5’ to 3’): the 5’ Illumina adapter (AATGATACGGCGACCACCGAGATCTACACGCT), a Golay barcode (12 bp variable sequence), a forward primer pad (TATGGTAATT), a forward primer linker (GT) and the forward primer (515F (Parada)) (GTGYCAGCMGCCGCGGTAA). For the reverse primer, constructs included (5’ to 3’): the reverse complement of 3’ Illumina adapter (CAAGCAGAAGACGGCATACGAGAT), a reverse primer pad (AGTCAGCCAG), a reverse primer linker (CC) and the reverse primer (806R (Apprill)) (GGACTACNVGGGTWTCTAAT).

For each 25 µl reaction, we combined 13 µl PCR-grade water (Sigma W3500, or QIAGEN 17000-10), 10 µl Platinum Hot Start PCR master mix (2X) (Thermo Fisher, 13000014), 0.5 µl of each primer (10 µM) and 1 µl of template DNA. The final concentration of the master mix in each 1X reaction was 0.8X and that of each primer was 0.2 µM. Cycling parameters for a 384-well thermal cycler were as follows: 94 °C for 3 min; 35 cycles of 94 °C for 1 min, 50 °C for 1 min and 72 °C for 105 s; and 72 °C for 10 min. For a 96-well thermal cycler, we recommend the following: 94 °C for 3 min; 35 cycles of 94 °C for 45 s, 50 °C for 1 min and 72 °C for 90 s; and 72 °C for 10 min.

We amplified each sample in triplicate (that is, each sample was amplified in three replicate 25 µl reactions) and pooled products from replicate reactions for each sample into a single volume (75 µl). We visualized expected products between 300–350 bp on agarose gels, and note that while low-biomass samples may yield no visible bands, instruments such as a Bioanalyzer or TapeStation (Agilent) can be used to confirm amplification. We quantified amplicons using the Quant-iT PicoGreen dsDNA Assay kit (Thermo Fisher, P11496) following the manufacturer’s instructions. To pool samples, we combined an equal amount of product from each sample (240 ng) into a single tube and cleaned the pool using the UltraClean PCR Clean-Up kit (QIAGEN, 12596-4) following the manufacturer’s instructions. We checked DNA quality using a Nanodrop (Thermo Fisher), confirming that A260/A280 ratios were between 1.8–2.0.

For sequencing, the following primer constructs were used. Read 1 constructs included (5’ to 3’): a forward primer pad (TATGGTAATT), a forward primer linker (GT) and the forward primer (515F (Parada)) (GTGYCAGCMGCCGCGGTAA). Read 2 constructs included (5’ to 3’): a reverse primer pad (AGTCAGCCAG), a reverse primer linker (CC) and the reverse primer (806R (Apprill)) (GGACTACNVGGGTWTCTAAT). The index primer sequence was AATGATACGGCGACCACCGAGATCTACACGCT, which we highlight as having an extra GCT at the 3’ end compared to Illumina’s index primer sequence, to increase the melting temperature for read 1 during sequencing.

The protocol for 18S is outlined fully in ref. 112. To target variable region nine (V9), we used the primers 1391f (5’-GTACACACCGCCCGTC-3’) and EukBr (5’-TGATCCTTCTGCAGGTTCACCTAC-3’). These primers are based on those of ref. 113,114 and are designed for use with Illumina platforms. The forward primer is a universal small-subunit primer, whereas the reverse primer favours eukaryotes but with mismatches can bind and amplify Bacteria and Archaea. In addition to deviations from the 16S protocol above with respect to primer construct sequences and PCR cycling parameters, we included a blocking primer that reduces amplification of vertebrate host DNA for host-associated samples, on the basis of the strategy outlined in ref. 115. We note that the blocking primer is particularly useful for host-associated samples with a low biomass of non-host eukaryotic DNA.

The following primer constructs were used to integrate adapter sequences during amplification. For the barcoded forward primer, constructs included (5’ to 3’): the 5’ Illumina adapter (AATGATACGGCGACCACCGAGATCTACAC), a forward primer pad (TATCGCCGTT), a forward primer linker (CG) and the forward primer (Illumina_Euk_1391f) (GTACACACCGCCCGTC). For the reverse primer, constructs included (5’ to 3’): The reverse complement of 3’ Illumina adapter (CAAGCAGAAGACGGCATACGAGAT), a Golay barcode (12 bp variable sequence), a reverse primer pad (AGTCAGTCAG), a reverse primer linker (CA) and the reverse primer (806R (Apprill)) (TGATCCTTCTGCAGGTTCACCTAC). The construct for the blocking primer is as such and is formatted for ordering from IDT: ‘GCCCGTCGCTACTACCGATTGG/ideoxyI//ideoxyI//ideoxyI//ideoxyI//ideoxyI/TTAGTGAGGCCCT/3SpC3/’.

Reaction mixtures without the blocking primer (that is, those for non-vertebrate hosts or free-living sample types as defined by EMPO) were prepared as described for 16S. For reactions including the blocking primer, we combined 9 µl PCR-grade water, 10 µl master mix, 0.5 µl of each primer (10 µM), 4 µl of blocking primer (10 µM) and 1 µl of template DNA. The final concentration of the master mix in each 1X reaction was 0.8X, that of each primer was 0.2 µM and that of the blocking primer was 1.6 µM. Without blocking primers, cycling parameters for a 384-well thermal cycler were as follows: 94 °C for 3 min; 35 cycles of 94 °C for 45 s, 57 °C for 1 min and 72 °C for 90 s; and 72 °C for 10 min. With blocking primers, cycling parameters for a 384-well thermal cycler were as follows: 94 °C for 3 min; 35 cycles of 94 °C for 45 s, 65 °C for 15 s, 57 °C for 30 s and 72 °C for 90 s; and 72 °C for 10 min. Expected bands ranged between 210–310 bp.

For sequencing, the following primer constructs were used. Read 1 constructs (Euk_illumina_read1_seq_primer) included (5’ to 3’): a forward primer pad (TATCGCCGTT), a forward primer linker (CG) and the forward primer (1391f) (GTACACACCGCCCGTC). Read 2 constructs (Euk_illumina_read2_seq_primer) included (5’ to 3’): a reverse primer pad (AGTCAGTCAG), a reverse primer linker (CA) and the reverse primer (EukBr) (TGATCCTTCTGCAGGTTCACCTAC). The index primer construct (Euk_illumina_index_seq_primer) included (5’ to 3’): the reverse complement of the reverse primer (EukBr) (GTAGGTGAACCTGCAGAAGGATCA), the reverse complement of the reverse primer linker (TG) and the reverse complement of the reverse primer pad (CTGACTGACT).

The protocol for ITS is outlined fully in ref. 116. To target the fungal internal transcribed spacer (ITS1), we used the primers ITS1f (5’-CTTGGTCATTTAGAGGAAGTAA-3’) and ITS2 (5’-GCTGCGTTCTTCATCGATGC-3’). These primers are based on those of ref. 117, and we note that primer ITS1f used here binds 38 bp upstream of ITS1 reported in that study.

The following primer constructs were used to integrate adapter sequences during amplification. For the barcoded forward primer, constructs included (5’ to 3’): the 5’ Illumina adapter (AATGATACGGCGACCACCGAGATCTACAC), a forward primer linker (GG) and the forward primer (ITS1f) (CTTGGTCATTTAGAGGAAGTAA). For the reverse primer, constructs included (5’ to 3’): the reverse complement of 3’ Illumina adapter (CAAGCAGAAGACGGCATACGAGAT), a Golay barcode (12 bp variable sequence), a reverse primer linker (CG) and the reverse primer (ITS2) (GCTGCGTTCTTCATCGATGC).

Reaction mixtures were prepared as described for 16S. Cycling parameters for a 384-well thermal cycler were as follows: 94 °C for 1 min; 35 cycles of 94 °C for 30 s, 52 °C for 30 s and 68 °C for 30 ; and 68 °C for 10 min. Expected